Putting AI to work across your customer data strategy

Early this year, Salesforce revealed that 47% of the 7,000 marketing executives it surveyed planned to increase their use of AI. In fact, of all the technologies listed in the survey — AI, social media tools, analytics tools, automation and voice technology — AI adoption beat out all the others.

AI use cases stretch across a broad spectrum of marketing applications, but customer data platforms are one area where the technology can dramatically impact revenue goals.

The four questions AI can answer

“If we could wave a magic wand, what do we all really want?” asked Blueshift’s Chief Growth Officer John Francia during his Discover MarTech presentation. He summed it up in four questions:

• Which customers are most likely to convert?
• What is the most relevant content that would drive these customers to engage with the brand?
• When is the best time to get in front of those customers so that they can make a decision?
• Where are customers most likely to respond and take action?

“If we could figure those things out — who, what, when and where — we would have it right. We would be able to do what we want to do as marketers,” said Francia, “Once you pull all that together, and get a unified view, then AI actually can answer these questions for you.”

To use AI effectively within the customer data platform, Francia says it starts with building a customer profile based on first-party data.

Marketers biggest obstacle: Pulling together the data

Salesforce’s survey found that the median number of data sources used by marketers this year is ten — and will likely grow to 12 by next year. As data sources become more spread out, the more difficult it becomes to build a single view of the customer. Blueshift’s own data found that 51% of marketers do not store critical customer experience data in one platform.

“A lot of people have customer data in a variety of places, which makes it really hard to make good business decisions,” said Francia. Datorama product marketing manager Emily Hoffman agrees. She says marketers are up against three fundamental challenges: Lack of a unified view across all of the marketing investments; lack of real-time insights; and, lack of alignment across teams.

“As a result, marketers are struggling to answer some critical business-driving questions,” said Hoffman.

First-party data is key

As Salesforce’s study made evident, marketers are using multiple data sources, including data warehouses, platforms tracking both transactional data and non-transactional data, email activity, desktop and mobile browsing, third-party data and more.

“How do we prepare our data for AI?” asked Francia, “It really starts with that first-party — you need to leverage your customers and create profiles based on them.”

For organizations beginning their journey into building customer profiles using first-party data, Francia says to focus on transactional data and browsing history first and move into adding activities like email opens rates and click-through-rates.

“If you can just get those things in there and build a profile with that information, you’re well on your way to using AI to actually enhance the experience and drive real results.” said Francia, noting how AI can help filter the noise to find the signals.

Top performers are adopting AI at higher rates

In Salesforce’s study, the 7,000 marketers surveyed were divided into three groups: High performers, moderate performers and under-performers. Seventy percent of the high-performers had a clearly defined AI strategy compared to only 35% of the under-performers.

Overall, 84% of the marketers polled by Salesforce reported using AI in some aspect of their marketing — up from 28% only two years ago.

“Whether this surge in marketers claiming AI use is due to net new adoption or increased knowledge of the role AI has played all along is unknown. In any event, marketers are turning to AI for various use cases, with personalization, segmentation, and deep data insights being particularly popular,” reports Salesforce.

Blueshift’s customer data platform uses AI across four key predictive functions: segmentation to identify high and low intent customers; recommendations to determine content; engaged time and channel engagement. A Forrester report on Blueshift customers found that, over a three-year period, AI drove the biggest lift in incremental revenue.

“AI can really drive the revenue and the results that your company needs if you just put it in the right place,” said Francia, “When you focus on the right customer, with the right message, across all of your mediums at the same time, you’ve got a really big impact on your business.”