Strategies for Leveraging Customer Insight Data
Scale optimizes marketing ROI
At marketing conferences and in various trade publications, some form of the following is often expressed: "In the era of Facebook and Twitter, who needs third-party data? People share everything." It's true that people do share an amazing amount of information these days. Yet with all that sharing, there's still a paucity of relevant data for marketing scale. Huh?
Even with all the indicators, comments, explicit references to being in-market, clicks, and more, there's still not enough actionable insight being generated for marketers to achieve reasonable scale for targeted campaigns without using the necessary descriptive data for look-alike analysis. Back to the Lexus example: it's helpful for Lexus to see a tweet from one person indicating that he's in-market for a Lexus. Lexus can reach that person with all kinds of messaging. That's just one person, however. Lexus can find some look-alikes based on similar online behaviors, but that's still likely a small sampling of the audience it needs to reach. In order to scale effectively, look-alike modeling, selections and messaging powered by the insights generated from the right data types are mandatory to identify and target that particular, high-value audience Lexus wants to reach.
While marketing budgets are often siloed by channels, 70 percent of online consumers display multichannel behavior and are four times to five times more valuable than single-channel customers. Thus winning brands will be those that present a consistent, logical, multichannel experience for their audience. To that end, leading marketers are increasingly finding ways to synchronize messages, offers and even campaign selection across channels. Doing so based exclusively on exhibited behaviors in one channel or another may be completely off target when applied broadly in many channels, however.
Deep, meaningful consumer insight provides an important Rosetta Stone, enabling translation and consistency from one channel to another. If Lexus knows nothing else besides the fact that 60 percent of the individuals who buy its RX model have children and an annual income above $90,000, it can use that insight to target placement and messaging of its advertising intelligently on television, online, in email, outdoors and in print. Then the more information it can layer based on an individual's channel behaviors, purchasing life cycle, etc., the more Lexus can use that information to tweak channel budgets, individual offers and more. A key basis for optimizing messaging and coordinating customer engagement across channels is cultivating the right descriptive data to refine those deep, meaningful consumer insights as a starting point for reaching high-value, desired audiences and individuals with consistency.