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Stephen Hawking famously said, “The development of full artificial intelligence could spell the end of the human race.” Before that happens, however, the emergence of AI-driven sales technology will almost certainly spell the end of selling as we know it now — and could spell the end of the line for businesses that don’t appreciate the power in the next wave of technology.
Imagine a digital sales assistant that responds to verbal commands. Those commands could be simple — “call the next most qualified lead in my queue,” for example. Or they could be complex — “provide an analysis of which up-sell offers have worked best for prospects like the one I’m working on.” Such assistants — when connected to large data sets of sales and marketing automation data — will allow salespeople to work at the speed of their thoughts, rather than at the speed their systems allow them to work.
Already, the consumer space is seeing an influx of products 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.
Except within a few trailblazing companies, the revolution will start with individual salespeople. That’s how smartphones, 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 at home and then began to notice a gap between those technology-enabled experiences and the lagging experiences they had at work. That creates 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 or a guided configuration function to configure price quote (CPQ) solutions, the ability to interact with sales management systems while in the car, and the rapid generation of new types of reports based on criteria spoken to the assistant by a manager and then viewed on a screen.
The next level would be the assistant as a partner — suggesting sales tactics, prompting the salesperson about content the prospect should receive, or prioritizing sales calls based on deal stage, deal value, or 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 – due to new competitive products, customer behaviors, economic conditions, and more — the patterns must shift as well.
All of this requires access to data, and not just a single data set. Applying machine learning to one set of data relating to the sales and marketing process is somewhat useful, but its impact will be limited. Machine learning must be connected to a comprehensive collection of data covering all aspects of the buying and selling process. To provide answers that are current and correct, AI must draw on customer data from marketing automation, performance data from the sales organization (including data about commissions, quote generation, sales 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).
When this is done successfully, the results will be groundbreaking. It will be as if every salesperson has an assistant at their side to help them make decisions and to arm them with planning advice, upselling and cross-selling suggestions, and access to content, almost instantaneously.
But the interface will be the most visible and, paradoxically, the least important aspect of an assistive sales solution. The data makes the interface a powerful tool instead of a novelty. Success depends on having complete data, up-to-date data, with the most important and accurate data given priority, and in a format that makes it compatible with whatever machine learning-enabled system sales is using. Having data accessible in an assortment of disconnected repositories will deprive the assistive intelligence solution of the ability to make the best decisions and will cause real adoption problems by making it harder, rather than easier, to close deals.
We may well see a new sales technology arms race as machine learning is combined with sales data. But it would be unwise to place the initial emphasis on the assistive technology. That new interface — with all the benefits of ease of use, speed, and flexibility — depends on a sound foundation of data. Success will come far faster for companies that prepare their sales data sources for the AI future.
Leslie Stretch is the CEO of CallidusCloud, a cloud-based sales effectiveness company.
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