Data driven

Data driven Agency Consulting firm Experts Specialists Consultancy

Build a data-driven strategy or project

For more than 30 years, Alcimed has been supporting its clients in the design and implementation of Data Driven strategies, from data acquisition strategy with the identification and collection of data, to the valuation of this data via data mining, as well as the implementation of machine learning algorithms and data visualization.

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    The challenges related to data driven approaches

    Companies are generating, retrieving and storing more and more data. Data Driven approaches allow the implementation of decision-making methods based on this data.

    Implementing a Data Driven strategy covers issues in change management to implement this strategy, as well as in operational execution:

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      How we support you in your projects related to data driven

      For more than 25 years, Alcimed has supported its clients in their innovation and new business development projects. With the evolution of technologies and associated practices, data is now at the heart of business activities, and its intelligent use is becoming a necessity. In this context, the consideration of a Data Driven reflection is often essential regardless of the type of project we are working on with our clients.

      Beyond placing data at the heart of our reflection and models in our projects, our team supports you in your specific Data Driven projects and in the implementation of your Data Driven strategy.

      Depending on your project, your context and your challenges, we support you in particular in:

      • The identification of your strategic data:

      Our experience in our clients’ businesses and our ability to lead work meetings enable us to identify critical data to meet our clients’ business challenges and strategic imperatives. If this critical data is available, it must be recovered. However, if it does not exist yet, we can create solutions with our clients to generate such data, to gather it, and to find what to do to achieve the necessary objectives.

      The identification of data sources and the collection strategy:
      We support our clients in identifying sources of internal data and in retrieving such data internally. We define how this data can be used to provide answers to their questions. We also help clients handle external data by sharing our knowledge of open data, of the various databases available on the market, of the procedures to follow in order to access both public databases and databases that we use.

      • Data analysis:

      Our data scientists and consultants have the analytical and technological tools for analyzing our clients’ data, enabling the analysis of qualitative data and quantitative data.

      • The implementation of machine learning algorithms:

      Some missions for our clients require the implementation of prediction algorithms or in-depth analysis. Our data scientists develop machine learning algorithms coded specifically for each project, adapted and adjusted to the challenges of our clients.

      • Ensuring your teams are familiar with data subjects:

      We participate in developing the internal culture of our clients around the use of data through training, discussion, organization of seminars or workshops, as well as the co-construction of internal methods and processes with our clients’ teams.

      Examples of recent projects carried out for our clients in data driven approaches

      • Redesigning the promotional model of a drug using a Data Driven analysis approach

        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.

      • Evaluation of digital solutions for real-life data collection for the creation of a new offer

        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.

      • Determination of an indicator to measure customer engagement via a Data Driven approach

        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.

      • Analysis of the regulatory context regarding the collection and the processing of sensitive data

        One of our clients, 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.

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