In just the past 12 months, “predictive analytics” has emerged as one of the biggest buzzwords in the B2B marketing space. At its core, “predictive analytics” assumes that powerful algorithms that are crunching lots of user data are able to anticipate future human behavior. When you apply this to sales and marketing, the benefit is obvious: not only are you able to fine-tune your marketing messages, you are also able to predict which customers are actually going to react to specific offers.
This obviously has a great deal of appeal for sales teams that are under pressure to deliver more, faster. And, according to Gartner, by 2018, half of all global enterprises will rely on some form of predictive analytics to guide future decision-making. Still, there are three important things that B2B sales teams need to keep in mind about predictive analytics:
#1: Historical data is no guarantee of future performance
Most of today’s predictive tools rely almost extensively on past data. They analyze past usage patterns, for example, to predict future usage patterns. Or they analyze which customers responded to a particular offer, and then make inferences about future behavior. But there’s just one thing wrong about that premise: historical data is no guarantee of future performance. In short, it’s impossible to predict the future with 100% accuracy. Yes, some trends do seem to persist, year after year. But there are also new trends and changes that seem to appear out of nowhere.
#2: Context matters
Context is especially important when it comes to the task of scoring and qualifying leads. Say, for example, a predictive analytics package analyzes web site visitors to determine which ones are the “hottest leads.” Using past data, it might be possible to track which website visitors downloaded a specific whitepaper, which ones watched a webinar, and which ones were clicking on social media icons for further engagement with the brand. But now consider the context – what if the recent barrage of visitors was somehow out of the ordinary, perhaps due to a competitor checking out your website, or some piece of viral content that is somehow leading visitors to your website for 24 or 48 hours? That might skew the data significantly and lead to flawed conclusions about what’s actually happening.
#3: Computers are still only as smart as the humans that program them
This might change with the current rise of AI-powered machines, but for now, computers are remarkably dumb machines. They can only do what humans tell them to do. That means any miscalculations in an algorithm made by a human user will be repeated and amplified by a machine. That means that any biases or beliefs of the human programmer will be transmitted to the machine. In short, machines are not
infallible because humans are not infallible.
Over time, of course, it’s within the realm of possibility that machines will become incredibly smart and sophisticated as the result of powerful algorithms and super AI-enhanced machines. But until that day, sales and marketing professionals are best off treating any predictions as a suggestion or guide rather than the ultimate truth.
IMAGE: Designed by Freepik
Dane
Dane Cobain is a published author, freelance writer, book blogger, poet and (occasional) musician from High Wycombe, UK. Before going freelance, he worked as a social media marker, now specialising in writing about publishing, technology and marketing.