Database : Model Behavior
How to tell a good analytics tool from a bad one
September 2008 By Li Zhou
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It's more than an idle question. The difference could mean meeting your goals or not, or having to go back to beg for more marketing dollars before the year is out. It could mean spending way too much on a media channel that's beginning to suffer fatigue and not nearly enough on a fresh media channel that could rocket your sales. It also could mean you over- or understaffed call centers because you weren't able to accurately predict customers' responses to your calls to action.
The marketing industry is beginning to learn the value of analytic models, but the quality of models out in the market range from so poor you may as well just guess to nearly spot-on perfect. What you need is a set of questions to ask, a few basic rules against which to judge the value of a model.
The Seven Questions
These are the essential questions to ask providers of marketing models to determine if the model is a good fit for your company. They drill directly into the most important elements of reliable, verifiable and predictive models-essentially those elements that are most useful to you.
How does your model account for:
1. New media for which you have no historical data? When a product or service has been marketed over certain media for a period of time, and you want to explore a medium you've never used, you're in new analytical territory. Even the best models must import assumptions and data from other sources to account for behavior in new media. Truly sophisticated modelers have become quite good at transferring this knowledge with a high degree of predictive accuracy.
2. Insufficient historical data? To have a rock-solid foundation, models should be based on a couple years of high-quality historical data. Many analytics groups use data that covers only a few months. You should always be wary of any models that call for only a small amount of historical data.
3. Diminishing returns due to media fatigue? All media tend to have diminishing returns as you use them over time, especially if you continue to raise the ante and invest more into them. The return on your first million spent is almost always larger than on your last million, all other things being equal. Defining the decay curve takes historical data that shows response (usually sales) versus marketing spend for each media. What you invariably detect is a curve that shows a startup investment to get to a high payoff zone, then a slow decline as returns diminish. The only way to realistically account for this is to develop models based on accurate data over at least two years. Models that do not account for this will miss their predictive marks.
Companies Mentioned:
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