Uplifting product sales in pharma with AI driven insights

Our client, a leading international pharmaceutical company, wanted to achieve a substantial uplift in prescriptions (Rx).

Stage 1 - Data collection and integration

To gain a holistic perspective, we merged multiple data streams. This included internal data such as action logs, sales figures, brand strategies, physician and facility profiles, external market data and public domain information.

Additionally, research data including physician perspectives, market research insights and first-hand insights from interviews with medical representatives and sales managers were integrated to further enrich our data pool.

Stage 2 - Data mining and ML models

Turning to advanced analytical techniques, we used pattern mining to identify distinct patterns or features with high success rate for each different combination and number of actions.

We en defined a critical "Winning Action Sequence" set to guide subsequent Next Best Action (NBX) strategies.

Stage 3 - Deployment of optimal sequences 

After the analysis phase, in which we meticulously examined nearly 6,000 sequences performed over six months, we derived the characteristic combinations that were performed by physicians with high Rx. This detailed analysis paved the way for us to identify the Next Best Action that promised to induce an uplift in Rx.

The implementation of our strategies has been very successful. Institutions covered by our targeted approach experienced 30% higher product sales growth than other institutions. In addition, the sales representatives who acted on the NBX insights drawn from our machine learning analysis saw an impressive sales growth rate – 1.5 times higher than their counterparts.