“2020 will be an exciting year for us. We’ll launch a few advanced devices and solutions that will have a transformative impact on patient lives and will position us as the leading provider of care in our therapeutic area,” said a general manager of a large capital equipment manufacturer.
The company’s president seemed cautious at best. “I’m hoping we’ll do better this time compared to our past launches. It worked just fine last time courtesy of the relationships our reps have with surgeons. However, this year will be different because we have to convince IDNs about our value proposition through data. I hope we’ve looked at the data hard enough.”
In response, the general manager called the vice president of commercial excellence. “Hey, we need to talk. This is about the upcoming launches in 2020. I want to make sure that we have looked at the data and done our homework.”
The vice president knew what was coming over the next few months. Leadership would be curious to know who their most valuable customers will be and how much market share they can realistically get. They’ll also want to know what their promotional investments should be to maximize launch.
Answering these questions requires data and analytics, which are not the company’s strong suit. Its commercial analytics are fragmented, data is fraught with errors, analytical capabilities are nascent, and sales leaders are comfortable doing things the old way.
While this scenario is a hypothetical one, this is a common occurrence in many medtech organizations, unfortunately. It’s not that medtech does not value analytics. Quite the contrary, advanced analytics and AI are becoming mainstream in R&D and product engineering, and many medtech companies are applying deep learning in designing innovative products that are poised to have a transformative impact on healthcare. However, as customers become more data-savvy and outcomes-oriented, commercial capabilities need to undergo a transformation. At this year’s AdvaMed event, a group of panelists agreed that the customers have changed but medtech commercial capabilities have not. Success in the future will be driven by bringing overall value to the customer, not just the clinician. Now, more than ever, insight into the customer (beyond the clinician) needs to work its way into the fabric of the company, from product development to commercial execution.
So where should companies begin? Should medtech companies start to build data, analytics, AI and technology capabilities within the commercial organization first or focus on some pertinent commercial business problems and show how analytics can drive transformational value? I believe in the latter.
Here are four current business problems that medtech companies can address by applying analytics and AI:
1. Price erosion: Medtech continues to observe significant price erosion across all major product categories. The consolidation in the healthcare ecosystem creates further risks of exposing price differentials and can lead to price “cherry picking” by customers. Proactive data management, analytics and purposeful investments in platforms can stop the downward spiral. For example, one of our large medtech clients saw over 5% improvement in EBIT through purposeful analytics, diagnosing price erosion and understanding the drivers behind it.
2. Missed forecasts: It’s no surprise that most medtech companies routinely miss their forecasts. Unfortunately, most don’t know whether it’s due to incorrect forecasting or underperformance. Not surprisingly, they don’t know how to course correct. AI can play a critical role in improving forecasting accuracy by:
- Predicting potential forecast deviations by triangulating across field intelligence, historical capital replacement and customer profiles
- Predicting the impact of market headwinds on share due to treatment protocol updates, competitive launches, reimbursement coverage changes, policy changes based on claims data, primary research data, etc.
One medtech company, for example, was able to realize 10% improvement in base forecast accuracy and reduced quarter-on-quarter variability by applying AI to the sales forecasting process.
3. Suboptimal inventory (too much or too little): While e-commerce has changed the paradigm of demand management through one-day delivery, medtech companies have reps who continue to carry stock in their car’s trunk or in their garage. The resulting unused inventory leads to many millions in lost opportunity. Commercial operations could leverage a variety of AI techniques to improve rep inventory management. Reps could rely on powerful AI algorithms to provide dynamic stock allocation recommendations based on a variety of internal and external factors.
4. The declining impact of sales reps: As customer needs continue to evolve, selling is becoming more complex and personalized. Reps are finding it hard to stay relevant and continue to add value to their customers. Today’s reps look at various reports but are left on their own to derive the insights that matter most. There seems to be an information overload but a lack of insights. Analytics and AI can play a huge role in converting the information to insights and providing personalized alerts and recommendations to reps. Imagine an algorithm alerting a rep that a contract with a key customer is due to expire and needs urgent attention, or that there are opportunities to cross-sell parts of a portfolio based on recent purchasing patterns. These insights can help reps tailor their selling process and drive greater impact. One medtech company that we’ve worked with was able to grow impactable sales by 15% over two years by providing reps with timely insights and improving customer engagement.
Waiting to build commercial analytics capabilities is not an option anymore. In future blog posts, we’ll dig into each of these business issues and discuss how, exactly, medtech companies can leverage analytics and AI to solve these problems.