There’s a whole spectrum of analytics techniques available to medtech leaders, and while innovations like automated personal assistants are great, it’s the middle of the spectrum where the greatest value is created, and from which companies can derive a competitive advantage.

Maria Kliatchko, ZS Associates

When we think of advanced analytics, we think of AI solutions like self-driving cars, automated personal assistants or cancer diagnoses by image recognition. But all too often, for commercial leaders and field managers, “analytics” is limited to dissecting some data with better visualizations and dashboards. Medtech needs to up its analytics game, but how?

Here are some techniques that can help, and that fall within five levels of analytics maturity:

Augmented data

Most organizations start their analytics maturity journey with better access to data through dashboards, reports, ad-hoc query tools and visualizations. A typical commercial leader needs regular, timely key performance indicators for sales, margins, productivity, etc., but the amount of  available data continues to grow. It’s no longer sufficient to produce more reports and let users figure out what to do with them.

Embedding alerts, showing outliers and warning signs or spelling out the insight as a suggestion are a few ways to augment the data to bring attention to what’s most important. Leading companies also enrich their data by assigning scores to customers, opportunities or other entities as a way of providing quick insights for everyone.

Optimization

The next level of decisions can’t be made by analysts visually slicing and dicing the data, even when it’s sufficiently augmented but requires optimization. Optimization is a mathematical model that minimizes or maximizes an objective function without violating constraints. Whether in SAS, Excel or specialized software, an optimization model can improve many operational and strategic processes.

Analytics-driven optimization in the size and deployment of a sales force can lead to revenue improvements of 3-5% relative to a typical non-optimized deployment, according to a study published in The Economist. Another model can optimize marketing mix, yet another can minimize inventory or determine the best pricing or discount strategy.

Prediction

Predictive modeling makes another step forward, enabling machines to sift through data to spot dependencies, leading indicators and to predict events of interest. Predictive models use a variety of techniques from linear regressions to decision trees and neural networks.

With the rapidly decreasing cost of computing and the availability of open-source code for many models, many companies now use predictive modeling for a variety of business problems, such as predicting customer churn or calculating the lifetime value of a customer. In many cases, insights that prevent undesired events or speed up desired ones may bring companies tens of millions in extra revenue.

Recommendations

Once machines can predict an outcome, they can be trained to suggest a response or share what has helped in similar cases. Prescriptive models can differentiate between potential actions and determine which will lead to better outcomes. “Next best actions,” which recommend sales tactics to the field, and “proactive launch,” which monitors product launches and suggests adjustments to strategies or tactics, are two of many applications that leading healthcare companies deploy to optimize sales.

Automated execution

While these four analytics levels provide insights, they depend on humans to take action. When a company can automate the decision and the action itself, it can derive the greatest value from analytics. We’re not talking about replacing the entire commercial function with robots. Automation doesn’t have to be super-advanced to bring outsized returns. Think about automatically shipping another set of spare parts based on seeing a depleting inventory at the warehouse. What if you could send an automated welcome package to a new customer, or an event invitation to a customer considering a switch to a competitor? These applications are achievable for most medtech companies.

Maria Kliatchko is a principal and the leader of ZS Associates’ medtech analytics practice. She has more than 20 years of experience working with pharmaceutical, medical devices and health-care companies on sales and technology strategy issues and implementations, including sales resource optimization, alignment, segmentation and targeting, as well as business intelligence, CRM and commercial data integration.

The opinions expressed in this blog post are the author’s only and do not necessarily reflect those of Medical Design and Outsourcing or its employees.