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Improving Cross Brand/Business Unit Engagement

Applicable Segment(s):

Speciality Retail, Fashion, Food & Grocery, QSR, Marketplace

Impacted Function(s):

Marketing

Solution:

Real-time CDP

Improving Cross Brand/Business Unit Engagement

Company

A multibillion dollar conglomerate based in the Middle East.

The Challenge

The Group is the master franchise for some of the leading international brands in retail, food & beverage, automotive, engineering, credit, etc.

The Group’s marketing team did not have a centralized, group-level view of customers across different brands and business units (BUs). This hindered the team from driving cross-brand or cross-BU customer engagement.

The Approach

The Group used a Real-time CDP to centralize all their customer data - from 11 different data sources, cutting across 19 brands and 9 BUs.

The CDP’s intelligence layer, supported by machine learning algorithms, helped create granular customer segments by creating centralized customer profiles across BUs and Businesses - to identify and enable cross-BU journeys.

With over 800 micro-segments across multiple brands, both the corporate and BU marketing teams were able to drive targeted cross-sell campaigns.

Below are examples of some high-impact campaigns:

  • Premium customers of the Group’s home furnishing business were targeted with cross-sell offers for high-end consumer electronics products.
  • High-value retail shoppers were targeted as potential customers for a premium luxury car brand.
  • Automotive customers who bought premium cars and did not return for a paid service after the complimentary service were nudged into visiting the Group’s third-party car servicing business that offers cheaper rates.

High-value retail customers targeted for automotive brands Cadillac and Ford

The Result

  • 10% lift in cross BU/brand engagement
  • 2% incremental revenues

The ROI of Algorithmic Retail Solutions

Learn how our customers improved key business
metrics with our made-for-retail solutions.

Home Use Case

Configurable Strategies for user affinities

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