Income can be a viable marketing tool but when advertisers use it to predict household spend it often falls flat, producing less than desirable results and leaving marketers frustrated. So what is the alternative that helps advertisers and marketers achieve greater flexibility and precision?
The analytical team at AnalyticsIQ took this challenge head on. Research and experience indicated there are two primary reasons for the misfire with household income:
Analytics prove that higher income households don’t always spend at a high rate. For example, the wealthiest 14% of US households are actually in the lowest 10% of spend while 16.5% of the least affluent households are in the highest 25% of spend. Using predicted or actual income alone does not necessarily identify the best revenue-producing customers.
Traditional income predictors do not take into consideration predicted savings. By addressing savings, greater clarity is available to the actual dollars available for discretionary household spend.
So What About Segment Spend?
Another challenge marketers face is the ability to get granular and predict the amount of available spend within a specific market segment. It stands to reason that a generic spend predictor will perform differently for travel than it will for apparel or home furnishings. This is where segment spend prediction by industry type comes in. By segmenting spend prediction into 14 industry sub categories, marketers and advertisers achieve new levels of precision and dramatically improves campaign results.
An example?
A large retail drugstore chain wanted to improve wallet share with their 50MM+ consumers. Spendex was used to identify expected health and beauty spend and target those under-performing customers with appropriate offers. The result – a significant increase in customer spend and wallet share.
Don’t waste precious resources chasing bad prospects. Consider category spend prediction as a viable option to improve your marketing results.