2949_SM_Ai_augmented_blog-1In a world where autonomy, mastery and purpose drive high performance, the very idea of a centrally directed automaton feels completely disjointed and jars the senses. How could we expect members of our sales teams, who grew up being told to think of themselves as CEOs of their territory, to do exactly what AI (or predictive analytics from HQ) tells them? Also, how can we assume that HQ knows best when it comes to client needs and preferences?

There’s a weak case for a prescriptive distribution model, but it can’t be implemented successfully in a free market society. However, there’s a strong case to be made for a model where humans rely on (or partner with) AI and machine learning to sell and market even better. In this model, the human would have agency to consider the suggestion but ultimately decide what to do. Outcomes are far superior when a person chooses which Google Maps directions to accept or ignore, a radiologist reviews an AI-generated analysis of cancerous tissues or a product designer selects AI-generated designs, so we end up following the guidance most of the time. Anecdotally, in just the first few months of ZS clients running AI-augmented distribution models, we have seen a 40 to 70% guidance acceptance rate. I expect that in a model where technology augments their capabilities, sales and marketing teams will end up using the suggestions most of the time. And they—like us—will follow the guidance because it will turn out to be better.

The AI-augmented model will periodically ingest large volumes of sales and marketing activity, transactions, advisor profile data and the outcomes of previous suggestions. It will then run the data through its algorithms to predict personalized journeys for every advisor in the database (an expected timeframe for the interaction, topic for the interaction and the medium of interaction). There are three reasons why sales and marketing will end up taking guidance from the AI-augmented distribution model:

  1. Increased selling time: Based on our work, it’s safe to assume that a sales person will save 30 minutes per day that’s currently spent wrangling advisor data, preparing for meetings or coordinating with colleagues: an extra 100 hours (15 to 20 selling days) of additional selling time in a 200-day year. There’s a similar savings for marketers, too. How much more revenue could this additional time yield?

  2. Improved quality of interactions: AI can quickly guide sales and marketing efforts toward higher-value advisors. AI improves the ability to reach target advisors and allocate the optimal mix of personal and non-personal interactions, which can then increase revenues. In my experience, not using AI translates to 100 to 200 missed opportunities in new assets or redemption prevention per territory per year.

 

  1. Better relationships with advisors: Our research on the advisor Connection Quotient (CQ) indicated that advisors place value on interactions that help them make well-informed decisions, achieve personal success, and feel valued. The AI-augmented distribution model develops a personalized, omnichannel journey to increase CQ. And the higher the CQ, the higher the level of business.

In a nutshell, here’s the business case for the AI-augmented distribution model:

  • Increased selling time = reach more advisors
  • Improved targeting of high-value advisors = more opportunities
  • Increased impact = close more opportunities

If you’re thinking that we’re going back to the basic levers of selling, you are 100% correct. Only this time, it’s the 21st-century version, where AI can turn human efforts into super-human results. And importantly, there’s nothing prescriptive about this model. Sales and marketing can opt to ignore the guidance from AI, but only those that do take the guidance will realize its full potential: superior results.


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Topics: financial advisors, distribution, asset management, Financial Services, artificial intelligence, connection quotient