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Machine Learning for Commercial Pharma: Less hype & more ROI

Wednesday, 05 Sep 2018

You have heard the buzz around machine learning (ML), but how can you leverage it to improve your pharma commercial operations?

Data foundation

One of the biggest pain points of data science is getting reliable & refined datasets, that is the starting point for any Machine Learning analysis.

That is one of the advantages of our platform, having integrated pharma commercial data warehouse (market & sales data, CRM & market access) makes easier reap the benefits of ML analytics.

Answering Commercial Effectiveness questions & validating the hypotheses

The sales team is the highest investment during the commercialization phase, any minimal improvement on the team efficiency has a big impact on the bottom line.

The key point is asking the right business questions and use ML techniques to answer them and test the hypotheses, such as:

Can we increase call frequency to certain customers, or are them over saturated so we would get diminishing returns?

Call Productivity hospitals clusters

See Call Productivity patterns example

Can we perform a more refined approach to targeting & segmentation, using account behaviour patterns over F2F calls with the data we already have?

Dynamic Targeting Hospitals with optimal calls

See Dynamic Targeting example

Can we use advanced forecasting for all the territorial levels to help reps & managers react within the sales cycle & attain their quota?

Hybrid Forecast

See Hybrid Forecasting example

Machine learning at its basics is about fitting models on observed data, and identify clusters having the same behaviour.

When asking the right strategic questions, these ML techniques can help senior staff validate the hypotheses and uncover hidden patterns to maximize ROI on commercial operations.

Nevertheless, the first problem to tackle is data quality and the existence of data silos spread all over the company.

Marc Mauri

Kaizen for Pharma

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