When SAP wanted to create a truly bespoke experience for readers of its content websites, the enterprise software company turned to machine learning. David Jonker, SAP’s vice president of thought leadership marketing, spoke with eMarketer’s Jillian Ryan about his team’s journey to implement artificial intelligence (AI) technology and why it’s an essential part of the company’s content marketing strategy.
How have you expanded the use of technology specifically for content marketing at SAP?
I have a small team that works on how we use data, machine learning and predictive algorithms to improve our content marketing efforts. We've been building a number of systems that use machine learning to analyze our content. For example, on our thought leadership website, the Digitalist Magazine, we’re analyzing all of the content and mapping every article’s relationship to one another based on the themes within those pieces of content.
It sounds like you’re assigning affinities between all of your content assets, but what’s the larger goal behind this investment in AI technology?
We’re aiming to personalize customer engagement through content. We’re at an early phase of the project. Visitors don’t see it in action just yet, but the goal is to let our audience find more relevant articles based on their behaviors and search history.
If people show up to our site and the article they see isn't quite what they're looking for, we could lose them.
Through machine learning, we’re building more sophisticated algorithms to understand our published content and match it to people’s interests. If people show up to our site and the article they see isn't quite what they're looking for, we could lose them. We want to make sure the content we deliver to them is as precisely targeted as possible. The better we can target the content, the more engaged visitors will be—and the more satisfied they'll be in the education they received on issues they're trying to address.
What was the genesis behind this development in SAP’s marketing technology?
Our sales team was asking how the marketing department could provide prospects with a more tailored experience. If we know a certain customer is interested in a particular topic or issue, can we make a landing page that’s relevant to them? A standard recommendation engine doesn't work for this level of personalization, so we spent some time dreaming up a new way to build machine-learning algorithms that would make recommendations and tailor content.
A standard recommendation engine doesn't work for this level of personalization.
One of our clients, the Toronto International Film Festival, had a similar problem and we built this technology for them. The personalized experience more than doubled performance results and engagement for them, so we figured we could test it for our purposes.
Is the machine-learning mechanism only used for content delivery, or do you also implement AI to create the assets?
The machine-learning side tinkers with how we deliver content. We don't use it to drive the content creation.
We have two approaches to content creation as it relates to thought leadership marketing. First, we’ve very deliberate about curating high-quality original content and long-form research on topics like digital transformation. The second element is community-sourced content. We're building a community of experts, writers and thought leaders in their industries and lines of business to contribute to our content properties.