September 8, 2015/Derek Choy/Decision Support

Today we arrive at the final part of our Six-Part Decision Support series. So far, we have reviewed the five key tenets of decision support design and the four most common categories of use cases. But even the best designed decision support program can fail to deliver results if it is not implemented well. Below we will explore three key lessons learned for the successful implementation of a decision support program. 1. Model as a Trusted Advisor Without rep adoption there can be no impact, so it’s critical to plan the roll-out of a decision support program very carefully. Several key questions must be considered: How do you announce the program to field sales reps and their managers? How will they be trained on the tools? And most importantly, how will they perceive this latest technical assistant in their work? We think of decision support like a golf caddy to a golfer: the caddy has great knowledge of the course and conditions, but remains in an advisor role.  Ultimately the rep, like the golfer, will still swing the club (and decide which club to use). It is imperative that sales leadership recognize and buy into this positioning, and then help carry the message to their entire organization. We’ve seen decision support programs stall during roll-out due to a key misperception – “the tool is here to do my job better than me.” A good decision support engine is an invaluable advisor to the sales rep, but only that. It never directs, only counsels. It makes Smart Suggestions that the rep can choose to take or ignore at their discretion. Proper positioning of this will influence the success of your roll-out immensely. 2. Think Big, But Start Small As we reviewed in Part 4 and Part 5 of the series, there are many use cases for Decision Support / Suggestion technology. It’s natural to see opportunity in most of these use cases, and ultimately you will want to apply the benefits of Decision Support across all sales and marketing activities at your company. But as with most new programs, it’s best to start small so you can have some initial success and then build from there. Pick an easily achievable goal so you get that quick win. Even better if it’s something that has high visibility across the organization. Once others see the positive impact, it’s much easier to create internal buy-in and expand to other relevant use cases. For that initial use case, however, be sure to cover the full decision scope for reps (all products, all actions), so the suggestions and insights are most useful and drive adoption. You’re looking to eliminate any risk of cynicism where others could say, “that wouldn’t work in my situation.” The greater the applicability of your pilot program, the faster you’ll build buy-in across the organization. 3. Plan to Learn Your preparation and design have been impeccable. Your strategy has accounted for every known market variable. Surely you couldn’t do any more to grease the skids of success. And yet, guess what – it won’t be perfect. It never is. But if part of your preparation is a plan to learn and iterate, then your path to ultimate success will be much shorter. Three things help the most:

  1. Speed up your learning by using A/B testing to explore variants of a strategy and see what is most effective before rolling the strategy out too broadly.
  2. Monitor reporting to see variances in suggestion adoption and effectiveness, to determine what seems to be working and what isn’t.
  3. Put in place a connected learning platform early on, so you can correlate rep actions (whether or not they were suggested) with results. A connected learning platform will allow for continuous learning, avoiding the need to collect and normalize additional data sets. It also makes generating experiments and A/B tests easy. A connected platform ultimately allows you to direct actionable “suggestions” back to those setting the strategy about which configuration changes will lead to increased impact.

The pharma market is more complicated than it’s ever been when it comes to communicating effectively with HCPs. But the truly scary news is that it will only get more complicated from here. Equipping your marketing and sales teams with an intelligent, customized decision support program will leverage the many investments you’ve already made in data collection, and it will help your teams add even more value in strategy design, optimization and execution. We hope this overview of decision support design planning, common use cases, and implementation best practices will assist you in your own program. And if you have any questions or would like our counsel on your particular situation, please don’t hesitate to contact us – we’re always glad to talk about decision support and provide “suggestions” of our own based on the multitude of data points and experiences we have gathered over the years!