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We live in a data-saturated world where a great many of our interactions with other humans happen online. It makes sense then that one of the most human of business activities — sales — is currently undergoing a digital renaissance. While the sales function has historically relied on metrics, today there is far more sales-centric data, and far richer data, than ever. It comes from social media, from website interactions, and from A/B tests, just to name a few.
To help make sense of all the available data and to improve sales effectiveness and efficiency, organizations are turning to machine learning. Smart machines are becoming trusted sidekicks in sales departments as they make opaque processes more transparent, provide analysis to inform decision making, and offload low-value tasks.
In our survey of executives at 168 large companies with at least $500 million in annual revenue, 76% of respondents said they are targeting higher sales growth with machine learning, the kind of artificial intelligence software that continuously learns from big data and optimizes recommendations in real time to sales staff. Moreover, more than two out of five companies have already implemented machine learning in sales and marketing.
Our research shows large companies are applying machine learning to sales processes along three dimensions, each of which adds algorithmic rigor to human intelligence and intuition, creating a dynamic new formula they hope will boost sales. The first dimension allows for a scientific approach — with data and clarity of process — in sales interactions. The second enables more data-driven experimentation within a sales and marketing environment. The third uses science to create more time to sell, that is, by automating the administrative tasks that get in the way of managing accounts, finding leads, and closing deals. Taken across these three dimensions, machine learning is creating speedier, more scientific processes for generating sales revenue.
Before machine learning came along, static databases, analytics from historical data, and experience and instinct steered execution — with performance improvements coming in set increments over time. With machine learning, real-time data can drive actions and process change along a continuous path. Hypotheses can be quickly formed, tested, and revised, enabling a new kind of workflow that can dramatically outperform previous ones.