The Challenge.

A fast fashion global retailer needed to increase customer acquisition, lower costs and target customer promotions effectively.  Historically, they would look at customer information that had been limited to demographic data collected during sales transactions. But today, their customers behaviors were changing, and more and more interactions were occurring on multiple channels, they needed the ability to better understand the behaviors, why they were changing and the impact these had on their business.


The Solution.

  • Created a data repository
  • Correlated customer data, customer purchase histories and profile information, as well as behavior on social media sites.
  • Tested and quantified the impact of different promotional tactics on customer behavior and conversion.
  • Used customer’s purchase and browsing history to identify needs and interests and then personalize promotions for customers.
  • Built an AI powered attribution model.
  • Monitored customer purchasing behavior and social media activity to drive timely offers to customers to incent online purchases.
  • Built an algorithm to track key themes that generate conversations in order to quickly pinpoint spikes in conversations.
  • Built monthly trend and anomaly detection dashboards with insights & recommendations.

The Results.

  • Reduced the CAC (Customer Acquisition Costs) by 32%.
  • Increased customer satisfaction scores by 16% and Reduced Churn by 21%.
  • Increased repeat buyers 11% in first 3 months.​
  • Using proper attribution modeling allowed for Marketing budget efficiency: decreased marketing budget 10%, while increasing revenue 35%
  • Increased brand awareness by 30% within the first 6 months​.
  • Increased database by 20%.
  • Having the ability to monitor and react to the buzz on a specific topics in social media, they saw a dramatic increase in the engagement rates.
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