Using Predictive Analytics to Optimize Strategy

Predictive analytics isn’t just for statisticians. Marketers can decode the cost-cutting properties of the analysis method to create more efficient marketing strategy. Assigning scores to customers according to their propensity towards certain behaviors is nothing new, but linking that behavior to specific marketing strategy decisions is an often forgotten method for conserving a marketing budget.

Optimizing advertising spending means creating not only a roster of rational key performance indicators (KPIs) but also a framework of spending guidelines which fine-tune consumer targeting practices and minimize resource waste.
That requires a value-focused strategy which eliminates “shotgun” marketing tactics and focuses on actionable insights and quantifiable results.
Here are 3 maxims for making the most of that fancy predictive analytics suite.
  • Use churn modeling to identify loyal brand followers: Don’t waste retention efforts and resources on loyal brand followers when those resources would be better spent on neglected audience segments.
  • Use uplift response modeling to identify consumers in frequent contact with the brand: Don’t waste efforts contacting consumers whom are already frequently engaged.
  • Use response modeling to identify consumers that are dead-end clients: Don’t mount elaborate campaigns to win over consumer segments that attach high-negatives to your brand, if their numbers are relatively insignificant.

 

Common-sense rules apply when acting on insights; data from multiple sources always requires context as well as a logical connection between performance goals and the constant flow of real-time data. Learn more about predictive analytics here

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