Conventional wisdom has it that if you target market to prospects that look like your clients, you will have great success. Conventional wisdom also has it that by doing something - anything - different you will likely see a change in performance - hopefully a positive change. All of this is true and I have experienced it many times myself. However, this approach merits a closer look to understand what the impact is on acquisition costs and therefore your return on marketing investment.
In a traditional marketing campaign, customer attributes such as demographics, lifestyles and behaviors are compared to the same attributes in the market. If they see a high presence of prospects that share the attributes as their customers, they market to them. Ultimately this technique generates new customers as intended. However, there may also be a higher number of nonresponders which, of course, they’d like to avoid.
If you consider that on a good day you may get a .5% response rate, then clearly 99.5% of the prospects will be non-responders. Hence by merely comparing attributes of customers to a market - without regard to how those attributes are integrated to tell a fuller story - may inhibit your ability to gain an incrementally higher rate of responders above the number of new customers that were acquired.
For example, let’s assume that your customers’ average age is between 35 and 45. So, you buy a list of prospects that are age 35-45. Unbeknownst to you, although that demographic likely contains many propsective responders, it may also have a higher ratio of nonresponders to responders. The end result is that although you get many new customers, you may also get a higher number of nonresponders and consequently your acquisition cost increase.
How do you get around this?
A predictive response model considers how a full plate of customer attributes is integrated to better differentiate the potential responders from nonresponders. By eliminating those that have a low probability of responding, you increase your overall average response rate thereby lowering your overall acquisition costs.
Although a predictive response model also adds incremental expense, your marketing campaigns may be large enough you need to carefully weigh the financial impact of the savings from not marketing to the low-probability-of-response prospects against the cost of the model. You will be surprised at how cost-effective a predictive response model may be.






