How Pizza Hut aims to turn weather forecasts into sales

It’s not enough to be first — just ask tech giants like Netscape and Friendster. And Pizza Hut.

The deep dish chain was first to online food ordering — an industry projected to hit $126.91 billion this year. But it didn’t keep pace with innovation and was later eclipsed by competitors. Now, nearly three decades later, Pizza Hut knows it’s time to get serious. The company is looking to data, analytics, and AI to learn more about its customers in order to boost digital experiences and sales.

So while the other aforementioned early entrants are no more, Pizza Hut is still spinning up pies, and there are lessons to be learned from its game of catchup. To find out more about the company’s approach to data, its partnerships, and why it chose build over buy for its machine learning technologies, we chatted with Tristan Burns, Pizza Hut’s global head of analytics.

This interview has been edited for brevity and clarity.

VentureBeat: Pizza Hut was fairly early to online ordering and digital customer experiences. How has the vision and approach evolved over time?

Tristan Burns: You’re right — Pizza Hut was the first brand to create an online ordering experience. That was back in 1994, in California. You could submit an order online, it would end up in a store, and it’d be prepared and sent to your house, which was pretty cool. And while Pizza Hut was quite early to the ecommerce game, I think no one would mind me saying we were kind of eclipsed by Dominos in the 2000s. They came out swinging, saying they were a tech company that makes pizzas and with some pretty innovative technologies. Now Pizza Hut Digital Ventures, the organization I work for that is specific to Pizza Hut International, is taking a tech-first approach to redesigning, reimagining, and recreating our ecommerce capabilities. We’re in the process of building and scaling out some pretty robust solutions. It’s a very, very data-centric and very customer-centric approach.

VentureBeat: Are you using AI and machine learning as well? What does that technology look like, and what role is it playing in the organization, specifically in regards to personalization and customer experiences?

Burns: Definitely. We’re in the early stages of an AI journey. And part of our machine learning program is to ingest customer behavior and a little bit about who customers are, where in the world they are, what the weather might be at their location, and then surface relevant product recommendations to them during their experience. We’re still early days in that process, but we’re building in-house capabilities to own it and with the hope that we can do a better job and have more specific outcomes. I think there are limitations when you use an off-the-shelf platform, and because we’re global and working across multiple different regions of the world, we have to be pretty nimble in how we implement and use AI. So those nuances and specifics mean we’re going to have a lot more flexibility if we own the experience and the platform.

VentureBeat: What are the more recent challenges the company’s been facing in terms of data and analytics? What have you been looking to execute or improve?

Burns: So we’re very much trying to improve our daily decision-making, and conversion rate optimization (CRO) is a huge part of that. We’re probably in the early to mid stages of a new CRO-led approach to designing our digital experiences. We have a lot of stakeholders, so we’ve had a lot of input from various corners on how the experiences should work and how we should go about building them. But we’re in a position now where we have to be really conscious of data and what the customer needs, and experimentation is a really big part of that for us now going forward. We’re becoming a lot more mature in making sure that we test and validate with data and user research.

VentureBeat: I know you tapped digital analytics company Contentsquare as part of these efforts. Why did you seek an outside partnership? What is it enabling you to do?

Burns: It’s been almost two years with them now, and I saw that the opportunity and the capabilities of what they were trying to do would just be so effective in getting to the bottom of the problems we were experiencing. We had a lot of what the problem was, but we didn’t have a why. And Contentsquare gave us the opportunity to kind of look right into the customers’ behaviors and get a far better understanding of what they’re doing on our platform. Now it primarily supports our CRO programs, but it also allows us to come up with and test ideas really efficiently. We can see customers do something unexpected or that might not be optimal and then run tests to see if we actually solved the problem.

VentureBeat: What more specifically are the capabilities you’re referring to? And can you give a specific example or anecdote of how you use the technology and the results you’ve seen?

Burns: Personally, I’m a big fan of Contentsquare’s page comparator, where you can superimpose the click rate, scroll rate, and attractiveness rate metrics over top of your experience. And one great example was we saw that customers were not immediately clicking on our deals page. And it became clear they weren’t sure where they needed to click, or even if the deal cards themselves were clickable. We hypothesized it was because we didn’t have a CTA (call to action), and so we ran a test and saw a phenomenal increase in the rate at which customers added those deals to their baskets. We estimated there was an almost $7 million to $8 million uplift in sales if we were to extrapolate the performance we saw over a 12-month period.

VentureBeat: What are the top considerations you found are important when applying data and analytics to customer experiences?

Burns: I think data is fantastic at telling you where you might have a problem and what customers are doing, but I believe you always have to supplement that with user research and insight to really get the full picture. So for any problem data analysis surfaces, we also want to attack it from a different angle. And if we see a problem in both the data and the research, that’s something we should look into solving.

VentureBeat: Is there anything else you think is important to know about all this?

Burns: One thing is the role of data within an organization, and the power tools such as Contentsquare can have to support the democratization and communication of data insight across maybe less data-focused teams, as well as leadership and other stakeholders. Traditionally, I think data people are not seen as being phenomenal communicators — you know, diving deep into a spreadsheet, coming up for short breaks, and then it’s back into the spreadsheets. And we’ve not always looked to hire or looked to focus on fantastic communicators within the data space. But I think that as data takes on a much more central role within organizations, it’s going to be really crucial for companies to think about their data people as strong communicators.