Identify user need for a discount to encourage full price shoppers


Propensity Models to Improve Response and Revenue


Predictive analytics is ambrosia for digital marketers who are trying to optimize "right offer, right person, right time" through their campaign management solution. To make it work, you need a combination of defined personas, a content management strategy, creative asset library, and technology (e.g., it's built into the segmentation tools of your campaign management or marketing automation software).

The end goal is to automate the offer selection and placement based on analysis and predictive models for your particular customer base. However, every marketer can get started by using pattern analysis in your existing response data to identify factors that lead to purchase behavior. Use that data (even manually) to improve your personas, segmentations, and send more relevant offers.

Recently, we worked with a major U.S. retailer to try to figure this out. The retailer includes propensity models in their analysis to identify the best segments by channel - in store, e-commerce, and digital (email, SMS, mobile apps, online advertising). Propensity is loosely based on an RFM (reach, frequency, and monetary) model, and incorporates past purchase data, online behavior, and social media status. Propensity models also help identify the need for a discount to encourage full price shoppers. In the end, propensity models end up being a new layer of analysis on top of the segments to improve the conversations with high value and ready-to-act people.

What is your story? Share with us your learnings. Is propensity modeling on your roadmap? What other challenges will you face?

Comments

  1. Clara Pagina19 January 2022 at 15:40

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    ReplyDelete

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