Decision support has become a vital tool in connecting brand strategy with execution, resulting in increased CRM satisfaction and better healthcare professional (HCP) engagement. When you roll out your decision support program, how can you ensure that you’ll get the adoption and long-term user engagement with the technology to achieve these results? Below are four essentials for maximizing engagement in AI decision support.
Empower sales and marketing stakeholders
Although there are many stakeholders in the launch of any new project, sales and marketing co-authorship from the very beginning is essential for success. There are several key stages in the project launch process where marketing and sales teams collaborate.
- Product Setup and Design: While sales teams have historically been removed from the strategy phase, we have seen projects with the highest engagement include sales reps as co-authors. As requirements are being set, this partnership is critical to understanding the end-to-end rep workflow, knowing what information can help decision-making, and identifying points at which rep input on HCPs is most effective.
- UAT and Training: User acceptance testing (UAT) and proper training provide another avenue for reps to give feedback early. This feedback provides context for constant refinement and growth to ensure campaigns are translated from the design table to execution as expected. An effectively designed project provides a variety of ways in which sales can communicate back to marketing.
- Hypercare and into Steady State: In these stages, the feedback loop is further refined to ensure campaigns are translated correctly and running effectively. An example of collaborative refinement at this stage includes the adjustment of active feedback channels to collect the most important information back for marketing.
Early and continuous collaboration between sales and marketing teams includes both logistical and psychological advantages. Logistically, you are able to address design and process concerns at early and flexible stages, building a project that reps will actually use. Psychologically, reps who feel they are co-authors have much more buy-in with a higher likelihood of promoting the project to others.
Identify and market the right metrics at each stage
Over the course of the program, the ways in which you measure success or engagement will evolve. It’s imperative to focus on the right set of metrics based on stage, and to communicate, or market, progress to your stakeholders. Metrics are categorized by three basic stages:
- Engagement: Engagement metrics serve as your foundation. Start with identifying how the platform delivers information, at what frequency suggestions are received, and if suggestions are going to the right reps with the right channel mix. From there, look at field engagement to measure the acceptance, dismissal, and engagement rates in conjunction with syncs.
- Execution: Once you have a good handle on engagement, measure field behavior by looking at execution rates of activity by channel, new content usage, customer coverage, messaging, and planning. To understand how HCP’s are responding, measure the number of interactions by channel, channel affinity, or message affinity.
- Impact: Following success with execution, you can look at metrics that demonstrate business impact. HCP behavior can be measured by customer journey transition, event attendance, open and click-through rates, consent, and objections. Business performance can be measured by an increase in prescriptions, sales, new patient, switch patient, treatment line, indication, or formulary.
The ultimate goal is to change HCP behaviors to drive business growth. Constant tracking across the process provides better insights for iterative improvements. Through metrics, you can evolve from tactical to strategic and adjust roles and strategies along the way. Use those metrics to communicate progress and success to all stakeholders and ensure long-term adoption and engagement.
Build a practical foundation to ensure scalability
The key to long-term engagement is to build a practical foundation to ensure scalability. Start by evaluating the type of competitive opportunities that decision support can address, perhaps a brand that is relatively mature, looking to respond to a competitive threat. Then consider the areas you might want to launch with decision support. For example, how you go to market with an oncology product will be vastly different from a primary care team or vaccines team.
Next, consider which priorities decision support should focus on. Do you want to reinforce messaging or address referral drop-offs? Finally, for those in a global role, determine which market is most appropriate to launch decision support. Perhaps there are markets with new leadership whose focus is digital excellence or markets with teams who have demonstrated willingness to experiment with novel go-to-market technologies.
Keep in mind there may not be a perfect order or starting point. Instead, use these thought starters and dimensions to be specific about where you want to start to ensure a strong foundation that can be built upon for impact where it’s right for your organization.
Leave room for human involvement in AI initiatives
AI comes with a perception of full machine automation that negates the need for human involvement. However, the most successful use of AI is a blended approach. By allowing machine learning to do what it’s good at and empowering your team to interact and engage with AI, you’ll achieve success.
In a blended, AI-assisted approach, a balance is created where team members see AI as an asset to their contributions and not as a replacement. We see AI drive engagement when machine learning incorporates human involvement through questions to curate and improve suggestions. This results in dynamic content that feels right for the rep and helps engagement.
For example, we always curate a list of the most important suggestions for a rep – in this case, let’s say we have provided the top 5 suggestions to a rep. Beneath those top 5, there are still valid suggestions in-line with the strategy, but including too many will deter reps from acting on any of them.
We can also start using AI to overlay some human considerations to further curate the suggestions. For example, can we predict where the rep might be on a given day to prioritize suggestions that are more en route? Can we assess when the HCP might be available given historical accessibility data? When these human elements are incorporated into your decision engine, we may determine a different order for the list of suggestions shown to the rep. Ultimately, all of this helps to drive content that feels right to your team and, therefore, helps drive higher engagement.
Companies that have had success with decision support all have at least one thing in common – high user engagement. High engagement not only leads to adherence of “next best actions,” but also provides critical feedback to refine strategy. For more on how to achieve better buy-in from your stakeholders and efficient collaboration between teams, listen to our recent webinar that discusses these four essentials in more detail.