As the digital engagement model for life sciences companies has evolved over the last five years, the critical importance of coordinated communication channels to maximize the effect of spend and outreach has dramatically increased. This has led to the rise of an omnichannel approach to not only marketing but digital engagement and field coordination as well. Since the onset of the COVID-19 pandemic, the level of direct access and expected standards of digital engagement have been significantly altered. As the industry tries to achieve optimal results for the lowest costs, omnichannel models and strategies can be leveraged to improve campaign connectivity and consumer outreach – but successful omnichannel requires a commercialization model that is integrated with sophisticated data and analytics technology.
Companies know that an immersive, personalized experience is key to patient and provider engagement. Without data-integrated campaigns, wasted spend and activity in siloed engagement channels not only cost more and are less effective but also lead to a potentially negative reaction from consumers (patients or physicians). When omnichannel interconnectivity is coordinated, the effect can be exponential between the channels involved, and the long-term brand experience impact can become a measurable competitive differentiation. As life sciences companies adopt core technology to meet accelerated digital demands, leveraging platform coordination drives predictive technology through a “next best action” framework. When all channels are coordinated, they’re not only metricized by activity but by attribution and consumer experience as well.
While companies have seen success from omnichannel strategies for years within the agency channel, the next stage of innovation exists in orchestration between CRM systems and outreach channels. It should be a standard point of measurement and attribution that can determine which activity in which sequence leads to a behavior change. Further, this attribution data should serve as the core reinforcement learning data for training and optimization of omnichannel activities. Unfortunately, the primary challenge tends to be temporal integration of engagement tactics and activities across marketing, sales and digital systems, which requires further orchestration for success.
EVERSANA recently took on the challenge to solve the industry problem of ambiguous targeting across field, marketing and patient services teams. We wanted to know: Which providers are impacting product script adoption and fills within patient groups and among other providers? To begin this study, we looked at a client’s product that was on the market for six months to see if we could leverage collective prescriber data to adjust targeting strategies and upturn the script trajectory – and we did. (Download the full case study, including research methodology and terminology.)
CLINICAL AND MARKET FINDINGS AND INSIGHTS
From the provider networks, our team was able to build bridges and connection paths from first adopters to find our next outreach targets. If you’re trying to get the whole community to take an action, such as prescribe a product, companies must target messaging and outreach to the providers at the center of these communities. For example, once you know which provider influences a community the most, marketing and field teams can take actions to increase the chances of influence, such as inviting the provider to speak at a conference where other providers from their community will be in attendance. By studying how to best influence and reach prescriber networks and educate them about new therapies and treatments, pharma would have an inside look at the most effective sales and marketing methods to improve script adoption. This led us to our next question: Is marketing more effective when targeted to connected physicians? (Download the full case study, including in-depth analysis of key research questions.)
GOING FROM GOOD TO GREAT: RECOMMENDATIONS
CONCLUSION: NEXT STEPS TO PREDICTABLE FORECASTING
One of the industry’s top challenges is accurate forecasting. Now, with a way to predict provider actions and script volume, companies can predict product performance better than ever before. With this case study, we were able to model a provider behavior that could lead to greater script adoption and treatments for patients. With this model, clients can flex their omnichannel commercialization and outreach strategies how and when they need to in order to meet product and patient needs.
A prediction alone is not interesting. A prediction that enables an action and learns from the outcome of that action is what creates a high-performance operation. (Download the full case study.)