Aktana’s software suite integrates the best decisions made by artificial intelligence and the best decisions made by human intelligence. It continuously learns from specific successes and failures, automatically incorporating results into its algorithms and recommendations. Further, since Aktana captures feedback directly from the field, the system is able to form an increasingly deep understanding of what works best when, for whom, and under what conditions.

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Aktana’s Proprietary Learning and Recommendation Engines employs partially-observable decision process algorithms and temporal difference learning to create superior field recommendations, and provide the basis for a constant cycle of reinforced learning. Aktana’s proprietary technology is based on a sophisticated artificial intelligence, and highly adaptive dynamic programming engines.

Aktana’s Proprietary Learning and Recommendation Engines are built around four key assumptions about the world of sales:

The World is Non-Linear

Customer response to messaging is not the easy lines of linear programming. Is the third customer visit in a month worth the same as the first? How much worse is a 3-week delay between visits, as compared with a 2-week delay? Aktana’s system is built from the ground up to factor in and learn these non-linear relationships, providing recommendations that better reflect the nature of the market.

The World is Multivariate

Meeting business objectives requires a constant balancing and rebalancing among competing priorities. No simple set of rules can account for the myriad objectives facing a sales and marketing team. Should you reach out to customers with the most growth potential, or customers with the highest current sales? What is the impact of seasonal promotions of particular products on all customers? What is the impact of other channels on the sales call?  Aktana’s software suite produces recommended actions that optimizes across all of the team’s objectives, and puts control of these balances into the hands of reps and decision makers. The result is a sales organization empowered to adjust its priorities within hours, instead of weeks.

The World is Dynamic

As new information comes to light, Aktana adapts. The rep’s life is filled with shifting promotions, changing schedules, and updated information.  The real-time nature of the Aktana system means that the AI’s recommendations are always up-to-the-minute with the changes within the Rep’s world.

The World is Learnable

Aktana’s proprietary partially-observable Markov decision models learn from every rep action, customer reaction, and from exogenous changes to the sales environment. Aktana combines this feedback with information about sales impact, creating ever more effective suggestions for the field, and providing HQ the tools necessary to evolve strategies to fit the market.

Building Smart Suggestions from smart technology.

Building smart suggestions from smart technology