March 2, 2017/Gil Blustein/Field Sales

When it comes to making suggestions to sales reps, there’s a lot to consider in order to ensure the right suggestions are generated for the right rep. For example, it’s not enough just to consider a rep’s call plan for engaging with providers; suggestions should also adapt based on events, market data and the brand’s overall strategy and messaging.

A key differentiator of Aktana’s Decision Support Engine (DSE) has always been our unwavering focus on creating suggestions that feel right to reps. The DSE accomplishes this by tracking the rep’s individual progress to goals, their recent and planned activity (along with their counterparts’) and practical circumstances like availability or proximity to a suggested provider. Now, drawing on more than 40 implementations of suggestions, we’ve also identified how to consider a rep’s personality, preferences and historical response to suggestions so that the right knowledge is generated at the right time for the right rep.

Aktana’s Rep Engagement Module, powered by Aktana’s Learning Platform, is an enhancement to the DSE which monitors both a rep’s interaction with suggestions as well as engagement with providers. Equipped with this insight, the DSE then prioritizes suggestions that are more likely to fit the reps preferences—within the boundaries of the defined strategy.

Like all Aktana products, the machine learning module is very configurable, allowing the brand or team to decide how much weight the module should have, which reps it should apply to and for how long. For example: we might choose for the module to have a greater impact on reps whose suggestion engagement is below a threshold, and only until those reps begin engaging with suggestions above that threshold again. Alternatively, we might choose for the module to have a more moderate impact for all reps, providing more emphasis to those suggestions that feel better for reps and increase the likelihood of adoption, especially earlier on in the adoption lifecycle.

By incorporating the likelihood that a rep will engage with suggestions (and interact with providers) at a specific date or time through a specific channel, suggestions are more likely to feel right and actually be used by reps. Ultimately, this is the only way suggestions can have any impact.