By Stephen Shaw
Businesses are “drowning in data but starving for insight”. But relief is on the way in the form of artificial intelligence (AI). The ability to skip right to the answers without even forming the questions will be the salvation of marketers. With AI, the analytical load shifts to machine learning algorithms.
Most of the major marketing automation vendors have already integrated AI capabilities into their platforms. But companies also have the option of outsourcing the analytical work to software-as-a-service (SaaS) platform providers that can help them benefit from the technology immediately.
One of those providers is Toronto, Ontario-based Daisy Intelligence, founded by CEO Gary Saarenvirta. The company’s AI platform can determine the best product price points, adjust the promotional mix to minimize cannibalization and identify optimal store locations and layout, saving merchandisers from needing to figure it out themselves.
AI is very much like magic because no one can ever say how it arrives at the answers it comes up with. Yet it offers clear advantages over traditional approaches to data mining and analysis, both in speed and precision, as Gary Saarenvirta explains.
Q: How did you come up with the “Daisy” name?
A: It was inspired by the first song ever sung by a computer back in 1961: Daisy Bell. And of course, that song was famously sung by HAL in 2001: A Space Odyssey.
Q: It’s a perfect name for an AI company. What was the genesis of your business?
A: My goal has always been to use math and science to make companies smarter. Take a retailer with hundreds of locations, a hundred thousand products and millions of customers it’s hard to figure out what’s going on, no matter how many analysts you have. Our vision was to build an autonomous decision-making system using AI. That was 15 years ago.
Q: Half of companies today report that they’re still struggling to create actionable insights. Why is that?
A: Data mining technology has always been aimed at a technical user with a mathematical or statistics degree. Which is why a gap still exists today between data analytics and business decision-making. It’s the main reason why analytics hasn’t become a strategic practice yet. The output of a predictive model is a numerical label or a text label, but what do you do with that? There’s no decision-making process wrapped around that label. If the label says two instead of one, what do you do?
Q: Is the problem a lack of data fluency?
A: Yeah. But what’s missing is a tie back to the P&L [profit and loss]. At Air Miles—fantastic company, it was a formative place for me—I remember every PowerPoint report declaring, “This is a 100% ROI [return on investment]…a 500% ROI…a 200% lift over random”. That was all wonderful, but then the client’s P&L figures wouldn’t move. You saw all of this awesomeness from a statistical perspective, but it wasn’t translating into business results.
Q: You mean data mining is more of a tactical tool as opposed to strategic?
A: Just think about the retail business. Maybe you did a shopper marketing campaign to sell Coke and you sold twice as much Coke this week as last week. But then you didn’t take into account that Pepsi sales went down and that juice sales went down. You didn’t measure cannibalization. You also didn’t measure the forward buying: people who bought two cases this week because they were on sale but who did not buy their usual case the following week. Marketers don’t bother to measure all of the ripple effects because that gets too complicated. I tell my customers “If I don’t move the P&L, then fire me because there’s no point doing analytics if you’re not moving the P&L”.
Q: When you’re knocking on doors, are you talking to the CIO or the CMO? Who do you have to convince of the merits of AI?
A: In retail, we want to talk to the CMO [Chief Marketing Officer] and the head of merchandising. Our goal in retail is to double the net incomes of retailers. We want to turn a 1% industry into a 6% industry. The people who care about that are in the C-suite. Our users are the retail operators, the merchandisers, the category managers and the marketers who use our technology on a day-to-day basis. In insurance and banking we go after the C-suite, the claims people and the risk people.
Q: Not only do retailers have to deal with the omnichannel shopper, they’ve also got to deal with a sudden deluge of interaction data.
A: That is a real challenge. One of the reasons I chose retail was because of the technical complexity of making use of all that data. Something like 50% of the world’s GDP [gross domestic product] is retail. So, if we can move the needle in retail, we can move the world.
Q: What phase is AI at? Is it still in a hype phase? An early adoption phase? Even a honeymoon phase?
A: I think it’s still in the hype phase. Let me explain. If you only analyze historical data, that’s called statistical analysis. You learn from history only. You have to have labeled training examples to train your algorithm, whether that’s linear regression, invented in 1805, or deep learning, which has become popular in the last five years. That’s all mathematical labeling. You create a label, namely a numerical number, e.g. this is a dog, that’s a cat. It’s just a label. It’s a mathematical process and it can only learn new things at the speed you collect new data.
Q: When you talk about deep learning, are you referring to neural nets?
A: Neural nets or support vector machines or support vector regression, it’s really all historical data learning. You can only learn a new mathematical pattern when you collect new data. If statistical analysis was a panacea, it would have had a greater impact long ago given the proliferation of statistical analysis tools. The hype is a whole new generation going “Wow, this predictive modeling stuff is really powerful.”
Q: What separates Daisy Intelligence from the hype pack?
A: What we do is different. It’s called reinforcement learning. Think about retail promotion. I have to pick 500 products to promote out of 100,000. But you can’t treat Coke and cheese and bread as three independent things. They’re all related. It’s the marketing mix that drives the result. In predictive analytics, I have to create a label for each mix. The combinatorial math tells me I could never come up with enough labels. So predictive analytics can’t work in that scenario. You have to use reinforcement learning. We can tell the retailer “here’s what you should promote, here’s the price and here’s the inventory allocation”.
Q: What’s the business case you make to the merchandiser sitting across the table from you when you talk about this?
A: We deliver decisions. If you execute the decisions, we’re going to grow your revenue by 3% to 5% or more and in a 1% net margin industry, we just doubled your profit.
Q: And you do that by optimizing price and shelf allocation and promotions?
A: And product selection. There are a lot of companies doing price optimization and forecasting, but I haven’t seen anybody help decide which products to pick. An example would be a product like ground beef. Consumers see ground beef promoted, they go “Oh, I’m going to make an Italian dinner”. So, you buy pasta, tomato sauce and produce. If you’re making hamburgers, you buy hamburger buns and condiments. But because I bought ground beef to make hamburgers, I might not buy hot dogs. And then there’s forward buying where you’re stealing from the future.
There is [also] the spatial geography. I’m not going to go out of my way to get a 10 cent discount on carrots. But if you’re giving gold bars away for free, I’ll drive across the world to get it. All the common-sense things that retailers know, like lower prices equal more sales and front page of the flyer is better than back page. All these common-sense rules we’ve assembled into a theory of retail like the laws of physics.
Q: What might merchandisers do differently by using your model?
A: Let’s use a household promotional flyer as an example. In the dairy category, I need to decide which products I’m promoting. Well, Daisy says “Put milk on the front page, cheese and yogurt on the inside page and here’s the price you should charge.” So, we tell the merchant “Here’s what you should do.” In promotional planning there’s way too many data points for analysts to look at. One very large retailer was looking to do 90 billion product forecasts three times a day. 90 billion. They had several million products and thousands of locations. They wanted to know three times a day what the demand for those product combinations would be at the store level. These problems are beyond human capability to compute. That’s the class of problems AI should focus on
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Q: Where else can AI have an immediate business impact?
A: Anywhere involving large volumes of data where highly complex decisions need to be made. In insurance, we’re doing fraud detection and predictive underwriting, for example, where you might get a million claims coming in every day, needing to decide what’s fraudulent. Another example is screening bank transactions for money laundering or speeding up mortgage approvals which might happen thousands of times a day. Again, just moving the needle 1% or 2% can have a significant financial impact.
Q: Is AI going to govern the real-time interaction experience of customers?
A: We’re analyzing 100% of the transactions and interactions of our retail clients across all channels: in-store, online, mobile, you name it. Using that data, we’re able to help our retailers give their customers what they want, which is having the products available that they’re interested in, at prices they find compelling and having the stock there when they go to buy the products.
AI risks, obstacles, solutions
Q: Is there a risk with AI that it will actually create greater distance between marketers and analytics because those decisions are now being handed off to a machine?
A: That’s a good point. It could happen. I think customers are always asking “So what’s inside the black box?” We try to be as transparent as possible. But it’s so complex. We might do a hundred trillion computations to come up with an answer. There needs to be some element of trust. But it’s not like the machines will take over all analysis and humans will do none.
Q: So, you’re saying there’s still a role for predictive analytics and the sister disciplines?
A: Yeah, certain problems. A customer acquisition challenge. A targeting challenge. Those are perfect predictive modeling opportunities. So, there are certain problems where predictive analytics will play a role and certain problems where reinforcement learning is better.
Q: What’s the on-ramp for AI in a company that hasn’t been down this path before?
A: Start with a big problem. Then look at business processes where people struggle, where there’s lots of data, super complicated trade-offs, dependent on old rule-based systems. Figure out how the math is going to help. If you’re going to create a predictive model, think about what you’re going to do with the answer. And there’s still a lot of data management issues that haven’t been addressed. You need to manage your master data, product hierarchies and historical promotions. All of that drives the quality of AI.
Q: Data quality is still an obstacle?
A: It still is. To me, it’s the proof point that nobody is really doing any strategic analytics because we haven’t solved that problem yet. With every retailer we work with, the first thing we do is tons of data management work. For example, you have all these different UPCs [universal product codes]—like all the different flavours of yogurt —which should be considered as one group. That data entity doesn’t exist in the vast majority of retailers. Large retailers only very, very recently, like in the last year or two, started to actually think about those things. Which is proof that analytics hasn’t even scratched the surface of possibilities.
Q: As a long-time data mining advocate, it must be gratifying to see all of this interest in AI.
A: I’ve been at this for 25 years and finally my vision is starting to gain traction. At times I thought I was out of my mind. So, yeah, it’s very exciting to have people buy into the vision.
Stephen Shaw is the chief strategy officer of Kenna, a marketing solutions provider specializing in customer experience management. He is also the host of a regular podcast called Customer First Thinking. Stephen can be reached via e-mail at email@example.com.