CognitiveDATA, a Merkle Company

By Terrell Sellix A matchback is the process of matching order records back to mailing-tape records to determine the actual source of those orders. Matchbacks have been used for years on a limited basis to try to pinpoint the source of unknown orders: typically 5 percent to 20 percent of orders. With the advent of the Web and the increase in multichannel marketing, understanding where your orders and customers are coming from has become harder to learn — and yet more critical to know — than ever. The shift has brought matchbacks into the limelight of customer order-tracking and results analysis. This Special

By Terrell Sellix A matchback is the process of matching order records back to mailing-tape records to determine the actual source of those orders. Matchbacks have been used for years on a limited basis to try to pinpoint the source of unknown orders: typically 5 percent to 20 percent of orders. With the advent of the Web and the increase in multichannel marketing, understanding where your orders and customers are coming from has become harder to learn — and yet more critical to know — than ever. The shift has brought matchbacks into the limelight of customer order-tracking and results analysis. This Special

The adage, “80 percent of your sales come from 20 percent of your customers,” is as true today as when it was coined many years ago. The real questions are how to identify those prospects and one- and two-time buyers who may have a strong affinity for your merchandise, and then how do you keep them buying? That is, how to turn prospects and one-time buyers into gold customers. Following are some strategies to test, roll out and then measure. If They Look Like Customers … Tactics such as calculating average order value (AOV) and/or lifetime value (LTV); modeling; and segmenting buyers

Multichannel marketers’ customer files change rapidly with new information being contributed on a daily basis from stores, catalogs and the Web. Even with the most strenuous efforts in place to qualify new-to-file transactions, your customer file no doubt continues to accumulate disparate and seemingly un-related transactions. These dynamic factors make it difficult to correctly identify and value each customer with traditional identity consolidation processes. Most merge/purge processes were developed more than 20 years ago and were never conceived to recognize the fluidity of movement, name change and channels in which customers interact today. Even the most advanced de-duplication processes use character-based logic and look-up tables