Creating Segments from Signals Using a DMP

Just what does c_product=="apple" mean?

Previously I outlined some thoughts on developing segments that focused on more strategic uses.  The end result of the top down process is 5-to-8 key personae that should be tracked as part of any management reporting process.

However, when working at the campaign level the process of defining segments is a bit different because we're now more interested in getting the most bang for the buck as quickly as possible.  Optimization and bidding decisions are the epitome of this tactical thinking and the more variations we can test the better.

Think blocks...

Nowhere is this distinction more evident than in the process of developing segments for digital targeting using a data management platform (DMP).  In this case we start with the signals generated by traffic, convert them to traits that are appended to each visitor and then create the segments based on those attributes. This bottom up process takes a lot of structure and quite a bit of restraint not to end up with a bazillion one-off segments.  

The organization of traits and segments is the most critical aspect of a successful implementation even more so than the script that generates the signals .  These folder structures (I'm using Adobe Audience Manager as a mental model) are "priceless", a term used by a colleague with more experience than I at the time.

Here are lessons learned from a recent experience:

• Use tag management to standardize or at least rationalize different tagging schemes before the signals arrive.  This becomes more important as the number of sources or sites increases.  Imagine trolling thru an unused signal report where each source has its own taxonomy.   Since these signals get converted to simple values, e.g. "apple", there is no other context provided than what you see.  The band aid is having to add traits just to define what you mean...this gets ugly as the "or" and "and" conditions get strung out.

• Separate traits into independent dimensions that represent specific elements of behavior, e.g. location, site structure, content elements, and events/actions.  You can deconstruct past campaigns into its component parts, but don't think too much about those segments at this point.  The goal is to resist the injection of complex logic too soon in the process which ends up decreasing the value of the traits over the long term because they are too specific.

• Think of segments at the DMP level as building blocks, not the final target group.  In this way we can easily mix and match at will at the destination or ad server side of things when we know what the campaign objective is.   We want to encourage reuse from both and immediate efficiency perspective, but also for analysis purposes later on.  By having building blocks we can look across campaigns for insights.

As a really simple example, consider building segments that a retailer can use for targeting it's customers.   There are at least two definitions here - site visitors (i.e., "h_referer") as well as those within a defined trade area (i.e., "d_postal").   These should be passed as two segments rather than one to allow different campaign rules to apply of various combinations at various points in time in the future.

• Visitor In-Area:  retain, defend, reward
• Visitor Out of Area:  understand
• Non-Visitor In-Area: acquire, make aware

Now imagine a dozen or more classes of building blocks...

As analysts we should be in the business of creating the building blocks, not a specific audience segment.