5 key considerations for predictive modeling and how it can boost your marketing efforts

5 key considerations for predictive modeling and how it can boost your marketing efforts

If you own a company in a large city like Dallas/Fort Worth, Austin, Houston and San Antonio, the competition for market share is fierce. But there are ways to overcome: Predictive Modeling. We’re entering a new era of Precision Marketing brought on by ever-increasing computing capacity. One result of that super charged computing power is the ability to process complex sets of data faster, and output a more precisely prediction of the relationship between marketing inputs and human behavior.

In short, new modeling techniques can bring marketing managers and business owners even closer to achieving the dream of “automated results!” Is there ever a guarantee? No. But there are ways to decrease spending, while getting increased results.

But here are five things to consider about predictive modeling:

1. Good model outputs depend on good inputs

Or, as the saying goes: Garbage in, garbage out. A company that specializes in predictive modeling can guide you on the types of inputs needed for a good model, and will supplement your own knowledge of category, competitors, and relevant brand attributes. At Creative Options, we understand business and can help you think through the model inputs that will lead to a robust and powerful model.

2. Try to be complete

One goal of modeling is to account for as many variables as possible that might influence the ways people behave. The more complete the data, the more complete the model and the greater its power to predict the things that will influence behavior.

3. Patience is a virtue

Because many powerful models are able to incorporate multiple streams of data (e.g., economic conditions, ad spending, social media activity, purchase behavior), marketers sometimes believe that modeling is like creating a stew: Just throw it all in there, and it will come out delicious on the other end.

However, to make data sets talk with one another, it’s often necessary to do upfront work (sometimes a lot of upfront work). That is particularly true if it is the first time a given model is run and the model incorporates different data streams from different sources.

But be patient. Once the data sets are aligned, magic will truly happen!

4. Be sure your modeling partner is up to date on modern modeling.

Earlier modeling approaches have been discarded because they are outdated and less effective. So make sure the modeling techniques are up-to-date.

For instance, many early marketing mix models are based on linear regressions (and many companies still use those approaches because that’s what their people know), which work well if there is a straight-line relationship between the marketing cause and the behavior effect. However, cutting-edge methods currently being developed will help account for the marketing activity may have an indirect or a lagged effect on behavior.

The modeling tools that are used can make as much of a difference as the quality of the inputs you’ve given your analytics partners in how accurate the model is. Some of those tools are obscure or very academic, but the members of your internal research department—or the folks in the modeling firm—should be able to explain what they use and why it makes a difference.

If they try to “wow” you with academic mumbo-jumbo, or if you feel you’re being asked to buy into a “black box,” trust your instincts and look for a partner who invites you into a clear and meaningful discussion about the approaches it uses and the reasons they are relevant.

5. Look for user-friendly outputs

If you’re the user of the model, you really don’t want a single “optimal” answer on a PowerPoint slide. Ideally, a model should be the “gift that keeps on giving.” You should be able to easily play “what if” games. So, when the chief marketing officer comes in and asks “What will happen if we increase our price in Wal-Mart by 50%?” you’ll be able to have an immediate answer.

You should look for the results to be delivered in an interactive tool that lets you adjust the inputs to see what impact they have on outcomes, such as market share, brand preference, revenue, or profits.

In Summary: Not every marketer has the kind of budget to develop a complete predictive model. However, there are different kinds of affordable predictive models for different marketing needs, such as strategy development, product-concept optimization, product-line optimization, and media mix modeling, to name a few. Predictive modeling and customer profiling are a must these days of Precision Marketing.

Think of predictive modeling as a hot, souped-up Ferrari. It’s sexy; it’s powerful; and, with the right knowledge, it can be a heck of a lot of fun to drive. Contact us today, and give our predictive modeling and customer profiling a test drive and watch your sales grow.

The comments are closed.