Predictive Analytics: Should Automated Content Selection Work by Segment or Individual?


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Two vendors made the same point with me this week, which is reason enough for a blog post in mid-July. The point was the difference between basing content selection on individuals and on segments. I have never considered the distinction to be especially important, since segment membership is determined by individual behaviors and individual-level decisions are guided by behavior patterns of groups. But the two vendors in question (Evergage and Jetlore) and another I spoke with earlier (Sailthru) were downright religious about the superiority of their approach (individual-level selections in every case). So I thought it worth some discussion.

First, let’s clarify the topic. The distinction these vendors were making is between selecting content separately for each individual and selecting the same content for all members of a segment. Of course, customers are assigned to segments based on their individual behavior and other attributes, but once someone is in a segment, the segment-based system ignores individual differences. Among segment-based systems, collaborative filtering uses product selections almost exclusively: this is the classic “customers who looked at this product also considered these products” approach, which doesn’t take into account other aspects of the customer’s history. Other methods build segments based on customer life stage, demographics, and similar broad attributes. It’s possible to build segments based on very detailed behavioral differences, but that’s likely to create too many segments to be practical.

At an operational level, the individual-level systems use automated analytics to rank all possible content choices for each individual using that individual’s data. Segment-based systems either use rules to select content for each segment or use automated analytics to rank content choices for the segment as a whole. The individual-level approach makes the most sense when there are many content choices to consider, as with retail merchandise or entertainment (books, music, movies, etc.). Those are cases where getting precisely the right content in front of the customer is much more effective than offering everyone the most commonly-selected items. Retail and entertainment marketers also usually have detailed history which supports accurate predictions of what the customer will want. Segment-based systems work best when only a few choices are available.  This means a separate segment can be created for each item or, more realistically, segments come first and items are created to serve them.

So does the entire debate really come down to using individual-level systems when there are lots of choices and segment-based systems when there are only a few? Not really: collaborative filtering can also handle massive numbers of options with great accuracy. The difference is that collaborative filtering doesn’t really consider much beyond a particular product choice, while a sophisticated individual-level system will consider other factors including the current context and the customer’s history. Done correctly, this should yield more appropriate selections. On the other hand, individual-level approaches require more data and more complex analytics, so there will be cases where a segment-based method is ultimately more appropriate.

Moreover, the two approaches are as much complementary as competitive. A segment can indicate customer state, such as just-acquired, satisfied, or churn-risk, which constrains the contents considered for offer by the individual-level system.* Or a segment-level system could chose the type of message to send but let the individual-level system pick the specific contents. Dynamic content within email campaigns often works exactly this way.

In fact, I’d argue that state-based segmentation is essential for individual-level optimization because states provide a framework to organize the masses of detailed customer data. Without tagging the customer’s current state during each event, it would be very difficult for even the most sophisticated analytical system to see the larger arc of the customer life cycle or to understand the relationship between specific offers and long-term outcomes.

All this has practical implications for marketers considering these systems.

– for individual-level systems, make sure they can look beyond predicting the highest immediate response rate to measuring impact on long-term objectives such as conversion or lifetime value.

– for segment-level systems, make sure they can take into account the customer’s past behaviors and attributes, not just the products they have recently purchased or considered

– for all types of systems, assess how they track and guide the customer through the stages of her long-term journey

You’ll want to consider other differences between content selection systems, such as number of items they can manage, how quickly they return selections, how they incorporate items without a sales history, and what data they consider in their analysis. Just remember that making selections isn’t an end in itself: you want to make choices that will create the greatest long-term value. To do that, it’s not a choice between individual and segment level analysis. You need both.

* See my June 25 post for a more detailed discussion of state-based campaigns.

Republished with author's permission from original post.


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