INCREASING LIFETIME VALUE:
EARN THE SECOND PURCHASE
With all the data coming in from both online and offline purchases, most retailers have a hard time sifting through the noise and identifying what it takes to convert one-time purchasers to multichannel buyers and repeat customers. Stephen Yu, Chief Product Officer at BuyerGenomics, discusses the value of studying multi-buyers and having a buyer-centric point of view so that you can start finding important patterns now and incorporating those patterns into your marketing plan instead of simply waiting for machines to get smarter.
Below is a lightly edited transcript of Episode 33 of the Inevitable Success Podcast with Damian and special guest Stephen Yu. (Listen Here)
Damian: So, today I had some very specific questions about acquiring the second purchase. That’s such a missed opportunity in almost any customer and retail database, and the stakes for getting that second purchase are tremendous, right? I know it’s different for every business, but what are some steps you can start with?
Stephen: Your data should be aligned so you know who only bought once, which is really hard. Imagine you don’t have a database that is buyer-centric. We talked about buyer-centric for some time, but let’s just remind ourselves that a buyer-centric database is a prerequisite for all of these things.
Damian: Actually, that’s a great point, because if you don’t have that kind of technology or view, you may not even realize you have this problem.
Stephen: Well, that’s the first thing we look at. How do we know this? Because we define a customer. Customers are made of what we call personally-identifiable information, which can be a name, address, email, all of that. Once you do that, then you start adding all the transaction data. Transaction data looks like a shopping basket, like when you go to Amazon and put things in the basket. Likewise, if I have all this data, I know exactly what to put in the box, what label I’m going to put on top of the box, and that the customer will receive the merchandise. Is that a database? Of course, it is, because it’s data, it’s organized, and has all the details you need to do that function. That doesn’t mean you’re counting these transactions, that’s different. That requires being in accounting mode.
Damian: Or these transactions are in different places, one on the website, one in the store.
Stephen: That’s a good point. Do you know that a lot of retailers have different systems for online and offline? What if you bought some big ticket item because you wanted to try it on, and then you go to the online website and buy a belt. Now, are you a one-time buyer or two? You have to be aware this is happening; that’s the first thing you have to do.
Damian: Right, and that’s specifically the multichannel buyer.
Stephen: Well, the other day I was schooled by my own daughter, who is in marketing as well, that I was mixing the words up—omnichannel and multichannel. I said hey, that’s your agency’s lingo. But she said something interesting, that if you can trace all the things back to the source of the campaign that they came from, they call it omnichannel. But if they just know that a customer used multiple channels, then they call it multichannel. So that’s one distinction. The point is that the view is not the channel, it’s not the product, it’s not division, it’s not brand—it’s the customer, so that you know that yea, Damian came to us twice, once online, once offline.
Damian: So, that brings me to a break-off point. I’ve just been doing thought experiences here, wondering how would I get a second purchase. One idea that I had was to get a list of all first transactions that people had and then look to see if there were patterns. For example, you would identify when people buy a particular product more often, and then determine if it’s the person driving that or the product, or some combination of those. It’s a jumping off point.
Stephen: No, it’s a very good question. Remember when we talked about different types of data?
Damian: All the time, that’s all we talk about.
Stephen: Well, let’s put that to use now. Okay, great, you have one-time buyers, now you know it. You have the database, you have the view, you know that 75% of your base never came back, and you know who they are now. The way you mimic and push them into the second purchase is to study multi-buyers. In other words, there’s a group of people who did the undesirable thing, right, and you want to push them to desirable behavior. Let’s dissect what those desirable people look like in terms of all the different data types we’ve talked about, like behavior. You also have to look at what kind of people they are in terms of demographics.
Damian: You know, it’s so funny. One of the interns here just told me a story. He said he’s been a New Balance shoe buyer his whole life. Then he grew up and had to buy them himself, and he didn’t buy them anymore because they were more expensive. So when he had a life-stage change, he changed his behavior tremendously.
Stephen: So, rolling back about two episodes ago, we talked about what types of data. Nobody exists on a one dimension. If you want to compare one-time buyers and multi-buyers, you have to look at it through the lens of behavior, product view, channel view, as well as the demographic view. If you do that, then you can mimic that behavior and push them in the right direction.
Damian: I think this is something where machines or technology can be really helpful to identify the signal in the noise and sort of discover that magic formula.
Stephen: Right. We’re living through it. Anybody who buys anything from a reputable online merchant will know it. I bought a door hinge the other day on Amazon, and do you know how many different types of door things showed up? By the way, human beings aren’t doing this, machines are. It is annoying, but this machine is working. Database marketing is not about being right every single time. It’s about increasing the probability of being right, and they did it, with a machine. And you mentioned all the noise within the data—can you imagine the types of data they had to deal with? All the product categories, product descriptions, and all that. Sometimes they’re right, sometimes they’re wrong, but they’re generally more right than wrong now because the machines are getting smarter. What if you’re a small guy and you can’t afford all these big initiatives, like machine learning and AI? I say, what do you think machine learning is? It’s nothing but automating things that you knew all along. If you ask a machine to take care of things you don’t already know how to do, that isn’t going to work. So, what do you do? You can do this with simple analytical tools. What I used to do, and still do, is sort every multi purchase in descending order of either dollar or number of transactions and see if the top value patterns, maybe top 50, tell you anything, and ignore the rest. And guess what? That’s good enough for you to pinpoint what the second offer should be based on the first purchase. Don’t wait for the machine to get smarter or until you can afford all this technology. You can do this right now.
Damian: You said something earlier about the people who don’t buy, and you made me think of a story. I once read that in World War II there was an aircraft engineer whose job it was to figure out how he could reinforce airplanes so that more of them came home. They were looking at all the places where the planes were getting shot more than others. One day this engineer said we’re doing this all wrong. We should be looking at the planes that don’t make it back home to figure out where we strengthen the plane. This also made me think about all the first purchases that people make that never turn into second purchases. Are there patterns cause you to admit when you should get rid of a product because it kills value?
Stephen: Yes and no. I can say similar things about how New York City Police used computers to find patterns of crime. They didn’t look at the crime-free area, they went into the areas where crime existed and put more cops there. Sometimes it’s that simple. Now, when you ask if the first product is the wrong product, I’m sorry to say it, but it depends. If you’re selling expendable things, like things that you must replace, then the first product is very important. A lot of telecom companies do it that way, a lot of magazine subscription guys did it that way. What you have to do is change your view from the product-centric view to the customer-centric view. If you do that, then you differentiate those small-timers by a lot vs the whales at the back. Let’s define what the problem looks like from a buyer-centric point of view, and then explain it through the first and second product, and all the other variables.
Damian: As you say that, I can quickly start to think of examples where it’s the person, not the product that, in situations where people didn’t repeat. I just think that at a high level there are probably so many people out there who are bargain discount hunters, that are just disloyal in general, and you could probably identify those customers.
Stephen: I call them barnacles or bargain seekers. People who drive ten miles to get a 20% discount, and then never come back.
Damian: Not to pick on the intern I mentioned earlier, but he said that his requirement now for buying shoes is the cheapest shoe that doesn’t hurt.
Stephen: Hey, that’s honorable. That’s his need. Everybody’s need is different. Is it price, is it quality, is its durability, is it style? What is it? Everybody is different. We have a client who sells very expensive Italian shoes. For their customers, it’s not about the price. It’s about exclusivity. How do we promote that as a second purchase? Then you really make a club out of this thing. You have to make an association. Now, let’s go back to what you just talked about. When you have all these one or two-time buyers that you’re trying to explain away. You want the buyer-centric view to come first, but don’t limit your view. Always set the problem statement from a buyer’s point of view, and follow the buyer life cycle as much as the seller’s life cycle. The pattern will emerge.
Damian: Okay, we’re going to wrap up here. We may cover in later episodes some of your specific go-to moves.
Stephen: There are a lot of go-to moves. One is not to wait for the machines to do it. By the way, you can do the same thing for discount purchases. Not everyone is always a full price buyer or always a discount seeker. Nobody is black and white. You have to start looking at their transactions and see if there is a pattern there. This pattern emerges only after you line up everything you know about the person.
Damian: All right, this was great. My takeaway is that you need to focus on this problem, because it’s a big opportunity, and you have to find your pattern. There is a pattern there. There’s going to be some signal, and it could be your magic formula.
Stephen: That and also we talked about not only how to identify these guys, the barnacles or what-not, but also what are we going to say to them?
Damian: That’s right. Maybe that’s what we’ll talk about. All right, until then.
Damian: If you enjoyed today’s episode, we ask you to please leave a rating and write a review. Or, better yet, share with another marketer. Be sure to subscribe to the podcast for new episodes. Also, check out the show description for complete show notes and links to all resources covered in today’s episode. If you’d like to speak to someone about any topics covered in today’s episode, please visit buyergenomics.com and start a chat with the team today.