(This blog series introduces emerging trends in sales planning that every commercial operations department must prepare to face in the future.)
Previously, we explored how new and emerging sources of data offer a rich opportunity for life science organizations, specifically how unstructured data will support answers to some currently intractable questions for sales planning. All of that data, however, can be meaningless without the appropriate analytical tools.
Several years ago, Peter Sondergaard of Gartner made one of the more hyperbolic comments about big data: "Information is the oil of the 21st century, and analytics is the combustion engine. “1 In life science, sales planning has always been based on analytics—for example, promotion response analysis helps identify the most responsive doctors and the optimal amount of effort across the field. Today, however, we have the data to go far beyond that.
Key Capabilities to Unlock the Value in the Data
Now we must figure out how to master the new methods to take sales planning to the next level. Here are three capabilities organizations must consider to evolve from individual analysis to systematic automated processes.
- Understand the data and its limitations. In the past, data from syndicated sources came relatively complete. While it required some cleaning and conforming, the effort was mostly to match datasets between each other. For all the gaps in sales data, a lot was done to project and complete the datasets so that it all added up to national trends. The same can’t be said about the new data sources. Whether one analyzes social media, collects clickstream data or mines call center records, the data is fragmented and incomplete, and possibly limited by collection method. Organizations must understand the data sources and pre-analysis processing methods to decide which analytic methods it can or cannot support.
- Match the analytical method with the task. New data requires not only new analytical methods, but also new architectures for collection and storage. First, establish capabilities for storing massive, unstructured data sets. Next, hire data scientists, a new type of business-oriented statisticians, and proficient with analytical tools who designs research and spots trends. Finally, master and employ a number of different sophisticated analytical capabilities, such as keywords cluster analysis, neural networks or text analytics, to build the models and find the insights.
- Merge technical skill with deep business domain knowledge. There are many ways to analyze big data. Having robust hypotheses based on business knowledge and observations are essential to get the needed insights. While data scientists are by training technical people, they must have or develop deep business knowledge to differentiate true insights from noise.
Early Uses of Advanced Analytics in Life Science Can Inspire Sales Planning
Some companies are already leveraging analytics to unlock new sources of data, but such use is far from widespread. Take for example one company in a competitive market that has been developing a model to identify brand switchers. It used analytics to look into anonymous patient-level data (APLD) to identify doctors likely to “switch in,” and to “switch out,” and developed two different messages accordingly, allocating extra effort for these types of doctors.
Identifying switchers, monitoring adherence challenges, predicting the impact of events such as allergy outbreaks; this all requires mastering both the new data and new analytical methods. The good part is that most life science companies have already started building those models, albeit in very targeted use cases. There is still much to do to go from individual analysis to a systematic, automated process. What can you do today to prepare to embed analytics into your sales planning?
The next trend we will explore is how adoption of data and analytics will help eliminate the cycle, and real-time resource allocation will become the norm.