Prior to the era of on-demand access to data sources enabled by the Internet, marketers were limited in their ability to segment their customers into groups to which they could market independently. In this era, customers were primarily segmented by who they were, not what they bought or how they behaved. Your age, gender, education level, address, family status, any life event, the automobile you drove, and your occupation and income level were the primary data used for this kind of segmentation.
This is the era that gave us defined personas such as “suburban soccer moms” and “urban dual income no kids” for marketing purposes. While this type of segmentation was better than a monolithic, one size fits all approach, it really only told part of the story.
Static personas such as these can’t tell the marketer anything about purchasing behaviour or stage of purchase lifecycle, which are the best predictors of marketing effectiveness. For example, you may be a member of the “urban dual income no kids” segment, but that alone doesn’t tell a marketer much about whether or not you are in the market for home furnishings or what styles you may like.
Today’s marketer has not only all of the demographic and lifestyle data available previously, but also a wealth of additional data on a customer’s purchase activity, marketing interaction, channel preferences, and social media interaction to add to their customer profiling and segmentation capabilities.
So, for example, the “urban dual income no kids (UDINK)” profile segment can now be micro-segmented to “UDINK: outdoor sports enthusiast/e-commerce value shopper/social reviewer/email channel preference”. This gives marketers additional, valuable information to optimize their marketing spend by delivering messages to consumers for only what they have an interest in, and using only the marketing channels they prefer. Every marketer knows that some of their marketing spend is wasted, this kind of micro-segmentation helps marketers with their total marketing spend ROI.
Taking this a step further, real-time shopping interactions, either online or offline through the use of proximity beacons bring the time dimension into the marketer’s ability to segment. Our “urban dual income no kids: outdoor sports/e-commerce value shopper/social reviewer/email channel preference” micro segment can now be augmented with: “is online now comparing pricing on outdoor tents” or “is in our New York mid-town store in the camping department”.
The time dimension of customer segmentation brings a new level of capability to the marketer. Not only can we now deliver relevant messages through the right channels, we can do it at precisely the right time.
A personalised message or offer delivered at the time of shopping comparison allows the marketers to move from a one-way communication model to more of a dialogue with the consumer, in effect emulating personal touch of a store associate, but with the advantage of “knowing” that consumer through their profile and activity data. “I see you’re looking at tents today. The new North Face models have just arrived” is an example of a message a marketer can deliver to our consumer, in time and in place, knowing though their purchase history and social media interactions that their preferred brand is North Face.
Consumers are the ultimate judge as to whether marketing delivered through insights, delivered in time, and delivered in place is a welcome change from yesterday’s shotgun approaches, but so far the results are encouraging for both the marketer and the consumer. Both are winners in this new world of ultra personalised marketing.