Case Study

Developing Data-Based Recommendation Engine

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The ask:

  • The pandemic was having significant impact on field force access to neurologists and engagement was limited due to the demand on their time.
  • We were tasked with identifying organisational and environmental causes to poor engagement, beyond just “the pandemic excuse” and create new ways of delivering customer interactions.
  • Our objective was to embed a data driven approach to create a 3D customer understanding for field teams & utilize their assets to re-invent ways of engagement through an innovative approach.

The Method: 


  • We conducted in-depth cross-functional team interviews to discover the root causes for lack of engagement and make subsequent recommendations

Build Recommendation Software

  • Data deep dive of content assets and 3-year Veeva sales interaction to define values of interaction types 
  • We subsequently built personas and allocated individual one key customer files for bespoke offerings 

Monitor & Assess

  • We continuously monitored performance of 10-week sales period alongside an control group and coached KAM’s on optimally utilizing recommendation engine 

The Results:

Metric Achieved

  • On target activity during pilot phase 55% higher than previously. The pilot group also had 69% more activity with customers and 41% more high-value interactions
  • The engine enabled KAMs to send x37% more messages, with a 55% increase in opened and 69% increase in VAEs compared to pre-pilot activity. Additionally, the pilot had 2.31 more face-to-face interactions
  • Client experienced an increase of patient registrations, benchmarking to pre-pandemic level.
  • Client won 2023 Pharmaceutical Marketing Effectiveness Award  

The Learning:

Company’s have all the content and data to effect behaviour change with their customers but require cross-functional collaboration and a functional program to escape the ‘normal’ ways of thinking

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