Salespeople: Ready to Meet Your New Assistant? Prepare for the Machine Learning-Driven Future

Last night at dinner, a tech-industry pal of mine was trying to puzzle out why the term “artificial intelligence” had suddenly become the in-vogue buzzword of the month. I pointed out that Marc Benioff has telegraphed that an A/I-like thing called Einstein will be the centerpiece of his Dreamforce keynote. That still didn’t satisfy him. “Why artificial intelligence?” he demanded. salespeople-ready-to-meet-your-new-assistant“Well, first off, we’re going with artificial intelligence because we don’t need artificial stupidity – there’s already plenty of natural stupidity,” I said. “But we also have a need to understand and employ a lot of data, fast, and in the most effective ways – and sales and marketing need the assistance in order to use that data correctly.” This did not satisfy him either – but it’s the truth. The next few years will see the emergence of not artificial intelligence, but what I prefer to think as assistive intelligence, enabled by machine learning. It will not replace the sales person, but instead amplify and accelerate the salesperson’s ability to understand and employ data – not just to improve productivity and boost performance, but to influence the customer experience for the better. We’re already seeing this in the form of things like Amazon Echo, Cortana and Siri. Almost 88 million devices with voice-activated functionality will be shipped this year, according to Strategy Analytics. That will rise to 347 million shipping in 2020, at which time some 970 million will be in use around the world. And, if the past is any indication, salespeople will start seizing on the technology. Once they’re used to it in their personal lives, it will inevitably start to play a role in their sales lives. That’s how smart phones, social media and even CRM (in the form of contact management software) became part of the sales landscape: salespeople experienced the power of the technology, and then began to notice a gap between the technology-enabled experiences they had at home and the lagging experiences they had at work. That creates what we like to call an “experience gap;” the wider that gap becomes, the more rapidly (and disruptively) it will be filled when conditions are right. As the interface for the fast transmission of some types of data changes from a display to the spoken word, it is not hard to imagine what that could mean for a salesperson: a spoken-word guided selling system in CPQ; the ability to interact with sales management systems while in the car; the rapid generation of new types of reports based on criteria spoken to the assistant by a manager, then viewed on a screen. The next level would be the assistant as a partner: suggesting sales tactics, prompting the salesman about content the prospect should receive, or prioritizing sales calls based on deal stage, deal value, and factors in marketing data that suggest likelihood to buy. This can all be enabled through machine learning – which discovers patterns associated with outcomes –automatically. The next stage is to alter the patterns based on context; when the framework the data exists in shifts – new competitive products, customer behaviors, economic conditions, etc. – the patterns must shift as well. All of this requires access to data – and not a single data set, either, but a comprehensive collection of data covering all aspects of the buying and selling process. Customer data from marketing automation, performance data of the sales organization (including data about commissions, sales performance, quote generation, content performance and other data), external data about the market, and even predictive data (such as the projected effects of changes to the sales process or product mix). Applying machine learning to one set of data relating to the sales and marketing process is neat, but it will be limited in its impact and best. Applying machine learning to all data about the sales process will be ground-breaking – it will be as if every salesperson has an assistant at their side to help them make decisions and to arm them with content, upselling and cross-selling suggestions and access to content virtually instantaneously. The constant chatter in sales management circles is all about how we turn “B players” into “A players.” This may be what takes “B players” to today’s “A player” level. However, it will not supplant selling skills; “A players” will still be better at using assistive intelligence than “B players.” What it could create, however, is a dramatic imbalance for the companies who implement it first, who could dramatically tip the competitive scales in their favor vs. their competitors who are latecomers to the technology. We may well see a new sales technology arms race as machine learning is combined with sales data. But it would be unwise to start with the assistance system (the sales equivalent of Echo, Cortana or Siri). That new interface – with all the benefits of ease of use, speed and flexibility – is the way we use a new layer of analysis, but that layer depends on a sound foundation of data. If you’ve worked with sales technology for any amount of time, you know that the place a sound solution must start is with the data. Assistive intelligence depends on having data in good condition, with the most recent and accurate data given priority, and in a format that makes it compatible with whatever machine learning-enabled system you wish to use. Having your data accessible in an assortment of disconnected repositories will deprive your assistive intelligence solution of the ability to make the best decisions and will cause real adoption problems by making it harder to close deals rather than easier. Stephen Hawking famously said, “The development of full artificial intelligence could spell the end of the human race.” The emergence of sales assistive learning will almost certainly spell the end of selling as we know it now – and could spell the end of the line for businesses who don’t appreciate the power in the next wave of technology. Want to learn more? Hear more about the promise of machine learning and A/I – along with other trends in education, motivation and innovation – at our Dreamforce session “3 Ways to Make Your Sales team Great Again,” Oct. 5 at 11:30 a.m. at the Yerba Buena Center for the Arts. Register here!

By Chris Bucholtz | September 30th, 2016 | Dreamforce

About the Author: Chris Bucholtz

Chris Bucholtz

Chris Bucholtz is the content marketing director at CallidusCloud and writes on a host of topics, including sales, marketing and customer experience. The former editor of InsideCRM, his weekly column has run in CRM Buyer since 2009. When he's not pondering ways to acquire and keep customers, Chris is also an avid builder of scale model airplanes.