Andy Quintana

AI, Relationship Intelligence - Why the approach you take to build it matters.

Blog Post created by Andy Quintana on Jul 16, 2018

In 2014, Accent Technologies set out on a mission to build an assistive technology that actually helps reps sell.  It wasn’t a mission to build a CRM but rather an extension of Sales Enablement and a dive into the Artificial Intelligence (AI) realm that would ultimately enhance the CRM.

 

With a few false starts Accent figured out a way to build out an AI system that can be diagnostic, descriptive, predictive and prescriptive (more on this one later).  Building out an AI system certainly takes time but a lot of thought was put into the approach we would ultimately take.  Here are a few things we discovered:

 

  • Leveraging only historical data is dangerous.

 

The approach many AI players take is purely predictive, leveraging only historical data, determining how a business succeeded in the past and how that would indicate what to pursue in the future.  This is standard data analysis - that tells you what you should pursue based on history.  This approach is short-sighted and there is a very big issue with it.  It solely relies on how a business may have performed in the past, but sales is a forward looking game. Sales reps spend a good deal of time identifying what works and what doesn’t work when talking to clients so they know how to better approach the next client.  They try to learn as much as they can from a client and position solutions that solve the problems, ultimately, wins that are attained have some common elements and those elements will be repeated.  But what if your sales strategy changes or the objectives change?  Your reps will follow the same pattern they are used to because they win with it.

 

  • Allow for input from the experts

 

So historical data is important but ONLY a piece of the pie for what is required.  Historical data doesn’t provide the business with an ability to identify the sales objectives. In other words, it never accounts for the “where we want to go” as a business.  Companies often hire consulting firms to figure out who their ideal customers are, what the ideal company profiles look like and that is driven by a multitude of factors like the marketplace, direction of the market, technology and human expertise, just to name a few.  So why discredit all that insight by using historical data to predict future business?  You can’t. And furthermore, you shouldn’t.  The sales leaderships experience, expertise and intent must be injected into the models and algorithms of a good AI technology.  Otherwise the technology will fall flat.  I know what you are thinking – That costs money and time for someone to inject that information into an algorithm, but have you considered the cost of not doing it?

 

  • Allow for adjustments

 

How does your organization account for the current sales strategy and a future one if you pivot? Meaning, how does the business get there? Essentially, you must operationalize your sales strategy.  With Accent’s CRM Supercharger, we help you operationalize the sales strategy.  Allowing for input into the models gives a business the ability set the course and direction for the sales team.  Now, the prioritized opportunities using AI models are not the ones the sales reps want to prioritize but the ones the business wants to go after. 

 

With Accent’s CRM Supercharger, we take a better approach and leverage historical data, sales objectives and sales strategy.  With machine learning algorithms the system can learn from the data it collects but add the ability to inject expertise, experience, intent and operationalize the sales strategy and you end with a winning combination. 

 

Andy - Accent Technologies, Inc.

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