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Table of Contents PREFACE...3 WHAT ARE PREDICTIVE ANALYTICS?...5 ADITYA CHOWDHARY - CLIENT SERVICES DIRECTOR MARKETELLIGENT...5 BEYOND SENTIMENTS...10 ZISHAN ANSARI - SENIOR BUSINESS ANALYST, TARGET CORPORATION...10 SOCIAL MEDIA STRATEGIES FOR THE DATA DRIVEN NEWSROOM...17 DENNIS MORTENSEN CEO, VISUAL REVENUE...17 TOO MUCH TALK IN ANALYTICS & TOO LITTLE ACTION!...24 AJAY KELKAR COO, HANSA CEQUITY...24 ACTIONABLE ANALYTICS...28 JAMES TAYLOR CEO, DECISION MANAGEMENT SOLUTIONS...28 UNDERSTANDING AND MEASURING SOCIAL INFLUENCE...34 ARUN SUNDARARAJAN - PROFESSOR AND NEC FACULTY FELLOW, NEW YORK UNIVERSITY S STERN SCHOOL OF BUSINESS...34 HOW SOCIAL MEDIA CAN REVEAL THE MYSTERY OF BRAND LOYALTY!...37 G.K SURESH - GENERAL MANAGER, ITC FOODS...37 SCRM IN 2013: NEXT GEN SOCIAL MEDIA ANALYTICS...44 BHUPENDRA KHANAL CEO, SIMPLIFY360...44 SOCIAL MEDIA BEST PRACTICES...53 ANKITA GABA - CO-FOUNDER, SOCIAL SAMOSA...53 INSIDE BIG DATA WHAT S IN IT FOR MARKETING?...57 DEEP SHERCHAN CMO, SIMPLIFY360...57 SOCIAL MEDIA PREDICTIONS FOR 2013...62 PRASHANT JAIN - SOCIAL MEDIA ANALYST, SIMPLIFY360...62 2
Preface Today enterprises are overwhelmed with the possibility of social media. It s been proven that it is no longer a fad and it means serious business. At this turning event of realization, enterprises are not equipped with right tools and human resources to leverage social media. On the other hand, we are seeing a rapid innovation in the field of social media tools and technologies social monitoring, analytics, marketing, intelligence, scrm, big data etc. As a result, we at Simplify360 tried to connect with different professionals active in this field of social analytics and learn from them about the future of social media. In this process we kept hearing predictive analytics as a repeating term. Hence we asked our team to reach out to the experts in the field and learn about what does predictive analytics mean in social media and its role in the future of social media. We would like to thank all the people who have agreed to share their experience and knowledge about social media. 3
What Are Predictive Analytics? Aditya Chowdhary Aditya Chowdhary, has over 16 years of experience in areas of Application Development, Marketing and Social Analytics. He has worked with global companies like GE, Dell. He was instrumental at Dell to setup the Social Analytics practice and very recently has joined Marketelligent. 4
What are predictive analytics? Aditya Chowdhary - Client Services Director Marketelligent Predictive analytics have been around for a long time and slowly these analytic tools are finding their way into the marketing and social media arenas. Predictive analytics use behavioural data from past to predict how individuals will behave in the future. For instance, your credit score is a predictive model including your repayment history and other information to predict whether you're a good credit risk or not. Predictive models commonly include a number of variables, such as number of late payments, and weighing factors that reflect the importance of that variable in predicting future behavior. These are commonly regression-type models. 5
Modern predictive analytics use similar kind of data to build models to classify people into different groups or predict their behavior. For instance, we might build a model that predicts how much of a product we'll sell if we lower (or raise) the price. While we won't be able to predict WHO will buy at the new price, we really don't care. We only need to know if we'll sell more at the new price. Thus, predictive analytics help us determine which marketing strategies will produce the best ROI (Return on Investment). How businesses use predictive analytics? Businesses use predictive analytics in a number of ways, one such way is discussed above. In addition, a number of tools, such as CRM (Customer Relationship Management) use predictive analytics to determine marketing strategies. Another type of predictive analytic is CLV (Customer Lifetime Value) which uses purchase information to classify customers into groups and determine the level of profit reflected by each group, which is used to build marketing strategies for each group. Descriptive models and predictive analytics Descriptive models are often overlooked as tools for generating predictive analytics because they suggest strategies that will generate better results without being able to quantify how much better the results will be. 6
Source: griibdesign.co.uk An example is the TRA (Theory of Reasoned Action). This model states that buying behavior is impacted by a consumers attitude and beliefs about the products, as well as the norms related to that purchase. This theory, ofcourse, underpins how social media works. Social media helps in building attitude toward products, based on the references from the most credible sources our friends and establishes norms of behavior when we see all our friends buying the product. So, why aren't these descriptive models being used more frequently in businesses? In part, that's due to poor exchange between businesses and academics,who seem to speak different languages. Predictive analytics and social media Social media analytics is a powerful tool for uncovering customer sentiment dispersed across the countless online sources. As businesses feel the pressure to gain new insights from social media, they require the analytics expertise to transform this flood of information into actionable strategies. There is a lot of internet chatter about social media analytics to predict the future. This field of predictive analytics is in its infancy, but there has been enough success to generate excitement about its potential. 7
In case of social commerce, any kind of buzz is a good buzz because it directly translates into customer demand. For example, keeping tap of all the trending topics on Twitter can be a useful exercise to predict future demands. This allows business to leverage social media and maximize sales. Though the concept is still in its infancy, 2013 is an exciting time where we could see some of these concepts being implemented. 8
Beyond Sentiments Zishan Ansari Zishan is a SAS certified predictive modeler with over 4 years' analytics consulting experience for global players in banking, insurance and retail domain. Zishan is currently part of Target Corporation's enterprise business intelligence team based in India that provides analytic solutions to problems in operational risk, loss prevention and profit protection area. 9
Beyond Sentiments Zishan Ansari - Senior Business Analyst, Target Corporation Source - http://provalisresearch.com/uploads/sentiment.jpg If you are reading this, you probably are aware of how things in the social media world usually work. You probably have used/seen/heard of tools that tells you about the sentiment of your customers, by analyzing what they are writing over various social media platforms. And I am sure many of you would have also seen software providers making tall claims about how accurate their engines are in predicting the correct sentiment of a company s customer base. There is no question about whether sentiment analysis is a powerful analysis technique or not, because it certainly is. The bigger question is whether it is the right thing for you or not? And if it is, then do you have the ability to apply it correctly or not? If you are looking for an answer to the question whether investment on sentiment analysis is the right thing for your business, then read further. In case you have already spent loads of cash on buying a sentiment analysis package, still read further to know how you can best utilize it. Why sentiments? Being a part of a smart and proactive organization that listens to all its customers, you would want to build a strategy where you: Listen to all that your customers are saying about your brand or a newly launched product Take actions based on customer sentiments Redress customer grievances by offering him or her some brownie points And in turn be loved by your customers for being a great listener 10
Well, this all works really well when you are running a Mom and Pop Store. Why? Because: You know who your customers are You understand their language You understand their moods, their emotions You know their preferences You know what will please/displease them How are things different in social media? Simple answer to this would be in more ways than one. It is important for you to understand these differences to figure out whether you need sentiment analysis or not.. Not every person is good in expressing themselves in text, and definitely not in just hundred and forty characters. And this poses a big challenge. Why? Because, there is no way to capture customer s facial expression through social media, nor is there a way to capture voice modulation to figure out customer s mood. With deliberately excluding the discussion about misclassifications that majority of the tools anyways do, there is one more aspect of language that I think should be considered, and that is dialects. In this very world of ours, where spoken languages change with every two hundred to three hundred kilometers, it will only be illogical for us to expect everyone to express their 11
sentiments in the language in which our software s NLP engine is trained. OK. So in that case will it be insane for us to demand for an NLP engine that has the functionality of identifying the sentiments from any language/dialect. No, it wouldn t be completely insane. But it is very likely that you will be paying a bomb to get such a service. Let us assume that you get an NLP engine that has all these powers. Tell me who writes a perfect language these days? If you think your customer does, then you are absolutely wrong. Even Shakespeare would have had a hard time in expressing himself in hundred and forty characters forget about an average Joe (Pardon me if you are Shakespeare or Joe!). We are living in an era where even native speakers are getting bad in grammar day by day. Languages are evolving, and so are the NLP engines. And, more evolved an engine is, more dollars you have to burn for it. Knowing your objective You will be solving the major part of the puzzle if you figured out what your objective is behind sentiment analysis. Because, after this you just have to pick the right approach to achieve your goal. If you are managing a customer service center and you receive 100,000 emails every day, and would like them to be classified as Positive, Neutral or Negative then you can use software that has the capability to do the job. That s all the functionality you need to achieve your goal. Rest of the work will be done by the executives to ensure each customer is satisfied. Looking beyond sentiments Say if you had kids. You find out on a Sunday morning that your kids were annoyed about something and were not looking happy. Being a good parent you give them permission to go out with friends. If you are a more involved parent, you would yourself go out with your kids, to make their day. A 12
parent very well knows what to do to change the mood of their kids. As a kid I have really enjoyed that. Sometimes getting annoyed for no good reason can also get you treats, just because your parents wants you to be happy always. But if you were a responsible parent, what else would you do? You would probably try to understand, why were your kids upset at the first place? Have they been getting annoyed quite often recently? What are the circumstances which makes them unhappy? A responsible company is no different from a responsible parent in this aspect. It will do a root cause analysis of what actions led to all this. And they can take actions to ensure that such circumstances do not arise in future. An organization with a dedicated customer service team can probably do all what a responsible parent do, given that they know who the customer is. It can spend resources to get in touch with the customer, understand his/her concerns, identify the causes of unhappiness and then act accordingly. It can also go ahead and analyze what were the things that led to such circumstances. So what should you do? Should you wait for a cheaper NLP engine with all the functionality? Or should you wait until your company buys a foolproof text-analytics software license? It is important to be clear about what exactly is your objective to perform sentiment analysis. You can then combine the results from a not so very expensive tool with other free tools or with your own data visualization tools for analysis. Stream Graphs One such freely available tool is stream graph. It helps you in analyzing what people have been tweeting about a brand/product over a period of 13
time. Figure: (Source - http://www.neoformix.com/projects/twitterstreamgraphs/view.php) The Stream Graph shows the usage over time for the words, which are highly associated with the search word. One of these series together with a time period is in a selected state and colored red. The tweets that contain this word in the given time period are shown below the graph. You can click on another word series or time period to see different matches. In the match list you can click on any word to create a different graph with tweets containing that word. You can also click on the user or comment icons and any URL to see the appropriate content in another window. If you see a large spike in one time period that hides the detail in all the other periods it will be useful to click in the area to the left of the y-axis in order to change the vertical scale. The bad part is that the free version analyzes latest thousand tweets. The good part is that there are open source resources (http://www.processing.org/) available to help you develop such graphs. Probably you need to invest some time to develop that. Remember, there are no free lunches and there are no free graphs either. 14
You can combine the results of the sentiment analysis with the results of stream graph analysis to infer what circumstances probably led to positive or negative sentiments. So even if your sentiment analysis says that 80% of your customers have neutral opinion and your sales figures are way below your forecasted sales, a stream graph can help you identify what probably is causing the low sale. Stream graphs present one way of visualizing the data. There can be multiple such ways of analyzing it. A good analyst will always find a suitable way of presenting the appropriate information that you would like to have in order to validate your sentiment analysis findings. And remember without validation, your results from sentiment analysis in the worst case can be as bad as that from a random classifier. 15
Social Strategies for the Data Driven Newsroom Dennis Mortensen Dennis Mortensen is CEO & Founder of Visual Revenue, Inc., whose Editorial Support Platform helps editors to better place content and provides real-time recommendations and predictive analytics to more than 250 global online publishers, including Comcast, The Atlantic, NBC Universal and Le Monde. He is the author of Data Driven Insights from Wiley and sits on the Board of the Digital Analytics Association. 16
Social Strategies for the Data Driven Newsroom Dennis Mortensen CEO, Visual Revenue Just as the Internet itself began to upend news and journalism in the mid1990 s, so too has social media added another layer to that upheaval. Perhaps even a greater one that doesn t just change the delivery and consumption of news and information, but the creation and molding of the stories themselves. To compare the changes, it would be one very large order of magnitude. Social : Newswire or Newsroom It s agreed that the prevalence of mobile devices and social media tools has turned everyone into a publisher of sorts. While not every individual can hope to monetize their tweets like a celebrity, every last tweet out there has the potential to affect journalists and the gathering of news. They may not lead the story, but they ve become parts of the stories told (man tweeting Bin Laden raid) and parts of the newsgathering process (Egypt and the Arab Spring). In the 24 months since Andy Carvin of NPR provided a model for futuristic newsgathering by tweeting at potential sources and confirming reports from afar, many, if not most, journalists have found their way onto Twitter and are using it to the same effect. In the same short span, this same move to the center of the news has happened in the newsrooms themselves in the way that content is pushed out to an audience. The Social Editor s role was created to evangelize and educate. With so many journalists of all stripes now savvy in social, it has become critical to determine and assign both ownership of the social channel within the newsroom, as well as ownership of the content that goes out under each individual feed. Defining Success in Editor and Publisher Terms 17
As more and more people in the newsroom have moved to publishing on the many social platforms available to them, the movement to measure that activity has followed. For most individuals, a somewhat limited view of success in social media is defined simply by response. Did I get re-tweeted? Favorited? How many @ replies do I have? What s my Klout score? This level of measurement is fine for the individual, but not for media companies where activity must be measured by the way it delivers audience, and by how that audience ultimately can be monetized. For online properties, if you want to increase your audience, that content must be exposed to new audiences on a regular basis. Publishers understand this, and most quickly go to Facebook, Twitter or other social media channels to expand their reach. Yet the true impact on their business success or failure is very difficult to measure. Sure, they can measure tweets and favorites and likes and other vanity metrics. It s easy can be a trap that is fallen into quickly. But and re- tweets and favorites and shares only have a secondary effect upon the business, and they hardly measure audience interaction. The editors in large newsrooms of leading editorial operations need to know more. Just as with the content and activity on their own websites and properties, they want and need to measure social media similarly. For example, what are the expectations of content published? If editors at The Atlantic, publish a piece of content at 1pm, it would be fair to have an expectation on how much direct output it will generate: How many views should this content generate? How many views does it actually receive? Is this content that should be shared on social media? 18
If so, when should it be shared to attain the maximum impact? As a process, editors and their social media tools should essentially follow a process of (1) finding and selecting the proper content; (2) pushing that content into the social channels; (3) measuring success and/or failure, and (4) learning and optimizing from it. When editors begin to answer these questions and follow this process, they make the definition of their success in social media more precise, and they move away from the vanity metrics of the consumer marketplace. It is one of the things that separate the genuine publishers from the everybody s a publisher publishers. Determining Success and Failure in Real Time The real-time nature of social media interaction makes it a natural complement to the newsroom. It is, however, that same fleeting aspect that makes real-time success measurements akin to capturing lightning in a bottle for them. It can only be done with some of today s more sophisticated tools. With the proper tools in hand, news organizations can have more exact measurements on social media. For example, they can have a means to determine the difference between tweeting content at 4:23 p.m. and 4:35 p.m., followed by recommended actions based on real-time performance and consumption data. Additionally, the best tools can tell editors whether 19
or not to include photos when sharing or tweeting, which accounts to share on, and sometimes, the value of sharing specific content multiple times. This difference can be significant for some properties versus others. Patterns for when and what to share exist, but they tend to be very much property-specific. They are so specific that, for example, one news organization can share content with equally powerful results from dawn til dusk, while another will only succeed in limited windows. What works for one news organization won t work for another, largely as a result of audience fragmentation having intensified to the degree it has. Figure 1: Historic Success Measurement for Leading Online Properties A comparison of activity for two leading online properties, showing hourly clicks into their article content via social media. Note that one property, in addition to an overall larger number of clicks per hour, never truly falls to zero, while the other does so almost every night. For the editor and the newsroom, this makes their social media activity only worth the time that they invest in measuring it -- not for the vanity metrics, but for the deeper ones. This is where their ability to know true success from simple failure will show whether their time spent sharing (to grow their audience) has been well spent. Using Predictive Analytics to Determine Your Next Step Editors today, particularly social media editors, live within what can be an 20
endless torrent of information, opinions and activity. They are called upon to quickly whittle it all down, and make quick, yet significant decisions about the placement and direction of content. Given social media s realtime nature, the use of predictive analytics can help data-driven editors to stay one step ahead of the game. Figure 2: Benchmark Performance Predicted for Two Leading Online Properties (24-hour period) A comparison of the expected social media activity for two leading online properties, showing hourly clicks into their article content via social media. Note that one property, carries a consistently higher level of activity that (a) peaks more sharply during key periods, and (b) maintains a significant level of inbound clicks (views) throughout the overnight period. Whereas these decisions were once made based on intuition, skill and instinct, there now exists a huge pool of data that, when properly applied, can enhance an editor s judgment with suggestions and the additional confidence to take decisive action. The genuine value of such predictive algorithms is in the ability to recommend specific actions for an editor within an editorial framework outlined by the organization. Separately, these two elements are important; together, they provide a powerful engine for editors to act immediately, secure in both their judgment and the interest of their publication. These new tools simplify data and tell a newsroom when it should tweet and also what it should be tweeting. The computerized suggestions take on the 21
role of a deputy editor: someone who knows the history of the data, as well as editorial values of the paper, and can therefore determine the best publishing strategy to follow. In the end, it is still the editor making judgment calls. They just happen to be faster and better calls. Conclusion Today s data-driven newsroom relies on many tools to keep pace with the real-time nature of communications. Social media is a natural fit for the newsroom, both in the collection and dissemination of the news, but it needs to be deployed within a editorial framework. Without one, the publisher may as well be an individual. But with a clearer, data-driven strategy for measuring the success and failure of the outbound social media, the newsroom of today will be ready for the news of tomorrow. 22
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Too much talk in Analytics & too little action! Ajay Kelkar Ajay Kelkar, Co-Founder & COO of Hansa Cequity, has over 20 years of experience in customer-driven marketing across a wide range of industries like Soft goods, Banking & Financial services & Retail. He has had exposure to a wide variety of business styles & cultures across Procter & Gamble, Britannia, Marico, Shopper Stop & HDFC bank. Too much talk in Analytics & too little action! Ajay Kelkar COO, Hansa Cequity Is analytics yet another fad? Is there much more talk about it than real solid action. It does seem so when you look around you as a consumer. Marketers still don t care, as much, about being relevant to you. You get that umpteenth credit card solicitation from the bank which has already sold you a card. And nothing about a physical Retailer shopping experience makes it personal for you! And yet your online persona seems to be treated differently & when you go to Amazon & other sites you do get a feeling of getting offers being recommended for you. And as a consumer you flit between your online & offline avatars & this becomes more & more obvious. What s the difference? Is there a category of organization which is able to leverage data far more effectively? Gartner says that only 20% of enterprise will use more than 50% of the total data they collect to gain competitive advantage. 24
This is what software architect Grady Booch had in mind when he uttered that famous phrase: "A fool with a tool is still a fool." Google-executive-turned-Yahoo-CEO-thought-leader Marissa Mayer declares "data is apolitical" and that her old company succeeds because it is so data-driven: "It all comes down to data. Run a 1% test [on 1% of the audience] and whichever design does best against the user-happiness metrics over a two-week period is the one we launch. We have a very academic environment where we're looking at data all the time. We probably have somewhere between 50 and 100 experiments running on live traffic, everything from the default number of results to underlined links to how big an arrow should be. We're trying all those different things." A recent Ad Age article carried this comment: British Airways spent almost a decade corralling passenger data from 200 sources into one database. It built infrastructure to support the number crunching, but perhaps the harder piece, said Simon Talling-Smith, exec 25
VP-Americas, is getting in-flight personnel to use the technology and data to create better consumer experiences. And BA introduced onboard ipads to send in-flight crews passenger-specific information, but Talling-Smith said encouraging staff to use them is still a challenge. "Probably half of the messages don't even get delivered," he said. Maybe there are some learning s here: 1.Analytics doesn t need you to solve a technical problem but a business & social problem. And most Business analysts have not spent much time in business roles. They are super specialised number crunchers without a sufficient exposure to business reality. Even if the managers have some exposure to business through experience across a variety of analytics projects, is it enough? Does this bring the analytics career into some jeopardy? Would analysts be able to grow in companies beyond a level or is it a parallel consulting stream only? 2.Analyts need to Story tell to embed analytics into the fabric of the company. But analysts are too one-dimensional & not embracing the intersection of technology, statistics & business. So analysts struggle to tell stories. Often I see journalists do a far better job with infographics in media. But information journalists are not wanting a career in analytics & so there is a gap in story telling. 3.Analytics is too theoretical. Not enough integration with systems has happened to push decisions to the point at which consumers interact with the business. This is far easier to do in new Online businesses which have built their systems around this capability. CIOs & technology teams in large existing offline businesses don t see this as important. 4.Average age of employees in online business is far lower. Younger people are adopting analytics far faster. They are getting exposed to it in their education & they are consuming it through their digital avatars. They see this often as a no brainer. Older executives are harder to convert to this 26
line of thinking. 27
Actionable Analytics James Taylor James is the CEO and a Principal Consultant of Decision Management Solutions. He is the leading expert in how to use business rules and analytic technology to build Decision Management Systems. James is passionate about using Decision Management Systems to help companies improve decision making and develop an agile, analytic and adaptive business. Actionable Analytics James Taylor CEO, Decision Management Solutions 28
The use of analytics, especially predictive analytics, to improve business results is a key focus for marketing departments around the world. The potential for new sources of big data, such as social media, to improve analytics results is getting attention too. To add value, though, any analytics must be actionable and acted on. Experience suggests that making analytics actionable is harder than it looks, with far too many analytic models sitting unused or failing to have a significant business impact. Part of the reason that organizations struggle with making analytics actionable is that they have a mistaken belief that using analytics will tell them how to change their business. They believe they can do some analytics and get a great aha moment that will tell them to start doing something differently or stop doing something else. They believe that analytics will give them huge, one-time improvements in their business. But they are wrong. Analytics will not tell you how to change your business. Instead you must change your business so that analytics can help you run it more effectively. You must change your business, change the way you interact with prospects and customers, so that your analytics can be actionable. To make analytics actionable many organizations are turning to Decision Management. Decision Management is an approach to analytics that focuses on decisions first identifying, modeling and managing critical business decisions and applying analytics to improve those decisions. Decision Management does not begin by asking what can we measure or even what does this data tell us. Decision Management begins by asking which decisions matter to our business? Which decisions, if made correctly, will move our business performance in a positive direction? How, exactly, do we move the dials on our dashboard? 29
By identifying the decisions that can be improved with analytics, Decision Management identifies where your business will have to change to account for analytics. It identifies the decisions that are made the same for every customer where analytics could target or personalize those decisions. It identifies the decisions where you segment customers by channel where analytics would segment them by behaviour. These decisions are often not the first ones that come to mind, however. Instead of focusing on big strategic decisions or on management decisions, Decision Management focuses on decisions about individual customers or prospects. These micro decisions are often hidden in an organization hidden because although organizations realize they make a decision about their customers, they do not realize how many they make. For instance, if you decide to send a marketing email to a subset of your customers you might think you have made just a couple of decisions such as what to put in the email and who receives it. And you might think that you could use analytics to come up with a much better email or target list. While you have made these two decisions, you have also made a decision for 30
each customer to either receive or not receive the email. Therefore, if you have 10,000 customers you just made 10,000 decisions one for each customer. Similarly, if your website has a thousand visitors each day and you have decided on a promotion to display then you have made a thousand additional decisions just today to display this promotion to each individual visitor. Organizations are increasingly realizing that these micro decisions can be made one at a time, treating each customer uniquely and relying on analytics to personalize and target the promotion, message or content delivered. By changing your business to think of these as micro decisions, decisions about a single customer or prospect, you can make your analytics actionable. Now your propensity models are actionable, for instance, because you make the email content vary depending on what each customer is likely to buy. Because predictive analytics tell you what s likely to be true for a given customer, they are actionable only in the context of a micro decision about that customer. This focus on decisions addresses one of the biggest challenges in developing actionable analytics. By focusing on decisions that must be made and by tying these decisions to the way the business operates, the metrics that matter to it, analytic teams ensure they understand the problem that must be solved and that they can clearly see what success looks like how they will measure the value of their analytics. Decision Management also creates a shared framework and collaboration environment for the business, IT and the analytics teams. Because all three groups understand decisions and decision-making, they can come to an understanding of the problem by identifying and prioritizing the micro decisions that drive the organization's success. Decision Management links these decisions to the business drivers and performance measures that have the most impact on the business. Modeling the decisions to be analytically improved clarifies and focuses analytic projects. Predictive analytic models can now be built to influence these decisions, 31
predicting the risk or opportunity in each customer or prospect. Because these decisions are high volume even small improvements can make a big difference, making it possible to begin showing a positive result from relatively simple analytic models. These decisions often need to be made in real-time so these predictive analytic models must be deployed into automated decisioning solutions such as offer or ad engines. Once built these solutions will need to evolve and improve both to increase the quality of decisions and to ensure that changing circumstances are reflected in the way decisions are made. Decisions are high change components, impacted by changes in markets, consumer behavior, regulation and data. Collecting data about what works, continually refining the predictive analytic models you use and optimizing over time ensure the best possible outcomes. Analytics are a potentially powerful tool in your toolkit, provided they are actionable. Focusing on decisions first, and on micro decisions, will make sure they are. 32
Understanding and Measuring Social Influence Arun Sundararajan Arun Sundararajan is a professor and NEC Faculty Fellow at New York University s Stern School of Business, and an expert on influence in social, economic and political networks. His award-winning research has been widely published in scientific journals. His recent op-eds have appeared in outlets that include Bloomberg, Financial Times, Harvard Business Review, The Mint and Wired. 33
Understanding and Measuring Social Influence Arun Sundararajan - Professor and NEC Faculty Fellow, New York University s Stern School of Business A couple of weeks ago, I had an interesting classroom discussion with my MBA students at the Indian School of Business about the relative influence of Shashi Tharoor (1.67M Twitter followers, Klout score 84) and Priyanka Chopra (3.49M Twitter followers, Klout score 85). That room full of digitally savvy future business leaders concluded, among other things, that: 1. Yes, both were indeed both influential Tharoor in the realms of politics and literature, Chopra in the realm of fashion; 2. The actual content in their Twitter feeds had very little to do with their domains of influence, and 3. Twitter (and thus Klout) was more a reflection of their real-world influence rather than the channel by which they garnered their influence. You might debate each of these points (as they did), and come to different conclusions about points (2) and (3) for other influencers. And you might end up being right. The point is, the science of measuring influence is still in its infancy, and there are still substantial gaps between actual influence in the real world, and what the data trails of our online networks capture. Here are three simple lessons that can guide us in the interim: Things that look like viral spread are often simply demographics It is often exciting to see application downloads, advertisements or product adoptions that appear to be spreading through a customer base, and makes all of us want to identify the influencers. However, there are many different reasons why such outcomes might cluster across customers who 34
are connected via a social media platform like Facebook or an email/im network. We have known for decades that birds of a feather flock together, or people who have social connections tend to be similar, a phenomenon sociologists call homophily. When you see clusters of connected customers making similar choices, it could simply be because they have similar tastes, which causes them to be friends, and these tastes (or demographics) are driving the choices. Or it could be even simpler. The choice itself -- for example, adopting Instagram -- could be causing the creation of the friendship ties, something we call selection. The trouble is, in the data, both these explanations look identical, and exactly the same as a third that your product or idea is in fact spreading virally because of genuine person-to-person social influence. But the marketing implications of this difference are huge. If you have real influence, a viral marketing strategy is the way to go the next time around. If not, a demographics-based approach would be better. Smart companies are therefore moving beyond network simulations, superficial analysis and measures like Klout, delving deeper into capturing the flows of data and measuring influence more precisely. The techniques 1 aren t too complex, and eas ily im plem ented you jus t hav e to know to look out for them. Lots of small contagions are generally better than one giant epidemic A series of studies Yahoo and Microsoft Research have shown that an overwhelming majority of influence-based spread over social media has very little depth. That is, over 99.9% of the total volume of online social contagion comprises one or two steps, and it is very rare for something to spread widely over Twitter, Facebook or any other emerging digital platform. 1 See, for example, Aral, Muchnik and Sundararajan, PNAS 2009, http://www.pnas.org/content/106/51/21544 35
This has important implications for how you allocate your advertising money if you are in fact targeting influencers to spread your message. In most cases, if you spread your budget over targeting a large number of somewhat influential people, rather than going after a few extremely highly connected ones, you will get better returns. It s not just the network, it s the content as well When the lights went out during Superbowl 2013, the marketing brains at Oreo rapidly generated the following ad content rapidly: A link to this ad, tweeted from @Oreo (which had, at the time, a mere 65,000 followers) generated 16,000 re-tweets and 6,000 favorites, (more than triple what President Obama s most successful tweet generated after the State of the Union speech), making this perhaps the most successful advert of the Superbowl. Often, in our quest for influencers, we forget that good content can create its own viral spread. Some companies like Buzzfeed are building a business on this idea. Extreme humor like theirs isn t always necessary what s critical to remember is that even in our brave new world of social marketing, the message is still at least as important as the maven. 36
How Social Can Reveal The Mystery Of Loyalty! Brand G.K Suresh Mr. G. K. Suresh is the General Manager - Brands with the Foods Business of ITC Limited the looking after categories of Staples, Snacks, Confectionery, Noodles and products. GK Ready to Eat has worked in a variety of roles from Sales to Trade Marketing to Brand Management. In his prior assignment, he was Head - Brands and Business Development with the Personal Care business where he oversaw the launches of brands like Fiama Di Wills, Vivel and Vivel Active Fair - across the intensely competitive categories of Soaps, Shampoos and Fairness Creams. He was also Trade Marketing Development Manager responsible for the development of Distribution and IT strategy for all ITC's FMCG products. How Social can reveal the mystery of brand loyalty! G.K Suresh - General Manager, ITC Foods Let s face it. Customers are no longer loyal or rather they are loyal but to brands that understand and engage with them in the new world. The messages carried by the advertisements are no longer compelling. For years, brands were able to suppress the consumers voice. But now, all it takes is a Tweet or a Facebook update from an irate customer and the world knows about it. Honesty has become most brands top priority. 37
Maintaining brand loyalty has suddenly become the biggest challenge. Moreover, the entire concept of loyalty is vague. All thanks to Facebook, where one person is interacting with several competing brands, all at once. In such a scenario, traditional branding exercises are no longer effective in getting the attention of customers. This is further complicated by the variety of devices where such micro eco-systems exist. This is the result of the digital convergence of culture, business and economy into bits and bytes. So, this is where lie not only the challenges but also huge opportunities for brands to understand their customers. Digging into the wealth of social media data, brands can today discover consumer insights like never before. There are 4 steps to understanding customer insights through social media: Find out what people are talking about and why Find out who is talking and influencing the crowd Use this intelligence to optimize your brand s message to impact in real-time. Measure your interaction and influence of the messages and optimize them. One brand that has successfully utilized the above steps is Bingo! with the launch of Bingo! Tangles on Facebook. With over 3 million fans, Facebook offered Bingo! a great window into understanding consumers conversations. Consumers use these forums to talk about their likes & dislikes and welcome new information on product as they find it appetizing, tempting & satisfies their need for variety. Consequently we decided to launch the new Bingo Tangles first on Facebook and give a chance to loyal Bingo! fans to discover the product and also taste it before it hit the market. A teaser contest was created for Facebook fans which encouraged them to decipher the brand name and the winners could taste the product before it was made available in the market. Thousands of fans participated in the contest and packs of 38
Bingo Tangles were sent to the winners. This campaign helped us connect with the brand advocates and also use Facebook as a launch platform for various other brands. Data is a vital raw material for building the business infrastructure in the information age. Brands that can put systems in place to access, process and utilize the data will be the most successful in connecting with the customers and influence their decisions. The key idea behind customer loyalty is customer retention. There are already different programs, which businesses employ like reward programs, referral programs and 1-to-1 marketing campaigns to ensure that customers stay with the brand. But social networks are taking over these programs in terms of gaining a deeper relationship with the brand. There are 3 ways to use social media data to improve and make social programs much more effective: 1. The Brand should genuinely care When you embark on the social media journey, be prepared to respond to negative as well as positive feedback and genuinely do something about 39
consumer problems. On the Aashirvaad Multigrains Facebook page one of the fan brought to our notice unavailability of Aashirvaad Multigrains atta in her area. We used this info to investigate the issue with our sales team and figured out that there was an issue with the sales person operational in that area. Action was taken immediately and we called back the consumer to validate that her problem had been addressed.we figured out that this was an issue due to the sales person operational in that area. Thus listening to a single fan, who was representing a cluster of consumers in that region, we were able to resolve issues faced by many such consumers in that region. 2. Engage without losing focus on your brands Many times the focus on the content posted on Social platform goes to extremes. Either it is too generic or it is too brand centric. There should be a proper balance. 3. Brands have to learn to converse with consumers as equals. Brands are too used to speaking to consumers from a position of authority & knowledge and not as a friend. But today brands need to learn to converse with the consumers on equal terms & be seen as an enabler. The key thing is to do it without compromising on the brand personality. Hence the voice of Bingo! is more youthful & contemporary; the voice for Aashirvaad is always joyful & optimistic while that of Kitchens of India is authentic & welcoming. 40
So we now know why and how social media can help decipher the mystery of brand loyalty. But the last portion and the most vital one, is to understand how to measure this in social media. There are 4 factors, which one must consider while looking into brand loyalty in social media: 1. First of all, what are customers talking about your brand and how? What are their attitudes and sentiments towards your brand? What is their feeling towards your brand? Are they neutral, friendly, hostile or ignorant? This helps brand not only understand the emotion but also map them along the brand attributes and identify the missing bits. 2. Secondly, to understand the emotional connection between a customer and your brand and measuring the strength of the bond. Most often an outburst by a customer is temporary, and can be mitigated easily. Hence, identifying such customers is vital. Many a times the reverse is true as well. There was once a complaint on Facebook by a consumer on the quality of Atta. Before we could begin to address the problem, 3 other consumers had responded asking the fan to check the storage conditions at the outlet of purchase as well as her kitchen. 41
3. Thirdly understand that the customer is likely to buy more than one segment of products from the brand. This sends much a stronger signal of loyalty. 4. And finally, know which platforms are more effective in communicating with the customer. Email, Social Networks, Mobile, TV, Tablets and the list goes on. The avenues where customers are present are wide. Hence identifying the top engaging platforms and optimizing them is vital. Mining social data and building your decision systems on top of it is the secret of successful customer retention. The main goal is to make an emotional connection with the customer in each interaction to increase referrals, retention and acquisition. 42
SCRM in 2013: Next Gen Social Analytics Bhupendra Khanal Bhupendra is the co-founder and Chief Executive Officer of Simplify360. Prior to this he was a manger and founding member at Marketelligent, an Analytics Consulting company. Earlier he worked at Fair Isaac (now FICO) as Marketing Analytics Consultant and served clients like Hartford Insurance, Smith and Hawkins, and Coca-Cola. Before FICO, Bhupendra worked at Global Analytics as Business Analyst, and lead a team of 6 people to develop and implement Enterprise Decision Management System for Sub-prime Banking in United States. 43
SCRM in 2013: Next Gen Social Analytics Bhupendra Khanal CEO, Simplify360 The time when people discussed tools is long past. 2013 will talk about business outcomes, KPIs and unified systems that streamline business functions. Social is moving towards becoming a general utility medium much like Phone or Email. Remember, even though these mediums are used for customer service, no one refers to them as such exclusively. Social is highly misrepresented in business circles. It is regarded as an advertising medium to amass Facebook fans and Twitter followers. This should end now. We should start looking at the business functions and use Social to achieve the business goals. The KPIs needs to be defined and best practices built up. And to make all these things happen, Analytics has a major role to play. Here are some of my recommendations. 1. Social is more than Engagement. Social means business. Enough and more has already been said about Social Engagement. Let us outgrow it. Let us talk about the real business impact. Obviously, the same business metrics work for Social that work for other mediums. The value has to be in one or more of the following: a. Increased Revenue 44
There are multiple avenues of revenue realization through Social. Companies are trying offering discount coupons and cross-selling products in each other s Social Channels. Most companies have not been able to generate leads for business. But this was true for all earlier adopted channels Television, Radio, Email etc. The leads are best generated and tracked with high confidence through PPC Ads. For Social to be successful, it is very important to have clear revenue goals and a strategy to back it. We, at Simplify360, have been very successful in generating a good number of leads from Slideshare. It works great for a B2B Model. b. Increased Buzz Social does not only have consumers. It has resonators too. And this can be highly leveraged by properly building a community and keeping them active. The message once gone viral is more powerful than several passively played ads in other channels. Reason the message flows in the form of recommendations between friends and connected individuals. The trust factor is thus high enough to show some amazing output. c. Cost Savings Take the example of a BPO operation. The average cost per call from a BPO company to the consumer costs an average of USD $0.1 (INR 5), while the cost of receiving a call on time from a customer is USD $1 (INR 50). The difference in the price occurs as the BPO Company needs to charge for resources waiting for the call too. This adds to the phone bill charge. Now take the example of a Social Contact Centre. Several people may post complaints on Facebook or Twitter, a few reps can quickly handle it and respond like an Internet Messenger. The benefit here is no one needs to remember each other s phone number or email id. Bonus huge savings on 45