Customer Intelligence Tames the Big Data Challenge A Harvard Business Review Insight Center Report

Customer Intelligence Tames the Big Data Challenge A Harvard Business Review Insight Center Report sponsored by

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Customer Intelligence Tames the Big Data Challenge A Harvard Business Review Insight Center Report The HBR Insight Center highlights emerging thinking around today s most important business ideas. In Customer Intelligence, we ll explore how customer intelligence drives innovation, the challenges of having customers on multiple platforms, cutting edge tools and technologies, privacy issues, and more. www.hbr.org

Table of Contents 1 Mining Big Data to Find New Markets Manish Goyal, Homayoun Hatami, and Angelia Herrin 6 Pin Down Your Customer Intelligence Objectives Tom Davenport 7 Customer Intelligence, Privacy, and the Creepy Factor Larry Downes 9 Marketers Flunk the Big Data Test Patrick Spenner and Anna Bird 10 Tracking the Customer s Journey to Purchase Emma Macdonald, Hugh Wilson, and Umut Konus 11 Turning Customer Intelligence into Innovation Scott Anthony 12 Why Email Marketing Is King Arthur Middleton Hughes 13 Meet Your Company s New Chief Customer Officer Fatemeh Khatibloo 15 Don t Build a Database of Ruin Paul Ohm 16 Understanding Customers in the Solution Economy David Midgley 17 What Data Can t Tell You About Customers Lara Lee and Daniel Sobol 19 Retailers Turn to Soft Surveillance to Fight Customer Anonymity Robert Plant 20 Customer Experience Should Be Part of Your Business Harley Manning 21 Using Market Research Just for Marketing Is a Missed Opportunity Werner Reinartz 22 Does the 18-to-49 Demographic Matter Anymore? An HBR Management Puzzle Horst Stipp and Jeffrey McCall 24 Use Big Data to Predict Your Customers Behaviors Jeffrey F. Rayport 25 How One Company Uses Customer Data to Drive Sales David K. Williams and Mary Michelle Scott 26 How to Find Out What Customers Will Pay Rafi Mohammed 27 The Big Goal Behind All That Customer Data Bill Lee 28 Free Customers Are More Valuable than Captive Ones Doc Searls 30 Case Study: Should You Listen to the Customer? Thomas J. DeLong and Vineeta Vijayaraghavan iv Customer Intelligence Tames the Big Data Challenge

hbr.org webinar summary Mining Big Data to Find New Markets Manish Goyal, Partner, Marketing & Sales McKinsey & Company Homayoun Hatami, Director, Marketing & Sales McKinsey & Company Angelia Herrin (Moderator), Editor for Research and Special Projects, Harvard Business Review OVERVIEW Even though companies today have limited resources, they still desire significant growth. Companies also are able to access more types of data and a greater volume of data than ever before, including real-time data from the Internet and social media. By mining this Big Data, companies can develop insights and identify micromarkets that represent opportunities for growth. When these data-driven insights are translated into specific plans and cascaded to the front lines, companies can produce exceptional results. Tools and technology are important, but even more important are committed leadership, having the right analytical talent, and focusing on specific areas that can create significant value. CONTEXT McKinsey consultants Manish Goyal and Homayoun Hatami coauthors, along with Maryanne Hancock, of the Harvard Business Review article Selling into Micromarkets described how companies can use Big Data to find new markets and drive growth. They provided firsthand examples and responded to numerous questions. KEY LEARNINGS Big Data can produce insights that drive business growth. For years, businesses have had a great deal of data, including CRM data and transactional data. But Big Data is different because of its: Sources Big Data involves pulling together data from multiple internal and external sources. This includes data from customers, channel partners, suppliers, web searches, social media, location data, and even external data such as weather and demographic information. www.hbr.org 1

Data from large data sets can take the guesswork out of selling. People have always talked about the art of sales. But with Big Data, art is being replaced by scientific analysis. Homayoun Hatami Scale Because so much data is brought together, the amount of data is now far greater than it has been in the past. Timeliness Data is now available in real time to show what customers are talking about in social media (via sentiment analysis) and what they are buying. The value of pulling this data together and analyzing it is to glean new and valuable insights. For example, retailers can use purchase data to estimate a pregnant woman s due date and can target relevant offers. Google used insights from data to modify the font color in its ads, boosting its rate of click-throughs and increasing revenue by $200 million. By mining Big Data, companies can identify micromarkets that represent opportunity for increased revenue. As many companies deal with the reality of constrained resources, they see analyzing and extracting insights from Big Data as a key way to improve the efficiency and effectiveness of their sales and marketing organizations. By putting Big Data at the heart of sales and marketing, insights can be leveraged to improve decision making and innovate a company s sales model, which can involve using data to drive real-time actions. Some of the ways Big Data can be used to drive improved sales and marketing performance include: Identifying micromarkets Big Data, which pulls in external data, can be used to identify new markets, which might be specific customer segments or geographies. Focusing on opportunities Many companies are largely focused on selling to and serving their existing customers. They create goals, budgets, and plans based on looking at past results from current customers. But by using Big Data, organizations can think differently; they can focus not on past results and existing customers but on the opportunities that exist in a market. Big Data will help identify new markets and opportunities that companies might not know even existed. Maximizing sales effectiveness By using Big Data, an organization can assess the targets it is pursuing, the way sales time and resources are being allocated, and what selling propositions and promotions are working best with different customer segments. Prioritizing opportunities rather than existing customers can entail risk. It should be supported by strong analytics. The insights on which decisions are based must be disseminated to all levels of sales and marketing, particularly the front lines. But companies that use analytics have shown that they can utilize their finite resources more effectively to pursue the best opportunities with the right propositions and promotions in the most efficient way. In using Big Data to identify new opportunities, practical prioritization steps include: 1. Defining the right granularity for your micromarkets This might be a specific customer segment, a geography such as a county, or a geographic radius such as 25 miles surrounding a sales rep. 2. Determining the growth potential By using external data, the total opportunity for a micromarket can be estimated with high precision. For example, a maker of plumbing products might look at data such as the number of construction starts in a market and the age of the housing stock. 3. Determining your share of each micromarket With the definition of a micromarket and knowledge of the opportunity, it is possible to determine your company s existing share of a micromarket. 2 Customer Intelligence Tames the Big Data Challenge

4. Understanding why there is variance in market share in different micromarkets inevitably, when different micromarkets are identified, a company s market share within them will differ. It is important to understand the underlying reasons for this variance. 5. Prioritizing growth pockets Having used external sources to determine the opportunities that exist, the opportunities can be prioritized and resources allocated to support these opportunities. Case studies showed that organizations generated growth by identifying opportunities and reallocating resources, without adding resources. The Big Data toolkit changes the way that data has traditionally been used. Manish Goyal A key to growth is using Big Data to unlock deeper and deeper insights. An example was shared of a faucet manufacturer analyzing its business, digging ever deeper to glean insights, and using these insights to decide upon actions to grow the business. Insights from this example included finding that: The company s overall market share in California looked strong, which might cause a company to conclude there were no opportunities for growth, but the market share by county varied by 4X. One area with low market share had no sales coverage, and another had no channel partners indicating obvious next steps in both situations. For the geography with no channel partner, analysis of social media identified the most influential partner in that area. In one geography, the company faced a strong local competitor. To compete more effectively, the company s analysis led it to conclude that it needed to better understand customer needs and strengthen its value proposition. It also needed to spend more on marketing to counter competition. Cluster analysis found another US geography with similar characteristics, leading the company to share best practices. The example showed that a granular analysis yielded insights, and each insight led to further analysis and an even deeper insight. As a result of such insights, a company can develop specific actions to drive growth. It is essential to get meaningful insights to the front line. Just developing insights at a headquarters location is not adequate. The speakers recommended building a Big Data toolkit to bring micromarket insights to the front line. It is essential that salespeople understand the insights that have been gained and believe in the validity of the data. This toolkit: Gets data to the front lines so it can be used and acted upon instead of holding data in a central location or in silos. Is predictive and forward-looking, as opposed to looking backward at performance and problems. Is consistent, standard, and repeatable, as opposed to time-consuming and one-off. The key to using Big Data is getting started by focusing on a limited set of outcomes that data can impact. It is important for an organization to realize that using Big Data is a journey. Businesses of any size can embark on and realize value from this journey. A good starting point is to determine a few outcomes that could be improved through analysis and to determine what data is needed to support this analysis. No company will have all of the data it wants, but that shouldn t preclude the need to get started. www.hbr.org 3

Other Important Points B2B. When analytics in business is discussed, it is often in the context of B2C companies. But this session and the examples discussed showed that analyzing Big Data also has tremendous value for B2B companies in identifying micromarkets and improving sales effectiveness. Leadership. Having leadership that understands and supports the use of Big Data and analytics is an important element of success. Google and Procter & Gamble were mentioned as organizations whose leaders are strong supporters of using data to make better decisions. Organizational structures. Some organizations have a centralized analytics team in strategy, marketing, finance, or another function. With this structure, the focus on sales opportunities often begins when a creative salesperson speaks with a data analyst, who becomes interested in analyzing a business problem/opportunity. In other organizations, analytical capabilities reside in small teams that are distributed throughout the company to support different businesses and functions. They are often closer to the action and might reside in the marketing department or in pre-sales. A key to success is business people working closely with analysts as opposed to handing off a project to them. Emerging markets. There is interest in using analytics to identify and prioritize opportunities in emerging markets. This makes sense because many companies see significant growth opportunities in emerging markets and are resource constrained. A challenge can be a lack of good data from these markets. Learn More For more details about customer intelligence solutions: sas.com/software/customer-intelligence To read more thought-leader views on marketing, visit the SAS Customer Intelligence Knowledge Exchange: sas.com/knowledge-exchange/customer-intelligence To get fresh perspectives on customer analytics from marketing practitioners writing on the SAS Customer Analytics blog: blogs.sas.com/content/customeranalytics To get fresh perspectives on customer analytics from marketing practitioners writing on the SAS Customer Analytics blog: blogs.sas.com/content/customeranalytics Mining Big Data to Find New Markets featuring Manish Goyal and Homayoun Hatami aired on September 5, 2012. This webinar summary was created for Harvard Business Review by BullsEye Resources, Inc. www.bullseyeresources.com. The information contained in this summary reflects BullsEye Resources subjective condensed summarization of the applicable conference session. There may be material errors, omissions, or inaccuracies in the reporting of the substance of the session. In no way does BullsEye Resources or Harvard Business Review assume any responsibility for any information provided or any decisions made based upon the information provided in this document. 4 Customer Intelligence Tames the Big Data Challenge

SPEAKER BIOGRAPHIES Manish Goyal Partner, Marketing & Sales, McKinsey & Company Manish is a partner in McKinsey s Marketing & Sales Practice. Based in Dallas, he helps clients focus and find growth in micromarkets. Homayoun Hatami Director, Marketing & Sales, McKinsey & Company Homayoun Hatami co-leads McKinsey s Sales & Channel service line and the firm s work in sales growth. He has a broad range of experience working with clients in Europe, the United States, and Asia to power growth through excellence in sales and channels. He is also a leader in knowledge and insight development. Angelia Herrin Editor for Research and Special Projects, Harvard Business Review Angelia Herrin is editor for research and special projects at Harvard Business Review. At Harvard Business Review, Herrin oversaw the relaunch of the management newsletter line and established the conference and virtual seminar division. More recently, she created a new series to deliver customized programs and products to organizations and associations. Prior to coming to Harvard Business Review, Herrin was the vice president for content at womenconnect, a website focused on women business owners and executives. Herrin s journalism experience spans 20 years, primarily with Knight-Ridder newspapers and USA Today. At Knight-Ridder, she covered Congress as well as the 1988 presidential election. At USA Today, she worked as Washington editor, heading the 1996 election coverage. She was awarded the John S. Knight Fellowship in Professional Journalism at Stanford University in 1989 1990. www.hbr.org 5

Pin Down Your Customer Intelligence Objectives by Tom Davenport that are pretty aggressive on the technology front and those that are more aggressive on the business front. Technologically Aggressive Customer Intelligence Applications Social networks-based offer and attrition models Online-sentiment analysis for social media Targeted next best offers (non-mobile or location-based) A few weeks ago, I was asked to prepare a workshop for a telecom company that wants to invest more in customer intelligence. My first question was, Can we take a week to go through all the possibilities? The problem with customer intelligence is that while everyone wants more of it and better versions of it there are many different avenues to take in pursuing it. No organization can pursue all of them at once (and this company didn t want its workshop to last a week), so the challenge is to narrow featured comment FROM HBR.ORG I hope that this article serves as a reminder and guide to those that read it of the importance of using customer intelligence planning to aid our businesses to succeed. Frank Woodman Jr. down the options fast. You need to reflect on your business situation and goals and consider only the initiatives that will offer the highest impact. For example, customer data and analysis can help you acquire new customers or keep old ones. Depending on which is your current priority, different tools and techniques will apply. The ability to present next best offers to customers (which I and some collaborators wrote about in a recent HBR article) is very high value if you are a retailer with lots of diverse and appealing products to offer, but perhaps not if you are a bank with a set of pretty standard financial services that don t tempt consumers to make impulse buys. Video analytics have great potential for bricks-and-mortar retailers who want to understand their customers, but probably won t do much for an online retailer or a manufacturer who distributes through many channels. Segmentation is a powerful tool, but it only works if you have the ability to treat different customers differently and many consumer products and services companies don t (at least if they are honest with themselves). What I did, therefore, for the telecom team was to draw up a quick list of capabilities an organization might choose to develop in the customer intelligence domain. The notion was that the managers attending would winnow it down for practical purposes at the outset of the workshop, and in the process gain a broad familiarity with the whole landscape of the topic. Perhaps the same list will be useful to you. I divided the possibilities into two categories: those Mobile or location-based offers Non-hypothesis-driven data mining (machine learning, etc.) Uplift or incremental modeling Automated voice of the customer text analysis Automated behavioral targeting for online ad placement Accurate attribution analysis for online advertising Multichannel, multivariate randomized testing Video analytics Business-Aggressive Customer Intelligence Applications Segmentation (and treating different customers differently) Attrition modeling (and taking the necessary actions to avoid it) Predictive models for customerservice episodes Single customer data warehouse with all touchpoints Marketing mix portfolio modeling Adaptive customer profiling (qualitative and quantitative) Loyalty and lifetime value-based pricing Simple or A/B randomized testing By putting something on the technologyfocused list, I meant to underscore that the problems it will cause you to wrestle with are primarily technological and when you successfully implement it, you ll be on the leading edge. On the business-focused list, the primary problems are not technological because the relevant technologies and analytical approaches 6 Customer Intelligence Tames the Big Data Challenge

have been around for a while. The primary issues are rather things like getting your entire organization to agree on a common definition of customer, establishing differentiated customer-facing processes for different segments, and so forth. Of course, some of these applications overlap, and some are more infrastructural than others. For example, the single customer data warehouse with all touchpoints would be useful as a building block for most of these applications (but is of course hard to do from a business standpoint). Customer Intelligence, Privacy, and the Creepy Factor by Larry Downes So think of this as the whole toolkit of customer intelligence laid out before you. It s now up to you to be clear on what you re trying to build, and then decide which tool is the best choice for the job. I suppose it would be theoretically possible for someone to create an algorithm or a set of business rules to do this for you to dictate, that is, what customer intelligence applications would make sense for your business. To my knowledge, however, no one has created that. I m curious to hear from HBR readers about how such decisions are being made by smart managers in various settings. What s your biggest problem in customer intelligence, and what does it mean for what applications you need to develop? While we re at it not that this list of 19 isn t long enough already what applications have I missed? u The relationship between large-scale customer intelligence data collection and privacy is more complicated than it seems. From the perspective of data analytics, for example, the bigger the data warehouse the less interesting information about an individual turns out to be. Marketers want to know intimate facts about individual behaviors, but only so they can fit them into increasingly refined demographic groupings of other individuals with similar behaviors and, they hope, similar interests. The more information available about more people, it turns out, the more privacy we actually get as individuals. You really can get lost in a crowd. Perhaps the best explanation for today s resurgent and generalized anxiety about privacy is that it just doesn t seem that way. When a novel information service appears to have zeroed in on one s deepest, darkest secret preferences, it s hard to resist a strong emotional response what might be referred to as the creepy factor. But there is almost always an explanation that, when understood in context, takes the creepiness out of the equation. Gmail users, for example, see ads along the side of the screen advertising products and services that often relate to the contents of recent email conversations. We know intellectually that there s no vast army camped out at some Google Ministry of Love, reading through the messages and looking for opportunities to connect them to contextual advertising. It s all software. But that software has gotten so good at reading our minds that it begins to look personal. Only another human and a devious one at that could have connected the dots so easily. That s the moment and we have them more frequently as innovative technologies accelerate their entry into the market when the creepy factor comes into play. Something happens that you didn t expect or hadn t experienced before, and you think, How did they know that? Right now, my Facebook page is showing me photos of three people you may know. I know all three. For two, the connection is obvious. For the third, the connection is eerily indirect. Until I understood what mundane data elements connected all three to me, I felt uneasy about Facebook. The company seemed to be an actual person, and a creepy one at that. As we record more information in digital form in hopes of sharing it with our intimate contacts and, less enthusiastically, with advertisers who pay for the services we love, it s inevitable that more of these visceral responses will occur. When specific data is used in novel ways, the initial response is often the creepy factor. The creepy factor, however, is the response to a novel use of information to provide a seemingly personalized response. Over time, the creepiness decreases. Most of us are now accustomed to customized Google search results, specific Gmail ads, and prescient Facebook recommendations. They no longer make our skin crawl. In response to innovation in customer intelligence, however, privacy advocates are calling for all sorts of new laws to protect us from ourselves. In reality, what they want most is a placebo to cure the creepy factor. Often, there s no need for legislation. Over time, consumers either adjust to what is an www.hbr.org 7

essentially inert new-information use or act through the market to change the practice. Consumer-enforced change is frequent recent examples include the cancellation of Facebook Beacon and Google Buzz and Apple s modifications to geolocation files stored on consumer devices. When consumers objected to how these services were perceived to be using information, the companies modified their practices or canceled the service altogether. In 2011, to take a specific example, LinkedIn users objected to a new feature called social ads, in which ads for a particular product or service included the profile photos of others in a user s network who recommended it. The creepy factor response was just too overwhelming, and the company quickly agreed to simply list the number of network members who recommended the advertised product. What we ve learned now, wrote Ryan Rolansky, the company s director of product development, is that, even though our members are happy to have their actions, such as recommendations, be viewable by their network as a public action, some of those same members may not be comfortable with the use of their names and photos associated with those actions used in ads served to their network. These are examples where constructive engagement with service providers led to quick resolution true market success. The dangerous alternative to rational discussion is to panic, and in our panic legislate, creating unintended consequences that unnecessarily raise costs for ourselves. After all, the more social the ads at LinkedIn, the more the company can charge its advertisers, keeping subscription fees lower and encouraging a larger and richer network. So a law banning the use of subscriber photos in ads, or something like it, would necessarily raise the cost of a service, perhaps more than the benefit to overall privacy. Legislation should be the last resort, one employed only to correct uses of information that remain disquieting over time. In the US, for example, we have privacy laws that prohibit the use of specific identifying information as a determinant in refusing to transact on reasonable terms. Fair housing, equal employment, insurance red-lining, and sexual harassment laws are all examples. So are, in their own way, privacy laws that restrict the acceptable uses of accurate but often misapplied data elements, including credit histories and health information. These laws in effect correct a creepy factor response that doesn t go away by itself or through market mechanisms. When new applications stimulate our creepy factor response (and more of them will enter the market all the time, thanks to technology trends making data collection and analysis cheaper all the time), the critical policy question becomes what to do during the initial, visceral response period, when creepiness is high. The stakes are high. For better or worse (almost certainly better), Internet users are hooked on the free software, content, and services that rely for revenue on information collection and use. So are the service providers. Legislate too soon and we kill valuable innovation in its infancy. Wait too long and consumers lose faith with the implicit quid pro quo of ad-supported services. My preference is to give the market the first shot. It s faster and cheaper than regulation, less prone to unintended consequences, and easier to tweak after the fact. For those who leap first to legislated solutions to emotional responses, better just to fume, debate, attend conferences, blog, and then calm down before it s too late. In the meantime, more often than not, the creepy factor will go away without the need for intervention. u 8 Customer Intelligence Tames the Big Data Challenge

Marketers Flunk the Big Data Test by Patrick Spenner and Anna Bird Today s top-performing marketers as rated by the managers (a profile we call Focusers ) have three key qualities: comfort with ambiguity, ability to ask strategic questions based on data, and narrow focus on higherorder goals. Together, these traits help them filter out noise and apply only the insights or data points that truly matter for longterm success. As marketers get better access to raw numbers and Big Data keeps growing, the importance of this filtering ability will only intensify. The Big Data explosion is driving a shift away from gut-based decision making. Marketing in particular is feeling the pressure to embrace new data-driven customer intelligence capabilities. No wonder a strong appetite for data is one of the most soughtafter qualities in new marketers. And yet, a recent CEB study of nearly 800 marketers at Fortune 1000 companies found the vast majority of marketers still rely too much on intuition while the few who do use data aggressively for the most part do it badly. Here are our key findings: Most rely too much on gut On average, marketers depend on data for just 11% of all customer-related decisions. In fact, when we asked marketers to think about the information they used to make a recent decision, they said that more than half of the information came from their previous experience or their intuition about customers. They put data last on their list trailing conversations with managers and colleagues, expert advice, and one-off customer interactions. But in today s volatile business environment, judgment built from past experience is increasingly unreliable. With consumer behaviors in flux, once valid assumptions (e.g., older consumers don t use Facebook or send text messages ) can quickly become outdated. A majority struggle with statistics When we tested marketers statistical aptitude with five questions ranging from basic to intermediate, almost half (44%) got four or more questions wrong and a mere 6% got all five right. So it didn t surprise us that just 5% of marketers own a statistics text book. Some are dangerously distracted by data While most marketers underuse data, a small fraction (11% in this study) just can t get enough. These data hounds consult dashboards daily and base most decisions on data. They have a plugged in personality type and thrive on external stimulation so they love data and all forms of feedback, including data on marketing effectiveness, input from managers or peers, and frequent interaction with others. We call these marketers Connectors, and they re exactly what most CMOs are looking for. But these types of marketers are actually severe underperformers (they receive much lower performance ratings from their managers than average marketers do). The problem is they don t have the statistical aptitude or judgment required to use data effectively. Every time they see a blip on the dashboard, they adjust and end up changing direction so often that they lose sight of end goals. In management positions, these people can wreak havoc by creating endless fire drills and preventing anyone from sticking with projects long enough to achieve the best results. Worse, many marketing disciplines (especially direct, digital, and loyalty marketing) unwittingly encourage these behaviors and end up magnifying the problem. That s because dashboards often capture response-based metrics such as clicks and open rates that aren t tied to more important measures such as customer loyalty or lifetime value and yet, marketers are rewarded for improving the response metrics. The best focus on goals and filter out noise The bad news for marketing leaders is that the ability to filter out noise is rare (only about 10% of marketers excel here) and hard to teach. The good news is that a wellguided team environment can protect noise chasers from themselves by providing blinkers that keep bright, shiny objects out of view. To drive effective data use, the best marketing leaders reiterate critical business goals constantly (to keep them front of mind despite distractions), teach marketers to put data front and center in their decision making, and sensitize marketers to common data interpretation mistakes. This enables even the most distractible data lovers to overachieve. For more information on the five marketer profiles and the characteristics of today s high-performing marketing teams, download our report here. u featured comment FROM HBR.ORG When you look at the data as individual bits, it can be very overwhelming. Successful marketers are starting to use predictive analytics to bring order and meaning to the data Brian Kardon www.hbr.org 9

Tracking the Customer s Journey to Purchase by Emma Macdonald, Hugh Wilson, and Umut Konus A customer will touch a company in many different ways before a deal is made. Before you rent your first ZipCar, you ll have talked to friends about it, checked ZipCar s website (and comparison websites), and maybe even called the company. From ZipCar s perspective, all of these touchpoints are important because if you hear bad reports or find the website and call center hard to manage, you ll very likely opt for the safe option of a Hertz or an Avis. Unfortunately, few companies have an overall picture of their customers journey toward a purchase because the information is all too featured comment FROM HBR.ORG To be able to gain insight into the sequence of connections with a brand and the intervals between them and the link to purchasing behavior sounds as significant as segmentation approaches have been up to now. Paula often stuck in a channel silo. An intercept survey that a customer might fill in upon leaving a website can tell you a lot about that customer s experience with the website, but it usually does not provide any information on where the customer will next experience the company. Surveying customers directly after their purchases to explain how they arrived at them means that you have to put a lot of faith in their remembering exactly what they did. A CRM system might let you know how customers moved between the website and the store, but it tells you nothing about how they responded to advertising or word-of-mouth reports. Two years ago, we came across a technique that does allow companies to document quite accurately how their customers actually arrive at a purchase. It is called realtime experience tracking (RET), and we first wrote about it for HBR in a blog last year. It was developed by a market research company called MESH Planning, with which we have been partnering to improve the RET methodology and identify applications for the data it generates. RET involves asking a consumer panel to send text messages on their cell phones every time they come across a given brand or one of its competitors over a period of a week to a month, depending on the length of the purchase process. The structured four-character message captures the brand, the touchpoint type (Saw a tweet about it? Saw it in a shop window?), how positive the customer felt about the encounter, and how persuasive it was. Respondents add further detail online and fill in surveys at the start and end of the study to record brand-attitude changes. Companies can tell how the customer journey works or doesn t from that sequence of text messages. Unilever, for example, could not understand why a campaign for Axe body spray wasn t working in Italy when it was performing well in Poland. In both countries, TV advertising was positively received. But whereas in Poland the ads were followed by high-street touchpoints such as the Axe Police attractive women who would arrest young men and spray them with Axe such reminders close to potential purchase moments were missing in Italy. Traditional econometric models based on spend by media type would have completely failed to pick up this problem. RET can also diagnose how attitudes lead to the next step in the chain, as one major international charity discovered. The charity, which relies on a large network of stores selling both secondhand and new goods to raise both revenue and awareness, recently applied RET in an effort to understand why direct donations (as opposed to store profits) to the charity were falling. The RET project revealed that the in-store experience of customers (and potential donors) was rather mixed; quite a few people felt that the stores were poorly organized and deduced from this that the charity probably wasn t very good at helping its beneficiaries either. They might well purchase goods at the store, therefore, but they did not go on to make donations. Armed with this insight, solving the problem was simple: a smarter layout, displays at the cash register about the charity s fieldwork, and encouraging staff to share their passion for the charity. Non-store donations have since been rising. Because data is gathered in real time, it can be acted on in real time too. PepsiCo recently used RET to fine-tune its relaunch of Gatorade in Mexico, repositioning the brand around sports nutrition. They soon found that experiences in gyms and parks (seeing posters or seeing other people drinking Gatorade, for instance) were twice as effective in shifting brand attitudes as similar encounters elsewhere. They were able to quickly shift more ad and distribution resources into these touchpoints and pass on what they learned as Gatorade was relaunched in other Latin American countries. 10 Customer Intelligence Tames the Big Data Challenge

Our first two years working with RET have confirmed its benefits in providing integrated insight, a vital first step toward holistic customer management. Its use is clearly spreading, and doubtless the market research industry will come up with new ways to exploit the rich real-time data RET produces. u Turning Customer Intelligence into Innovation by Scott Anthony It s a paradox of the information age. The glut of information that bombards us daily too frequently obscures true insight. Intelligence should drive better innovation, but unless it is strategically collected and used, it functions like a summer beach novel an engaging distraction. Thoughtful companies intertwine customer intelligence throughout the three phases that characterize most successful innovations. Innovation starts with discovery where an innovator pinpoints an important problem to solve. Ground-level intelligence is critical to this part of the process. While companies are increasingly using detailed analytics to fine-tune pricing, packaging, and product performance, analytics have their limits when it comes to finding the next big idea. After all, data only exists about the past discovering untapped opportunities typically requires a heavy dose of primary research to tease out what the customer needs but cannot easily articulate. Consumers don t do a good job reporting what they currently want or do, let alone what they might want or will do in the future. Procter & Gamble is famous for its deep commitment to these kinds of anthropological approaches. For example, in the early 2000s, P&G investigated the cleaning habits of Indian consumers who washed garments by hand. At first glance, that s a counterintuitive place to look for new growth, because those consumers are unlikely to buy P&G detergents formulated for washing machines. But since hand-washers constitute 80% of the home-based washing market in India, it was too big a market to ignore. P&G observed that many consumers were in fact hand-washing garments using machine-oriented detergents to take advantage of their superior cleaning benefits. However, the chemical formulations weren t intended for hand-washing and could cause abrasions or burns. Insight in hand, innovators next blueprint a solution to address the identified problem. In the case of P&G in India, the idea the company ultimately commercialized was Tide Naturals, a special formulation that lets hand-washers get the cleaning benefit of Tide without suffering the downsides of machine detergents. There are substantial opportunities to generate real-time intelligence by involving customers in the blueprinting process. For example, four years ago, the Indian company Godrej & Boyce was working on an idea for a small, battery-powered refrigerator to reach the 80% of Indians without refrigerators. The team working on the idea brought an early prototype of the concept to a rural village and showed it to 600 women. Navroze Godrej, who leads the company s disruptive growth efforts, describes how the event was a way to get instant feedback, allowing Godrej to co-create with these women. It was also here that the final color we went with ruby red was decided pretty unanimously with 600 women. P&G has a number of mechanisms to facilitate this kind of real-time customer input, such as a specially designed Home of the Future and Store of the Future 30 miles north of corporate headquarters in Cincinnati; online networks such as VocalPoint, where hundreds of thousands of mothers provide feedback on products; and the regular practice of bringing real consumers into its offices. The final stage in the process is to iteratively test an idea by executing smart experiments to test key assumptions. Does the product (or service) solve the consumer problem it was intended to in a way that generates repeat use and repurchase? Will consumers purchase at the required price point? Can the idea be reliably delivered at scale? Do the economics work? Ideally, tests to answer these kinds of questions aren t run in the laboratory, but with real customers in everyday settings. Presenting early ideas to customers to get their feedback provides vital intelligence that helps increase the success rate and sustainability of innovation. featured comment FROM HBR.ORG This is a good reminder that customer data serves multiple purposes and that prospects are often the best sources of new product ideas. Don Nanneman www.hbr.org 11

There are a number of ways to generate this kind of real-world input. Many consumer-facing companies use employees as customers to test new concepts inside their walls. For example, the headquarters of Unilever s India operations contains a street with shops and kiosks selling Unilever products to glean insights from employee customers. Business-to-business companies can consider bringing rough ideas to customer councils, running pilots with select customers, or even using booths in industry conferences to gauge interest in new ideas. Since the goal is learning, companies should ensure that they keep tests simple and focused. Affordable online tools such as LinkedIn, elance.com, Survey- Monkey, Wix, Amazon s Mechanical Turk, Appmkr.com, and Google SketchUp can complement live testing to accelerate effective, affordable experimentation. Companies seeking to more formally intertwine intelligence with innovation should consider three straightforward starting points: 1. Mandate that everyone in the company increase the amount of time they spend with customers however much time your company is spending, it is probably not enough. 2. Find simple ways to make customer conversations more frequent. Consider forming a lead user panel or creating an online community such as those offered by CommuniSpace. 3. Build a little-bets lab, a mechanism by which you can selectively introduce early ideas to the market. For example, at beta620.nytimes.com, users can test-drive early experiments offered by The New York Times Company. Littlebets labs facilitate the thoughtful process of strategic experimentation that typifies successful innovation. Want more intelligent innovation? Start by intertwining intelligence and innovation. u print, or direct mail ad is what it is. On email, the ad is much more. Because of electronic links, those who open your emails can do their own research: they can explore and see any of the thousands of products you sell. They can see the colors and sizes. They can, and they do, read ratings and reviews. They can put products in their shopping carts and buy them. Fine, say the TV folks, but shopping cart sales through emails are seldom more than 5% of total sales. Nothing to write home about. What these detractors seem to willfully ignore is that emails create impressions that lead to sales through other routes. Some of these routes can be tracked. The recipient can open it or delete it. If she opens it, she can click on it, perhaps buy something or print out a coupon and take it to a store. Finally, if she puts things in her cart but does not buy, you can send her an abandoned shopping cart email that usually yields 29% of lost sales. Why Email Marketing Is King by Arthur Middleton Hughes In a business world obsessed with gaining more customer intelligence, you would think that email marketing would get more respect. But just look at media spending. According to emarketer, this year US companies are spending about $64 billion on TV, $34 billion on print ads, and $39 billion on Internet advertising. And how much are they are spending on email? For that, we have Forrester data: only about $1.5 billion. Of course, compared to other media, email messages are dirt cheap to send. With TV, you are spending on ad agencies, creative studios, and cable channels. With print ads, you are helping to keep newspapers and magazines alive. Direct mail costs more than $600 per thousand pieces. With email, there are almost no costs at all. But its low cost only makes the argument stronger that email marketing is the most cost-effective advertising method available today. Certainly email beats the competition from a measurability standpoint. With TV, you do not know who is watching your ads. Ditto with print. Even with direct mail, you cannot be sure that your mail has been delivered or that anyone reads it when it gets there. With email, you know within 24 hours exactly which messages have been opened, by whom, what links the openers clicked on, and what part of your message was working. A properly structured email message provides this benefit to the marketer because it provides benefits to consumers. A TV, But note that, in many cases, she also does things that are hard to track. She can get in her car and drive to a mall to buy the product. She can pick up her phone and order it. She may be prompted to do research on Google for better prices of similar products or discuss the offer with her spouse or a friend, leading to a possible purchase later. These are all the behaviors that provide the rationale for TV or print advertising. My point is that emails prompt the same kinds of behaviors. Thus, there is an offemail multiplier. For every purchase in an email shopping cart, we can fairly assume that there are some number of other nontracked profitable purchases that occur because of the arrival of the email a number that quantifies all the non-tracked behaviors that email recipients engage in. If you are going to make a case for investing more heavily in email marketing, you have to determine this off-email multiplier to account for all the sales your emails can be expected to generate. How can that be done? A retailer I ve worked with that has 900 stores and is very active with email campaigns recently did a great study. It took a group of 105,000 customers in its loyalty club database, divided them into three groups of 35,000, and marketed to 12 Customer Intelligence Tames the Big Data Challenge

What are the skills that enable a CI leader to take on the formidable challenge of owning the customer relationship and being the organization s customer advocate? There are three: the three groups differently, as shown in the chart below (click to see a larger version). Thanks to the loyalty program, it was able to see all subsequent purchases by these customers. Direct mail has a higher response rate than email. But note that direct mail costs about 100 times as much. Meanwhile, the data collected by the retailer allowed it to calculate its off-email multiplier (a simple matter of dividing the percentage of online sales by the percentage of in-store sales generated by email-only marketing). It is 3.76. In other words, for every email shopping cart sale, this retailer gets 3.76 other, typically non-tracked sales due to the email. What might your off-email multiplier be? Zero is of course possible, but studies to date suggest that a number between two and three is typical. Once you factor in your off-email multiplier, it s a very safe bet that email will beat all your other marketing methods in terms of return on investment. As email marketing gains more respect, marketing intelligence will meet customer intelligence. u Meet Your Company s New Chief Customer Officer by Fatemeh Khatibloo Customer intelligence is at an organizational inflection point. This practice, which is largely the evolution of database marketing, has become a critical driver of business strategy for global organizations in nearly every industry and vertical because it supports decisions with data. In this way, CI s value extends well beyond the marketing organization. But what does a successful CI professional s future look like? The answer lies in the rise of a new type of executive: the Chief Customer Officer. Why CI pros should aim for the CCO job A decade ago, the only option for an ambitious CI professional was the CMO s office, even though in most enterprises, that seat will be filled by an executive with brand and media expertise. Today, the new Chief Customer Officer role a position that I believe most smart companies will create in the upcoming decade is well-suited to the skills of a CI leader. My colleague, Paul Hagen, writes extensively about the emerging CCO and was good enough to share some of his raw data with me. In his research, he reports that these individuals typically have extensive sales, marketing, and operational backgrounds. But when we dug more deeply into their previous work history, we found titles such as Consumer Engagement Lead, VP, CRM & Loyalty Program Development, and VP, Direct Response Marketing all customer intelligence positions. The ability to interrogate data. I m not suggesting that these folks need to run SPSS or sit in front of a business intelligence tool all day. But they do understand the importance of data in addressing business questions. They know how to build a hypothesis, mine the data, test a solution, and validate. And most importantly, these individuals can translate data insights into strategic business language to gain adoption and credibility for their approaches. The ability to speak IT. Marketing and IT have never been more interconnected. But there s still a fundamental language disconnect between these groups. Many customer intelligence leaders have bridged that gap already they know how to translate data insights into the language of business to gain credibility and drive adoption. As organizations transition from product-centric to customer-centric, CI leaders are ideally positioned to build a data-driven business case to justify the organization s marketing technology and business intelligence needs. The ability to describe customers realistically and actionably. For decades, agencies and market researchers described customers in terms of a handful of archetypes and personas. But that s not realistic in today s omni-channel, multidevice ecosystem because marketing s promises of right place, right time, right offer require microsegmentation and granular customer understanding. CI leaders already understand this. They manage preference centers, they use propensity models to define and meet customer needs, and they leverage tools to recognize customers across channels and devices. The future of customer management means individually optimized touchpoints, and CI pros are already halfway to the finish line. What skills should CI leaders hone to succeed as CCOs? The role of the Chief Customer Officer will vary by industry and organization. But www.hbr.org 13

Hagen has identified three attributes that these leaders must possess: 1) a passion for customer experience, 2) a strong personal brand, and 3) operational know-how. These leaders must: Act as the voice of the customer. It sounds like a platitude. But to be recognized as an organization s customer advocate, CI pros should use their access to data to create customer journey maps that track customers across all channels and touchpoints and introduce these tools across the organization at every opportunity. Use the maps to question and challenge decisions that impact the customer and support these challenges with data and insights. featured comment FROM HBR.ORG This is a brilliant piece! As a Business Development Specialist for a major Fortune 50 company, we ve been moving in this direction for some time now. Jeremiah LeBlanc Become tempered radicals. In October 2001, HBR published a piece by Debra Meyerson in which she advocated four approaches to becoming a unique type of change agent: 1) disruptive selfexpression, 2) verbal jujitsu, 3) variableterm opportunism, and 4) strategic alliance building. Leaders who practice this type of change management gain trust and build a personal brand that earns respect without strong-arming. CI leaders, in particular, should learn how and when to apply these methods because without them, it can be hard to gain executive-level visibility. Get operational expertise, and fast. CI leaders often spend time delivering insight to colleagues in operational functions. But instead of being just a service provider, engage these individuals at a more strategic level. Consider trailing field sales agents or following a product development cycle from inception to market. Consider how these organizations and processes use data, how they function, and what operational challenges they face. Lead cross-functional task forces outside of the normal marketing and IT purviews, and look for opportunities to pilot and lead entire customer-focused functional teams. I believe that customer intelligence leaders, with their deep and broad understanding of customers, are the natural choice to lead organizations along the path to true customer centricity. They are the future CCOs. Agree? Disagree? I d love to hear your thoughts. u 14 Customer Intelligence Tames the Big Data Challenge

Don t Build a Database of Ruin by Fatemeh Khatibloo Many businesses today find themselves locked in an arms race with competitors to see who can convert customer secrets into the most pennies. To try to win, they are building perfect digital dossiers to use a phrase coined by Daniel Solove massive data stores containing hundreds, if not thousands or tens of thousands, of facts about every member of our society. In my work, I ve argued that these databases will grow to connect every individual to at least one closely guarded secret. This might be a secret about a medical condition, family history, or personal preference. It is a secret that, if revealed, would cause more than embarrassment or shame; it would lead to serious, concrete, devastating harm. And these companies are combining their data stores, which will give rise to a single, massive database. I call this the Database of Ruin. Once we have created this database, it is unlikely we will ever be able to tear it apart. I have become convinced that my earlier bleak predictions about the Database of Ruin were in fact understated, arriving before it was clear how Big Data would accelerate the problem. Consider the most famous recent example of Big Data s utility in invading personal privacy: Target s analytics team can determine which shoppers are pregnant, and even predict their delivery dates, by detecting subtle shifts in purchasing habits. This is only one of countless similarly invasive Big Data efforts being pursued. In the absence of intervention, companies will soon know things about us that we do not even know about ourselves. This is the exciting possibility of Big Data, but for privacy, it is a recipe for disaster. If we stick to our current path, the Database of Ruin will become an inevitable fixture of our future landscape, one that will be littered with lives ruined by the exploitation of data assembled for profit. But we can chart a different course in various ways. I think our brightest engineers can develop innovative privacy-enhancing technologies that will enable new techniques for data analytics that minimize costs to privacy. I hope that public institutions and industry, through self-regulation, will devise ways to better balance the burdens on privacy and the benefits of Big Data. If nothing else, I anticipate that society will slowly develop new norms for engaging with the massive amount of information collected about us, creating informal rules governing when and how it is appropriate to release, collect, and use data, the way minors have learned to speak and listen carefully on social networks. But every one of these correctives requires the same thing: time. We need to slow things down to give our institutions, individuals, and processes the time they need to find new and better solutions. The only way we will buy this time is if companies learn to say no to some of the privacy-invading innovations they re pursuing. Executives should require those who work for them to justify new invasions of privacy against a heavy burden, weighing them against not only the financial upside but also against the potential costs to individuals, society, and the firm s reputation. Companies should do this not only as a matter of good corporate social responsibility but also because it will likely square with the government s recommendations for protecting privacy, which seem to advise caution and deliberation, under the banner of context. Earlier this year, Federal government officials released two privacy reports the White House s white paper and the FTC s final privacy report that together describe a national privacy policy for the foreseeable future. Although the two reports vary on some particulars, they both point to context as a central, important, and fundamental measuring stick we should use to assess decisions that bear on personal privacy. The FTC report offers three broad recommendations: Privacy by Design, Simplified Choice for Businesses and Consumers, and Greater Transparency. In discussing the second recommendation a call for simplified and more transparent choice the FTC suggests a carve-out. Companies do not need to provide choice before collecting and using consumer data for practices that are consistent with the context of the transaction or the company s relationship with the consumer, or are required or specifically authorized by law. Under this standard, it might be consistent with the context, for a company in a direct business relationship with a customer to use that customer s information to deliver ads for its other services, but it might be inconsistent with the context thus requiring notice and choice to sell that information to third-party advertisers, the FTC explains. Similarly, the White House white paper defines a Consumer Privacy Bill of Rights, which would protect, among other things, Respect for Context. Consumers have a right to expect that companies will collect, use, and disclose personal data in ways that are consistent with the context in which consumers provide the data, the paper explains. These parallel pronouncements mean that companies that deal with personal information (meaning all companies, really) need to focus much more often than they featured comment FROM HBR.ORG Paul, I ll be quoting your piece in tomorrow s Forbes online. Thanks for the interesting ideas. Chris Taylor www.hbr.org 15

have on the history of privacy practices in their industries. Although neither report defines in depth what it means by the word context, to me the message seems to be do not push the privacy envelope. Companies that use personal information in ways that go well beyond the practices of their competitors risk crossing the line from responsible steward to reckless abuser of consumer privacy. The lesson is plain: compete vigorously and beat your competitors in every legitimate way, except when it comes to privacy invasion. Too many companies have learned this lesson the hard way, launching invasive new services that have triggered class action lawsuits, Congressional inquiries, and media firestorms. These companies knew that they were treading where others had feared to go. This may have felt like an exciting opportunity. It should have felt instead like perilous risk-taking because it meant hurtling beyond the contextual borderlands defined by past practice. u Understanding Customers in the Solution Economy by David Midgley Companies in all varieties of B2B markets have moved beyond selling products and services to offering complete solutions to their customers. Alstom keeps trains ready to run each morning for railroad operators rather than just selling the rolling stock to them. General Electric helps hospitals manage and use patient data rather than selling them the equipment and software to do the job. Hilti provides and maintains power tools for builders. Rolls-Royce runs the engines you see on the wings of your plane. Syngenta offers rice farmers planted fields. From the provider s perspective, selling solutions allows companies to differentiate themselves in commoditizing markets and to benefit from economies of scope across multiple profit and service capabilities. For customers, these solutions offer better value than the products and services that went before. After all, who would not prefer a solution to their business problems rather than simply buying services and products? My INSEAD colleague, Professor Markus Christen, and I have been researching solution strategies in a number of industries. We believe that it is the way of the future and that we are moving toward a solution economy where organizations focus on what they are really good at, relying on their suppliers solutions to take care of the rest. Getting there, however, is going to involve a bigger change than most suppliers realize most particularly in the way they gather information about their customers. For a start, there has to be general agreement on what the word solution actually means. Customers and suppliers often have different definitions. B2B customers regard a solution as something that helps their business. That is, a solution increases their revenues, lowers their costs, or reduces their risks and in doing so boosts their overall profitability. The trouble is suppliers don t always think about their solutions from that perspective. Many define a solution as a package or bundle of the products and services they already offer. And what they already offer may have no explicit link to an individual customer s business objectives, since the bundles of products and services are constructed to meet generic needs. Achieving a solution economy will require these providers to change their mindset. But this is not the whole answer. The solution itself has to add real value not seen before. If not, for all the interest shown by the supplier, the customer is going to view the solution simply as a volume discount offer. Creating that new value will require suppliers to combine their expertise with their understanding of the customer s business needs. This calls for changes in how B2B companies gather customer intelligence. Specifically, customer researchers need to: Ask different questions much more often. Most companies customer research focuses on how the customer uses or perceives the supplier s products and services rather than on how these help achieve the customer s business objectives. There needs to be a change, therefore, in the types of questions companies ask their customers and how the resulting data is interpreted. What s more, traditional market research is episodic, whereas the nature of solutions requires a more ongoing, relationshipbased approach. Periodic market research surveys or focus groups should be replaced by continual data collection from all interactions between the supplier and its customers. Observe the customer directly. Solutions often represent major innovations and, as such, customers may not always be able to grasp the value immediately. Traditional research techniques (surveys, focus groups, etc.) need to be complemented with observation of how the customer currently operates. And not observation by typical market research or salespeople rather, observation by personnel with the necessary technical and business expertise to spot opportunities. This is, of course, a challenge when there are large numbers of customers. Technology can help here. One company we know maintains sensors in customers factories so that it can monitor how the relevant parts of the customers operations work. The bottom line is that customer intelligence for the solution economy will look 16 Customer Intelligence Tames the Big Data Challenge

very different from what most companies do today. Customer orientation will become paramount. All customer-facing personnel will be involved; verbal, visual, and quantitative data will flow continually; and this data will be interpreted by people looking through the eyes of not just their company s expertise but also from the perspective of their customers. u Trends can be identified more quickly and precisely than ever before. But the fact remains that any trend, however early it s identified or robustly defined, can t tell you how to succeed. What Data Can t Tell You About Customers by Lara Lee and Daniel Sobol Across industries, companies are using the vast amounts of user-generated data to guide innovation of new products and services. But data mining does not equate to developing customer intelligence. Human behavior is nuanced and complex, and no matter how robust it is, data can provide only part of the story. Desire and motivation are influenced by psychological, social, and cultural factors that require context and conversation in order to decode. Data can reveal new patterns that point a firm in the right direction, but it can t indicate what to do once there. It reveals what people do, but not why they do it. And understanding the why is critical to innovation. A Wink or a Twitch? Think of the last time someone winked at you. With that simple gesture, the person was able to communicate. Yet, how did you know what it meant? Anthropologist Clifford Geertz posits that all of our behaviors are imbued with socio-cultural significance. Interpreting their meaning and the motivation behind them requires what he calls a thick understanding that comes from detailed observation of people s interactions and their environment. In the wink/twitch example, customer intelligence would only tell us that there was eye movement not what kind or what it meant. It misses the thick understanding that is critical to meaningful innovation. Several years ago, a client engaged Continuum to design new products for base of the pyramid (BOP) families in urban Brazil. We set out to understand the needs, values, and motivating desires of the people for whom we were designing. In conducting our field research, we observed that virtually every family owned a television. This was not a huge surprise any report can tell you the rising percentage of technology ownership among families in emerging markets. But when we dug deeper, we learned that the TVs were not status symbols or signs of increasing wealth; they were safeguards. Because of the violence prevalent in the favelas where these families lived, parents feared their children going out at night. What these parents really wanted was a way to make the living room more entertaining than the streets. Customer intelligence might have told us the percentages of TV ownership among BOP Brazilian families, but it never could have illuminated the why. Building on this insight, we leveraged our client s capabilities to transform a staple product geared toward parents into an engaging experience designed for kids. In prioritizing parents deeper needs, our client regained market leadership. When the Data Trail Goes Cold Increased computing power, ubiquitous consumer tracking, and ever-moreeffective data-mining techniques do offer significant advantages to business. When Clorox entered the green cleaning market in America, routine trend analysis had revealed that while the overall cleaning products market was stagnant, the green niche was growing. Basic consumer intelligence indicated that consumers were becoming more environmentally conscious but that people often didn t know how to act upon their changing values toward green. The company s own intelligence suggested the emergence of a new and underserved segment of chemical avoiding naturalists who had not been attracted by existing offerings from Seventh Generation and Method. But that s where the data trail ended. Abandoning quantitative data analysis, Clorox conducted in-depth interviews and in-home ethnography to better understand the psychology, unmet needs, and underlying values of these naturalists and what it would take for them to switch to green home-cleaning products. The insights gained allowed Clorox to stake out a positioning of natural vs. sustainable that resonated with this segment. They designed not just a product but an entire experience comprising utility, accessibility, aesthetics, information, and emotional resonance. Clorox Green Works has been credited with not only dramatically expanding the market for environmentally friendly cleaning products in the United States, but also helping the sustainability movement gain traction by revealing that for mainstream consumers, environmental concerns are first and foremost about what s in me, on me, and around me. Knowing Too Much In an effort to be more customer-centric, companies today often jump to apply their customer intelligence to guide product and service innovation. Armed with data, companies feel they know their consumers. But knowing about someone is not the same as knowing them. Confusing the two is the difference between a transaction and relationship. www.hbr.org 17

Recently, we spoke with a young man who had soured on his medical provider when they leveraged customer intelligence to help him. While driving to work, Kurt received a call from a woman on behalf of his doctor s office. The system showed that he had not refilled his depression medication in several months. She asked him if he was still taking the medication and reminded him that it was important. Kurt found the call extremely uncomfortable and paternalistic. It s weird to be an adult and have some stranger call to tell me to take my meds, says Kurt. It felt like someone was watching me, making sure I followed orders. Kurt s doctor likely had the best of intentions: keeping Kurt well. But Kurt was left feeling that the office had exploited its knowledge of his behavior to control him. In fact, Kurt intentionally stops taking his medication every once in a while to evaluate whether he still needs it. Yet the doctor s office took none of these factors into account in its interaction coming off as Orwellian instead of caring. There are certainly ways to use customer data to strengthen relationships and improve people s lives. But those actions and interactions must be thoughtfully designed to respect people s values and build trust. To innovate for a future in which consumers desires and habits change as quickly as their mobile devices, businesses must be nimble in delivering emotional connections beyond just functional utility. That requires understanding customers as people nuanced, dynamic, and unpredictable not just collections of data. u featured comment FROM HBR.ORG Wonderful article! Beyond the arguments of quantitative vs. qualitative research, this speaks to the heart of customer engagement Jmiller 18 Customer Intelligence Tames the Big Data Challenge

Retailers Turn to Soft Surveillance to Fight Customer Anonymity by Robert Plant Families that shop together have long represented a missed data opportunity for retailers because there has been no way to collect information about children if their mom is the only person interacting electronically with the store at the register. That s about to change. Facial-recognition software can now identify groups sizes and estimate members ages, which could allow stores to provide the customers with targeted displays. For example, a car dealership could put minivan ads on monitors as a family walks up to the showroom door. If a tree falls in the forest and no one hears it, does it make a noise? That age-old question resonates with the specialists who provide retailers with customer intelligence. In their case, the question is this: If a shopper leaves without making a purchase, is there any way for a store to know she was there? And why didn t she become a paying customer? Until recently, customer Intelligence has focused on understanding consumers through historical data. For example, Tesco uses vast data sets culled from decades of customers Clubcard use to make pricing decisions, influence shoppers to buy specific items, and build loyalty. But that approach doesn t tell a firm about a new or transient customer. Can anything be learned about shoppers who don t carry loyalty cards? What about cash customers? Casual browsers? Shoppers who come in as families or as gaggles of teenagers? The technology ecosystem known as the Internet of real-time data is moving us toward a better understanding of what these consumers are thinking and doing. This ecosystem, which will become increasingly integrated into the fabric of consumers lives and society as a whole, is an extension of the Internet of things an estimated 50 billion sensors that are capable of sending event data to anyone authorized to listen in. Many homeowners, for example, have connected their alarm systems to security companies; many drivers accept having devices such as mileage trackers in their vehicles. In retail environments, companies are aiming to be able to identify all shoppers, connect them with their shopping profiles, and either sell them something or at least gain enough data about them to help make a sale during the next visit. One way to do all this is to encourage shoppers to use an app while they re in the store. That lets the retailer track their movements while sending them coupons or suggesting purchase ideas. High-speed processing is a must because customers don t linger long. Leading proponents of this approach use the data-talks-to-data concept developed by IBM s Jeff Jonas and his team in the Entity Analytics group. An emerging and more radical approach known as soft surveillance, makes use of concepts such as DNA testing that are more usually associated with security services. To ensure the safety of passengers and workers on buses in the UK, drivers have been given swab kits so that if a passenger spits, for example, they can take a sample that is then compared with a national DNA registry. Policies like this have led to a 50% drop in crime in Merseyside, Liverpool. Researchers at the Fraunhofer Institute have created a system of electronic noses that can smell explosives or other chemical substances. When combined with laser scanners and embedded into the walls of buildings such as airports, these sensors can detect threats, alert security services, and track a person carrying a suspicious substance. With modifications, this technology could identify perfumes worn by shoppers and send related ads and discount offers to video screens in elevators or corridors. Fraunhofer has also pioneered eye-tracking systems that can identify consumer preferences in stores. Retailers will soon be able to use this technology to present a shopper with a coupon or other value-added service as they browse. Futurist and hacker Pablos Holman of Intellectual Ventures has shown how an RFID reader can wirelessly glean details from a credit card that never leaves your pocket. In theory, at least, data of this sort could be gathered at building entrances. To prepare themselves for this new age, retail executives should follow three steps. Decide on the overall data-collection goal and determine how invasive the company can be, given the laws and standards in each region where it operates. For example, the EU s data-protection provisions are stricter than those in the US. The company also needs to think about how its data-collection efforts might affect its relationships with customers. Develop and execute prototype solutions to gain an understanding of the opportunities that these systems and their data present, how these move the firm closer to its customer-intelligence goals, and what ethical frameworks need to be established to protect both the company and the consumer. Work toward creating a unified framework for the integration of systems and data. This involves combining softsurveillance data with information from existing business-intelligence and analytics solutions. Sophisticated new data-gathering systems are designed to both improve profitability and help the consumer save time and effort. But companies need to beware of making shoppers feel that they ve entered an Orwellian world where Someone Is Always Watching. u www.hbr.org 19

featured comment FROM HBR.ORG Gaining non-purchasing customer data is incredibly powerful. Feedback tools have enabled retailers to hear straight from the in-store browsers, and the data can be mapped against other data sets to provide a holistic view across the channels. Simon Rowland Customer Experience Should Be Part of Your Business by Harley Manning What s the best way to optimize your customer experience? Why not fix it where it happens? Improve the experience on your website. Improve the experience in your retail locations or call centers. This strategy makes perfect sense and it aligns nicely with the way your company is probably organized with the website, retail locations, and contact centers each in their neat little silo. But based on our research, this natural strategy doesn t work because it lacks any understanding of the larger, cross-channel journeys that your customers take. For our new book, Outside In, we researched a number of companies that overcame the multi-channel dilemma systematically by applying business discipline to the practice of customer experience in an integrated way. Here are three of their most effective strategies. Create an enterprise-level customer experience team Is the answer to blow up your channel silos and organize your company in a radically different way? No. Each channel needs experts with specialized knowledge, like how to code an app for a smartphone or how to design signage for a retail location. As FedEx concluded, the real answer is to create a team of experts with specialized knowledge about customer experience and to place them outside of any silo. That s why the shipping giant formed the Channel Strategy and Orchestration team in 2008: to solve the problems that occur when customers move from one channel to another, like going from a website to a phone agent. Through a research process that focuses on understanding customer journeys, the team identifies opportunities for improvement. Then they plan improvement projects and engage the relevant business owners in their efforts. To date, the team s reception by those business owners has been extremely positive: They re typically aware that they have a problem but aren t in a position to fix it without someone to coordinate efforts with their peers in other channels. Uncover and map customer journeys Qualitative research methods like those used by FedEx reveal customers real goals, perceptions, and behavior, including how they choose interaction channels and why they switch channels. Customer journey maps visually illustrate those findings by showing the series of events that make up a customer s interactions with a firm over time. The maps help companies find problems that occur in the white space as a customer passes from one channel to another. To help guide initiatives aimed at transforming the company s customer experience, Virgin Media in the UK set out to map customer journeys. As the largest Virgin company in the world, it s also the UK s largest mobile network operator and its second largest provider of residential broadband, home phone, and pay-tv services. Therefore, the challenge for Virgin s customer experience team was to create a consistent Virgin-quality experience not just across multiple channels but across different product lines as well. 20 Customer Intelligence Tames the Big Data Challenge

How could they understand that level of complexity from the customers perspective well enough to improve it? The team started by mapping six unique journeys, including joining (subscribing), paying, and getting help. Their map, an ever-evolving work in process that s been in use for years, is striking: a giant sheet of brown butcher paper covered in red pieces of tape and multi-colored sticky notes. It links all six journeys together in a continuous flow that crosses five functional silos within the business. more commitment than what companies typically apply to practices they perform routinely in business disciplines like marketing, pricing, and logistics. But the business benefits of improving customer experience across channels benefits that are now clear from our research and others remain in the cloudy, undisciplined nice to have parts of most businesses. It s time to put customer experience on par with other business disciplines so that every part of the business thinks from the customers perspective from the outside in. u Appoint a chief customer officer Over the past six years, we ve seen an increase in the number of companies that have a single executive leading customer experience efforts across channels and business units. Whether firms call them chief customer officers or give them some other label, these leaders sit at high levels of power in organizations as diverse as Cleveland Clinic, Fidelity, General Motors, and The Washington Post. Companies typically appoint a chief customer officer (CCO) to drive change that needs to cut across channels and business units. In the case of Walgreens, CEO Greg Wasson personally recruited a CCO to help bring about his vision for transforming the company. That vision includes reinventing the pharmacy by letting customers do things like ordering prescriptions online then picking them up at in-store kiosks. That in turn frees up pharmacists to get out from behind their counters which are lower in Walgreens new store formats and spend time counseling customers. Completely transforming how you interact with customers is a larger, more complex task than simply optimizing channels to work better in concert. Walgreens CCO does a number of things to make that transformation happen, including organizing facts gathered through customer understanding programs to clarify what customers actually want. Based on that understanding and the company s strategy, he leads change management efforts aimed at improving the customer experience. And he measures results to make sure that transformation efforts are actually happening and producing positive results. Adopting these three tactics requires no Using Market Research Just for Marketing Is a Missed Opportunity by Werner Reinartz In most companies, customer intelligence that is, the collection and analysis of customer data is largely used for improving customer relationships. Understand your customer s buying cycle better and you can target your mailings and product offerings more effectively. Smart companies realize that intelligence about their customers can actually lead to a lot more than greater marketing effectiveness. It a powerful tool for identifying innovations, especially with the opportunities for data gathering afforded by smart technologies and social media. To begin with, companies can gather data about their existing customers and use that to dream up new products and services to offer to those customers. Take Fenwick, a leading forklift truck manufacturer in France. Fenwick installed data-collecting sensors and radio frequency identification (RFID) technology in its forklifts, through which it amassed valuable information about how customers used its equipment. It used the resulting knowledge to develop new service offerings, including remote monitoring, a customer-specific intranet, and a school for forklift drivers. Today, 50% of its 500 million in revenues comes from services developed over the past 15 years. The Internet has provided a huge boost to this kind of intelligence gathering. Many firms deploy web-based tools to let customers autonomously make purchases, check their purchase records, track shipments, or simply access information about products and product applications. Over relatively short time periods, these customer interactions create a massive database in which customer profiles, transactions, and inquiries are all associated. The great advantage for organizations here is that they can draw on potentially very large samples, which makes analysis of the data very robust. But intelligence in the Internet age isn t just about tracking what existing customers think about the products they buy and how they use them. Companies can also find opportunities by broadening their definition of customer intelligence to include www.hbr.org 21

analyzing public discussions by consumers in the online world. This was exactly what Schwarzkopf, one of the leading hair-care companies in Germany, did when I worked with them recently. The company wanted to improve the performance of its website in terms of traffic and sales. To understand what hair-care consumers were actually looking for, we monitored and interpreted (via word-interpretation algorithms) millions of consumer conversations in blogs about hair-care problems and products. What we found was that, in their online conversations, consumers focused on their hair-care problems rather than on the attributes of specific brands or products. As a consequence, the company restructured the website so that consumers came to the product offerings through their hair-care problems. Instead of a list of brands and products, the home page now focused on hair types (stressed hair, thin hair, blonde hair, and so forth). The consumer came to a choice of appropriate products by navigating through a discussion of hair problems. Website traffic tripled after the changeover, and word-of-mouth activity in online blogs referring to the website increased more than tenfold. New applications like Facebook s Glimpse are potentially a very valuable source of this sort of intelligence. Glimpse builds LookBooks of peoples likes, visual depictions of preferred products, conversation topics, celebrities, and so forth. They allow firms to understand the context that customers put brands into and open up strategic opportunities that managers would otherwise miss completely. The challenge in obtaining this kind of intelligence is identifying the online environments or groups that hold the best data. Is your company s market research department up to the challenge? u featured comment FROM HBR.ORG Nice article! 360 Customer Intelligence is the Holy Grail for any organization. amitsomani Does the 18-to-49 Demographic Matter Anymore? An HBR Management Puzzle by Horst Stipp and Jeffrey McCall even though both have about the same 18-to-49 rating. So yada yada yada, the days of bluntly going after the 18-to-49s were over, she had been arguing. But Donny, who d spent 40 years in consumer advertising, had a gut feeling that no such technology, no matter how dazzling, could ever beat the value of aiming ads at 18-to-49-year-olds. He needed to convince her of his viewpoint before they met with the corporate parent s marketing head tomorrow to plan the next phase of the company s American campaign. Donny and Kaisy had just flown in from the US and were being chauffeured to their Bologna hotel when Donny suddenly told their driver to stop. Kaisy looked puzzled as well as jetlagged. No point in arguing abstractions, Donny said, throwing open the car door. I m going to show you what I mean about the value of the 18-to-49 demographic. He and Kaisy had spent most of their flight debating how to approach a new American marketing campaign for their employer, Fiero, an Italian stove maker. Kaisy, a techsavvy 26-year-old, was advocating a whole new approach to understanding consumers. A vendor named Settop Analytics could harvest household TV-viewing data from the cable companies and cross-match it with survey findings and credit card information from merchants to tell Fiero that, for example, 4% of the viewers of a Fiero ad on the show Modern Life are in the market for a stove, compared with just 2% of the viewers of America s Got Brains, (Editor s note: This fictional Management Puzzle dramatizes a dilemma faced by leaders in real companies. Please contribute by offering insights, solutions, and stories from your own experience.) Donny was now leading Kaisy out of the car and into a showroom that he had happened to notice from the road as they were passing by. It was full of appliances, including some from Fiero. All your new, high-tech metrics that show you which TV shows work well for selling 22 Customer Intelligence Tames the Big Data Challenge

which products that s all fine and good, Donny said. But at the end of the day, demographics trump everything. Come meet your customers and yourself. In the elegant interior, high-tech lighting contrasted with the exposed brick and beams. Let s find an 18-to-49, Donny said, making his way through the showroom. In the refrigerator area, he startled a young couple by making an attempt to communicate in broken Italian. Fortunately, both spoke English, and they brightened when Donny explained that he and Kaisy worked for Fiero. Donny asked them to talk about themselves. When they hesitated, he did it for them: You re young, Donny said. You re building your lives and your careers. And maybe a family? They grinned shyly and nodded. You need lots of products to fill up your life. You probably have a new apartment? They nodded. You re looking for a refrigerator now, but maybe a stove later. The economy here sucks excuse me, I meant to say it s terrible. But you have good jobs, you have a bit of money, and you like to spend, if you can find value. Right? They laughed. Right, they said. Donny turned to Kaisy. You see? he asked. Kaisy jumped in: How old are you, if I may ask? The man said he was 28. How old is your youngest sibling? Twenty-one. Do you think he ll be shopping for a refrigerator or a stove anytime soon? He s riding a skateboard in the piazza, the man replied. Kaisy said to Donny, Eighteen to 49 is vast. It s a hodgepodge of demographic groups that have very little in common. She said to the man, It might even include your parents. No, no, no, they are in their 50s, he said. But as a matter of fact, they just bought a new stove! They got a new apartment when my brother moved out. He added in a low voice, Not a Fiero, I m sorry to say. You re too good to be true, Kaisy said. To Donny, she added: Listen to what he said. A lot of consumer spending happens from people outside the 18-to-49 demo. Fiero doesn t even advertise to that age group because we re so focused on 18-to-49. The driver, who had gotten out of the car and found them, was peering at them questioningly. We have to go, Donny said. Donny, you don t seem to realize that Settop Analytics gives us a real chance to get beyond demographics, Kaisy said as they abruptly departed, leaving the couple looking baffled. Our current advertising strategy of searching endlessly for channels that deliver the biggest numbers of 18-to-49 eyeballs is way too crude. With Settop, we can be more precise. Hearing no response, she added, Using Settop s technology, we can focus our advertising on real buying intention, not some random slice of the public based on age. We can know what the return is on every aspect of our ad campaign. We can target our dollars to TV shows that appeal to people who might really buy our product. Donny spoke up: What if that vendor is selling you snake oil? he asked. And even if there are some good data, the reality is that this new technology would be a nightmare. Imagine trying to make sense of all those statistics that might or might not be telling you something you need to know. Cable TV data, credit card data, survey data imagine all the trial and error, putting this or that ad on this or that show, and waiting for the numbers to come back. I d feel like I was back in high school biology, trying to grow fruit flies. You don t like it because it s science, she said. But what Settop really gives us is freedom. We can free ourselves from the 18-to-49 mind-set. We can put our ads on shows that deliver likely buyers of stoves, whatever their ages. That s the case I m going to make tomorrow to Sergio, our new marketing VP. This is the new world, Donny. I have a sense that Sergio is going to be very receptive to it. The old guard is gone at Fiero, and Sergio is new blood. Donny stared out the car window. You know, Kaisy, I ve been hearing about the death of demographics for a long time. But think about that guy s brother, the skateboarder. Our ads on his favorite shows may not have an impact on him right now, but while he s thinking about which skateboard to buy, he ll also be making a mental note about the Fiero brand. And if our ads are effective, he ll keep that mental note in his head for a long time as he eventually gets married and starts a home. On a fundamental level, advertising is about building image and creating memory. Buying time on a show that delivers 18-to-49s is taking refuge in safety, Kaisy replied. That s what Fiero has done for decades. It s like Nobody ever got fired for buying IBM. At Fiero, nobody ever got fired for buying up a bunch of ratings points in shows that deliver high 18-to-49 numbers. But Sergio s in charge now, and I m hoping he ll listen to reason. Question: Should Fiero stick with its approach of appealing to 18-to-49-year-olds or use technology that reveals the impact of specific advertising channels? www.hbr.org 23

Use Big Data to Predict Your Customers Behaviors by Jeffrey F. Rayport It s tough to make predictions, especially about the future. So said Yogi Berra, baseball great and amateur philosopher. Sensible (and amusing) as it sounds, his dictum no longer rings true. The Age of Big Data has arrived and, with it, the ability to predict the future is increasingly a part of a new business reality. Whatever your discipline, doing business today means immersing yourself and your organization in a wealth of messy, unstructured, real-time data from customers, competitors, and markets and finding ways to use such data visibility to see what s coming. Advantage lies in a capacity to predict the future before your rivals can whether they re companies or criminals. Consider how the New York Police Department is using Big Data to fight crime in Manhattan. According to a series on Big Data in The New York Times, the NYPD and other big city police departments are using data-crunching technology to geolocate and analyze historical arrest patterns while cross-tabbing them with sporting events, paydays, rainfall, traffic flows, and federal holidays to identify what NYPD calls likely crime hot spots. As immortalized in a Smarter Planet commercial from IBM, such insight can help deploy officers to locations where crimes are likely to occur before they are actually committed. The beauty of such Big Data applications is that they can process web-based text, digital images, and online video. They can also glean intelligence from the exploding social media sphere, whether it consists of blogs, chat forums, Twitter trends, or Facebook commentary. Traditional market research generally involves unnatural acts such as surveys, mall-intercept interviews, and focus groups. Big Data examines what people say about what they have done or will do. That s in addition to tracking what people are actually doing about everything from crime to weather to shopping to brands. It is only Big Data s capacity for dealing with vast quantities of real-time unstructured data that makes this possible. For example, retailers like Wal-Mart and Kohl s are making use of sales, pricing, and economic data, combined with demographic and weather data, to fine-tune merchandising store by store and anticipate appropriate timing of store sales. Similarly, online data services like eharmony and Match.com are constantly observing activity on their sites to optimize their matching algorithms to predict who will hit it off with whom. The same logic is being applied to economic forecasting. For example, the number of Google queries about housing and real estate from one quarter to the next turns out to predict more accurately what s going to happen in the housing market than any team of expert real estate forecasters. Similarly, Google search queries on flu symptoms and treatments reveal weeks in advance what flu-related volumes hospital emergency departments can expect. Much of the data organizations are crunching is human-generated. But machine sensors what GE people like CMO Beth Comstock called machine whispering when I talked with her this past summer are creating a second tsunami of data. Digital sensors on industrial hardware like aircraft engines, electric turbines, automobiles, consumer packaged goods, and shipping crates can communicate location, movement, vibration, temperature, humidity, and even chemical changes in the air. As the volume of both human and machine data grows exponentially, so too will organizations ability to see the future. The net of all this is hardly a cold quantitative world. Rather, as marketers and machine systems learn more about our attitudes and behaviors, they re likely to achieve greater intimacy with consumers and customers than ever before. Yes, there is the risk of an Orwellian nightmare if the inferences from Big Data become too intimate and too intrusive and end up in the wrong hands. But there is also the opportunity to deliver services and marketing with unprecedented precision and accuracy, meeting and exceeding customer expectations in preternatural ways at every turn. Knowing the right time to deliver the right message (or action) in the right place before the time has come will bestow extraordinary power to those who wield such intelligence with intelligence. Use prediction wisely and Big Data has the potential to make the world small again. That is every marketer s dream: getting closer to customers. u featured comment FROM HBR.ORG Jeff, great thoughts here. What excites me about Big Data is that one can use it to avoid prediction altogether. Erich Joachimsthaler 24 Customer Intelligence Tames the Big Data Challenge

How One Company Uses Customer Data to Drive Sales by David K. Williams and Mary Michelle Scott to share. Using that data wisely (and treating the customer partnership with impeccable care) will be the soundest economic decision a company can make. u Additional reporting for this article was provided by John Erickson, VP of Training and Support for Fishbowl. featured comment FROM HBR.ORG How much data should you know and record about your customers? What should you do with the information you gather, and how much is too much? Every company needs to answer those questions for itself. Here s how we approach the task at our 100-person inventory software company. We collect as much business-specific information about every customer as we possibly can. The results range from a few pages gathered to see if our product was the right fit for the customer to sometimes even dozens of pages of highly detailed and even personal information such as individual names and roles within the company. The data goes into a master customer-relationship management, where we put it to active and regular use. We never share or sell the data. However, the secret to our success is that we use the information we gather in a very high-touch and very personalized way. We try to have our account leads make a personal call to every account approximately every quarter. The goal of these calls is to get clients to suggest the ways to tune the software, services, and processes what to add, what to consider, and possibly even what to downgrade to conserve cost. When we use the data this way, customers are generally happy to give us progressively more. We estimate that 10 15% of our revenues are won (or retained) as the direct result of our use of this specific customer information. In a down economy, that 10 15% could mean our entire profit, won or lost as the result of information we ve gathered and how we use it. We use customer data to direct our product development priorities and features as well. For example, years ago we got significant feedback from customers about improvements they like to see in software that manages customer returns. Based on this information, we built the basic pieces and released them this beta version as a means to get feedback about how our customers felt about the core functionality. We fully expected them to say, So far so good now go and finish the other 75% of what we asked. Instead, they said, It looks great, and that s all we really need. The feedback saved us from having to overbuild and overcomplicate, and it saved approximately $200,000 in development resources. In another case, sales data from a data report we affectionately call ProCat (revenue by product category) was able to tell us that we needed to make some tweaks to how we sold one of our core products. This early warning arrived long before we d have seen the issue in our standard sales reports. We immediately implemented the changes, and sales of our core product doubled immediately. Had this gradual decline continued, it would have created a major catastrophe approximately 12 months down the line. The cost to build the ProCat report each month is essentially free. While businesses should clearly obey legal requirements and respect individual privacy, we believe that any business should gather as much data from their customers as they can possibly get. It s the personalized and high-touch use of the data that makes customers willing (and even happy) The thing I like about this article is that you present data usage tactics that are both simple and powerful and can be used by virtually any company, big or small. Jim Watson www.hbr.org 25

How to Find Out What Customers Will Pay by Rafi Mohammed It s one of the most fundamental decisions that every business must make: What price should I charge? The right answer to that question is a company should charge what the market will bear in other words, the highest price that customers will pay. Unfortunately, few companies use this approach. Instead, prices are usually set using that s the way we always do it processes such as marking-up costs, matching competitors, hallway discussions, and backof-the-envelope calculations. Sound familiar? While easy to compute, these methods don t incorporate the most important component of setting a price an understanding of how much customers will pay. As a result, these prices have no correlation to what the market will bear. This leads to lost profits. So how do you find out how much your customers will pay? It s simple: Ask them. Next, ask a series of general satisfaction questions, and be sure to include the following pricing-related questions: featured comment FROM HBR.ORG Pricing my services at the right level is one of my biggest headaches (at the moment). What you suggest is something I hadn t thought of trying, but I sure will now. Thewritevintage Of course you can t ask customers directly how much they are willing to pay they ll likely shade the truth (by giving a lower price) to their benefit. That said, there are a variety of ways to better understand how your customers think about your price. In my work, I often interview customers, seeking to identify areas of disconnect profit opportunities where customers view pricing differently than management. Here are the secrets that I use to better understand how customers think about pricing: First, position the interview as a customer satisfaction survey. Your goal is to understand what customers like or value about your product or service and gain insights that will better serve them in the future. Customers appreciate being asked how they can be better served and often become chatty. 26 Customer Intelligence Tames the Big Data Challenge

1. What rival products did they consider purchasing? If customers tell you they do not bother to look at other products, this is a clear signal of an opportunity to raise price. Alternatively, those who consider several alternatives are likely to be more price sensitive. This question is the start of creating a pricing-related customer segmentation identifying characteristics of customers who care about price and those who don t. In a B2B environment where prices are individually negotiated, this segmentation is critical to determining the right price for each customer. The Big Goal Behind All That Customer Data by Bill Lee 2. What do they think about your price: too high or too low? Don t probe too much simply ask and listen. Some interviewees will discuss at great length while others will clam up. Take what you can get on this question and move on. 3. Ask what other features they would like to be added to the product? These insights will help companies better understand what customers value and what they re willing to pay a premium for. This can lead to innovation: new addon offerings as well as good-better-best versions. I always like to ask what insurance options customers would value too. 4. Ask what they like and don t like about your pricing strategy. This open-ended question provides interviewees an opportunity to discuss pricing. 5. Ask if they like the way they purchase your products or if there are other ways they would prefer to buy your product. Most companies sell products using à la carte pricing pay per transaction. But there are many other innovative pricing techniques that can be used to sell a product (e.g., two-part, all-you-can-eat, guaranteed future price, success fee, rental, lease, etc.). If customers prefer a different method of setting prices, be open to meeting their needs. Answers to these five basic questions lay the foundation for new profit and growth pricing initiatives. So what do you think? Does your company charge what the market will bear? Are you wary of asking customers pricing-related questions? What other questions would you ask? u Big Data is working hard to get into the minds of customers and uncover accurate information about how the customer really feels, thinks, and responds to products, services, advertising, and brands. But like eager field scientists exploring a verdant new continent, companies attempting to navigate and leverage Big Data risk getting lost in the weeds. Now still early on in our understanding of Big Data s true impact is the time to avoid the most common mistake that companies make when embarking on tech-based research about customers: losing their way amidst all the complexity of systems issues, technical possibilities, and implementation snags. Instead, when it comes to Big Data, your firm must stay focused on the business outcomes you want to achieve. And in many firms, the most promising outcome is unleashing vast stores of hidden wealth their customers can create. Thus, the most important question regarding Big Data at almost any company is this: How much are your customers really worth? Uncover the Hidden Wealth of Your Customers It s a remarkable fact that most companies can tell you to the penny how much their furniture is worth but don t have a clue about the real value of their customers. Neither do their bankers or investors. And of course, customers are the basis on which firm value rests. The most forward thinking companies are adopting some form of Customer Lifetime Value (CLV) to assess the value of customers to their bottom lines. But by now, CLV is highly limiting and narrow it only assesses the value resulting from a customer s purchases of your products and services. And in today s increasingly networked, social media-infused reality, that s missing a whole lot of customers who can generate value far beyond anything they buy. Purchasing is just one way and often not the most lucrative way customers can create value for your firm. That s because customers are your most credible and persuasive marketing and sales resources much more knowledgeable about buyers needs and much less expensive than the resources you re probably currently using to grow your business. Big Data needs to capture this reality and focus your organization on it. Expand the Value You Harvest From Customers Consider a customer we ll call Catie. She s a solid, if modest, purchaser of your products and services. Let s say she purchases $6,000 per year, resulting in a profit of $2,400 (feel free to use your own numbers for a solid if modestly profitable customer in your business). Her CLV might be $7,000 or so, and that s where even companies with advanced analytics stop. But Catie blogs regularly and participates in a variety of social media and professional networks networks that have plenty of potential buyers. And she s respected. She s also interested in speaking at her professional association meeting and would be willing to talk about the success she s enjoyed on an important project www.hbr.org 27

success that your product or service helped to make possible. She also scores high in your Net Promoter Score surveys, but at the moment (like most promoters ), she s never actually referred a profitable customer to you. That s because you haven t reached out to her to do so. And that, in turn, is because these characteristics of Catie s and the potential value they would bring haven t shown up from Big Data. By the way, Catie is not fictional. She s a composite of actual customers such as Salesforce.com s MVPs (Most Valuable Professional), SAS Canada s Customer Champions, and other Rock Star customers that I write about in my book, The Hidden Wealth of Customers. Now suppose you identify her as a modest buyer but a high-potential referral and lead-generating source. So you engage her by giving her speaking opportunities, you co-present at her industry association, host a webinar featuring her, invite her to local customer forums, and give her a leadership role if she s interested. And suppose as a result of these activities over the next year, Catie generates four direct referral customers and another 17 additional leads who become customers (remember, unlike the webinars and speeches by your executives, hers are much more credible to buyers because she s one of them). That makes her referral and influencer value to your firm (after allowing for other factors that influenced purchase decisions, along with the cost of supporting Catie s advocacy) equal to $33,080 or 13.8 times her value as a purchaser. A primary goal of Big Data must be to show you this potential and to record it as it s realized. a job done or provide an enjoyable experience is likely just not that important to their lives in the grand scheme of things. So let s take customer value up a notch, to a higher, richer dimension. SAS Canada, for example, gives its Customer Champions opportunities to take leadership positions in its customer communities, increasing their professional networks and building their professional reputations. Salesforce.com seats its MVP customers in the front row of its massive annual Dreamforce event and keeps them updated with important industry information. In our example with Catie, you can help her build her professional visibility with speaking and webinar engagements and increase the reach of her blog by providing insider information that other bloggers don t have access to. Companies are creating much greater satisfaction than getting a job done. They re helping customers build social capital. They re improving customers lives. Other leading firms such as Procter & Gamble, Lego, and Hitachi Data Systems engage in similar activities. Does your data capture such opportunities? Does it show which customers are influential on the web? Which are plugged into and respected by networks attractive to your firm? Does it reveal customers who want to gain recognition in their peer groups and who have great stories they can tell (based in part on help from the product or service you provide)? Make sure you re not getting bogged down in Big Data. Instead, focus it on unleashing the hidden value of your customers. u Expand the Value You Create for Customers Improving the customer experience is a fine idea. But companies often take it to extremes. It s always a good idea to look for new ways to create value for customers. But focusing only on doing so through your product or service is entirely onedimensional. The hard reality is that your product or service, however great it is however much it helps your customers get 28 Customer Intelligence Tames the Big Data Challenge

Free Customers Are More Valuable than Captive Ones by Doc Searls Put down the customer. Step away from the marketplace. That s what Craig Burton once said to a clueless marketing officer at a meeting we both attended a few years back. It was one of the most right-on things I have ever heard uttered inside a company. It also comes to mind every time I hear unwanted surveillance of customers rationalized for marketing purposes or how Big Data lets a company know a customer better than she knows herself. We hear lots of that jive lately, and it makes full sense only to business people talking to other business people. To most customers it s creepy, regardless of how many Chief Experience Officers get hired or how many sales pieces lauding the Chief Executive Customer get distributed. What s rarely heard amidst all this talk about customer intelligence is the customer s own voice, expressing her own agency as an independent actor in the marketplace. Instead, many companies continue to talk to themselves about acquiring, managing, controlling, and locking in customers and to create systems for that, described with marketing euphemisms that fool vendor and customer alike. One example is loyalty programs that are often nothing more than coercive gimmicks to provide discounts that aren t or rewards that are barely worth the hassle especially when they make customers carry around different cards for every store, each with its own way of delivering a relationship experience. The problem with having as many different experiences as there are vendors is apparent only from the customer s side of the marketplace. And, as long as solutions to customer experience problems are sold only to vendors who use those solutions to increase switching costs and other annoyances, the frictions for both customers and vendors will only increase. The best way to fix the customer s experience problems is from her side of the marketplace: the demand side. And, in fact, many demand-side solutions are being developed and moving forward, mostly below the supply side s radar. The intelligence behind these solutions is the customers own. I m involved in that work, and I m here to report on it. For the past six years, I ve led ProjectVRM at Harvard s Berkman Center, fostering development of tools and services that give customers two advantages: 1. Independence from vendors; and 2. Better ways of engaging with vendors. VRM stands for vendor relationship management. Think of it as the customer-side counterpart of CRM, or customer relationship management. With VRM, customers have ways of speaking their wants, needs, and preferences in relationships with individual vendors and simultaneously at scale in the open marketplace across many vendors. For example, think about what a pain it is to change your contact information for every vendor separately. If you could do it once for all of them, that s working at scale for you. The first and most obvious tools for indefeatured comment FROM HBR.ORG You [Bill] raise the excellent point that unless we humans know what to look for, [Big Data] won t capture relevant information and will give us skewed results. Dorie Clark www.hbr.org 29

pendence are browser add-ons for blocking ads and tracking of users. According to ClarityRay s Adblock Report issued in May of this year, the overall rate of ad-blocked impressions in the US and Europe is 9.26%. Even if we discount the source (Clarity- Ray s business deals with ad blocking), the rate of ad blocking is substantial. Mozilla shows 170.5 million downloads of Adblock Plus, with more than 3 million downloads in the last 30 days alone, and an average of 13.9 million daily users. That s for just one add-on for one browser. People are also taking action against unwanted tracking. All the major browsers support some form of Do Not Track (DNT) signaling by browser users to websites, and Microsoft is committed to turning it on by default with the next version of Internet Explorer. But to engage, VRM can t just draw lines in the sand. It will also provide ways to cross those lines, offer a handshake, and back that handshake by demonstrating new and better ways of doing business. For example, at ProjectVRM, we want to add dialog to DNT (informally, we call it DNT-D). With DNT-D, the user can signal an openness to engagement with the website. One dialog might go like this, entirely in the background... USER: I don t want to be tracked, but I m open to dialog. we can move forward. SITE: Here are the interfaces to our CRM system so your VRM system can interact with it. USER: Good. From now on, my browser s r-button will tell me when I m at your site that we have a working relationship, and I can see what s happening on both sides of it. That s just one example. You can also expect VRM tools and services to do one or more of these other things as well, just for starters: Provide a way for the individual to gather and store his or her own data from transactions in a reusable way. Provide ways for managing the use of that personal data in relationships with vendors. Provide ways to intentcast demand for products and services to whole markets, outside any one vendor s silo. Provide means by which levels of trust can be established between customers and providers and put to use. Provide ways for individuals to express their own policies, preferences, and terms of service,which can be matched with those of companies open to negotiation. Serve as agents for individuals, known as fourth parties. You can see a growing list of these on the ProjectVRM wiki. You can also follow them on the ProjectVRM blog or with the #VRM hashtag on Twitter. You can join the conversation by getting on the ProjectVRM mailing list, which has more than 500 members. You can also join Customer Commons as a customer. (Customer Commons tag line is We re the 100%. ) I launched ProjectVRM because I wanted to make good on a promise laid out by The Cluetrain Manifesto, a business bestseller I coauthored with Chris Locke, David Weinberger, and Rick Levine six years earlier. While Cluetrain is best known for its 95 theses (e.g., Markets are conversations and Hyperlinks subvert hierarchy ), it summarizes its case in a statement that appears above all the other theses, under the line If you only have time for one clue this year, this is the one to get. We are not seats or eyeballs or end users or consumers. We are human beings and our reach exceeds your grasp. Deal with it. VRM is how customer reach will exceed vendor grasp. With VRM, customers will prove that the intelligence that matters most is their own and that free customers are more valuable than captive ones. When that happens, vendors will have to deal with it or find the marketplace stepping away from them. u SITE: OK. What would you like us to do? USER: First, share the data I shed here back to me in a standard form, specified here (names a source). SITE: OK. What else? USER: Second, it s OK for you to monitor my activities on this site for the purposes of our mutual interests and for managing the performance of your services. But don t track me with third-party cookies, tracking beacons, or anything else that monitors my activities outside this site. Case Study: Should You Listen to the Customer? by Thomas J. DeLong and Vineeta Vijayaraghavan SITE: OK. What else? USER: Here (points to a source) are my other preferences and policies and means for matching them up with yours to see where we can agree. SITE: Good. Here are ours. USER: Good. Here is how they match up and Natalia Georgio knocked on the door of her new marketing director s office. Elizabeth Gardos hadn t done much with the space yet. Aside from two chairs, the desk, the computer, and a picture of her daughters, the office was empty. You need to get some art in here, Natalia observed. I know. It s been a busy two weeks. I want to put up some photos of the dancers, Elizabeth said. The two women worked for Delacroix, an avant-garde dance troupe based in New York that had five companies touring the US and Canada. Natalia, a former dancer, was the organization s executive director. She d hired 30 Customer Intelligence Tames the Big Data Challenge

Elizabeth, another former dancer, because of her decades of marketing experience, most recently at Violet, a fast-growing woman s athletic clothing company. Despite the stagnant economy, Delacroix was growing at a healthy pace, in part because of its policy of keeping ticket prices reasonably low. Still, Natalia thought the company needed better marketing to support its expansion strategy. Elizabeth, who d been keen for a new challenge and to return to the dance scene, had jumped at the opportunity. So why did you want to meet? Natalia asked. I have some ideas I want to run by you some things I ve noticed in these first couple of weeks. Great, let s hear it. I m really surprised that Delacroix has never surveyed or gathered information of any kind from customers before, Elizabeth said. Yeah, that s not really our thing. We take our lead from the dancers, not the audience. Then what you do here isn t really marketing, Elizabeth said cautiously. It seems like marketing s only responsibility is to decide how long shows should run, how to advertise them, and what to charge for tickets. You promote the shows and people show up, but you don t really know who your customers are or why they come. You mean, we promote the shows, Natalia said, smiling. You keep saying you, but you re part of this company now. We. Sorry. You re right; marketing s role has been limited up to now. But part of the reason we brought you in is to give us some new ideas. Natalia had a mandate from Delacroix s board of directors to take the company in new directions: to explore international engagements and television and film opportunities. But she was nervous about formulating her strategy without any research upon which to build a case for risks worth taking. She d explained that to Elizabeth during the interview process. I think we should start, at a minimum, by doing a simple customer survey, Elizabeth said, just with people who have signed up on the website and clearly want to be in communication with us. We can gather some basic information and find out what they like best about our shows. That will give us some insight into other audiences and markets we should target. That s certainly an idea, Natalia said, choosing her words carefully, but it would also be a big change for us. There are some people in this building who will resist. She was referring to Henry Delacroix, the company s founder and artistic director. While Natalia was technically in charge of the company, Henry still exerted a lot of influence. So it won t be an easy sell? Elizabeth asked. No, I don t think it will. It s All About the Dancers Why do we want to ask what our audience thinks? Henry said. We don t care what they think. Elizabeth and Natalia glanced at each other across the conference table. Henry, come on. We care that they come to the shows, Natalia said. Of course, I know our audience is important. But our business depends on the creative expertise of our artists. Henry forced a smile and turned to Elizabeth. When people come to our shows, they expect to have an unbelievable, unique experience one that they ll never forget. How can people tell you what they want if they haven t ever seen it before? If we ask them what they want, we ll end up doing Swan Lake every year! Natalia shifted in her seat uneasily. She knew that many of the board members not to mention the dancers felt as strongly as Henry did about maintaining artistic control. If Delacroix were to shift to a customer-centric approach, it could lose some of its best people. Elizabeth cleared her throat and passed Henry a brief report. Here are some examples of how my previous firm, Violet, used social media to better understand customers, get feedback on products, and test new ideas. She paused as Henry gave the report a cursory glance. This allowed us to make better decisions about how to price our products, how to go to market, and when to take risks with new products. I know the product in this case is very different: We re a dance company. But there are similar benefits here. Take your fan website, for example Yes, 90,000 people have signed up, Henry replied. Right, it s great. But you I mean, we don t really engage with those people. We have practically no data on which of them are coming to our shows and why, which means we have no idea which opportunities to pursue next or how to present ourselves to sell ourselves to the media or to corporate partners, Elizabeth continued. We re feeling around in the dark. It may work for giants like Apple to tell customers what they want, but that won t work here. It has up to now, Henry said, smirking. But the game has changed. You re thinking about different ways to expand. I know we want to go international by the end of the year, but we know little about what, say, a London audience would really want to see. We will tell them what they want to see, Henry shot back. Many companies operate this way. Tiffany doesn t survey the women of the world asking what kind of jewelry they want. The company has faith in its designers and their imaginations and it gets a better product as a result. We can t keep our artists inspired and innovative if we start letting customers tell us what to do. Natalia looked at Elizabeth apologetically, but she recognized that Henry had a point. Delacroix wasn t founded to meet customer needs; its mission, from the beginning, had been to push the boundaries of modern dance. Henry s relentless pursuit of that goal had brought the company much success. What about what happened last year at the Joyce? Elizabeth asked. Natalia looked at her new hire. She had done her research. Delacroix had invested a big chunk of its marketing budget in promoting the three-month run of a new show. Expected to be one of its best ever, the show featured dancers in huge masks. Unfortunately, the masks seemed to terrify all the children in the audience. There were walkouts at every performance in the first few weeks, and then parents flooded the website, as well as ticket and review sites, with complaints. After much internal deliberation, the company decided to put a note in the program warning that the masks might be scary for children under eight. Natalia had www.hbr.org 31

wanted to do more, maybe even remove the masks from the show, but a few board members held strong to the principle that they shouldn t bow to customer dictates. But they also told her that they didn t want to see that sort of mistake again. It was an unfortunate incident, definitely a failure on our part, said Henry. But we featured comment FROM HBR.ORG A very nice post, Doc. This seems to be a topic that will loom larger in the near future. Robert Fornango learned from it: No more scary masks in our shows. But if we had an ongoing dialogue with our customers, we might know these things ahead of time know that they want more kid-friendly performances or that we need to market certain shows to certain segments of our audience, Elizabeth countered. We could avoid these sorts of failures. Her experience, especially at Violet, made her a firm believer in the power of information. New ventures, new partnerships, more exposure you can t gamble with these things. You have to get it right. Customer research will help minimize our risk, Elizabeth said. Henry shook his head. You re just not getting it OK. Natalia stood up. We re not going to resolve this today. Elizabeth, can you email that report to Henry and me? We ll discuss it offline. Just One Survey? Natalia was in a cab and headed home when her cell phone rang. It was Henry. You re not really considering this, are you? he said as soon as she d picked up. Yes, I am. Elizabeth has some great points. How are we going to enter international markets if we don t know anything about the audiences there? Natalia looked out across the water as the cab started across the Brooklyn Bridge. How am I going to compete with every other company that wants a chance at TV if we don t have the customer research? Those Hollywood execs will laugh us out of the room, never mind the corporate partners. And what will you do when our customers ask us to put our dancers in Smurf costumes? Will you order one for Sophia? Natalia smiled at the thought of the company s most famous and most difficult dancer dressed as Smurfette. Stop being dramatic, Henry. They re not going to tell us to do something ridiculous. Fine, not the Smurfs, but you know we ll have to take fewer risks, to err on the side of selling tickets instead of pushing the creative envelope. You were a dancer, Natalia, one of our best. Remember the mission? Remember how energized you felt by it? Of course I remember. I also remember that it s part of our mission to bring modern dance to as many people as possible. Natalia tried to keep the frustration out of her voice. She knew as well as anyone how much Delacroix relied on the board members and dancers for innovation. But I was brought in to professionalize this company, to expand on what you started. And customer research real marketing may be part of that. Elizabeth knows what she s talking about. She ran the whole customer initiative at her previous job, and it was a huge success. She sold clothes, Natalia, Henry said. Fair enough. But what s the harm in a survey or two? If the customers give us outrageous feedback, we don t have to listen to it. But if we ask what they think and then don t give it to them, we ll alienate them. We ll damage the relationship, Henry said. And you know there s going to be resistance from the board. Natalia was not surprised. The business types would be all for it, she knew, but the members with an artistic background the ones who understood the creative process would never approve it. She pictured each of the board members, sitting around the table at their next meeting only a few weeks away. Henry was right. They would probably divide on this issue, almost evenly. Besides, Henry continued, we re doing fine. Why rock the boat now? Now feels like precisely the time to rock it. The board is expecting big things from us over the next year, risky things we ve never tried before, she said. And we re going to be successful only if we trust our own creativity and our own instincts not customer research. Natalia looked out the cab window. She felt tired. You still there? Henry asked. Yes, she said. Well, think about this. We can t have it both ways. Either employees come first or customers do. You know where I stand. Where do you? QUESTION: Should Delacroix launch a customer research initiative? 32 Customer Intelligence Tames the Big Data Challenge

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