Personalized Marketing Strategy: Data-driven model to identify the right content for the right audience


  • CASE STUDIES OUR MUSINGS
  • September 17th, 2020
  •   2092 Views

Marketers spend countless hours mining and burying themselves in a sea of data in order to provide personalized customer experience and to stand out from the rest.

Too often, while marketers invest time and effort collecting data, they do not have a clear strategy to leverage the collected data to drive decisions. By leveraging advanced analytics to unite real-time data meaningfully into consumer insights, companies can map a better personalized marketing strategy.

The Problem


One of the largest home improvement retailers sought to move away from a heuristic marketing campaign model to a data-driven statistical model to optimize their marketing spend.
As their transformation partner, Mu Sigma helped them turn their whiteboard strategy into a marketplace reality using advanced analytics.

The Mu Sigma Approach


With the increasing complexity upending the legacy marketing models, we partnered with the marketing analytics team to help them develop a more data-driven marketing strategy to:

•   Target customers with more personalized content
•   Identify behavioural patterns in sales, browsing, demographics, etc.
•   Realize better incremental revenue

Through our Art of Problem Solving (AoPS) framework we empowered our partner to drive better visibility into the complexity of the problem space. We aimed to solve our partner’s problem by creating robust and scalable statistical models.

The Impact


Our partnership helped the client in delivering the right content to the right audience for multiple subscription mails and campaigns being sent out regularly to consumers and prospects.

•   ~$3M in incremental revenue realized through the support provided for over 20 Direct mail campaigns
•   60 Million recommendations sent out every week
•   Global scoring tables set-up with regular update cadence for usage in various ad-hoc campaigns by email & social media teams
•   Revamping existing models using advanced modelling techniques to improve audience selection for campaigns