The internet is a rich source of information and is used by buyers to research new applications and offerings well before ever engaging a vendor and salesperson. Along with massive growth in offerings, this is a major reason why sales teams are facing increasing challenges to successfully sell and attain targets.
One approach that is being continually trumpeted by vendors is the use of artificial intelligence (AI) for improving sales effectiveness. Using AI in sales is relatively new; the first efforts were made around 2014. These early efforts were primarily around creating a numeric ranking, scoring for leads and opportunities based on historical records of win and loss data, to enable both salespeople and managers to identify those deals worth pursuing. Other early uses were in designing and recommending cadence and playbooks for inside sales teams as to when to contact and what means to use to communicate with either inbound or outbound prospects.
Over time, these initial efforts have spawned additional initiatives across the sales activity spectrum; here, we will examine more recent offerings from vendors.
The early lead and opportunity scoring capabilities were historically based on a measure of whether the lead or opportunity was a good fit, as indicated by win and loss record(s) and primarily drawing from an organization’s internal sales force automation data. More recently this has been expanded to use activity data and timeline progression to measure whether an opportunity is on or off track in terms of the recognized steps that result in a win or a loss, and to analyze voice and text interactions. The analysis of voice and text exchanges between seller and buyer can provide key clues as to what messages resonate and how to deal with objections or competitive vendors. The aim is to provide guidance both in terms of confidence in the probability of the deal being won and also as to what activities and conversations are most effective in winning business.
For inside sales teams, predictive models have been built around understanding the sequences and methods of outreach as well as inbound responses that improve the chances of both making successful contact and creating either a qualified lead or a sales win, if selling using an inside team. One area that many vendors are focusing on is often referred to as “next best action” where AI can do more than tell you what has happened, and actually provide recommendations about future action, linking existing observational information to actionable insights and the actions themselves. Through 2024, Ventana Research asserts that one-half of organizations will deploy AI- and machine learning-based technology that will assist field sales with next best action recommendations.
These topics are covered in more detail in my Analyst Perspective: The Art and Science of Sales from the “Inside Out”.
Incorporating third-party firmographic data, and data such as voice analysis, sentiment and buyer intention, have resulted in newer applications of AI in sales and, increasingly, the broader area of revenue. One such area is in predicting customer churn, which utilizes a variety of data sets such as product utilization, service and support records, NPS and other customer satisfaction scores, and media reports concerning financial and economic health of the customer. Aligned with this churn prediction are also upsell and cross-sell prediction capabilities.
All the above information can also be used to help in understanding traditional sales forecast that primarily focus on new business as well as the broader definition of revenue forecasts that include not only new business, but also look to project revenue from existing customers in terms of both renewals, and expansion and cross-sell. As in many of these examples, AI should augment professional judgment rather than replace it. I discuss more on this in my Analyst Perspective on Sales Forecasting: Have the Process and Technology for a True Revenue Forecast?
Other areas in which AI is playing a role are helping sales teams navigate buyer organizations, identifying influencers and reporting structures as well as indicating where others in the seller’s organization have had success with a particular buying organization, or ones very similar.
Finally, a newer approach focuses less on the opportunity and more on the seller as, at the end of the day, deals rarely close themselves - sellers do. Combining the historic record of opportunities won and lost with profile data about sellers can identify which characteristics mark a seller for success with an organization’s product set, customer profile and geographic location. This is a more data-driven approach, matching seller and buyer characteristics that can be used for both hiring as well as micro-targeting skill training needed to improve performance of the existing sales team. And perhaps in assessing whether certain sellers may be better suited to an account management or customer service and success role. I cover more on this topic in my Analyst Perspective: The Science of Sales Professional Effectiveness.
Many of these vendor initiatives should be reviewed by organizations as the basis of a longer-term strategy of utilizing newer technologies to aid sales teams. But whichever application proves of interest, any AI capability, although of increasing help, is not yet ready to replace the human interaction required in many sales situations and most definitely does not remove the need for focused activities designed to engage and create a trusted relationship between buyer and seller. Your sales team will still need to sell.