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Business Analytics

What Is Your Customer Data Telling You?

Predictive analytics is the art of finding signals in your customer data that suggest conversion, and leveraging those data points for more effective and efficient marketing. Basically it’s putting data behind sending the right message at the right time.

Identifying what actions contribute to conversion allows you to create an audience segment on the verge of conversion so you can hit them with the message that will seal the deal. Identifying that segment also improves customer experience (you’re not hitting people who are not likely to convert with the same message, which can be annoying) and media efficiency (you’re not paying for impressions or clicks from people who aren’t ready to convert).


How Prove Uses Predictive Analytics Modeling

Predictive analytics can be used to build a chain reaction, identifying what actions indicate a propensity to take the actions that indicate a propensity to convert, and customize messages to that funnel stage.

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Predictive analytics can go deeper and use data to predict a segment’s average lifetime value, and average order value. The analytics teams at Prove work with clients to evaluate available data and desired set(s) of actions to determine the right modeling approach:
 

  • Propensity Models: these are the “crystal ball” models that allow you to predict future customer behavior and related metrics based on past data. The level of detail of this model can help you make media buying and offer/discount decisions so that you’re spending money toward acquiring the most profitable customers.
     
  • Descriptive Models or Cluster Model: these models use data to develop advanced customer segments, so you can match different customer types with their likely behaviors based on demographics, purchase history, and interests/preferences. They can be used to expand and constrict audiences within campaigns so that you’re reaching the people most interested in what you’re saying or selling.
     
  • Decision Models or Optimization Models: these models are the engine behind matching product recommendations –upsells, cross-sells, next sells– to the right users.


Our cross-functional teams take these models to the next level and build content matrices to map messaging to your desired actions and implement the technology to get those messages to your data-driven segments.


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Jamie Stevenson

Jamie is the architect of Prove's Business Analytics department, and she has overseen the growth of our most successful clients. Would you like to know more about our approach to business analytics? Ask us a question.