Personalized Campaign Management Powered by Data


  • CASE STUDIES
  • January 28th, 2020
  •   8053 Views

It’s time you get to know your customers better.
They inch closer to a conversion every time they receive a highly relevant offer. Creating personalized omnichannel experiences for customers can help retailers retain and expand their share of wallet.

THE PROBLEM
A leading membership-based retailer was targeting customers with common campaigns that didn’t resonate with them. This reflected as low membership renewal rates. More than just a solution, they were looking for a thought transformation to:
• Improve customer retention
• Increase purchase frequency

THE MU SIGMA APPROACH
Mu Sigma delivered a comprehensive Targeted Campaign Management framework.
This tool enables holistic campaign management including planning, launch, and monitoring. Through an outcome-driven Art of Problem Solving, we meticulously implemented some crucial capabilities:
• Customer segmentation – Whom to offer to
• Product recommendations – What to offer them
• Automated lifecycle management — When to offer them

THE IMPACT
This Targeted Campaign Management tool reduced campaign design and rollout time by 80%. The highly relevant recommendations and membership renewal reminders sent through 80M+ messages over customers’ platform of choice have attracted incremental annual revenue of over $10M.

Download the case study to know more.

« Previous

Retail Stores Emerge as the Top Advertising Medium

Every consumer goods brand today looks to digital media as their go-to advertising channel. Trackable and easy to navigate and scale - this channel has quickly dominated the brand media options. However, the buzz around it also masks the fact that it’s cost per impression is high, and scaling the target audience with digital is increasingly expensive.

Next »

Decoding Consumer Behavior using Agent-Based Modeling

Mu Sigma is enabling next-level consumer insights through Agent-Based Modeling (ABM) – a predictive-prescriptive technique to observe consumer behavior in a simulation of their market environment and predict demand accurately.