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The Challenge

A large sporting goods retailer needed to personalize at scale. 

While the company had plenty of insightful customer data, it hadn’t been used in a way that allowed them to deliver personalization that increased conversation rates.

As part of this goal of personalization, the business also needed to be able to identify customers’ intent to purchase in real-time. It was particularly difficult to do so for customers who were new or anonymous.

Furthermore, the company also needed to be able to offer product bundles, created with various customer groups in mind. In order to so, they had to find a way to identify the best combination of products to bundle, find the optimal price for each bundle, and identify the key behavior traits of various customer groups. 

Picture of the Object Edge Process

The Solution

Our team of experts created an analytics engine that processed both past data and real-time data. It then identified top products and the categories from which customers were likely to purchase. 

This engine then was able to predict purchases from anonymous and new customers by combining third party data with the first few clicks of the user session.

Then, we processed massive amounts of data to produce effective product bundles. We performed a price movement analysis to understand price and sales patterns, and derived insights on customer purchases based on groups. This information allowed the company to market more effectively.

data graphics-customer segments and rfm analysis

Tools Used:

  • Google Analytics
  • Know Your Customer
  • A/B testing with Optimizely
  • AWS Kinesis for stream processing

The Results

The company’s conversion rate increased by 28%, resulting in a broader customer base and increased revenue.

The additional data analysis also equipped them to more informedly market to their potential customers and make product decisions. 

data graphics-product views-inventory analysis
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Mockup of the Designs

More Case Studies

Using Data to Drive Personalization and Conversion

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