|Solutions Guide||Summary of Charts||Research Report|
Sales Mastery’s AI-for-Sales Solutions Guide (released 6/25/19) profiles 130+ applications from over 200 researched, with another 55 more “to be evaluated.” Today, there is no fully integrated AI solution to augment the performance of sales organizations across the full Customer Lifecycle Management framework. As a result, we categorized solutions according to the aspects of sales/sales management that early adopters are looking to augment and enhance.
AI-for-Sales Optimization Categories
- Market Segmentation/Sales Intelligence Analysis
- Sales Process/Training
- Lead Generation/Management
- Appointment Setting
- Stakeholder Engagement
- Content Management
- Sales Conversation Analysis
- Solution Configuration Management
- Forecast Management
- Key Account Management:
The report focuses on those firms have or are in the process of implementing AI-for-Sales (160 respondents). Unsurprisingly, Lead Generation/Management topped the list (51% had implemented), followed by Forecast Management/Sales Analytics (42%). Sales Intelligence and Sales Activity Analysis tied for third (39.6%), followed by Sales Coaching Support (32%) and Content Management (31%). The numbers fall of further for the remaining categories. (Totals can exceed 100% because respondents were asked to check “all that apply” for solutions being implemented.)
The chart below shows, in order, the benefits of these various AI-for-Sales initiatives.
What is surprising about these results is the leader: Increased Revenues/Sales Person. Over the past several years, when asked the benefits of implementing CRM, increased revenue was always in 10th or 11th place!
Compared to previous studies we’ve conducted on emerging sales technologies, the initial study data are more positive than we have ever seen. But the experiences of firms that have firsthand knowledge with these solutions, even at this nascent stage of applying AI to transform aspects of their Customer Lifecycle Management, cannot be ignored.
The conclusion from these early adopters: “Do It or Get Left Behind.” This was, by far, the most repeated suggestion. A key driver behind this is that with machine learning, AI technology will contribute to these solutions becoming more effective over time. This gives early adopters a competitive advantage over slow movers that may prove sustainable over time.