ANALYTICS POWERHOUSE: A solution to your product launches
Introduction
Competitive landscape and the increasing influence of pharma stakeholders have nudged brands to look for innovative ways to stay ahead of competition. Companies are betting on multiple product launches as a means to grow their revenue. Having worked with some of the leading pharma brands, we in Mu Sigma propound the need for an analytics powerhouse to support faster and effective decisions during launches. The analytics powerhouse requires pre-built modules that can be conceptualized by keeping a perspective of information flow across decision supply chain pertaining to post launch environment. Here’s what MUST constitute the analytics powerhouse.
Pre-emptive problem universe: For a complete picture and better impact, companies will need to look beyond point problems. They will need to preempt the connections between questions and problems that could arise post launch. For example, it is a known fact that the fate of a product is sealed in the first few weeks of launch. Measuring the success would require close tracking of uptake by early adopter segments and sales force execution on them, followed by a need to analyze broad adoption behavior and barriers. Once the critical adoption rate is achieved, the focus changes towards identifying growth drivers. This is the fundamental component which can provide an impetus for the design of a launch powerhouse.
Integrated analytical dataset: A centralized dataset constituting all data sources needs to be created. This can serve as a single source of truth for launch information. This is especially helpful when there are several teams working in tandem to deliver. Rather than each team performing redundant activities, there can be a single dataset used across all teams, rendering consistency as well.
Self-service tool: Ad-hoc requests from leadership requiring quick data pulls are very common during launch environment. There needs to be an enablement platform that will visualize data in a manner consumable by various stakeholders. This platform (e.g. Spotfire, Tableau, and QlikView) on top of integrated launch dataset can enable analysts to extract data quickly and drag/drop variables to analyze data with basic visualizations.
Reproducible analyses: Most product launches have similar analyses, which need to be refreshed regularly in order to see the progress or changes with newer data week-on-week. Establishing code libraries and result templates can help us run recurring analyses effectively. For instance, having a software repository (Gitlab/SVN to store code libraries with pre-defined naming / coding standards) and output templates with pre-defined presentations through charts and tables using a combination of big data analytics software and services.
Anomaly detection and interactive deep dive: Where data is in question, it will be absolutely critical to quickly detect anomalies or unexpected patterns using data science consulting. Stakeholders can be alerted for corrective actions in a timely manner. Various algorithms can be deployed to identify these anomalies and customized in a manner that makes sense to respective stakeholders. For instance, a regional sales director may be more interested to understand whether or not some of the top physicians have been reached by his sales reps. Also, this could help decision makers probe further on the reason for anomaly and define implementable corrective actions.
Test scenarios and measurement framework: All of these efforts finally boil down to measurement and scalability. Various simulation techniques and scenario planning approaches can be leveraged to forecast the test outputs. A test control framework can help validate the outcome. For instance, Sales leadership may want to test higher call activity to a physician segment before scaling it to the whole nation. Enhanced call activity can be run on a few pilot territories to assess the returns compared to holdout/control territories. The outcome can help make informed decisions on increasing the sales force size at a national level.
Conclusion
Organizations across the industry spectrum, not just pharma, are looking at ways to capture and capitalize data analytics services as a means to their informed decision making process and competitive edge. It would definitely do well for forward-looking organizations to invest in an analytics powerhouse with all / some of these pre-built modules. For all good reasons, this powerhouse can help organizations trudge through paths never seen before and implement innovative product launch approaches never ventured.