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The sales and marketing industry has been abuzz with talk of predictive analytics, machine learning and artificial intelligence (AI) this fall, especially on the heels of a flurry of AI updates from Microsoft and Oracle, Salesforce’s recent Einstein announcement at Dreamforce, and Google’s unveiling of its efforts in machine learning and AI yesterday.
In all of this hype, I’ve encountered several conflicting definitions and explanations of what AI really means.
Those of us close to the space know that AI, at its core, is actually foundational technology that’s been around in the consumer world for over a decade.
Think of the amazing intelligence behind Google Photos, which uses facial recognition technology to organize your images for you, as well as the highly accurate music recommendations that you get from Pandora based on your likes and dislikes. We see similar examples in major league baseball (remember Moneyball?) and, of course, the fast-evolving world of Uber, Waze and self-driving cars.
As AI enters the enterprise realm — in what Constellation Research predicts will be a $100 billion market by 2025 — it’s important to shift our focus away from science fiction perceptions, and instead look toward the specific business outcomes that AI can produce.
To help cut through the noise, I’ve outlined below four key roles that analytics plays in the sales and marketing landscape. Keep in mind that each of these approaches delivers insights based on sophisticated data processing, modeling and other scientific techniques — all of which are important aspects of AI.
[Read the full article on MarTech Today.]
Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.
