Over the years, I’ve had the opportunity to work with a number of companies that lie at the intersection of audiences, data, and technology. As time has passed, many of the challenges associated with using data to reach potential customers have been solved. Amassing audience information? Check. Recognizing a prospect across platforms and devices? Check. Integrating information and insights into a range of sales and marketing technologies? Check, again. Many of the fundamental problems of data-driven marketing have been recognized and addressed.
Now, we’re dealing with harder problems. Which isn’t to say the earlier challenges were easy, but rather that the new problems weren’t initially foreseen. For example, now that marketers have access to so much data, how can they evaluate its quality? How is data quality maintained over time? How can it inform smart planning, support decision-making, and fuel marketing programs that are engaging but not creepy?
Much of the thinking around using audience data comes from the B2C world, whose brands and marketers were among the first to see the potential for reaching and engaging customers online and through social channels. That is rapidly changing and more B2B brands are not only thinking about their customer data, but are also rethinking the ways they put that data to work.
Companies like VanillaSoft are making it easier for inside sales teams to use social channels to reach business buyers - and set up a cadence and process for efficient interactions. Even just a few years ago, the idea of reaching a B2B purchaser via SMS or Facebook or Twitter would have raised eyebrows. Now, they have become part of the daily routine and are recognized as valid channels for engagement.
This type of engagement depends on having accurate contact data, the type supplied by companies like DiscoverOrg. They do a great job of constantly vetting and refreshing their information but contact is just one piece of the puzzle; what about the rest of a marketing team’s data? That data also needs to be evaluated and refreshed on a constant basis, while also bearing in mind customer privacy, their preferences for engagement, and what approaches are most likely to be effective.
The challenge is that the current volume and velocity of data simply can’t be managed manually. To be successful, marketers need to rely on AI and machine learning to do the heavy lifting. While in its early stages, this approach is already paying dividends to forward looking marketing organizations.
These systems can do so much more than simply ensure data quality. Technologies, like those from Zylotech - a self-learning customer data platform that enriches audience data, predicts purchase behavior, and helps increase sales - are able to ingest, normalize, append, and segment customer data in new and interesting ways. These platforms are becoming incredibly sophisticated, transforming audience data into customer intelligence and putting that intelligence to work for sound business results.
That jump is a game-changer. For the first time, B2B marketers have an intelligent virtual ally in their corner. This doesn’t mean that marketers can take a hands-off, automated approach - not by a long shot. Marketers need to apply their own intelligence and creativity to develop campaigns and programs that harness machine learning and AI while preserving customer trust. It’s a fine line and one that is constantly shifting. Thankfully, there is a feedback loop that allows marketers to see what’s working or what might be causing prospects to flee.
AI can learn those business engagement boundaries, but human intelligence needs to trace their outlines for the AI to learn and operate effectively. That’s a higher level function for both marketers and AI. It may sound daunting, but it will soon come to seem obvious. Machine learning and AI - coupled with our own creativity and intelligence - will have a huge upside and are poised to rewrite the rules of marketing.
Greg Peverill-Conti is a Zylotech contributing writer.
If you liked this post, check out our recent blog post: How AI and machine learning are impacting B2B: 3 great use cases for CDPs.