What does Big Data allow marketers to do with segmentation that was not possible before?
Big Data is like the population in some emerging countries. There is a lot of it (volume). It’s incredibly diverse (variety). And it’s growing at an extremely rapid rate (velocity). This has created unprecedented economic opportunity for businesses.
History has proven that businesses often don’t move at the speed recommended by pundits to take full advantage of these kinds of opportunities. Now, with Big Data-enabled improvements in analytics, including segmentation, organizations can realize the financial benefits and evolve along the Big Data journey.
For years, segmentation has been fundamental to engaging customers and achieving superior financial performance. Traditional schemes have relied on demographic, firmographic, survey response, and customer behavior data. The emergence of Big Data has created tremendous variety and timeliness of customer information. Thus, segmentation can now unearth greater customer insight.
The Big Data-enabled possibilities are enormous, but we focus on three areas that can bring relatively quick wins.
1. Increasing usage of text mining – The importance of text mining has grown with Big Data because much of that data is unstructured. A common example of this is text data such as customer comments. Text mining enables conversion of this text into structured data that marketers can use in segmentation. For example:
- A customer enters many sentences worth of frustration with a company in an online text box.
- Text mining this unstructured data results in categorizing the customer as “unhappy” due to “service issues.”
- This customer categorization becomes an input to a segmentation scheme.
- The segmentation scheme then classifies the customer into an appropriate segment, such as High-Value-Dissatisfied or Low-Value-Dissatisfied, leading to appropriate retention tactics.
2. Leveraging social network data – Let’s say, company iSell has a product BuyMe. Now assume there are three individuals—HappyBuyer, ProspectFriend, and JoeStranger—who share a similar profile based on demographics and other traditional data. First, Big Data can enable iSell to know that HappyBuyer and ProspectFriend are “linked” in social networks, but JoeStranger is not linked to any buyer. Second, since HappyBuyer has a long relationship with iSell and is passionate about BuyMe, he may have a positive influence on the purchasing activity of ProspectFriend. Third, while the traditional data-based segmentation scheme would have placed ProspectFriend and JoeStranger in the same segment, ProspectFriend and JoeStranger could potentially be in different segments based on their propensity to be influenced for purchasing BuyMe.
Also, typically, value segmentation schemes have classified customers as high or low value based on past or expected future spending. Now, a portion of these low-value customers could be classified as high value based on their influence within social networks.
3. Updating segmentation frequently – Historically, in most organizations, segmentation is “refreshed” only periodically, between monthly to once a year, driven by data refresh schedules and associated process challenges. My colleague, Epsilon CTO Chris Harrison, notes that “Technology exists for marketers today to place individuals into segments in real time and, perhaps more important, move consumers from one segment to the next in real time.” As customer data is being refreshed in a timelier manner than in the past, it’s only logical that to benefit from such information, organizations must improve on the segmentation refresh frequency, driving it towards near real time.
As organizations adopt Big Data, there will be many challenges. Therefore, it’s critical to achieve quick wins to build and sustain organizational momentum. In addition to technology solutions, key skill sets such as network analysis and text mining need to be developed to execute the strategies outlined above. These skills can be built internally or leveraged through partnerships. Regardless of the approach used, the path forward is exciting and one that will result in superior customer experience, as well as strong financial performance when done right.
Amit Deshpande, Epsilon
A golfer and a passionate player in customer data, Amit says he feels like Tiger Woods on the fairway when engaged in marketing analytics. “I get really excited when I do my job; I get paid for doing what I love to do,” he says. As senior director of Epsilon’s analytic consulting group, Amit explains his job simply as helping clients in manufacturing, transportation, technology, and retail grow revenue profi tably. Not so simple is how he and his team go about achieving it. “We look to gain customer insight through segmentation, profi ling exercises, proper measurement, and cross-channel attribution,” he says. Prior to Epsilon Amit directed marketing analytics at Federal Express, where he helped move the company from a CRM focus to an integrated marketing approach. When not having a ball with data, he enjoys playing with his daughters, ages six and three.
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