Using Data To Drive Personalization

Su Li Rivera, SVP of Marketing at Rakuten Rewards, delves into the power of data-driven personalization strategies in the e-commerce landscape. Discover how Rakuten, a pioneer in online shopping rewards, leverages data to enhance customer experiences and drive engagement. Show NotesConnect With: Su Li Rivera: Website // LinkedInThe MarTech Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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  The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth, and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iHereEverything.com.

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  Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world-class marketers that use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.

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  Welcome to the Martech Podcast. I'm Benjamin Shapiro, the executive producer here, and today we've got a special episode for you, which is guest hosted by John Sintraus, the president of Mutinex. John and the Mutinex organization help marketers get more value out of their work by knowing more about the value of past decisions and predicting the value of future decisions through Growth OS, an AI-driven marketing attribution software. And today, John is going to take the mic and have a conversation with Sue Lee Rivera, the vice president of marketing at Rakuten. All right, here's a special episode of the Martech Podcast, guest hosted by John Sintraus, the president of Mutinex.

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  Hello marketers. My name is John Sintraus from Mutinex. And joining me today is Su Lee Rivera, who is the SVP of Marketing at Rakuten Rewards, which, since its founding in 1997, has helped shape the way people shop online, offering cash back, deals, and shopping rewards on the world's largest selection of products and services. To date, its 17 million members in the US have earned over 3.7 billion in cash back at their favorite stores. Yesterday, Su Lee and I talked about Rakuten's business model, and today, we're going to continue our conversation by discussing how Rakuten uses data to drive customer personalization.

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  Okay, here's my conversation with Su Lee Rivera, the SVP of Marketing at Rakuten Rewards. Welcome back, Su Lee. Great to continue our conversation.

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  Yeah, thank you.

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  So can we start our conversation today by thinking about personalization? And lots of marketers have struggled with marketing to the masses, but then taking that really down into an individual personalized experience. And you started to touch on that yesterday. Can you talk a bit more about how you balance the mass appeal, but also being very personal in terms of the relevance of your brand?

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  So with Rakuten, we are a marketplace, which means the breadth that we have in terms of brands really hits, as you called it, the masses, right? There's something for everybody, which, as we do from an acquisition perspective, we can go broad because once we bring you onto the platform and you see brands that you recognize or retailers that you love shopping at, it's much easier to start connecting you with those brands. But as you mentioned, once you're on our platform, how do we make sure that we're showing you things that are most relevant to not only your preferences, but when you might need to see them? For instance, we have all just gone through back-to-school shopping. It's a prime time of understanding, all right, what type of merchant mix are head of households looking for during the time of back-to-school,

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  which extends two months or so, versus the holidays, where it's a lot more about gifting, and you might be shopping at brands that aren't for you, but necessarily for those on your gift list. So from a data perspective, I think what we really Try to do is based on the signals that our members are giving us, based on the offers that our brands are giving us, making a tighter connection and bringing our members to those brands. The way we do that, to be able to create a personalized space, is we rely on the development as well as the use of many different proprietary models within our data warehouse. So we have a robust marketing analytics team. We also have a really dynamic data science team in our data center

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  that basically we can create preference models, as I mentioned yesterday, churn models, and other areas that we can assure that we have more of that one-to-one connection. And based on the signals we get back is how we refine those models. Because we might recommend something to you, but if you're not clicking or engaging or shopping with that merchant, we shouldn't keep pushing you to it, even if, even if that's the preference of a merchant looking for new to file. So I think we certainly look at finding, not necessarily squeezing for optimizing, but making sure that we're continually sending you different merchants or retailers, that you're then giving us signals back of yes or no. However, I will talk on the other piece of going broad. On the growth side, where we look at bringing in new members,

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  I use an analogy constantly with our team around being an accordion, that when we go out into the marketplace and marketing five, 10 years ago was very much around squeezing and optimizing and targeting in 18 different variables to get to the one person that you think would be interested in your product. Now, much more like an accordion, you have to pull in a lot and go very wide and see who's interested, what signals, and then begin to kind of optimize throughout. But we constantly have the motion of go broad and then optimize, go broad then optimize. Because certainly with the changing markets, changing preferences, different generational preferences,

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  we need to constantly build our marketing models to create that openness and then start to optimize within.

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  That sounds like a pretty smart strategy. There's lots of discussion about people having gone too far into personalization of performance and not doing enough to bring in new users and sort of to drive the top of the funnel. Would you agree that the market broadly sort of gone too far, too low and then not doing enough to sort of enrage brand desire, as they like to say?

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  I think it's a little bit of both. Where are you having that very personal connection? Might be in a place within email or within push where the consumer has raised their hand and said, yes, I'm interested in having more personalized offerings versus in the broad marketplace. Yes, I agree that discovery is definitely of interest, but from an efficiency standpoint as well. I think there's something nice about having my preferences and the things that I'm interested in show up on my Instagram feed versus what my husband's Instagram feed looks like or my 16-year-old, God forbid, what his Instagram feed looks like. There is something around it's curated, but not so specified where I was talking with

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  somebody the other day, the level of marketing precision where you think your phone is listening to you gets a little creepy, right? Like, we all get a little creepy about that.

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  Absolutely.

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  So finding that, like I said, I think it's finding the right balance of where is it okay to have a little bit more discovery? And then where is the consumer asking for more of that one-to-one connection?

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  Yeah, I think the balance is critical. So thinking back to the comments you've made around signals, how do you collect and analyze data? What are some of the signals that you source? Where do they come from over and above the interaction with the platform from your consumers?

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  So we at Rakuten, once a member engages with us and basically they give us consent that they are allowing us to, through their transactions, be able to collect data on their shopping habits as well as transactions. Because ultimately, unless we have those signals, we won't be able to get you your cash back. So it's with high trust that we utilize that data and then be able to turn it around for personalized offers that if there's a certain merchant that you prefer, when they have Great deals, we want to be able to tell you about it. Similarly, if there are merchants similar to where you like to shop, or as you mentioned, are there such great deals out there in areas like travel? And we want to make sure you know about it.

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  That's where we are sending back and forth that information and data collection between the consumer and our platform. So I think for us, the utilization is much more within our walls, within the one-to-one communication we have with our members, but as well, making sure that we're supplying based on our promise of giving you the best deals. Let's say if Sephora is having their beauty day sales, then we're able to make sure that our shoppers who have beauty preferences and are purchasing beauty products throughout the year, get that signal. So we absolutely, I think, from a perspective of being able to have relevant and timely offers is where we connect data with our members.

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  So it sounds like we're listening to very specific signals in terms of what they've already done and what they're interested in, but you're also pushing them to maybe discover something else or to know about something that they might also be interested that might be adjacent or a little bit left of what they would typically do.

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  Absolutely, because I think ultimately, especially the breadth, you know, as I mentioned yesterday, we have 4,200 different merchants. That covers so many different verticals that you might have come to us for savings on retail, beauty or apparel. But like I mentioned, if we have offers in travel, we can help you get cash back there. If we have Ticketmaster and you like experiences, we can help you get cash back on tickets. So I think while people come in and enter through one particular merchant, the breadth of our offering, we like to help with that discovery that there's more ways to save.

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  So everyone's talking about AI and everyone's looking to leverage that in a way that makes sense to their business. I'm sure you're doing experimentation. Can you talk a bit about the impact of AI in your business and what you think the potential is for that?

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  Yes, AI certainly has been the buzzy word for the past two years. We have embraced it. One of our key principles at a Rakuten Global perspective is speed, speed, speed. That includes not only adoption of new technology, but bringing it to help us execute at a faster clip. So for us, I think where we look at in AI are two main areas. One, in terms of productivity and efficiency. So how can we do our jobs faster, better, smarter, even in parallel, kind of anticipate what's ahead that we could be leveraging to make us faster in market? And secondly is AI in terms of performance. So obviously, within marketing, we're looking at not only operational efficiency, but also the efficiency of our investment and our marketing dollars.

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  And is it helping, though, on the personalization subject? Are you using any AI to help drive that recommendation engine to your customers?

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  Yeah, so in a couple of different places. One, we are within some of the proprietary models we use in-house. Like I mentioned before, we have within churn models or within other relevancy models where we're constantly sending signals back and forth around similar retailers or other member behaviors that are signaling that maybe they'd be interested in a certain deal. The timeliness is certainly where we're utilizing AI there. We're also using it as a way to improve even our marketing efficiency and things like email. So the signals that, based on what subject lines or what copy, all of those different factors feed back into the model that we can then use. And again, instead of what was previously A/B testing,

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  be able to dynamically create email copy as well as image selection based on model development.

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  Can you share any examples of your personalization approach? Are you able to quantify how that improves effectiveness of your marketing? Have you been able to just sort of get at that with any of your measurement techniques?

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  In certain areas, we've had, largely speaking, any place that we've done more personalized offers in terms of higher cash back rates based on preferences, we tend to see an increase. Thinking about maybe one or two case studies, even within our member communications, we've moved towards some of that more personalized offers. And when we do that versus, let's say if we have a promotion on a weekend, we might be able to send you, here's the top 20 retailers that have cash back this weekend. And that's kind of a blanket at assuming you were behaving like everybody else on the platform. So let's consider that our A side. The B test of having a dynamic platform where the merchants that appear in that promo

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  are driven by your preferences or preferences of people that had shopped at similar retailers as you. We've seen a huge increase in terms of not only engagement, but converted trips, which we view, converted trips meaning not only did you take a shopping trip, but you actually made a purchase. So that's a success in our view for not only the merchant or the retailer, but also for our member where they were able to get cash back. So we, in an environment, I'd say probably last year, as we moved towards this more of like dynamic retail selection, we've seen increases in the double-digit percentages of performance.

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  That's fantastic. Thank you for sharing those cases and experience. I think we might finish today with the subject of transformation. I mean, you are already a very dynamic company, you're the leading edge of e-commerce. But is there a transformation journey for you? Are there specific initiatives that you're sort of trying to reinvent or do even better maybe into the future that you could share with us?

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  Transformation, I feel like, is another phrase that's used often in marketing, many times coming from consultants coming in and trying to change marketing departments and make them more efficient. But truly, for us, transformation has become something continual. In an environment within marketing in all different sectors, every day is a different problem to solve, opportunity to approach, new ways to buy media, to improve our investment, et cetera. So for us, transformation is a continued effort. Removing that word, I think there's also foundation. Change that actually we went through in the past several years. And it seems like, again, in marketing, every five years, you're completely upheaving your martech stack. It's never quite the same.

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  But for us, it was around tooling and certainly making sure that we had all the right pieces in place. So the Lego blocks for martech for our foundation. But secondly, a data overhaul and making sure that there's continually cleaning our data and making sure that we are putting the right inputs throughout. With a changing team, you can see data flows change based on people's preferences or how they set campaigns up, et cetera. So for us, those key pieces of foundational changes, such as, as I mentioned, our kind of martech stack, but then also our data infrastructure and making sure that whatever is coming through and the data that we're using to power those models is high quality. And then layered on top of that is being able

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  to constantly transform or change or adjust or drive change in the market at a much faster speed than any time previously. So for us, again, like it's been an evolution in the past several years to catch up. And then look, AI shows up. So now we're on a different journey altogether.

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  Always something to think about and constant change. Su Lee, could you just give us a concrete example of a transformation initiative that you've moved forward with recently?

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  So recently, looking at the data and signals of what was coming through our shoppers, we developed an entirely new program to increase loyalty as well as support where our members are buying and saving money. And that is with Rakuten Plus. It's a new offering that will be coming out soon. It is around luxury shopping at 35 plus retailers where you will have an always on 10% cash back. It's a new subscription model coming through Rakuten. And it's an example of transforming not only our offering of what we have today in terms of bringing the best to our members, but actually branching off a new product altogether to support and look for savings in all new places, such as luxury.

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  Fantastic. Thanks so much for sharing that example. That actually wraps up this episode of the Martech Podcast. I want to say a huge thank you to Su Lee Rivera from Rakuten Rewards for joining us.

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  Okay, that wraps up this episode of the Martech Podcast. Thanks to our guest host, John Centros, the President of Mutinex, and his guest, Sue Lee Rivera, the Senior Vice President of Marketing at Rakuten. If you'd like to get in touch with John or Sue Lee, you can find a link to their LinkedIn profiles in our show notes, or you could visit John's website, mutinex.co. Or if you want to get in touch with Sue Lee Rivera, you can head over to rakuten.com. Just one more link in our show notes I'd like to tell you about. If you didn't have a chance to take notes while you were listening to this podcast, head over to martechpod.com, where we have summaries of all of our episodes and contact information for our guests. You can also subscribe to our weekly newsletter, and you can even send us your topic suggestions or your marketing questions, which we'll answer live on our show. Of course, you can always

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  reach out on social media. Our handle is martechpod, M-A-R-T-E-C-H-P-O-D, on LinkedIn, Twitter, Instagram, and Facebook. Or you can contact me directly. My handle is Ben J. Shap, B-E-N-J-S-H-A-P. And if you haven't subscribed yet, and you want a daily stream of marketing and technology knowledge in your podcast feed, we're going to publish an episode every day this year. So hit the subscribe button in your podcast app, and we'll be back in your feed tomorrow morning. All right, that's it for today. But until next time, my advice is to just focus on keeping your customers happy.

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  Thanks for listening to the Martech Podcast, an I Hear Everything production. Looking to launch or scale a podcast like this one for your brand, then visit ihereverything.com.