• by Michael Kaushansky , July 23, 2013

We are quickly moving to a world of cross-channel hyper-targeting.  The notion of database-precise targeting is not just for direct response campaigns anymore, but now can be used for brand campaigns.  The promise is to provide a laser-focused effort in delivering not just an offer, but also a brand experience to a targeted individual.  The most critical piece in the process is to have the right data to know whom to target.

For most brands, their existing customer data is the most effective method of defining which future customers they should go after.  Sounds like a simple task, yet in reality it’s anything but.  Several barriers exist, including the low accuracy of “cookie-synching,” and misclassification of customers into the wrong segments by relying only on online activity. Most importantly, some brands are simply not set up for access to their customer databases.  Brands must store their customer data in a simple and accessible manner, then carefully select the most reliable data sources/providers and enhance each customer’s profile.

Even with properly organized customer data, some advertisers have reported offline-to-online match rates as low as 10%, with only 50% accuracy in simply identifying gender, let alone deeper attributes about a consumer. 

To bring consistency and accuracy to data matching, analysis, and targeting, I have defined a five step process: AESMA.  These steps help convert valuable/proprietary first-party information into a highly competitive consumer targeting strategy:

1.     Access – Enable selective access to customer data, ideally using a data warehouse.  To improve match rates and data accuracy name and address as essential, email helps but is not required. 

2.     Enhance – Select a data enhancement provider with proven match rates above 50%.  There are a handful of primary data providers who aggregate consumer data using name and address for matching.  The match-key is kept fresh using the NCOA process (USPS’ National Change of Address).  Secondary data providers rely on these primary sources, you may as well go direct.

3.     Segment – Develop a dual segmentation framework using data native to a brand to create intuitive/proprietary segments and use third-party data to create empirical/data-driven segments.  A matrix between the two segmentations will provide a deeper understanding of existing customers and help define a strategy of whom to target in the future.

4.     Model – In most cases you will end up with too much data.  Use modeling techniques to isolate and remove redundant data and highlight only the handful of variables that matter in identifying your target prospects.  For example using Household Income would reduce the need to also use a wealth indicator, etc.

5.     Action – Having all the data and analysis is only half the story unless you do something with it.  Use the modeling output to help craft a more focused campaign across all channels.  Certainly digital channels allow for more targeting parameters (e.g. DSPs, Facebook, and email); howeverm you can also aggregate results by Zip code and target using traditional channels (OOH, Television and radio).

Soon all channels will allow for some level of hyper-targeting, even in television with the advent of set-top-box targeting.  Starting to enhance and segment your customer data now for CCHT will provide enough background and experience to be ahead of the curve.