It seems like an ideal – even obvious – application of big data analytics:  correlating sales activity and customer results to determine how field sales reps can become more effective. Of course, company and product strategy would be fully integrated, to ensure relevancy to the business challenges at hand. And, these insights would be packaged into an intuitive, easy-to-use tool for field sales.

Unfortunately, getting value from big data analytics is rarely simple. At Aktana, we focus intently on this challenge, and we’ve found that meaningfully improving rep behavior is a complex task. The results can be compelling – even game changing – but the path to achieving them holds many formidable obstacles.

The Aktana solution is anchored in what we call the Smart Suggestion: a practical, recommended action that balances strategy, customer understanding, and field realities. These Smart Suggestions are produced by our proprietary technology, which is based on sophisticated artificial intelligence and highly adaptive dynamic programming engines. Smart Suggestions are proactively offered to reps via the Aktana software application – always at-hand, dynamically updated, and integrated within a rep’s workflow.

Importantly, Smart Suggestions are just that: suggestions. Reps can reject recommendations, for example, when they believe they see a superior course of action. Smart Suggestions are designed to extend and enhance – not replace – rep knowledge and decision-making ability. In this way, Aktana captures the benefits of human-computer symbiosis, combining the best of artificial intelligence and human intelligence.

For this approach to work, it is imperative that sales reps trust the software and are willing to accept a recommendation from it. We believe that a rep’s decision to use our software hinges on the quality and presentation of our Smart Suggestions, and not surprisingly, getting that right is no simple task. In our work at Aktana over the last several years, we’ve identified four key requirements to ensure rep usage of our suggestion technology.

Suggestions must “Just Feel Right”

First, any suggestion made by software to a field sales rep has to “just feel right” – that is, it must seem like the right action to take. Ideally, the suggestion should simulate what a top sales rep would do, given the same robust data set and enough time to digest the information. Even a rejected suggestion must feel like a viable option – a rep will never trust a tool that makes bad suggestions.

We’ve also discovered that suggestions must be accompanied by the underlying reasoning – reps are more likely to trust a suggestion if they understand the logic behind it. We have seen other approaches to creating rep suggestions that are single-factor based or non-contextual and, consequently, reps tell us that those suggestions “feel off.” Most often, the software providing such suggestions operates as a black box, with limited or no visible rationale supporting the suggestion.

Aktana creates “feels right” suggestions using our proprietary recommendation and learning engines, which incorporate the essential non-linearity and variability of field sales interactions. This sophisticated artificial intelligence technology is critical to the effective simulation of human decision-making. We complement this technology with our years of experience regarding how to set coefficients to mirror natural, best-in-class human behaviors.

Suggestions must be Easy to Consume

The field sales rep’s life is already fairly complex, requiring the rep to remember and balance such myriad factors as sales strategies, product details, competitive product data, marketing promotions, customer details, and buying history. Reps won’t typically hunt down a suggestion within a hard-to-use tool, even if they know the suggestion might have value.

All too often, complex or poorly designed tools discourage reps from using them in the field. The Aktana product suite addresses ease-of-use directly: providing a few suggestions at a time, integrating them into the rep’s natural workflow, and providing for always-on mobile access. Aktana is passionate and relentless on the issue of rep usability: we have spent thousands of hours with reps, co-designing each and every screen and workflow element within our application.

Suggestions must be in Tune with the Business

A good suggestion technology can be very powerful – actively and constantly shaping rep decision-making, and creating meaningful change in rep behavior. However, unless the suggestions accurately reflect business priorities, rep actions could harm the business.

This is harder than it would seem, because companies frequently have inconsistent or even competing priorities. For example, a pharmaceutical company may wish to limit physician visits to control costs, and at the same time, want to promote physician use of a proprietary drug. The technology must balance these competing objectives in suggesting the rep’s next visit. Should the rep visit the nearby physician with a lower priority (less costly) or the physician across town with high prescribing behavior (higher script potential)? To stay relevant to business priorities, the suggestion technology must weigh the relative importance of various priorities within the context of each rep decision and customer situation.

Further, these business priorities frequently change, demanding a dynamic response from the suggestion algorithm. An implementation requiring manual updates with every shift in business intentions would quickly become outdated.

To address these challenges, Aktana’s technology employs multivariate optimization technology that can incorporate all relevant factors.  In addition, we use non-linear functions to reflect how various priorities would realistically interact. We designed our software so that company strategy, customer data and rep information feed continuously into our engine. Once set up and appropriately tuned to the organization, Aktana’s Smart Suggestions can dynamically – and automatically – adjust to changing conditions.

Suggestions must Continuously Improve

However, being dynamic isn’t enough. A good suggestion technology must actually learn. For instance, when a rep dismisses a suggestion, the technology must incorporate the reasons for the rejection, and apply that learning to improve suggestion relevancy in the future. To maintain rep trust, we’ve invested in learning algorithms to ensure that our Smart Suggestions get better over time.  Aktana’s technology adapts to rep preferences, adjusts to customer differences, and incorporates best-practice learning.

Unlocking the value of big data analytics to improve field sales rep effectiveness is a worthwhile, but surprisingly difficult endeavor. At Aktana, we have spent years honing our proprietary artificial intelligence technology for exactly this purpose. Aktana’s Smart Suggestions provide customers with a uniquely effective way of dynamically shaping rep behavior, acting upon the most up-to-date business priorities and capitalizing on the newest customer insights.