The last few years have given rise to ever increasing discussions on how the use of predictive analytics and machine learning will transform the profession of selling. It is quite compelling to consider how this technology may take root and how it might impact the profession of professional selling.
First let’s consider what we mean by predictive analytics and machine learning. Real AI is intended to “think” on its own as opposed to a technology like Alexa or Siri which is just trained to recognize specific language or words and translate this to a command. True AI are systems are intended to actually understand context and nuance, and to make decisions and provide insights that are the result of reasoning and analysis. Further, the intent of machine learning is in fact literal; to make better decisions as time goes on; to in effect learn from decisions today and make better decisions tomorrow. In this way, machine learning strives for what humans strive for, sometimes successfully (and in my case, sometimes not), to learn from our mistakes and make better decisions as time goes on.
So how would all of this work in the world of sales? Most of us have experienced predictive analytics during our interaction with platforms such as Amazon, eBay or Spotify. These systems understand our buying behavior and preferences well enough that they become quite adept at making suggestions on other related or similar products, artists and concerts we might enjoy. Sometimes we buy based on these suggestions. In this fashion, these technologies drive revenue growth. But this AI in the B2C world. How might this work in the more traditional B2B world of complex sales where professional salespeople are an important part of the purchase equation?
Here are some of the ideas being suggested.
- Improving the likelihood of contacting someone. A technology like InsideSales.com will for instance, suggest to a seller what are the best days and times to reach out to a potential contact to schedule a meeting. This can improve meeting counts and assuming all things are equal, more meetings will ultimately translate to more revenue.
- AI technology is able to capture information about buyer preferences. What pagers on your web sites are they looking at, what email marketing has been opened or forwarded, and does any of this behavior occur elsewhere within a targeted client. If this can be done, and it can, a seller should be able to better understand a buyer’s needs, interests, and this should be helpful if put to use in ultimately taking a buyer through the purchase journey.
- AI can be useful in better qualifying leads which means that salespeople can target which accounts, and which leads have a better probability of closing and spend time and focus on those. More efficient use of time should lead to more revenue.
- AI should be able to listen to key social channels in a way that salespeople just don’t have the time to do so. Listening to comments about products and services would help the seller where he or she is meeting or exceeding expectations, and where they are falling short.
- One of the ways I think will ultimately be of most benefit to organizations will be to use predictive analytics and machine learning to illustrate how the specific behaviors and activities of high performing individual are different from medium and lower performing individuals. The truth is, higher performing individuals do things differently than others. Sometimes what they do is obvious, but sometimes this gets lost or is misunderstood. An organization that can really develop a detailed and fact-based understanding what top performers do different and better can use this knowledge to train and coach others to replicate this behavior. Predictive personality assessments have certainly proven this point; different sales roles for different companies require different behavioral competencies.
- AI should also measurably improve organizational capacity to get a new salesperson up to speed. Today, the standard approach is to look at the information that is in the CRM related to a specific account. However, CRM systems contain only what salespeople have entered into the system. Imagine, if by using analytics, a new recruit could see a detailed history of all communications between everyone in both the buyer’s and seller’s firm as well as a network analysis of all parties involved in both firms. Clearly, a new recruit would be able to come up to speed far more quickly, dramatically accelerating time to productivity.
- Finally, AI is useful to improve forecasting. While the ability to have more accurate forecasting in my view provides a benefit disproportionate to the firm as compared to the seller, both do benefit. AI can look at velocity of communications between a buyer and seller and can compare this to other transactions that have closed (or not), and over time can provide considerable insight about the probability and timing of when specific deals are likely to close. Having better forecast data should free up sales managers to do more of what they should be doing to improve performance – coaching and leading their teams to better outcomes.
While AI and machine learning will bring substantial benefits to firms that successfully deploy this technology, some things will not change. Salespeople will not be replaced by machines. Salespeople will be provided with insights and understanding that will allow the good ones to become better and improve their ability to serve their clients.