Identify Trendsetters for Targeted Campaigns
Trendsetters are those ultra-valuable customers who simply must have your latest products, are willing to pay full price, and have influence over a broader social network to compel others to also engage with your brand. Wouldn’t it be great if you could figure out which of your customers are your trendsetters? The ones who have clicked through your past campaigns, browsed multiple pages, downloaded your content, and purchased without even needing a discount. Of course you value all of your customers, but this group requires some special attention. To answer the question: Yes, it would be great. And to answer the inevitable follow-up question: Yes, you can actually do it, and we can help you get there.
Getting there is a three-step process and, as usual, it starts with data. First, be sure to collect any and all profile information about your customers. Reach into as many source systems as you can get your hands on. Purchase data, website behavior data, social profile data, and web-activity data such as page views, ecommerce, and in-store purchase data should all be collected. If you can gather this information, then you can create what we call the Profile Attributes part of your trendsetter equation. It’s simple enough, since much of this data should be at your fingertips with features available in any ESP worth their salt.
The next step is to build the more dynamic components of the equation; those which track the constantly evolving actions that represent how a trendsetter behaves. Different than the Profile Attributes which are more static, these Activity Attributes are constantly changing and refreshing. The trick is to detect the patterns of behavior over time. This is where real-time decision engines like QuickReact churn through data from multiple systems and look for the pieces of the puzzle to inform the marketer when a pattern is unfolding. Data such as web-activity details gathered through tracking scripts, mobile shopping session variables, email opens, click-throughs, and social profile variables from the various social outlets are critical. For example, Trendsetters read about products that aren’t even released yet much more than they read about reviews of last year’s products that are now on sale. Ever wonder who is looking at the preview of your new Fall Line and ignoring your “Clearance Items”? It’s probably your trendsetters. The trick is to build dozens of these rules that run constantly, and then use the outcome of that rule as a building block for the last step in the process.
The last step in the process is to put it all together and synthesize down to one simple attribute to depict the series of more complex Profile Attributes and Activity Attributes that got you there. In the example above, you can see how a simple outcome called “Trendsetter in Action” is the result of a combination of Profile Attributes and Activity Attributes. Missing from the picture, but implied in the rule-logic is the concept of time. The challenge with detecting trendsetters is twofold, you need to both find them AND take action when they are exerting trendsetter influence. In this example, the “Trendsetter in Action” is exerting their influence right now, so specific campaigns and calls to action should spring from the newly uncovered behavior called “Trendsetter in Action.”