The Promise of Artificial Intelligence (Part 1)

2019-05-15 Blog

Leveraging AI in CRM to the advantage of Sales Managers

Introduction

Even in 2019, the successful adoption of popular CRM systems encounters a lot of resistance. Some of this friction comes from the people it supposed to help.  Some of this friction comes from the technology itself.  The current generation of CRM systems is based on designs that are 20 years or older. Although they were built to take advantage of the internet, the current generation of popular CRM systems was not designed to take advantage of the most impactful technology of the last 50 years – artificial intelligence (AI).

What is AI?

After many years of false starts, advances in neuroscience, control theory, and statistics combined with the exponential growth in processing power have bolstered AI to the point of yielding practical business benefits.  AI can loosely be defined as systems modeled on the human brain. The term broadly covers many different approaches to intelligence.  Cognitive computing, machine learning, deep learning, predictive application programming interfaces, natural language processing, image recognition, and speech recognition all fall under the umbrella of this definition of AI.

What this means to a Sales Executive

While this is all very good in theory, what does it mean in practice? Today using a CRM system built from the ground up has many bottom-line benefits for both sales executives and sales reps. In this post, we will examine the benefits of AI for sales managers. In the next post, we will explore the potentially even more significant bottom-line benefits for sales reps.

For sales managers, AI offers several ways to help them do their jobs. Principal among them is forecast prediction, named account scoring, and territory optimization.

Forecast prediction – Predicting a forecast accurately is one of the sales manager’s most important but most demanding jobs. In the Olympics, they don’t ask figure skaters to rank their individual performances. In sales, asking the players to score their own results is the rule rather than the exception. Self-forecasting creates many problems AI was built to solve. Savvy reps can be expected to game the system. They can “sandbag” the forecast by underestimating the amount of revenue they will bring in, or “blue sky” the forecast by overestimating the amount of business they will close. Either way, they make the sales manager look bad.

Forecasting is also tricky by nature because it changes every day.  Deals slip, die, and come back to life continuously. Accurate forecasting is even more demanding when upper management surrenders to their anxiety and demands constant updates because they don’t like to be surprised.

What if forecasts where based on actual data? If forecasts tracked actual sales calls, follow up calls, web activity, and economic changes (among many other factors) they would be a lot more objective. Forecasts would be scientific. Best of all, what if the sales manager could get the forecast without asking the sales rep. To put the icing on the cake, what if the forecasting algorithms could learn from past quarters in a process called machine learning and get better and better over time? This is what AI promises.

Named Account scoring – Some accounts show up ready to buy. These accounts are so rare they are called “bluebirds.” On the other extreme, there are large, established, complex enterprises that spend money year-in-and-year-out. Because of their size, named accounts are prone to macroeconomic cycles, and can waste a lot of seller’s time and never buy in down years.  What if there were a way to boil down all the complexity of these named accounts throwing in economic and government data to objectively decide where to spend your organization’s limited resources best using predictive analytics?  This is what AI promises.

Territory optimization – Optimizing sales territories is a constant challenge for sales managers and often delay the start of annual sales.  Sales territories are usually either too vast and result in lost opportunities, or too small and result in wasted effort. Like forecasts, sales territories are subject to constant change. Factors such as market structure, competition, and demographics that should trigger territory changes are often overlooked because of their complex interactions. What if there was a way to optimize territories fairly, objectively, and quickly using deep learning? This is what AI promises.

Conclusion

Of course, the benefits of AI are not limited to sales managers. In the next part of The Benefits of Artificial Intelligence, we will explore how AI algorithms such as lead scoring, opportunity scoring, opportunity next best action, recommendations based on market signals, automated response based on interactions, and conversational AI can all help the rep do a better job of selling.