AI/ML in Sales: How to Get the Right Data to Succeed
Today’s business environment is becoming increasingly competitive. As a result, sales organizations need deeper insights and access to data to stay ahead of the competition. One way sales teams are getting a competitive advantage is through artificial intelligence and machine learning (AI/ML).
However, while many companies are looking into and adopting AI/ML technologies, success relies on more than just the algorithms within the tools. Organizations need the right data in order for AI/ML to “learn” to be truly effective. To put it simply, machines can’t learn and predict without the data to teach them. With that data, machines have a basis of information to build up from, continue to add additional data, and eventually derive learnings from it.
In all honesty, machine learning (ML) is starting to feel a lot like a buzzword—high on hype and light on substance. Almost any technology article or release today mentions the use or benefits of the latest machine learning and artificial intelligence (AI) capabilities, but nobody really dives into the backend requirements to get the most out of it.
But the truth is that any company can add machine learning to its platform—there is nothing particularly new about the technology itself. In fact, the idea of machine learning dates back to 1959 when computer gaming and AI pioneer Arthur Lee Samuel coined the term. His first test bed was using ML algorithms so that computers could learn to predict the best moves in the game of checkers. What makes machine learning so revolutionary today is the vast amounts ofdatathat is now available and can be analyzed to form predictions.
Advancements in cloud technologies and online systems have brought about the democratization of data processing, opening up a bevy of new opportunities for AI/ML learning and insight. For the first time in history, companies are benefitting from what I call a “data trifecta.” Innovations in data processing have empowered companies with:
These three factors together create an undeniable opportunity for any organization to transform not only its offerings, but also how it serves customers.Amazon and Netflix are great examples of effective AI/ML use.
Over the years, Amazon has learned about its customers, starting with the kinds of books they liked to read and later moving to merchandise, movies, and more. Its platform has learned user behavior patterns, enabling it to predict when and what customers might buy and provide recommendations for other products to consider. This is only possible because Amazon has the access and rights to a vast treasure trove of data.
As Netflix has grown from mail-order tv and movies to a full-blown streaming service, it has gathered subscriber viewing behavior. It has used that data to serve users with related movies and tv series based on what viewers have previously watched and/or rated. Netflix has even gone so far as to provide a percentage rating system to show viewers the likelihood that they’ll enjoy the suggested movie or tv show.
According to a McKinsey AI report, Netflix applied ML to their proprietary data to improve customer search results, avoiding what could have amounted to a potential $1B revenue loss annually due to canceled subscriptions.The same is happening in the enterprise world.
