originally posted to TargetMarketingMag.com
How well do you know your customers? Sure, you may have a grasp on some basic demographic information and order history, but how well do you really know them? Enough to deliver experiences tailored to their personal preferences? Enough to tell them which “Imaginative Time Travel Movie From the 1980s” they might enjoy next?
Even if you’re not in the business of streaming media, marketers across industries would do well to take a page out of Netflix’s book. The streaming giant has raised the bar on personalization, serving highly tailored recommendations based on any number of data signals — from viewing history to user behavior (browsing, scrolling and search patterns) to time, device and even location that a user is logging in from. The result is a new breed of picky consumer — ones who expect us to know exactly what they’re looking for … even before they do.
Using the data that surrounds our day-to-day marketing activity, and a little bit of math, leading companies are using predictive techniques to better serve prospects and sell more. And while the term “predictive” may conjure up images of a man behind a curtain, a Magic 8 Ball, of late night TV’s own Madame Cleo … magic it is not. Predictive marketing analyzes the data already contained within your native technologies — CRM, marketing automation, etc. — and data from across the public Web to identify and prioritize your top quality leads, accounts, campaigns and marketing activity.
Despite knowing just how influential a well-implemented personalization strategy can be, it’s still a tragically underutilized tactic and, according toone study, 71 percent of companies fail to personalize their Web experiences today. The reason is simple: Data is more abundant than ever, but the ability to process, analyze and derive actionable insights from this always-growing mountain of data is no small feat. While the Netflixes of the world may have the budget and headcount to dedicate to predictive personalization initiatives, SMBs are often left scratching their heads — overwhelmed and unsure of where to start.
In B-to-C, technology marketers can use most often provides Web users with helpful product or service recommendations. Personalization engines can look at a customer’s history to recommend products that algorithms dictate they’d be likely to buy. Based on a combination of past purchases, browsing history and any number of other factors, data is helping B-to-C companies surface the products and offers that are most likely to convert visitors into buyers.
In B-to-B, predictive generally takes the form of highly tailored educational content — targeted to prospects with attributes that indicate they have a high likelihood to buy. Based on how well the characteristics of a prospect align with where a company’s seen success in the past, B-to-B marketers can use predictive tech to engage prospects with content that they know is most likely to resonate. With the ability to know exactly how likely a prospect is to become a customer, sales benefits from the ability to prioritize and personalize follow-up to ensure that they’re addressing a specific prospect’s unique needs.
Netflix is but one of countless services that is transforming consumer preference and expectations. Amazon, Spotify, Facebook and LinkedIn are all examples of experience-focused companies, leveraging big data to predict what users want and tailor their experience in real time.