Buyer Retention with Predictive Analytics

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Buyer Retention with Predictive Analytics

Executive Summary

The omni-channel retail client caters to eCommerce and in-store customers, as it operates as a warehouse club across America. The challenge was to retain club members with data-driven insights into their purchase patterns.

The solution was to identify patterns in the historical business data, and then apply decision-tree modeling for customer segmentation, apply linear regression to rationalise the media marketing cost per channel and then deploy right marketing mix to engage current members.

The client saved 30 percent in marketing expenses with data-driven camapigns. The effort was directed to members who are close to the renewal cycle, and are unlikely to renew the membership. The prediction data with prescriptive models was also available on a single click for business users, elimiating IT intervention, effort and time costs.

Project Highlights

  • R based Statistical Computation
  • Linear Regression Modelling
  • Decision-Tree Modelling
  • Best-Effort Modelling
  • BI, Visualization and Reporting

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    Executive Summary

    The omni-channel retail client caters to eCommerce and in-store customers, as it operates as a warehouse club across the Americas. The challenge was to retain club members with datadriven insights into their purchase patterns.

    The solution was to identify patterns in the historical business data, and then apply decisiontree modellig for customer segmentation, apply linear regression to rationalise the media marketing cost per channel and then deploy right marketing mix to engage current members.

    The client saved ~30% in marketing expenses with data-driven camapigns. The effort was directed to members who are close to the renewal cycle, and are unlikely to renew the membership. The prediction data with prescriptive models was also available at single click for business users, elimiating IT intervention, effort and time costs.

    Project Highlights

    • R based Statistical Computation
    • Linear Regression Modelling
    • Decision-Tree Modelling
    • Best-Effort Modelling
    • BI, Visualization and Reporting

    To read more, download the copy

    arrows-new-1

    To download this resource

    Fill in the details below





      I wish to be contacted by eInfochips


      For all career related inquiries, kindly visit our careers page or write to career@einfochips.com

      Executive Summary

      The omni-channel retail client caters to eCommerce and in-store customers, as it operates as a warehouse club across the Americas. The challenge was to retain club members with datadriven insights into their purchase patterns.

      The solution was to identify patterns in the historical business data, and then apply decisiontree modellig for customer segmentation, apply linear regression to rationalise the media marketing cost per channel and then deploy right marketing mix to engage current members.

      The client saved ~30% in marketing expenses with data-driven camapigns. The effort was directed to members who are close to the renewal cycle, and are unlikely to renew the membership. The prediction data with prescriptive models was also available at single click for business users, elimiating IT intervention, effort and time costs.

      Project Highlights

      • R based Statistical Computation
      • Linear Regression Modelling
      • Decision-Tree Modelling
      • Best-Effort Modelling
      • BI, Visualization and Reporting

      To read more, download the copy

      arrows-new-1

      To download this resource

      Fill in the details below





        I wish to be contacted by eInfochips


        For all career related inquiries, kindly visit our careers page or write to career@einfochips.com