With all the great data that data providers are making accessible today, it's possible to interact with consumers in ways that were never before possible. The tremendous breadth and depth of available data moves consumer views from one-dimensional to multi-dimensional, helping to paint a much more complete picture. The implications positively impact the entire value chain from the marketer all the way to the consumer. For all the progress however, we’re still very early on and there are still several misconceptions about the successful application of data.
One of the biggest misconceptions is that optimal data consumption is on an "as needed" basis since data is expensive and more data may not add incremental value. Utilizing data for targeting is only one of many applications however. One of the most important and innovative applications of data isn’t for targeting at all but rather enabling marketers to implement more effective customer segmentation strategies.
Many transaction-centric B2C companies rely on effective segmentation to align messaging with business objectives in order to maximize LTV (lifetime value). Successful implementation of segmentation helps these companies define business models, build customer loyalty programs, and further value proposition discussions. The exercise of creating an effective segmentation strategy should result in a comprehensive understanding of the various types of customers as well a coherent plan for achieving business results.
When helping marketers address their segmentation strategies, the first step is to understand what the objective of the marketer is. Typically objectives are either tactical or strategic. Tactical segmentation strategies typically encompass cross-selling and upselling opportunities. For a financial services client for example, it may involve messaging certain “gold” card members in order to move them to “platinum” card status which may yield much higher fees. Strategic initiatives are usually much broader and align to business objectives. For example, determining that users within certain segments may require less support than others would allow a company to deploy capital more effectively.
The next step is to construct the segments. This is where the tremendous amount of data available makes all the difference. In years past, marketers relied on single data types or sources for segment construction which typically yielded no additional insight or lift in performance. Today’s data landscape allows us to combine diverse data sets such as transactional data, intent data, attitudinal data, social data, occupational data, and even survey response data to gain a much more complete understanding of the users that make up marketer segments. As the knowledge of these consumer segments becomes deeper, we see greater accuracy within the models, not to mention greater flexibility. This results in a smaller error rate and allows for much greater performance and scalability. When you can begin to close the loop with incorporating revenue and profit data, the conversations between the CMO and CFO become a lot easier.
As new methods of data capture from innovative companies are deployed, we’ll continue to see more and more great data come to market. With an expected influx of massive amounts of rich data, it will be important to continually refine models and segmentation strategies. Developing operational discipline around building and maintaining well-informed segmentation will become more and more critical to marketers as fragmentation continues. I envision a tremendous amount of innovation in and around this area from both the buy- and sell-side in the coming years.