15 Fatal Errors in Pharma Analytics
Data is critical to any business operation. It provides the information that enables the commercial operations team to make informed business decisions. Setting up pharma analytics may sound as simple as buying a reporting a tool, but experience can dictate the difference between a quality set of impactful, widely utilized analytics or an expensive set of reports that do not drive sales.
Do any of these “fatal errors” resonate with you?
1. Reports not in sync with reality: bad data, incorrect calculations, dated territory alignments
2. Information overload: too many metrics, hard to read reports, reports disparate or not integrated
3. Measuring all things: rather than boiling down to a small amount of metrics that people should focus on
4. Not measuring utilization: how do you know the report is even needed or being used?
5. Not allowing for simple self-analysis: put the power in hands of user, starting with simple sorting and filtering and advancing to proactive alerts
6. Analytics not tied to user process: better to understand the process first and build analytics that support it
7. Late reports: if you are going to miss the usual delivery time, send a timely message out or prepare for an overload of emails and phone calls
8. Allowing people to think reports will perfectly match incentive compensation reports: the IC plan may have nuances that the reports don’t capture, like using monthly data instead of weekly data or other administrative rules
9. Trying to please everyone: not everyone’s favorite metric can be included
10. Not having a core team of report designers that includes users from the start: analytics cannot be viewed as a home office driven initiative
11. Not using Field Trainers to train their colleagues: doing so establishes regional, informal resources and encourages buy-in
12. Trying to hide a mistake: rumors abound in the Field. If a metric is wrong or a report needs to be updated, admit it and take responsibility for fixing it. Trust is hard to recapture once lost
13. Not using enough visualization: graphs and charts make it easy to understand trends – a picture is worth a thousand words
14. Thinking that reports are static: they actually will change as the business grows and changes; they are never complete
15. Not being strategic early on in design process: will you be adding additional sales forces? More data sources? Selling more products? Using team selling? All these factors should be designed into the technical infrastructure to make it easier later on to add these capabilities
If any of these mistakes sound familiar, then it may be time to rethink your analytics. Want to learn more? Download one of our free whitepapers.