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The Analytics Revolution of 2018: Transforming Insights to Action


Scott Beauchamp discussed the future of analytics in the life sciences industry-and look at how peers in other industries have reaped the benefits of being early movers.


The life sciences industry is undergoing a transformative shift. Under the new paradigm, patients are seeking greater transparency, physicians are losing prescribing autonomy, hospital consolidation is growing, and value-based medicine is paramount. This new reality demands a complete re-imagination of commercial strategies.

Given these changes, 2018 must be the year that analytics fulfills its longstanding promise: to truly change decisions and drive tangible value. Many life sciences companies have already responded to the shifting industry tides by investing in sophisticated data and analytics capabilities. However, while 70 percent of companies rate sales and marketing analytics as “very important” or “extremely important,” only two percent of organizations claim that their analytics efforts have had a “broad, positive impact.”

Based on APT’s work driving value through analytics at leading organizations across many sectors, this article discusses the future of analytics in the life sciences industry-and examples of how peers in other industries have reaped the benefits of being early movers.

Moving from diagnostic to prescriptive

Over the past few years, life sciences companies have made massive investments in commercial analytics. These investments, combined with declining data storage costs and increasing CPU power, have enabled more sophisticated, complex, and precise capabilities than ever. However, this newfound power has fallen short of its promise; instead of changing decisions, its insights often remain diagnostic.

While big data generates a myriad of trends and patterns, it rarely helps brand leaders decide what their next action should be. Imagine your multi-million dollar machine learning investment identified eight new prescriber segments and four potential patient triggers based on past data. Now what? Should you heavy up detailing on the new segments, invest more in non-personal promotion (NPP) to gain access to these groups, or improve your speaker programs to feature key opinion leaders with these profiles? Leading organizations are realizing that they need the people, process, and technology in place to rapidly act on new insights.

In 2018, brand leaders will continue to invest in infrastructure to achieve truly prescriptive analytics. For many teams, rapid experimentation is the critical capability needed to translate insights into action. Business experimentation, or what we call Test & Learn, is based on the idea of a clinical trial: trying an idea with a subset of customers or markets, and comparing the results for that “test” group to results for a “control” group that received no change. This Test & Learn approach can be the final step to close the loop within your existing infrastructure, generate prescriptive recommendations, and drive real business value.

Optimizing the customer journey

The customer journey is not only an increasingly central pillar of commercial strategy, but also an area ripe for business experimentation. Beyond mass marketing, both patients and physicians are looking for messages relevant to their specific situation and stage of brand engagement. While life sciences companies have reacted by investing heavily in new technology to better understand the customer journey, many have not seen it pay off. Instead, the most leading-edge teams are leveraging in-market experiments to optimize their targeting of different segments.

Two industries that have excelled at improving the customer journey through a Test & Learn approach are the financial services and retail industries. Consumer banks, for example, have long had the infrastructure to test and optimize their marketing content, channels, and timing based on key customer segment traits and past behavior. Rather than relying on hypotheses about customer triggers, they actively test these initiatives for nearly real-time insights. Then, they use learnings from the small-scale tests to inform broader pivots throughout the customer journey and maximize results.

Many retailers also rely on continuous testing of customer journey interventions. For example, retailers may use analytics to learn that shoppers with a certain spend history and demographic profile respond best to email promotions. They can then test various combinations of timing, promotion depth, and messaging to tease out the true cause-and-effect impact of each campaign. Only through in-market tests can they answer questions such as: which customer segments should have received an discount with their online purchase? Which ones would have responded better to a loyalty offer? Which will spend with us again even without further promotions?

Life sciences organizations have parallel questions for their physician and patient base; they should learn from the examples set by banks and retailers, and look to experimentation to unlock the promise of analytics.

Scaling causal analytics is a key competitive advantage

The ability to scale up this rapid, data-driven decision-making structure is key. In the era of big data, patterns are everywhere, but companies must ensure they do not confuse correlation and spurious trends with true cause-and-effect. Scale is another area where life sciences companies can look to other industries for best practices. Some leading credit card issuers, for example, run thousands of test vs. control analyses per year because they have invested in the necessary automation, training, and infrastructure. They not only measure and optimize nearly every campaign, but they also run “meta analyses” to identify overarching insights that cut across multiple initiatives.

In the coming years, one-off analyses will no longer be sufficient in the life sciences industry. Brand leaders will increasingly strive to achieve a similar scale of data-driven decision-making, especially as the industry becomes more competitive.

To navigate the sweeping changes of 2018 and beyond, brand leaders should focus on unlocking the full potential of analytics-just as innovative organizations from across retail, banking, and other industries have already done. This year, life sciences companies should focus on translating insights to action, actively testing to de-risk innovations, and ultimately driving growth.

Scott Beauchamp is Vice President at APT, a Mastercard company.

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