Cloning Your Best Customers (1,173 words)
It's also important to remember that data validity is sometimes questionable. Inferred or enhanced data can produce inaccuracies. Data entry errors or self-reported data from a customer increase the chance that the segmentation process will not be as accurate as possible.
Using tested statistical techniques will result in the sample set segmenting themselves into "like" groups with similar household characteristics. These groups may be used for further profiling. New suspects entering the database may be measured by "key" household characteristics. The characteristic sets can be used to go back to the main database for profiling; appending those key predictive data characteristics to the entire customer database. From this set of predictive characteristics, a modeling score can be created to score customers or prospects even though there is an absence of any significant purchase history.
SCORE THE DATABASE
The next step is to rank the database according to household characteristics by matching the most similar characteristics to those prospects. Take what was discovered about the key characteristics within the various groups and create a scoring method and algorithm that will be used to score all the records in the database.
After identifying those with the highest score, it's advisable to test the hypothesis by employing a number of staged and balanced marketing efforts. Direct your marketing efforts to each segment, including a control group which receives nothing but which will be monitored and measured and compared to the overall effect of the campaign(s).
From the tests conducted, and by comparing the results to the segmentation and profiling strategies, you can determine which group responded best. Revisit that profile within the responding customer segment and validate all the key characteristics that proved to be potential predictors.
FIND "LIKE" NAMES
After the house database has been exhaustively mined for those "best" customers, it is time to try conquest strategies from outside databases. Remember, a house list will always be more predictive and responsive than a compiled list.