We partnered with a leading pharmaceutical
company to develop HCP Persona Analytics
Engine leading to an incremental 35%
lift in their engagement

We made a visible and measurable impact to our client's business


Incremental lift in engagement


Lift in pilot campaigns


Industry overview

Maintaining an effective digital relationship is challenging when Healthcare professionals are constantly being reached across various channels and mediums.

The problem

While the pandemic saw an uptick in digital adoption, it also translated to a higher degree of digital fatigue with the HCPs like never before. Having successfully scaled digital reach in the first leg of the company’s MCM journey, the clients now wanted to design a persona engine that could effectively help them cut through the noise and stand out.

Our role

The clients sought our help in rationalizing the HCPs digital persona, and developing a digital analytics engine that could effectively provide content recommendations at scale to HCPs.

Our approach


Based on our experience across industries, we designed a three-pronged approach to stage the experience for the client’s end customer, the Health Care Professional (HCP).

  • Defining the Digital Persona of the HC.
  • Providing Content Recommendations at a user-level based.
  • Adopting an experimental mindset through a Test & Learn framework.

To understand the digital persona of the HCPs, our first milestone was to bring together a unified view of the HCP. This brought together everything we knew about the customer, everything that the customer interacted with, and interactions with the sales reps.

The next step was to create a persona engine that could effectively capture engagement patterns from these three pillars. Machine Learning algorithms are extremely scalable and effective in capturing behaviors that stand out. Hence, we developed multiple segmentation models to carve out mutually exclusive segments of HCPs across key specialties.

Once the personas were carved out, we moved on to providing recommendations for each used. To this end, Ganit developed Content Recommendation Models using advanced ML techniques. These recommendations were based on the HCPs past engagement, similarity across HCPs, and similarity across content types.

How did we enable consumption?

We tested the effectiveness of these models before they were rolled out. For most Brands, the recommendation models were able to zero in on ~50% of the target population that would drive ~80% of the overall engagement, helping the team move towards focused targeting instead of a Mass Targeting approach.

We also held multiple Learning Sessions to onboard and evangelize the idea with stakeholders across the Marketing, Customer Analytics and Leadership Teams.

A valuable difference

Our impact

We piloted the campaign on ~50% of the customer base and across 10+ brands to test the effectivness of the analytics system, leading to an incremental 35% lift in their overall engagement.

Success stories

See the impact that we make on our
cross-industry client base.