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Marketing Analytics in Retail

Started ‎02-16-2022 by
Modified ‎08-23-2022 by
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Team Name Complex_Processors
Track Retail
Use Case

Marketing Analytics

STP Framework

Segmentation, Targeting and Positioning (STP) is a common strategic model in today’s marketing approach. It reflects the increasing popularity of customer centric marketing strategies over product differentiation strategies. The audience focused approach in marketing e.g. helps to deliver more relevant and targeted communications to commercially relevant segments. STP therefore goes hand in hand with marketing personas.

What Is the STP Marketing Model?

The STP Marketing Model can help you to analyze your product offering and the way you communicate its benefits and value to specific groups.

STP stands for:

  • Segment your market.
  • Target your best customers.
  • Position your offering.

The model is useful because it helps you to identify your most valuable types of customers, and then develop products and marketing messages tailored to them. This allows you to engage with each target group more effectively, personalize your messaging, and – ultimately – increase your sales.

Machine Learning in STP Framework:

Our use case will be to implement ML techniques in each of the 3 phases of STP framework.

  1. Segmentation: We will use clustering techniques to divide customer and market into segments.
  2. Targeting: We will use CLTV, RFM analysis techniques for this step.
  3. Positioning: Outcome of previous 2 steps, will be used for positioning our services.
Technology SAS, Python, Azure
Region EMEA
Team lead Puspanjali Sarma
Team members  @Ponvani @martinlouis2012 @MustaphaMomoh 
Social media handles

https://www.linkedin.com/in/m-martin-louis-25283b12b/

https://www.linkedin.com/in/ponvani-muthukumar-5b05a130/

https://www.linkedin.com/in/mustaphaunubi/

https://www.linkedin.com/in/puspanjalisarma/

Is your team interested in participating in an interview? Y
Optional: Expand on your technology expertise  

 

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Last update:
‎08-23-2022 05:59 PM
Updated by:

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