You’ve identified your use cases, implemented your brand strategy and orchestrated a successful roll out to the field. But what happens next? As we all know, change is inevitable. Additional data sources become available, new competitors arise and, as a result, strategies change. If you want to achieve continued success with your decision support program, it’s critical to put the right mechanisms in place to keep your program and business in sync.
After more than 40 deployments, we’re fortunate to have a number of long-term customers—brands that have deployed suggestions and insights for more than a year. Together, we’ve benefitted from watching their programs evolve, uncovering strategies for implementing and maintaining a successful decision support program along the way. In this blog post, we’re going to share their best practices for keeping suggestions fresh and engagement strong after the initial excitement of rollout subsides.
Start Strong: Set Yourself Up for Success
Embed Suggestions and Insights into Your Larger Operations Strategy
Intentionally incorporating suggestions and insights into your operations plan is one of the first steps to ensure the long-term success of your decision support program. At one pharmaceutical company, for example, each internal brand team appoints a dedicated colleague to lead a cross-functional decision support team that involves business technology, SFA and the commercial team. This not only encourages buy-in from stakeholders across your organization, but also establishes a clear process for regular evaluation and improvement.
Get the Field Engaged from the Start
The more input your field force has with the design and deployment of your suggestions program, the more invested they will be in its long-term success. As you prepare to launch your pilot, ask reps from the field to work hand in hand with the internal brand, sales and compliance teams involved in the implementation process. In addition to mitigating some of the heavy lifting for the home office, the credibility generated by peer involvement in program design is invaluable for making adoption stick.
Be Agile: Practice Continuous Improvement
Designate a Core Suggestions Team to Review Feedback and Execute Change
Once you’ve configured the engine, set up a regular meeting for brands and districts to relay feedback from the field and analyze dismissals within the tool. What insights do reps wish they had? Are there any suggestions that reps habitually dismiss? With Aktana’s configurable platform, brand teams have the agility to handle this level of ongoing change, making content tweaks or minor rule adjustments as brand priorities shift. For larger updates, such as incorporating a new data source or business rule, it’s better to go through a comprehensive requirements-gathering process with your data science partner.
Continue to Build User Confidence with Suggestions that “Feel Right”
We designed Aktana’s decision support engine (DSE) to learn from every interaction. By assessing past behavior (like call cycles, visits or HCP availability) and context factors, the platform produces suggestions that reflect the routines and preferences of each rep. Leveraging these machine-learning insights minimizes the possibility of issuing impractical suggestions, like telling a rep to visit a no-see doctor, that might cause a rep to turn away from the platform.
Similarly, Aktana’s Rep Learning functionality identifies dismissal patterns based on variables like channel, customer type and segment. Equipped with this information, the DSE can proactively skip suggestions that are habitually dismissed, replacing them instead with those reps are most likely to find valuable. Although these are better described as product features rather than best practices, our customers have identified them as integral to ensuring trust and engagement don’t erode over time.
Stay Current: Share New Functionality and User Insights with Your Team
Incrementally Roll Out New Use Cases
During the requirements-gathering process, you probably left a few use cases on the table in the interest of facilitating adoption by starting small. As your field force becomes increasingly proficient with suggestions, they’ll want to know how else the decision support tool can make them more effective—a great cue to roll out the next wave of road-mapped priorities.
Keep your team informed of any platform improvements and begin to implement those that align with your brand strategy. If your reps are tasked with triggering approved emails, for example, integrate your marketing automation tool to add value and keep suggestions relevant to those who use them daily.
Share Engagement Insights with Your Entire Sales Force
If you’re monitoring engagement internally but not sharing the data, you may be withholding valuable information from your greater sales organization. When one pharmaceutical company began releasing territory-level engagement metrics to reps and district brand managers (DBMs), they saw noticeable results. Equipped with a better understanding of why reps were adopting or dismissing suggestions, DBMs found it easier to identify and act on coaching opportunities that may not have been visible before. At the same time, internal teams placed more emphasis on training DBMs to understand the deeper calculations behind the rules that drive suggestions, empowering them to provide clear explanations when questions from the field arise.
The Payoff is Worth the Effort
A decision support program isn’t something you can set and forget. Maintaining engagement and growing adoption requires constant attention, dedicated resources and a sound operational plan to transform user feedback into stronger brand strategy. But when your sales team can spend less time mining data and more time engaged in valuable conversations with HCPs around patients and their well-being, it’s certainly worth it.