In the pharmaceutical industry, the digital revolution is forcing sales processes through an inevitable evolution from traditional face-to-face sales, to digital and multi-channel engagement.
With complex, long running, multi-channel sales cycles touching many stakeholders before a purchase decision is made, the needs of sales and marketing are changing fast, and pharma companies are trying to keep pace through technology modernisation programmes.
For modernisation to work at scale, sales force effectiveness (SFE) tracking and measurement will also need an uplift. The KPIs and metrics used need revamping to create cross-functional, standardised, globalised models built using the digital data now available. These KPIs are defined correctly and work correctly too – the data underneath is valid, the calculations are all correct, their visualisation is actionable, and well formatted for mobile access.
This might sound a basic aspect of KPI testing but consider the specific nuances & complexities of the Pharma world and you will quickly see this is no mean task. Challenges in the pharma world include:
- – 100s of different markets and territories
- – Strict legal and regulatory requirements from processes and data perspective
- – Consumption of reports by sales teams on the road using a range of devices
- – Siloed CRM deployments
- – Unifying data and reporting on third party data like IMS
- – Blending in sales data from acquired companies and partner business for specific drugs and therapeutic areas
Handled incorrectly this adds risk to your project – after all, the sales team is going to set objectives and decide on next sales actions based on these KPIs. It’s paramount the KPIs are designed correctly and tested thoroughly from many perspectives before they are rolled-out to the sales team.
You need a well-defined Data/KPI Testing Strategy to successfully address the challenges. The strategy and execution plan should consider the nuances of how your industry operates and how your specific business processes and technology landscape are to be redesigned and implemented.
Here are 5 best practices we have used to ensure your pharma sales KPIs are error proof:
- 1. Run cross functional workshops to capture all test requirements & scenarios. Consider structured workshops with the teams involved in your transformation programme – Sales and Marketing teams primarily, but also Legal and Compliance, Technology Implementation, Programme Management, Data and KPI Implementation. This will help you understand various aspects of the roadmap, the milestones being set, what objectives need to be achieved at each milestone, and the quality assurance and stage gates to be enforced at each. You will also understand the risks and mitigate as much as possible in the test strategy and understand the critical paths on the roadmap to align your test plan. Conversations with all the teams ensures you have not missed anything critical and opens vital communication channels for ongoing collaboration and governance.
- 2. Account for localised needs when standardising tests. This is even more important in the pharma world where various markets not only have different local requirements with respect to insights; they have different legal and regulatory compliance requirements when it comes to prescription, patent, pharmacies and doctors’ data. Your test plan should consider the similarities and differences in the markets, languages and translations used, priorities and urgencies for business teams on these markets.
- 3. Not all KPIs are made equal. It is important to understand the user landscape with respect to which KPIs are most important for role types and the actions they will drive. Bringing the target audience into consideration for your test plan helps design the test cases for insights and Visualisations to as close as they will be actually used. You are building the functional and user experience elements in the test plan by doing this, which leads to higher user adoption when the test plan is executed and approved by the real users.
- 4. Know where the data comes from. In order to design robust test cases, you need to understand the KPIs themselves and the journey data takes to create the KPI. Consider what data is used, how that data is maintained at the source, what transformations are applied, what calculations are done to arrive at the KPIs, how a change in any data element would impact the KPIs, which KPIs depend or are inter-related to each other. There are tons of questions like these for consideration to make sure you cover proper “sun-shine path” test cases, not forgetting special cases and exceptions.
- 5. Regression test new KPIs against the old definitions. Finally, if you already have some KPIs/Reporting in place (which you most likely do) and are in the process of either modernising them, or you are building the new KPIs in phases; it is important to consider how the new KPIs vary from the earlier ones, if there is traceability from old to new and how the users are going to be impacted from the change. In addition, if the KPIs are built and rolled out in phases, then think about how the upcoming release could impact the earlier ones, due to inter-related KPIs or difference KPIs based on same set of data sets. This brings in the importance of considering and planning for Regression testing in context of the new and the old KPIs, and between new KPIs developed in different phases of the project.
Of course, every KPI/BI programme is different, and the methodology needs to be flexible and extendable to cater to the nuances of your specific scenario. Making sure you have a methodology will ensure risks are reduced as much as possible and all the best practices derived from experience and learnings are applied to ensure successful testing efficiencies and smooth working product.
To know more details about how we did it for our clients, or to discuss your specific case, please get in touch.