We love this slide. It shows as a Life Science industry where we have been, where we are and where we need to go in terms of our analytics capability. Unfortunately, most companies aren’t as far along the curve as they may think they are or hope to be.
The y-axis shows the level of innovation in the analytics platform while the x-axis shows the increasing business value. Obviously, as we move along both axes, from Reporting to Prescriptive Analytics, the quality and value of analytics improves the shorter the “action distance”. By taking a good look at each point in the evolution – you will be able to identify where your organizations stands and perhaps identify ways to further move your company along the analytical continuum and truly take your analytic capabilities to the next level.
At the time, “reporting” must have seemed great, now, not so much. “Reporting” was essentially self-contained reports that consisted of looking at sales data, usually a couple months after the product left the warehouse. This was extremely inefficient and impractical at helping determine and guide decision-making. Few companies remain here, and those that do are typically niche companies usually with an ultra-orphan product.
At the next stage, the pharmaceutical industry was able to begin correlating between promotional events that took place (sales calls) and outcomes (sales). This leap forward came as a result of an explosion in the quantity of data and the increase in the granularity of the data and computer technology advances. However, while descriptive analytics can show why something in the past happened, its value at helping alter the future is limited. Much of pharma continues to play in this space (although not many companies will admit to it).
The dashboard-era brought about the concept of near “real-time” monitoring in pharmaceutical sales. We all gleaned on to dashboards that used a traffic light system of go, caution or stop. However, for many, dashboards became a place to dump all data, not just useful data. It was easy to compare sales in a territory to the national average, but was it useful? Did it take into account the uniqueness of a territory? Was there a way of acknowledging that just because something worked in Chicago it may not work in Santa Monica? Although dashboards have become a bit more refined and provide sales reps with better and more current information, the data is not all that useful in accurately describing how to get to the future sales state that you’re looking for. Another bolus of pharma remains here.
This is the era of the expensive coin toss. Predictive analytics were designed as an algorithm that “learned” over a lengthy period of time with inputs and analysis from a robust team of people. It was supposed to be the beginning of “Artificial Intelligence” in the pharma industry. But then a funny thing happened on the way to the doctor’s office. Predictive analytics is ill suited to the pharma industry.
The complex and rapidly changing pharmaceutical business model in conjunction with incomplete and fuzzy business data confused the learning algorithms that have worked just fine for other industries.
It is of no surprise that those in pharma that have tried this have not been happy with the results, making them hesitant to try the next step.
We believe that pharmaceutical companies who boldly move towards and accept prescriptive analytics will be the ones that flourish. Prescriptive analytics start with a very specific objective and end with a path telling you how to achieve that objective. These proprietary algorithms uniquely understand and work within important constraints such as different reimbursement models in different areas of the country, your sales structure and even your marketing prowess. These are some of the very things that the predictive algorithms have failed to automatically identify. Unfortunately the predictive hangover has meant that few companies have dabbled in prescriptive and none have truly embraced the concept.
At ASI we can assist you in moving along the analytic continuum with products to maximize the benefit you receive from your investment in your analytic program. Reach us at 610.265.9400 or email@example.com.