Data Driven Consulting

One of our clients, a pharmaceutical industry leader, wanted to rethink the promotional model of a drug in its portfolio (which physicians to target, how often, through which channels) using a Data Driven approach.

The objective of our project was to find, through a quantitative analysis, the promotional model that would enable the best ROI, by optimizing the targeting of physicians and the promotional mix, all based on a mix of sales data, budget data, data on the promotion carried out and also targeting data from external sources.

As our quantitative analyses were limited (little data, sometimes poorly informed), we supplemented them with a qualitative analysis to find the ideal model enabling the best return on investment for our client.

We supported one of our clients, a leader in the healthcare sector, who wanted to explore the opportunity to diversify its activities through the integration and use of digital solutions for the generation and collection of real-life data (Real-World Evidence, RWE). For this project, our teams evaluated the different data capture technologies available on the market, their characteristics, their advantages and their limits, as well as the existing approaches for their use in France in RWE.

Following our analysis, we defined 4 approaches enabling our client to integrate and to set up these new selected digital data services and established an operational action plan to carry out pilot projects. In the end, a pilot was successful and our client was able to launch a new differentiating offer.

We developed a global customer engagement indicator for one of our clients. The objective of this indicator was to use all available customer data, particularly in terms of responses to communications, to manage activities: to understand what actions triggered customer engagement to make the best future decisions.

Our methodology consisted of two parts. The first part was to create a common definition of what “customer engagement” was for our client and to define the data available for the creation of this indicator.

This resulted in an external investigation (bibliographic research and interviews with key players) as well as an internal investigation via exchanges with the various stakeholders in the company. The second part consisted in retrieving this data to bring out, in real-time, the engagement indicator at different levels of granularity for our client.

An industrial player wanted to set up a European project based on the collection of sensitive data in external databases.

After having carried out a first pilot which consisted in collecting data and setting up a machine learning algorithm in a European country, our client wanted to work its way through the regulatory context of the European Union and of several other European countries.

The objective of our project was to better understand how to implement the collection of sensitive data and its processing in these other countries in Europe. We therefore focused on the GDPR and national laws to provide a global vision of the regulatory environment and the different steps necessary to set up the project that our client wanted to implement.

A leading pharmaceutical company was interested in exploring the potential of external patient data in the United States and in Germany for an artificial intelligence initiative in the field of diabetes.

In a first step, Alcimed provided an overview on the major public and private data owners in the two in-scope countries. These data owners were then classified by our team in terms of data volume, quality, relevance and accessibility.

Based on these criteria, we prioritized some data owners as potential partners for our client and finally co-defined together a preliminary data acquisition strategy for this specific initiative.

In this project, Alcimed combined for a pharmaceutical laboratory its field investigation skills with its ability to understand the data science environment in healthcare in order to qualify the data sets useful for launching a phase III clinical trial. This qualification was based on an assessment of data quality, quantity, volume and accessibility.

Our project resulted in a recommendation on the partners to be favored on the international scene in order to develop a “data-centric” clinical trial and the avenues of collaboration to be considered.

Alcimed supported an aerospace player in the development of the business model of a Data Lab whose objective is to accelerate and promote projects using Big Data in several areas of space (spatial data analysis, telemetry data analysis, etc.). This initiative allows the availability of various data under a data as-a-service model.

To do this, our team carried out a benchmark of best practices from industrial players using Data Labs in other sectors as well as an internal study of our client’s needs and expectations.

This allowed us to guide our client on the best development strategy to set up including the organization, the type of services to offer, the benefits and constraints to take into account, etc. Alcimed finally delivered recommendations on the Data Lab’s offer and business model, from its structure to the types of services.

To support our client, a leading construction and public works player, in predicting its business volume, Alcimed developed a machine learning algorithm to predict, based on historical public data and before they are all officially referenced by the local authorities, the total number of building permits filed in the current month.

This project enabled the client to anticipate its sales forecast and to adapt several of their activities in advance.