Customer Engagement Impact Assessment
Over the last decades, life sciences companies have been transforming their go to market towards omnichannel. Most companies now have an organization, processes, technology, and the data eco-system to reach HCPs through a variety of channels. Therefore, they can now start to optimize their marketing mix to maximize the impact of their customer engagement strategies. This would ensure the limited attention time from HCPs is maximized, improving the ROI of any customer engagement team member.
Scope
We are experts in the customer engagement landscape, we can leverage the various sources of data that you have started collecting to ensure a model is holistic enough to provide insights.
In fact, we use the data from your commercial engagement system (both master data and transactional), your digital asset management system, HCP portals data as well as any external data sources such as sales and aggregated spends to build an optimization model that would define the optimal marketing mix for the commercial team and the optimal engagement mix for the medical teams.
Methodology
The team would always start with a data audit to validate the quality of the data available before building the model. A decision on the model complexity can be made based on the assessment to ensure the quality of the deliverable reaches the expectation. In fact, if the initial quality of such data dimensions e.g surveys made for NPS is not good enough, the variable may be removed from the potential model to ensure the validity of conclusions.
Once the data is audited and the variable of the model understood, the team would start building the statistical model. Levering the experience on both the data itself and the life science industry, the team would run multiple iterations while validating with the various stakeholders that the inclusion and exclusion of the variable remains logical.
Deliverables
Once the initial draft model structure is completed, the team would focus on improving the explanatory power of the model by fine tuning the model and the model variables. The adjustment of time lags for example, would be a typical aspect of this phase. This process would yield into a final model that would enable an optimization of the marketing mix based on the impact of each channel.
Audit report and recommendations (business and technical)
Variables selection for Model
Statistical Models for relationship between channel mix and its impact
