Major Retailer Grows Active Customer Base, Revenue, and Profit Through Consumer Analytics and Machine Learning
Personalised
Customer Insights
Stronger
Customer Loyalty
Increased
Buying Activity
Higher
Revenues & Margins

“We really appreciate the high level of service and the commitment shown by the team both in addressing our requirements and meeting our timelines.”

Challenge

This leading retail chain of more than 1000 stores struggled to grow revenue and maximize margins. They tracked more than 3 million loyalty card customers but dealt with increasing dormancy and a large pool of inactive accounts. Lacking effective analysis, the explanations for customer inactivity remained exlusive.

On the product side, the retailer sold items from other suppliers in addition to their higher margin, in-house brand. Although they collected terabytes of data from millions of transactions per month, the volume and diversity of the data inhibited the company’s ability to manage and analyze it. The data delivered little to no value. The retailer needed better data management and new, more modern analytics to deliver the insights required to catalyze growth.

Featured services

Data Preparation

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Business Intelligence

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Data System Architecture

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New Application Development

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Business Intelligence

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Data Analytics – Operational

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User Interface Design

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User Self-Service

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Solution

Kanerika began with the basics – establishing a solid foundation of good data management practices for high-volume, high-diversity data environments.

With this foundation in place, Kanerika’s data analytics team worked with the retailer to design and deploy innovative consumer analytics and machine learning algorithms that delivered actionable consumer and product insights. Kanerika introduced a new scoring model based on the RFM (recency, frequency, monetary) approach to customer segmentation and developed a new customer classification model based on purchase pattern and personalized consumer information. The classification model pro-actively identifies customers likely to become dormant so the retailer can target engagement efforts.

Kanerika also added association rule mining, a machine learning method, to the customer experience that nurtures customers to create more profitable market baskets, thus maximizing margin growth.

Results

  • Major Increase in Active Customers

    The retailer saw a 133% increase in buying activity from existing loyalty card customers (from 30% to 70% active).

  • Increase in Total Loyal Customer Base

    Simultaneously, the loyalty program grew from 3 million to 8 million total subscriptions, a 2.6x increase!

  • Higher Profits

    Customers create more attractive market baskets that include higher-margin, in-house brand products with the addition of association rule learning algorithms. The retailer can better target campaigns using improved customer insights.

  • Higher Total Revenue

    With more loyal, active customers and better market basket insights, the customer has driven increases in revenue.

Why Kanerika?

While Kanerika’s profound expertise in data analytics and machine learning enabled the delivery of the new system, the retailer also strongly valued Kanerika’s focus on building a trusting business relationship “for life.”

The retailer recognized that Kanerika promised a flexible team of experts focused on delivering results and committed to exceeding expectations.

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