Customers want everything, now. By adopting an AI-powered personalized marketing approach, retailers are able to determine the best time to deliver the right offers to the right individuals, to get the right results!
Hear from Jason Mathew, Director of Personalized Marketing Products, Symphony RetailAI as he explains more about AI personalized marketing to Mike Troy, Editor-in-Chief at Retail Leader.
- Download whitepaper: Let’s Get Personal – 5 requirements for personalized marketing success
- US retailer Giant Eagle selected Symphony RetailAI’s personalized marketing solution for its robust campaign and offer management capabilities. Learn more.
- Read Video Transcript
MT: Hello, I’m Mike Troy, Editor of Retail Leader. I’m here in Irving, Texas at the Symphony RetailAI Xcelerate Conference. I’m joined here in the booth with Jason Mathew and you are in charge of the personalised marketing piece.
JM: That’s right.
MT: And it strikes me that there isn’t a hard space right now for personalisation.
JM: That’s true.
MT: There’s got to be a lot of interest in what you’ve got going on.
JM: There is. I mean, customers want everything on demand these days, right. So what personalised marketing is able to do, is figure out the best time to deliver those products, those product based offers, to customers based on the purchase patterns they have, the way they’re shopping, different segmentations and do it in a timely way. So yeah, our product is very good at evaluating customers and what their goals are, not only purchasing but their convenience goals and then allowing us to deliver offers that match those goals.
MT: Elaborate a little on the time. Are you saying like the time of day, the time of empower?
JM: Right, so we are able to get the purchase history from retailers today, from our clients. And so what we can do, is we can determine, using artificial intelligence, when that customer is next likely to make a purchase. And that’s based on their past purchase patterns. And we’re able to do that by household. So if we can anticipate when you’re likely to shop and based on what you’ve purchased in the past, that’s how we can time that promotion, based on that same product, to be just before you’re about to make your next shopping trip.
MT: If I’m likely to purchase the product though, you don’t always want to serve me an offer if I’m going to buy the item anyway, right?
JM: That’s true. That’s true. And it depends on the goals of what you’re trying to do as a client. So if your goal is to reward a customer, to maintain them as a loyal shopper, then yes, you do want to try to give incentives for customers who are already buying your products already. But if it’s a customer who you’ve detected through this technology, that they’re starting to spend less, let’s say in a category.
JM: Or they’re starting to, maybe they have a potential for defecting and starting to shop with a competitor, then what you want to do, is try to incentivise those customers with products that start to try to encourage them to spend in categories that they may not be purchasing much in, or they’re starting to decline in.
MT: How do you help me avoid…there’s a lot of landmines with personalisation.
MT: So let’s say Mike Troy hasn’t bought dogfood in a while. You don’t want to really send me…if my dog passed away, or you know, there’s other examples, like a pregnant woman or an elderly parent, you know, all of that kind of stuff. How do you avoid those situations?
JM: So, there are certain, you know, categories that are sensitive categories, that you do want to steer clear of, unless you’re seeing very recent purchase patterns. But primarily, we try to take that into consideration through a smart process on our…in our software So for example, you may be highly likely to purchase a product, but if we see that there’s not been any movement of that product in your preferred store for some time, that even though you’re highly likely, based on your purchase history to purchase it, why would we want to send that to you if you go to the store and it’s out of stock, right? So things like that, we can use intelligence to determine what to give you and what not to give you, based on other data points.
MT: So obviously, the flipside of personalisation is privacy, because you can’t do personalisation if you don’t have data that people have willingly surrendered and know that they’ve surrendered, and you’ve given people permission to use to, you know, serve me something of value.
MT: What’s…what kind of conversations are you having right now with retailers about balancing those two things?
JM: Yeah, the…the good thing is that with the product that we have today, we don’t capture any PII information, so we have a…
MT: Personally identifiable…
JM: Information, that’s right. So we’re not storing like a name, address. We’re not storing email address, any of those types of things. We just have a retailer identifier. Typically, it’s like a hashed version of a loyalty card. And so what happens is, we work with channel partners to deliver those personalised offers, using that identifier. And then it’s typically the retailer themselves or the channel partners that does that merge back to the PII information, to then actually get it into the hands of the consumer.
MT: Okay. That sounds complicated and technical.
JM: It is, yeah. We are able to do that more easily with mobile, by being able to deliver…because then you can use a mobile identifier to deliver those personalised offers to a device ID as opposed to, you know, the consumer themselves. But the good thing is, like I said, we have no PII in the system, so it’s less of a risk when it comes to those sticky client conversations.
MT: Yeah, yeah. Well, it’s going to remain a balancing act for some time, I think. We see that with what’s going on with big tech companies right now. They’re in a lot of hot water about what they know about people and what they tell people they know about people, and it’s a little weird.
JM: It’s always been something very important to our executive leadership, that we’re very transparent about that, that we do all we can to protect consumer privacy, where Symphony RetailAI’s involved. So I think we’ve done a good job of that so far and we want to continue to do that.
MT: Is there a…does this somehow integrate with CINDE, the conversational interface that you all have to…personal assistant, I guess?
JM: That’s right, yes. So you know, what we’re trying to do is, we’re trying to take a lot of friction out of the campaign managers’ experience when they’re using a tool for personalised marketing. So for example, typically with these types of tools, you’re manually going about the process of identifying target households, based on, you know, different criteria, like how much they’re spending and all these other things. With CINDE, CINDE can actually autodetect households that are at risk, or households that have opportunity to move the needle in a certain direction. And so CINDE can then provide those households directly into the personalised marketing tool, to be targeted with smart campaigns.
MT: And say, Jason, you could try this promotion.
JM: It’s more like you know, the campaign manager comes in on a Monday morning and they’ve already got a list of potential households that…where it’s recommended, here’s some that you’d want to run some personalised marketing campaigns against, in order to achieve a certain objective. So rather than it being something where you have to go and try to ask the system to provide you information, it’s already sort of waiting there for you, because of CINDE. So it’s a powerful combination between the two and I think it tells a pretty good story.
MT: Oh, right. Excellent. Thanks Jason.
JM: Thank you very much, I appreciate it.
MT: You take care.