TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHTS

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1 TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHTS A CASE FOR ADVANCED SEGMENT ANALYSIS TO TRANSFORM THE CUSTOMER EXPERIENCE, CREATE OPERATIONAL EFFICIENCY AND DEFINE NEW BUSINESS MODELS. BY ROHIT CHAUHAN There is no shortage of stunning numbers to characterize the amount of data consumers will generate over the next few years. Perhaps the most telling statistic is the amount of money global businesses will spend on analyzing big data in 2015: $125 billion, according to IDC. That would put big data spending at a higher level than the GDP of all but 60 countries in the world. This big data is so valuable that 70 percent of all global enterprises currently purchase external data, use third party data in their business models, and fully 100 percent will by Big data is unharnessed in terms of its velocity and volume. Preparing for big data strategy has moved quickly from preparing personnel to creating the operational structure to deal with it. As the amount of data grows, it shows itself to be unrefined and lacking in the value that can come from processing it into a more actionable form. In much the same way a diamond is formed, advanced analytics must apply pressure on unrefined data in order to make it actionable. However, many companies and their management are still lost in the mass of data coming at them on a daily basis. In a late 2014 survey of 259 marketing and business-development executives at large and medium-sized retailers, financialservices companies, and consumer-products companies, Forrester found that one-third of respondents felt the term big data was very confusing. 2...COMPANIES EXPECT THEIR SHARE OF SPENDING ON MARKETING ANALYTICS TO ALMOST DOUBLE IN THE NEXT THREE YEARS, FROM 6.4 PERCENT OF MARKETING BUDGETS TO 11.7 PERCENT. 3 This evidence of big data intimidation is supported by many studies; among them, a February 2015 Duke University study of 228 CMOs shows that companies expect their share of spending on marketing analytics to almost double in the next three years, from 6.4 percent of marketing budgets to 11.7 percent. But companies are not using all the data they have, and they report lower than expected levels of contribution for this strategic investment. 3 In order for big data to be refined and made actionable, it will require the pressure of advanced analytics, followed by marketing adoption of the learnings found within the unrefined big source. This paper will show that four emerging data trends will enable the necessary evolution from big data to advanced customer segment analytics. This new approach will be enhanced by transactional data and secured by new technology and compliance regulations. 1 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

2 1 1. FOUR EMERGING TRENDS Devices are proliferating to create more data. In 2015, more than 2.6 billion new electronic devices will be sold globally. 4 All of them will create some kind of data, from web-based interactions to social preferences to product-based research. There is no shortage of data. But an enterprise can only act on data that enables it to increase the speed and accuracy of its decisions. Data must transform the customer experience, create operational efficiency, and define new business models. Four emerging trends will drive the growth of big data and its transition to advanced analytics. In order to make data actionable, it will need to be secure, subjected to proper compliance, and ultimately enhance the consumer experience. These four trends will also lead to operational efficiency and create new business models diamonds of data rather than unrefined rocks. A. Mobile: Mobile devices will continue their momentum to become the device of choice for communication, commerce, and payments. More than 2 billion mobile phones will be sold this year. And according to Gartner, by 2018, 88 percent of them will be smartphones. The big data approach will yield large amounts of information on mobile user locations and demographics. But in order for it to make a difference in decisions and consumer influence, it will need to include transactional data. As mobile commerce and mobile payments increase, that anonymous and aggregated data can make an actionable contribution to a company s data set. B. Social: The same Duke University study mentioned earlier showed that growth in social media spending is also expected to continue its sharp climb. Currently accounting for 9.9 percent of marketing budgets, it is expected to make up almost a quarter of spending 22.4 percent in the next five years. But demonstrating the impact of that outlay on corporate performance remains difficult for most companies. Only 13 percent of marketers report that they can measure it even though 61 percent said they feel pressure from CEOs and boards to do so. The amount of data driven by social media can be staggering, yet largely meaningless if taken in bulk. Because it is unstructured (does not contain a predefined data request), it is harder to make actionable. But by using it to map the customer experience and conduct sentiment analysis, big social data can be refined into actionable pieces. C. Internet of Things: Just as social and mobile media can produce enough data to make it intimidating, the Internet of Things has the potential to outpace its output. IDC estimates that, as of the end of 2013, there were 9.1 billion IoT units installed, with that number expected to reach 28.1 billion by 2020 following a compound annual growth rate of 17.5 percent. At an October 2014 AdAge Data Summit, OgilvyOne digital manager Bill Mullin predicted that the different nature of consumer data that could be entered into devices would change product design, retailing, and marketing. His example was a tennis racquet that Ogilvy is designing and marketing with Babolat Tennis (Rafael Nadal s company). The new racquet in development is loaded with six microchips that sense everything about the racquet s owner (including location) and usage from where the ball hits the strings to swing velocity to correct swing form. The data has enabled Ogilvy to build a tennis database that will be aggregated into a Pulse score. Join the community, download the app, and the user can compare his or her performance worldwide and track progress in the Pulse score (or lack of it). It is an example of how the IoT will include wearables and smartphones, with both those categories becoming payment platforms. As they do, they will add scale to the transactional database, narrowing the scope of actionable data for companies. IoT data can change business models. 2 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

3 2 D. Cloud-Based Data: The astronomical amount of data available has been enabled not by the data warehouses and servers that most companies built before 2005, but by open-source platforms like Hadoop that give data scale and speed. The awesome scale of velocity and volume of data requires companies to master open-source, cloud-based technology and use it as well as secure it. Just as data storage and usage change at high speed, security changes as well. The structure of data management and security has changed since the advent of Hadoop and its open-source approach. Before Hadoop, companies that aggregated, stored, and then analyzed data did so in huge data warehouses. Those warehouses filled the need for companies that harnessed transactional data, demographics, customer interactions, and the digital behavior of their customers. From that came the science of customer segmentation, basic data analytics, and predictive models. Enter Hadoop. It is a software platform that transcends storage constraints and enables the kind of advanced analytics that will produce actionable insights for social, mobile, and IoT data. Hadoop enables data set combinations and detail that can be processed quickly and provide greater access to more internal sales, marketing, and analytics executives. 2. BREAKING DOWN THE 360-DEGREE CUSTOMER VIEW These four trends will truly enable 1:1 marketing, which has been a nirvana of sorts since the advent of big data. It is not completely achievable because no campaign or analytics effort can effectively produce customizable treatments for every single customer. However, advanced segmentation analytics can move companies closer to 1:1 relationships. And as they continue the journey, they can provide customers with deeper, richer, and more meaningful experiences. New data connections, many from the emerging trends previously identified, will enable advanced segmentation analytics. The complete data set that emerges from the four sources mentioned, as well as from Internet traffic analytics, has come very close to producing a 360-degree view of the customer. For example, merchants have the capability to understand where their customers shop, what they buy, what they prefer in terms of needs and values of products, and what networks they identify with. Most importantly, retailers can be informed about what customers do when they re not shopping with them. They can see all angles. But a 360-degree view is not enough to make advanced segmentation decisions. In order to see that picture with clarity, transactional data is critical. Let s look at two examples to illustrate. First, suppose a communitybased bank has the mission of providing a full-service menu of credit, debit, mortgages, mobile banking, and affluent-level products. It has anonymized data sets to show credit history, outstanding debt, and current product usage. It has rich demographic information, social network analysis, customer journey information, and minimal transaction information, based on banking transactions. The bank has a 360-degree view of the customer. But the ability to make the data actionable only comes to life when a richer set of transactional data is used. Transactional data (anonymized and aggregated) can inform purchase behavior. For the local bank, with the current environment and the importance of the deposit relationship, building the segmentation based on how consumers interact and use the checking relationship is critically important. By carefully examining a customer s or member s current account inflows and outflows by transaction type (looking at direct deposits, withdrawals, check-writing behavior, ATM usage, POS debit card use, ACH, and the like) and evaluating meaningful breakpoints that tie to profitability metrics, a holistic picture of engagement and total value across all segments can be created, so primary and secondary account relationships can be better understood. The additional insight can then be applied to all kinds of marketing programs: upgrade strategies, product development, solution positioning, cardholder communication activities, and marketing channel tactics. Those are action items for the bank. They create advanced segments. They improve the customer experience. 3 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

4 The second example of advanced analytics is for merchants. Let s suppose an international merchant wanted to increase its presence in the US. The big data strategy would include traditional research such as site surveys and demographics of prospective new areas, which can be effective at a basic level. The actionable data strategy would leverage actual transaction behavior and modeling to drive the business expansion strategy further than in the past. Adding a relevant competitive set of merchants across both domestic and international customers results in additional levels of insight. The big data and the advanced segmentation analytics created by the transactional data enable the retailer to improve operational efficiency and create an entire new level of insight by creating a look-alike segment. The merchant is then able to model the spend behavior of its highestspending and most loyal customers and score a set of customers that spend exclusively with the competitive set to develop a best prospect customer segment for insights and analysis. Spend behavior of consumers is a much stronger predictor of future spend behavior than are surveys or traditional demographics. In this case the advanced analysis identified and quantified the opportunity markets and zip codes to drive the brand s prioritization of markets and produced a deeper understanding of where the brand should expand in the US, given its unique brand appeal to both domestic and international consumers. The analysis also included a broader spending profile of both the brand s best customers and the prospect segment that identified noncompeting merchants. The merchant used this information to either partner during expansion or as leverage for adjacency decisions, given that they are currently high affinity merchants for these customer segments EXAMPLES OF ADVANCED CUSTOMER SEGMENTATION As transactional data integrates with big data sources, advanced analytics can find best customers, define profitable pricing strategies, and move closer to the power of one customer. To be more specific, actionable data, if protected by compliant security, will produce the following examples of advanced segments to drive insightful customer experience improvement and operational efficiency: Affluent Segmentation: The challenge with affluent marketing has been to define spending behaviors and parse them into nondiscretionary and discretionary spending. Big data analytics can easily define overall spending and overall income. That has a level of actionability, especially in marketing high-end product. However, apply the lens of transactional data and the affluent customer spend patterns magnify. Companies can see how apparel purchases have a range of nondiscretionary, mixed, and discretionary spending. They can see how gasoline and groceries are completely nondiscretionary. And they can see how cruise lines and jewelry are completely discretionary. Companies can act on this advanced segmentation by pricing based on the level of discretion and marketing products based on shopping behavior rather than overall income. NON-DISCRETIONARY MIXED DISCRETIONARY GASOLINE GROCERY STORES APPAREL DEPARTMENT STORES EATING PLACES CONSUMER ELECTRONICS CRUISE LINES JEWELRY 4 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

5 Purchase Sequence Analytics: Transactional data completes the shopper journey picture and allows companies to act on different points of that journey. Example: Let s take the center point of a purchase journey as a gasoline purchase. By analyzing that big data set, purchase behavior that happened before and after can be segmented. For example, 30 percent of all gasoline purchases over a period of time and similar geography were preceded by a convenience or pharmacy store purchase. Twenty-five percent were followed by a sports or athletic facility purchase. Transactional data can be aggregated for further analytics and can predict next behaviors. That s advanced analytics, and it s actionable to all merchants along the purchase journey. t-2 20% Dry Cleaning t+1 25% Sports/Athletic Facility t-1 30% Convenience/Pharmacy Store t+2 20% Eatery t=0 activity ALL (100%) Buying (3-4pm) Spend Density Analytics: While digital marketers are familiar with click density, spend density matches shopper spend to zip codes and then to more specific locations. Example: A department store chain can make inventory, pricing, media, and even expansion decisions based on the total spend by zip code. Rather than judging a zip code based on real estate values, actual spend data can define where customers are making purchases. Linking Transactions: Transactions can be linked with location and weather, for example, to produce new data sets. Instead of answering a big data question such as What is my average revenue for the month of December? a restaurant can advance analytics by asking What is my average ticket size when the temperature drops below freezing? Operational efficiency would improve as a result of that insight. 5 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

6 4 Testing the Hypothesis: Cloud-based data has made social sentiment measurements accessible. Companies can still have theories about why key business metrics are increasing or declining. But when analytics can track this spend revenue and compare it to social sentiment scores, a different lens can show essential detail. For example, if sentiment scores are low and are reflected in a business downturn, the company can analyze one level deeper and find the common elements of the negative sentiment. If those sentiments include bad customer service, for example, it can take steps to improve. 4. THE PROMISE OF PREDICTIVE ANALYTICS As the emerging data trends pave the road to actionable data, advanced customer segmentation analytics will gain speed and agility. But the results take different forms. It s important to understand how the three different levels of analytics correlate to the use of big data and actionable data. The most basic form of analytics will still be descriptive; descriptive analytics have a place for companies in almost every vertical, most importantly merchant categories. US ecommerce, for example, was up only 6.5 percent for total retail sales during the holiday sales season of That number, based on transactional spend data, is an essential piece of planning for Holiday 2015 inventory and media budgets. However, descriptive analytics will not be as rich as the next level of data, which is the most valuable kind predictive analytics. The merchant planning for Holiday 2015 will need benchmark comparisons and the most beneficial customer data and share of market to predict the changes needed. Predictive analytics might tell the merchants planning for 2015 to increase prices on new inventory in high-income zip codes and to benchmark social media sentiment before making staffing decisions. Combining descriptive and predictive analytics produces the kind of advanced analytics described in this paper. Predictive analytics and the actionable segments produced carry a critical time factor. They expire almost as soon as they are produced. Such is the nature of a global market in which gas prices fluctuate, individual markets address currency changes, and consumers in general continue to cautiously emerge from the global financial crisis of Customer data and the advanced segments that result from it must be updated in near real time. Transactional data provides that speed and urgency. The value of analytics goes down as time elapses. Hence, companies should be looking to integrate analytics with constant change in mind. Example: The affluent customer that receives and direct response offers for high-yield bonds may be in the market for more lucrative and long-term offers. But a financial institution will never know without advanced segmentation driven by transactional data CONCLUSION The speed of data waits for no company. Several market factors have driven the size and speed of data over the past decade, but the time to wait has passed. Smart companies are embracing social, mobile, and Internet of Things data to increase the volume of customer data, but more importantly to benefit from the positive pressure of advanced analytics. Cloud-based data will increase speed and access. The entire landscape will be one that is best addressed by transforming big data into actionable data, thereby increasing the value of the process by producing customer insights, creating advanced segment analysis, and transforming the customer experience. 1. IDC Worldwide Big Data and Analytics Predictions for 2015, 2. Forrester Blog, 3. Duke University MasterCard Advisors Use Case, MasterCard Advisors SpendingPulse, Jan This document is proprietary to MasterCard and shall not be disclosed or passed on to any person or be reproduced, copied, distributed, referenced, disclosed, or published in whole or in part without the prior written consent of MasterCard. Any estimates, projections, and information contained herein have been obtained from public sources or are based upon estimates and projections and involve numerous and significant subjective determinations, and there is no assurance that such estimates and projections will be realized. No representation or warranty, express or implied, is made as to the accuracy and completeness of such information, and nothing contained herein is or shall be relied upon as a representation, whether as to the past, the present, or the future. 6 ADVANCED ANALYTICS DATA + INSIGHTS = RESULTS

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