According to Accenture, the new world of B2B selling requires several imperatives to survive and thrive. The first of these imperatives is to “rediscover your customer.” In this blog, we’ll explore the prevailing theme that historical customer insights are irrelevant, and how to gain real insights into what your customers want now.
This idea of disregarding historical customer insights is a scary one. Sales and marketing leaders and salespeople have been doing this for their entire careers. Now we’re told “throw it out.” Well, you might ask, “What do we do in lieu of historical insights? How do we get our arms wrapped around the ‘now’?”
As Accenture suggests, the leaders of tomorrow are modifying their organizations’ sales priorities and are using predictive analytics and AI engines to derive key customer insights. But how? Which AI engines and what data are being used? We all want insights and are becoming increasingly reliant on data, but are there rich sources of data that we’re missing? Are the best, most useful insights closer than we think?
We offer a resounding “yes” to that question. Yes, there are rich sources of data that lead to incredibly precise insights: They are the very people that make up your sales force. Your salespeople. They are the ones interacting with your customers. They are the ones experiencing the changes that take place over time. There is gold to be mined in the heads of your salespeople; you just have to find a way to get at the information so that you can analyze it and use it to make better decisions.
Accenture suggests that we need to equip our sellers with situational awareness, enabling them to better respond to changing customer conditions. Organizations can only do this with a mechanism that extracts vital customer behavior details out of the minds of their sellers and uses that information to develop recent, relevant insights to changing customer behavior.
The best, most accurate way to accomplish this is by examining deal-level data—real details related to real deals your sellers are pursuing. So, do we just extract the information from our CRM? Well, to say that CRM data has questionable hygiene is an understatement. This is a problem that organizations have been struggling with since the introduction of CRM. Sellers are notorious for putting the minimum amount of deal level information into their CRM when they log their opportunities. It may be getting better over time, but it is still falling far short of adequate. With highly questionable CRM hygiene, where do organizations go to mine the data? To their sellers.
To uncover meaningful customer insights, you must develop a reliable way to gather very detailed deal-level customer information. You need a way to pull information out of your seller’s heads that favors fact over opinion, allowing you access to details that can be used to produce insights. This would give you a reliable way to determine the best sales approach to lead to a win in each buying situation. And it is quicker, easier, and less expensive than you think.
Our research has revealed that there are over 25 buying factors that influence buyer behavior. That’s a lot to consider. What organizations need is a way to narrow the field and identify what matters most. That is where AI and machine learning come into play. By gathering seller-provided forensic information of won and lost deals, machine learning can be used to spot patterns in buying factors and how those factors influence unique buying situations. Sellers have very detailed information in their heads about deals they’ve pursued. Getting that information out of their heads and into a database is the first start.
October 18 at 1:00 p.m. ET
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