Last month I discussed the tradeoffs that data vendors make in their data consolidation process in terms of breadth, depth and accuracy, and how that may affect your intended use of the data. This month I'll discuss how to measure the external data that you are considering for the customer and prospect dimensions of your CRM-ready data warehouse.

Putting the right information into the hands of your customer care team means providing them with the timely and relevant information that your customers have directly volunteered to your organization when establishing their profiles and executing transactions. It also means providing a large measure of data originating beyond the walls of the organization.

Ranking customers across several dimensions of activity within your organization and correlating those highest ranking customers to their attributes also generates high value to the organization. Effective prospect lists can be generated, and cross-selling and up-selling opportunities become more evident.

Match Rate

Match rate is the measure of the quality of the external customer attributes. Matching applies at both the row and column level. That is, matching is both a factor of how many of your customers' rows were matched at all, as well as how many attributes were matched. Data vendors that compile consumer data use numerous sources. If your consumer set is not participating in phone books, surveys, contests, registrations, etc., not all the data will be available.

Match rates of interest will be overall row match rate, overall column match rate and column match rate on particular columns of importance. For example, if you sent the vendor 1 million records for enhancement and 750,000 were matched with at least some columns, the overall row match rate would be 75 percent (which is not bad). If, of those 750,000, the average record had 25 of the 50 columns matched, the overall column match rate would be 50 percent. If marital status is measured separately and is found on 80 percent of the rows matched, then its column match rate is 80 percent.

Quality Checking

I encourage you to understand the methodologies employed by the data vendors for gathering the data they propose to match with your data. Although the vendors will state the likely match rate, you will want to quality check the data with trial data from the provider. Performing a quality check will enable you to measure the actual, applicable match rate.

How do you determine if one of your customers actually owns a dog and has six wheels on his preferred automobile as the augmentation claims? You could call a random sample, but how would that go over with your customers? Rather than make these awkward calls or perform a ruse ("I'm a college student doing a study..."), enlist a separate surveying company. The surveying company should choose a random, but statistically significant, set that covers the numerous possible cross sections of consumers in the set of data. Keep in mind that you will undoubtedly receive a similar level of quality on prospect lists you may want to generate from that data provider in the future.

Softer measures of responsiveness, customer service, a commitment to continued sourcing, vendor stability, etc., should also factor into the equation. The process may become part of your pipeline for responding to market conditions, so it will be important to know how quickly they can respond to your needs.

Add price into the measures, and you have a foundation to compare data providers for your CRM-ready data warehouse. Quality and accuracy are continual tradeoffs. If the match rates don't bear out, perhaps another data vendor would be better for your situation. When comparing the offerings, always do so with a view toward usage. No vendor will match all columns for all customers. Allow for some measure of error that is acceptable for the way the data will be used. You're probably not asking for elements that are not interesting to you, but you may have some flexibility over the accuracy of each element. If you already have a data provider and have not tested the data, now may be a good time to do so. Build annual checkups into your quality processes.

In the 21st century, those companies that can engender the highest levels of customer satisfaction through the effective use of knowledge will be the winners. Quality external consumer data contributes to the attraction and retention of the best customers.

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