The traditional pharmaceutical sales and marketing model, which relies on sales reps and medical liaisons to detail physicians, is in the midst of a sea change. Like everyone today, doctors and other prescribers want and need to receive more information through digital and diverse channels. Notoriously slow to adapt to market changes, big pharma is striving to evolve its practices, yet most of these organizations remain unclear on what to do and how to adapt to reinvent the model.
Change won’t be easy. The number of existing communication vehicles is immense, from direct rep emails and automated marketing messages, to videos and whitepapers, to key opinion leader targeted symposia and more. Add to that the myriad messages each vehicle carries. To achieve the best results, the channels and messages must work synchronously and in harmony.
While this trend offers significant potential for improving customer relationships, it means coping with the kind of change for which the industry is not well prepared. For example, much of today’s data is crude—basic channel propensity or frequency response—rather than tied closely to actual “treatment” response.1 Moreover, there has been no systematic attempt to codify, shape and track more interesting responses to treatment.
But rather than view this as an insurmountable challenge, the most progressive organizations are taking advantage of a unique opportunity to improve overall communication while lowering the cost of delivering targeted messages. Superior analytics and execution methodologies will be cornerstones of this transition.
The bottom line is that planning, coordinating and controlling the multichannel, multi-message flow of information so that it supports pharma objectives across multiple brands is both a great emerging challenge and opportunity.
An Opportunity to Grow
Navigating this transition raises fundamental questions for most pharma sales and marketing organizations about how to manage marketing strategy most profitably: What messages to deliver at what time and to whom, which channel lever to pull and how frequently, and more importantly, how to use this opportunity to make changes that optimize operations.
Other industries facing this challenge have emerged much stronger by applying data and analytics to identify the “best” marketing strategy to boost returns. Insights gleaned from these sectors point to three factors for success in managing the transition:
- Leveraging data assets in both execution and analysis,
- Using analytic methods dynamically and in a continuous learning loop, and
- Precisely managing the execution of the marketing strategy.
Data
Commercial data is used almost exclusively today by pharma in the analysis and planning of the sales operations strategy. For example, data analytics is central to efforts aimed at determining the right pacing of visits to prescriber segments and what sampling strategies are most effective. What’s missing is the use of dynamic market data and a more complete picture of the HCP experience as captured in transaction data stemming from routine rep workflows and sales operations.
This is because, until recently, no platforms existed to enable integration of dynamic data into operations. Reps have instead been expected to conduct their analyses outside the workflow of their standard CRM tools. The types of dynamic data that will pay dividends when applied well include, but are not limited to, changes in prescribing behavior, response to electronic messages, attendance at symposia, and formulary changes significant to a prescriber’s patient population.
Analytics
Pharma is deeply focused on the use of analytic methodologies to shape detailing and sampling strategies, many of which segment physician populations based on prescriptions written and their economic value. These are sometimes refined with other measures, such as estimates of prescriber propensity toward digital messaging or mapping of their influence networks. That is all good. And yet much more is possible. For example, other industries use analytic tools to dynamically identify specific customers who respond to specific messages in ways that support the brand and increase revenues. There is no reason pharma cannot do the same.
Pharma multichannel marketing (MCM) presents the added complexity of not only finding the right message for a prescriber, but also the need to optimize the combination and sequencing of multiple messages, communication channels, details, samples and standard office visits. Data and data-mining tools are available to devise marketing strategies that sort through this complexity and improve performance. It is up to sales operations to recognize the possibilities and take action.
Execution
Seamlessly translating marketing strategy and dynamic data into operations activities requires specific capabilities. Real-time dynamics and tight integration into the CRM system can power the coordination of field and in-house staff required to execute strategy across the multichannel, multi-message network of possibilities. Poor execution may lead to customer loss of interest or worse, brand damage. Alternatively, a robust, real-time decision support engine (DSE) that can translate strategy, dynamic market data and customer interaction history into actionable suggestions and insights for sales will achieve the desired result. In short, carefully conceived, faithfully executed strategy will deliver improved performance.
Finding the Best Strategy
Overarching success depends on having a process for devising a good strategy and continuously improving it. Ideally, this involves working with marketing, brand and sales operations teams to codify existing marketing strategies in flexible ways so that as new learnings about what works well emerge, they are quickly integrated into the strategy and seamlessly rolled into execution.
The strategy development process should also include measures of marketing effectiveness, each with a business and technical implication. Candidates might include:
- Prescribing volume – number of scripts written and re-written
- Receptivity to message content – propensity to read emails, open links and respond to invitations
- Depth of relationship – amount of time spent with representatives
- Commitment to product – market share or prescriber use of samples
Each of these metrics provides a different insight into prescriber behavior and marketing effectiveness. Analytics programs can use such measures as predictors or as targets for improvement. Furthermore, all captured information about interactions with the individual providers as well as their demographics, practice information, and market dynamics can be inputted into machine-learning algorithms.
Smart analytics use multiple machine-learning approaches to identify high-performing changes to strategy within the context of this learning loop. This is achieved with the understanding that execution never follows strategy exactly. Because the professionals carrying out the strategy hold prescriber and market knowledge that has not been codified in the strategy or captured in data, their judgment augments actions in a way that they believe improves performance. Much of the time, these deviations lead to real positive gains. Learning algorithms look for patterns in the actual treatment of prescribers that are tied to improved market performance and capture them as strategy enhancements. This is the learning feedback loop.
Where to Start
Successfully navigating this new multichannel, multi-message world can be difficult if attempted all at once. Fortunately, change management models offer sequential processes paced by how aggressively a team wants to proceed.
The linchpin is an integrated DSE, CRM and Learning system that enables:
1) The distribution of suggestions and insights supporting marketing strategies to reps within their existing workflows.
2) Collection of feedback from reps on provider reactions to marketing efforts.
3) Tight coordination with headquarters’ marketing actions.
4) Accurate collection of specific treatments to individual providers.
5) The application of tailored machine-learning algorithms to guide a strategy-execution learning loop.
With such a system in place, the key is to iterate strategy based on the data, the execution realities and the analytics, which identify how strategy impacts HCP behavior and what deviations yield improved performance. Translation of these learnings back into strategy then fuels the next revolution of the learning loop.
Begin with existing strategies. Then balance the resources invested in an upfront analytic exercise with execution on well-formed hypotheses of how customer treatments improve strategies and execution over time. Don’t let analysis paralysis get in the way of climbing on board the learning wagon. Looking backward has its limitations when the world is so quickly moving forward.
Footnotes:
1. We use “treatment” here to mean all the marketing actions that are experienced by the HCP. For example, all types of contacts with the brand or company, including the timing, and messaging of those contacts.
