Examples of AI Solutions Usage
Matt Hellman a Transformation Leader at Microsoft writes in a September 2018 article titled, “How AI is transforming sales and marketing” in The Marketing Journal. A strong case study where a global MNC used AI enabled deal classification, data input enhancement, account analytics, and external data feeds to transform their sales organization in Nigeria resulting in “a substantial increase in sales productivity and forecasted Y-o-Y revenue growth.” A textbook example of traditional analytics techniques revolving around structured data in global enterprise sales organizations. Applications usually cover risk computation, lead scoring, price-bots, external data feeds and sales leaderboards etc. and deliver incremental value for the most part.
Non-conventional AI and its importance in the world of sales
- Popular AIML blog machinelearnings.co writes in an article titled “Applied AI in enterprise sales” that 70% of sales conversations happen over the phone (Voice data volume)
- Conversations are much more content rich. Nuances like sales tactics, competitive tactics can be easily captured in voice interview. (Voice data quality)
- Acquiring voice data is a path of much less resistance when compared with having a salesperson fill in data into a system of record like the CRM (Voice data acquisition)
Voice is hitherto the most underutilized source of data and intelligence, specifically in the world of sales. If data is indeed the new oil, then the voice is the sweet, West Texas Intermediate of the sales world. Inc.com believes that conversational agents and speech recognition are the top 2 AI trends to watch in 2019. We are at the cusp of bringing this disproportionately powerful data source that we call Sales Tribal Knowledge into the ambit of algorithmic treatment and advanced analytics frameworks like natural language processing/generation.
When all else held equal, we believe it is the “Sales Tribal Knowledge”, the nuanced understanding of which sales tactics are useful in a given scenario, and a sense of what will be the competitor’s next tactics determines the deal outcomes.
“SO WHAT?” ASKS THE SALES LEADER
Why does a sales rep outperform her or his colleague in a similar selling situation by as much as 500%?
In a similar selling situation, it boils down to differences in selling behavior. As a sales leader, your goal is sales behavior transformation. And sales leaders know this. The same 2017 Harvard Business Review whitepaper which wrote about many AI-enhanced revenue generations pilots being underway said that “These pilots must be designed differently than pilots for traditional analytics, one aspect being a constant focus on behavior changes required by end users of the analytics.”
As long as there are sales organizations there will always be outperforming and underperforming salespeople. The question is, whether AI can enable rapid learning of highly context-specific sales behaviors that are successful and guide the salesforce at scale? Will this be a more direct way of transforming sales behavior as opposed more indirect methods like sales performance dashboards? And finally, can AI thus minimize the vagaries of sales performance by transforming the sales organization into a self-learning organism?
If today’s sales leaders want to find answers to these questions about AI’s potential to fundamentally change how we sell, they would do well to borrow from AI’s origins and coming-of-age stories. As has been the history of AI, the actual value and transformative impact of its building blocks – when used in the right combination – is much greater than the sum of their individual value. And it is this value that marks a step function improvement instead of gradual progress, in how we function or in this case, how we sell. As is often the case with the evolution of AI, many of the key building blocks of the step function improvement in how we do business are already long in place. Now, it’s about time to exploit AI to actualize their higher order business goals.