8 Things Marketers are Doing with Big Data and Analytics

8 Things Marketers are doing with Big Data and Analytics
The full impact of using Big Data for marketing is yet to come; however, here’s 8 ways some marketers are applying analytics to their big and small datasets today. Are you on the Big Data train?

1. Developing a 360-Degree View of Our Customers

One size fits all does not fit anymore. People are more likely to respond to brand experiences that are tailored to their individual needs and desires and to interactions in which they are remembered and rewarded for their previous actions. What is more, for maximum effect, these experiences have to be integrated and adapted across channels. Not being aware of a recent customer service challenge, for example, can leave a salesperson blind to a customer’s reason for not renewing a contact. Customers generally perceive our companies as single entities; a salesperson’s lack of knowledge of what has transpired with customer service can increase the customer’s dissatisfaction, making them feel even more insignificant to the company. In a better scenario, if we know what a customer has viewed on our website, we can tailor our messaging to reflect those interests. If we are aware of their past purchases, we can make more intelligent recommendations.

Scott Epskamp, president and cofounder of Leapfrog Online, encourages us to invest adequate time discovering the true nature of our customers and their preferences across channels and throughout the sales cycle. In his experience at Leapfrog Online, without this knowledge it is impossible to optimize our marketing and sales efforts for the customer. Instead we optimize for the channel. He cautions against having a digital bias: “While digital might make the most sense for information building, interacting live with a sales rep at the right stage of the sales cycle could make the difference between a research project and a sale.”

Piecing together a comprehensive view of our customers that makes this level of customization possible requires us to be able to combine traditional demographic data (for example, age, income, occupation, home ownership, nationality, and residence) with behavior captured from across relevant online and offline touchpoints—call center data, web analytics, social engagement—in real time. To build contextualized experiences requires that we also have access to situational data such as our customers’ location, the time of day, and the device they are using, and even what the weather is like where they are located. Building a comprehensive view does not mean that we capture all the available data that is about our prospects and customers; only relevant information. We do not need to know everything that our customers discuss on social media, only that which is applicable to our business.

Most companies are not yet at the point where they can match every customer interaction to an individual person. For example, if we visit a company’s website three times, once on a mobile phone, once on a tablet, and once on a desktop, most companies’ systems will think these were visits from three different people. Customer Stitching is the process of being able to link all of these devices to a single user. In the absence of this capability, we do not have an institutional memory of our interactions with our customers; it is very easy for us to inadvertently make them feel insignificant.

Many firms may need to upgrade their technology to realize the benefits of big data. According to SCRIBE’s State of Data Integration report, only 16 percent of companies surveyed have fully integrated systems.10 Web-derived data often resides in a different system than data derived from call centers or social media, which limits our ability to form a comprehensive view. Further, data streams may not be organized using the same definitions; systems may define a lead or a customer differently. Solving these infrastructure challenges upfront is critical to our ability to create reliable analytics, to integrate data at the level of the individual, and to deliver a more remarkable customer experience.

2. Fill in the Blanks

How do we build an enhanced view of our customers in the absence of complete data? In some cases we can obtain third-party data to augment our customer profiles. An e-mail address, for example, can help us incorporate new data into a customer record.
Analytics can be used to create synthetic data, a proxy for authentic data that simply does not exist. For example, Data Fusion is a process that integrates multiple datasets of different, yet similar survey respondents, to develop a deeper understanding of a targeted group of customers. Data fusion works when there are strong underlying common characteristics between the two groups of survey respondents and those common characteristics explain the consumer behavior being evaluated. Validation techniques are used to prove the model and evaluate the reliability of results.

Data fusion is often used in marketing to make inferences about media exposure and buying behavior for a given segment. By merging data about a given segment’s media behavior (TV viewing, for example) with a data stream about a similar segment’s buying behavior (purchase of an over-the-counter pain reducer), we can extrapolate what the TV viewing behavior of people who purchase a given over-the-counter pain reducer may be. If we are marketing over-the-counter pain products, this information would allow us to make more informed choices about where and when we might advertise on TV in the absence of data obtained by direct measurement.

3. Smarten Segmentation

Segmentation is a process of breaking down an overall market into smaller groups based on common needs and motivations. When broken down in meaningful ways, segmentation allows us to prioritize our efforts and design more effective products, services, and marketing experiences for each targeted group or individual.

In the past we segmented our prospects and customers by demographics, reflecting how data was collected and how media was purchased at the time. Our segmentation became increasingly sophisticated when we augmented this data with transaction records from loyalty programs and credit-card purchases. Today we are able to segment our customers and potential customers in even more useful ways. In addition to being able to describe who our customers are and what they buy, we are able to get at the where, how, and why underlying their behavior.

Rick Kash, the vice chair of Nielsen and coauthor of How Companies Win, illustrates how segmentation has changed using the dog food industry as an example.11 Working with demographic data, we have traditionally segmented the dog food industry into categories like “owners of medium-sized dogs,” “owners of large dogs,” or “owners of small dogs.” Today, we can view the industry in terms of what actually influences customer behavior: the types of relationships owners seek with their dogs (the why). This leads us to market segments with personas, or representative archetypes, such as “Pampering Parents,” “Performance Fuelists,” and “Minimalists.” This additional dimension is critical; it allows us to more clearly identify each segment’s needs and desires, the triggers that prompt them to act, and the owners’ criteria for making purchase decisions. This proprietary insight is a very real source of competitive advantage in a time when customer-centricity matters.

A closer look at Pampering Parents, for example, reveals a market segment that treats their dogs like children. Often empty nesters, these people generally own small purebreds. They purchase dog food on the basis of their pet’s taste preferences, rather than on price or nutritional value. Viewing their dog as a love object, these pet owners have low price sensitivity. In contrast, Minimalists view their dogs as functional objects, low-cost employees, or living, breathing alarm systems. This market segment skews toward large, rural households with utilitarian hounds. Highly price sensitive, these owners look to buy the least expensive dog food brands in the largest sizes. A third segment, Performance Fuelers, are people who live active, healthy lifestyles and view their dogs as partners in their outdoor activities. This group wants the best for their dogs, and they define the best dog food on the basis of nutrition and taste. Performance Fuelers tend to be single or young families with large purebreds.

By segmenting the dog food market in this way, differentiated product and marketing strategies for each segment become clear. One brand will not satisfy the Pampering Parents and the Minimalist segments; however, one company can, by offering several distinct products, each with its own packaging and marketing strategies. Kash refers to each of these groups as demand profit pool, reflecting the basis upon which they are derived—by their demand rather than their demographics—and the potential profitability they represent. In his experience, at least six different demand-profit pools comprise most markets or demand landscapes. Predictive analytics allow us to determine how likely a customer or prospect is to be a part of a given demand pool, linking our customer base to our strategy.

4. Improve Targeting

More precise knowledge of prospects and customers, including their preferred communication channels, allows us to customize key elements of our content experience efforts. When situational data is layered on top, we can further refine customer experiences to reflect where people are, what they are doing, the devices they are using, the time of day, and even the weather.

For example, to drum up business during the off-season, an indoor waterpark resort applies a geofilter to its data to target parents of students residing in zip codes where school is cancelled due to inclement weather. Targeted web display ads served to parents when they are online, encouraging families to grab their bathing suits and head over for some indoor fun, have increased attendance by 30–50 percent.

Combining customer data with advanced analytics creates even more powerful targeting opportunities. Next-best offer (NBO) analytics help us estimate the probability that a customer will be interested in a targeted offer. When the rules and algorithms of NBO are combined with search engines, we can create cross-selling experiences such as, “You may also like…,” which often result in higher average order sizes and happy customers. For instance, the fashion retailer Forever 21 posts personalized recommendations for items on the bottom of their “reset your password” page, knowing that they have your attention and some degree of purchase-intent.

At-risk models can help us identify changes in customer-usage patterns, making it possible for us to predict when a customer may be losing interest and target interventions to retain their business. Using IBM’s Centralized Customer Decisioning Platform, which consolidates customer data and provides marketers with what-if analysis tools, a financial institution was able to increase its retention offer acceptance by 33 percent.12

5. Prioritize Customers

Customer-centricity involves accurately identifying the value of our customers and generating appropriate opportunities and experiences for those whom it makes sense for us to serve. Segmentation focuses our efforts and enables us to better prioritize at the segment level. We can go one level deeper by using predictive analytics to score each of our customers in terms of their own customer lifetime value (CLTV).

The CLTV represents our best estimate of a customer’s financial value to us over the lifetime of our relationship. (It does not quantify their contributions to product innovation or advocacy.) A predictive measure of future profitability, CLTV has its roots in past purchase behavior, but is adjusted to reflect future expectation of value. If we have faith in our estimates, CLTV can help us allocate our resources more effectively, offering our most valuable customers differentiated, premium experiences. It also allows us to quantify our companies’ overall customer equity. The sum of all of our CLTVs, customer equity is an alternative and customer-focused method of measuring corporate value.13

Given the importance of referrals to our ability to generate revenue for many of our businesses, we may also choose to estimate a customer’s referral value or CRV as this often differs from their CLTV. V. Kumar, J. Andrew Peterson, and Robert Leone presented a methodology for calculating CRV in an article in Harvard Business Review.14 According to their model, a customer’s CRV is calculated by estimating the number of successful referrals that he or she may make. Value is calculated differently for referrals for business that would have come in anyway (in which case we only count the acquisition costs we saved) and those that would not have joined without the referral. Our most effective prioritization efforts would capture both our customers with the highest CLTV and those with the highest CRV and encourage them to refer and spend more, respectively.

Working with customer data from a telecom company, Kumar et al. found that they were able to successfully influence customers in each of these groups through targeted marketing efforts. By offering financial incentives for referrals, they were able to encourage customers with high CLTV, but low referrals, to make successful referrals. Similarly, by offering cross-selling and up-selling opportunities to high CRV customers, the team was able to increase their average CLTV. Interestingly, when combined, these marketing techniques also improved the CLTV and CRV of the company’s least valuable segments.

6. Identify Influencers

Today’s customers trust recommendations from third parties, making influencer marketing one of the most effective ways to attract prospects and customers.

influencer marketing is one of the most effective ways to attract prospects and customers. Click To Tweet

Knowing where influence lies allows us to identify, engage, and support people who have credibility, sizeable networks, and an ability to motivate those networks to act on our behalf. Influencers can be journalists, experts, analysts, regulators, members of our broader customer ecosystem, and even family and friends. Their influence varies depending upon the type of purchase and where a prospect or customer is in the experience journey. Nurturing shared-value-based relationships with influencers can help us build brand awareness, create and activate content, spread the word about product launches, and be a partner in crisis management, product development, competitive analysis, and event promotion.

Social listening and social network analysis, which helps us predict someone’s potential to influence based upon their connection and interactions with others, can help us do both. Popular blogger outreach identification tools include Klout, Inkybee, BuzzStream, and GroupHigh. Using GroupHigh’s influencer software, the family entertainment company, Chuck E. Cheese’s, was able to enhance the effectiveness of its social media strategy, of which 40–50 percent is focused on influencer relations. By identifying and analyzing family-focused bloggers with adequate social influence and engagement, the company was able to build relationships with more than 650 influencers and generate more than 75 million monthly unique visitors to their website.15

7. Capture the Voice of Our Customers

To be successful, our companies must offer our customers products, services, environments, and experiences that we know they what, not what we hope they want or think they want. By interjecting our customers’ voice into our business decision-making we can improve our relevance to our customers.

Well-managed Voice of the Customer programs (VoC) that define customer insights clearly and have processes in place for taking action can help make this possible.16 Most VoC programs utilize surveys, e-mails, and customer comment cards to better understand their customers’ experience. Increasingly, companies are establishing customer communities to provide ongoing connection with customers and prospects. This running dialogue allows us to get to know them in a way that is not otherwise possible, yielding macro- and micro-level insights. We can explore ideas for new products and services innovation as well as test incremental improvements in our content or advertising.

Tracking and analyzing customer sentiment as expressed in reviews, ratings, recommendations, and comments in social media is another essential component of our customer listening effort. Social media monitoring allows us to identify what is being said about our products and services, brands, companies, competitors, and industries; anything really. Social media tools, such as Sysomos, Radian 6, Social Mention, Lithium Technologies, ViralHeat, and Meltwater Buzz, mine this unstructured data to determine underlying attitudes. As we saw with IBM Social Insights, social monitoring allows us to proactively address prospects’ and customers’ needs and desires and to incorporate their sentiments into our messaging and other engagement efforts. To truly reflect the market’s feelings, sentiment analysis needs to incorporate offline sources of information as most word of mouth takes place face to face.

8. Measure and Optimize Our Marketing Efforts

According to Gartner’s U.S. Digital Marketing Spending Survey, on average marketers spend 25 percent of their budgets on digital activities17—which should make it easier to evaluate our efforts as we can quantify clicks, views, shares, and track how people are discovering us. However, today’s marketing mix is complex and interconnected, making it challenging to evaluate the effectiveness of our marketing efforts with certainty.

Closed-loop analytics measure the value of each digital marketing interaction toward a desired outcome, allowing us to identify what is working and what is not and make mid-stream adjustments. This end-to-end picture also allows us to accurately demonstrate marketing’s impact on key business performance measures like revenue, profit, loyalty, and advocacy metrics. Using webtools like Google Analytics, we can optimize our marketing efforts by running comparative champion-challenger analyses of our content in real time. Similarly, dynamic optimization of ad content allows us to break ad content into layers, so that we can overlay customized features and test the effectiveness of variations.

There you have it. 8 great ways to use Big Data and Analytics to amp up your marketing and put real numbers behind your strategy. Be sure to check out our other blog posts on digital marketing.

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About the Authors

Larry Weber and Lisa Leslie Henderson are the co-writers of this Digital Marketing guide. Larry is the CEO of Racepoint Global, an advanced marketing services firm. A globally known expert in public relations and marketing services, Larry has successfully built companies and brands and is passionate about the future of marketing. Lisa is an observer, synthesizer, and writer who draws extensively from her background in marketing and consulting. Lisa and Larry have collaborated on two guides to date, The Digital Marketer, and Everywhere: Comprehensive Strategy for the Social Media Era. To stay current on their thinking, frequent www.racepoint.com/thedigitalmarketer and follow them at @TheLarryWeber and @ljlhendo.

Buy on Amazon: The Digital Marketer: Ten New Skills You Must Learn to Stay Relevant and Customer-Centric

10. SCRIBE, “The State of Customer Data Integration 2013,” Scribe , https://www.scribesoft.com/docs/reports/Scribe_SDI-Report-2013.pdf.
11. Rick Kash and David Cahloun, How Companies Win (New York: Harper-Collins, 2010)
12. James Taylor, “The Case for Centralized Customer Decisioning,” IBM Software Thought Leadership (White Paper) New York: IBM Corp, July 2011.
13. For more information about measuring CLTV and customer equity see Peter Fader, Customer Centricity (Philadelphia: Wharton Digital Press, 2012).
14. V. Kumar, J. Andrew Peterson, and Robert Leone, “How Valuable is Word of Mouth?” Harvard Business Review , October 2007, https://hbr.org/2007/10/how-valuable-is-word-of-mouth
15. GroupHigh, “Chuck E. Cheese’s Takes Family Entertainment to a New Level With Blogger Outreach,” GroupHigh.com, accessed December 28, 2013.
16. Andrew McInnes, “Taking VoC Programs to the Next Level,” Forrester Research, Inc. (blog), May 16, 2011, https://go.forrester.com/blogs/11-05-16-taking_voc_programs_to_the_next_level/.

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