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Successful Selling: Acing Advanced Analytics To Drive Commercial Growth

Executive Summary

Advanced analytics are being applied across different industries – from retail and transportation to hospitality and health care – to inform and enhance sales operations. In this final installment of a four-part series focused on sales strategies, Maria Kliatchko and Bhargav Mantha of global sales and marketing strategy consulting firm ZS explain how medtech companies can deepen their understanding of and better deploy their advanced analytics capabilities to drive commercial growth.

As a medtech commercial leader, you may have puzzled over why the uptake of a newly launched product differs across regions, or why some customer segments react to your investments differently than others. You may have wondered also which customers might be eager to try the new device or approach, and which would rather stay with their familiar, tried-and-true routines. Additionally, you may have considered the utility of being able to predict where your inventory should be at any given point so that you never lose a sale due to stock-out, while you also minimize the chance of overstock elsewhere.

As an industry, we’re starting to see medtech companies use advanced, predictive and prescriptive analytics to clearly and definitively address these scenarios, sometimes as early as three to six months before they become a problem. And yet, many of the technologies powering advanced analytics, including artificial intelligence and machine learning, are still poorly understood by many medtech leaders, and even in many cases by their analytics and IT departments. These technologies aren’t magical, but they can be extremely powerful when deployed the right way. Sifting through and learning from patterns found in data could provide medtech companies with deep insights and timely recommendations to make informed commercial decisions. 

About The Authors

Bhargav Mantha ( is an Associate Principal, and Maria Kliatchko ( a Principal and Leader, of the Medtech Analytics Practice at ZS, one of the world's largest business services firms specializing in sales and marketing strategies.

 In the past, these advanced analytical techniques were largely available only to the front-runners of the digital age—to Google, Amazon, Uber and other high-tech giants. But in the last few years, these front-runners have begun to make their tool sets available on Main Street in the form of powerful cloud computing, big data storage, and analytical mining software and libraries. Thus, mainstream companies have started to invest in and get significant early results from these advanced analytics approaches across their commercial, supply-chain, manufacturing and other functions, and some have even started to innovate with analytics-first product development and data monetization ideas. And with MarketsandMarkets Research estimating that the health-care industry alone is projected to invest $30 billion into data analytics by 2020, the opportunities are extensive.

 That being said, advanced analytics is at the peak of the hype cycle and, given its touted impact, it’s easy to get over-excited and invest in areas that ultimately won’t be valuable or profitable. To realize the early promises of analytics programs—before committing big budgets—successful companies would carefully select advanced analytics use cases with a high potential for profit growth, encourage exploration and experimentation, and continually measure the ROI of these pilots.

Let’s explore what such early use cases might be, how to not succumb to the hype, and how to separate real obstacles from some complacent old-world thinking.

Real-World Use Cases And Early Successes

In the world of provider consolidation, many of medtech companies’ customers are now large, sophisticated organizations that pursue clinical benefits with significant economic and public health objectives. They make decisions based on outcomes and economic value, leveraging a lot of their own and public data, forcing sales reps to enhance their relationships with HCPs through data-driven, value-focused negotiations. The rapid advancement of technology and ensuing competition have commoditized many of the formerly innovative products, putting more and more pressure on medtech margins.

In the face of these pressures, finding an edge through analytics becomes especially important. Here are three examples of how the use of advanced analytics has enabled medtech companies to realize multimillion-dollar incremental revenue and cost savings, with the potential to dramatically increase growth over the next few years and make a real difference:

Launch monitoring: A diagnostics company with a critical, high-profile launch invested in a “launch room”: a set of real-time dashboards to track the product uptake across geographies and customer segments. The dashboards provided all commercial leaders, including sales, marketing and operations, with the contextual insights needed to troubleshoot launch issues together. These insights were both diagnostic and prescriptive, and recommended specific programs and incentives depending on the uptake across segments. In the background, the analytics engine pored over data sets to compute the probable impact of different drivers, separating naturally slow-to-adopt customer segments from the impact of poor execution. When issues were spotted (such as one regional team’s reluctance to use the incentives that headquarters designed, or a deviation from the prescribed amount of inventory to stock), decisions stood exposed, and resulted in immediate remedial actions such as leaders talking to their teams. As a result, the company’s launch was successful and its approach has since been replicated in the company’s overall launch strategy.

 Customer retention: One medtech manufacturer has discovered significant customer churn, in some quarters losing more than 20% of its customer base. While headquarters and the sales force both had anecdotal explanations of why customers stopped using their products or switched on and off between their products and a competitor’s, no one knew which explanation was the most accurate or, more importantly, how to prevent it from happening. The company combined a few relevant data sets and experimented with a predictive churn model, looking at how various customer attributes and environmental variables affected customer churn patterns. In the process, the company sifted through a few hypotheses for what type of data might be more predictive—such as weather, inventory and supply issues, and customer demographics—to create a model that could confidently predict a relatively small set of high-value customers (those with annual revenue of $500,000 or more) to be at over 60% risk of defecting in the next three to six months. Using a white-glove approach with the customers now allows the company to prevent a large portion of them from defecting, saving the company more than $10m in revenue.

Pricing and contracting risk management: A diagnostics manufacturer contracted with several distributors to sell two different stock keeping units (SKUs) to end customers. Some distributors engaged in fraudulent activities by selling a lower-priced SKU to the end customers but claiming a rebate for the higher-priced SKU from the manufacturer. However, the company’s homegrown contracting risk analysis model quickly detected this fraud by analyzing the purchasing and rebating behavior of thousands of distributors and assigning them a risk score. The manufacturer discontinued the lower-priced SKU and changed contract terms with the distributors, preventing millions of dollars in loss. Another company leveraged a predictive model to predict the likelihood of revenue losses when smaller customers were being acquired by larger customers, due to uneven contract terms between the two customers, and thus was able to create more equitable and mutually beneficial contracts with the new customer entity.

In each of these use cases, advanced techniques including predictive analytics and machine learning were used to target costly problems, and yielded a significant return on investment. In addition to preventing multimillion-dollar losses, these technologies created confidence in possible outcomes and an understanding of the key skills and techniques required.

The Path Forward

Not all advanced analytics efforts are as successful as the ones described above. There were also many fits and starts where the problem was either too hard to solve or not the right one to solve, or the data wasn’t clean enough to earn trust in the results. However, the most typical barrier to success isn’t a bad attempt, but no attempts at all, such as when an organization lacks the skill and understanding to solve these types of problems. Organizational mindset is one of the greatest obstacles preventing companies from gaining confidence in advanced analytics and using it to solve real-world problems.

Companies that have been successful on this part of the journey have many things in common. In addition to having the right mindset for change:

  • They have executive sponsorship and a C-suite champion of the transformational nature and potential of analytics.
  • They innovate and invest on a smaller scale in use cases that align with their most burning problems.
  • They don’t let a lack of technical sophistication stand in the way of these efforts.
  • They leverage the resources that they have and encourage experimentation by assigning a skilled mix of curious, data-savvy, technical, domain-deep people to run pilots, without investing in too much technology.
  • They don’t hesitate to seek partnerships with analytics service providers who bring data science and advanced analytics skills to both build the first models and jump-start the internal skill-building effort.
  • They visibly lead the change throughout the organization to embed the data-driven mindset and appreciation for analytical insights.

 Once companies find success, they can begin to scale, operationalize and centralize their analytics investments in data, technology and people, enabling them to go after bigger problems.

 The old sales-driven, individual-customer-focused business model no longer helps to grow revenues and preserve margins; new analytical approaches are quickly evolving across the industry to optimize everything from inventory to sales coverage, minimize supply chain disruptions, discover new customers and prevent old customers from defection, and drive contracting based on outcomes and economic value. To sustain this level of competition, medtech companies need to invest in analytics, and it’s not a question of if or when, but where and how. Making small investments in the right problems can lead to big results, thereby paving the way to developing skills and competencies, and eventually data and infrastructure for the truly transformational changes down the line.

[Editor's note: Views expressed by guest columnists do not necessarily reflect those of Medtech Insight.]









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