Artificially Intelligent: Good Data. Poor Execution

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    Kismet Research Robot, MIT Museum -  Creative Commons CC0 1.0 Universal Public Domain Dedication

Kismet Research Robot, MIT Museum -  Creative Commons CC0 1.0 Universal Public Domain Dedication

Artificial intelligence and machine learning have made great advances in the understanding of customer actions in retail markets, consumer products and services, and online transactions. With large numbers of discrete interactions and a structured buying process, predicting group behavior can be quite precise in guiding marketing, as well as sales planning and investments. Unfortunately, extrapolating these same techniques to the world of low-volume, high-value, complex enterprise sales is challenging.

The enterprise buying process is complicated, hardly ever the same twice and dynamic human interaction creates an infinite number of possible scenarios. Lead scoring and predictive analytics have been applied in this setting to prioritize leads and opportunities or forecast potential revenue, but they can take you only part of the way toward analyzing and winning enterprise deals.

What gets in the way? Well, to net it out, humans.

Using a CRM system requires data entry by humans. No getting around that. Common problems are poor data quality and completeness, which come from the constraints of a complex sales process and over-engineered CRM systems. Requiring data collection that does not help the sales team win deals or drive efficiency, leads to bogus, unreliable information. It also defeats predictive analytics and analysis.

To traverse the last mile between what a machine can do for us and being fully equipped for enterprise sales, you must focus your CRM system on your sales people.

The first step is to talk to your sales people. Not executives, not sales managers, not consultants, but the folks who must use the system day in and day out. They have the answers. They know what they need the system to do to give them the desire and motivation to improve the quality of your CRM data.

The second step is to get as much as possible of the business of selling back into your CRM system. If you are not forecasting, performing pipeline review calls, reporting or coaching your sales people in your system, then all you are doing is telling your team that the system does not matter that much.

The third step is to really give them something of value back. Only do this when you have completed the first two steps. Give them something that they would not expect. Here’s an idea that has worked for our clients. Pay your sales people faster. Yes, you read that right. Pay them their commissions faster. Maybe easier said than done in some cases, but get them something of true value.

So, what can you expect from these efforts? We recently worked with a $200M technology company to show them how improving CRM usage and following an integrated sales process in just their North American sales can help them increase win rates from 10.9% to 23%. This would give them an incremental revenue lift in just this one group of around 44%.

In the enterprise space, artificial intelligence cannot make up for gaps in any of these areas. Each opportunity has a story with a unique journey and cast of characters. We look forward to the day when these stories can be reliably codified and analyzed. In the meantime, we are dependent on the skills of the sales team and the processes, tools, and training we deploy to support the sales team.