How Balkan Consumers Will Continue to Shape the e-Comm Industry - Corina-Andreea Bulimar, Mastercard

In this talk, Karina Bulimar, leading business development for Mastercard services in Southeast Europe, discusses how Mastercard has evolved beyond a payment company into a technology-focused organization. She highlights key consumer trends in Bulgaria, particularly the rise in card payments and a shift in spending from essentials to experiences such as travel and entertainment. The speaker emphasizes the importance of leveraging data for understanding customer behavior and market share, utilizing tools for A/B testing and predictive analysis. For personalization, she presents dynamic yield technology that enables tailored shopping experiences based on user behavior and preferences. The session concludes with a focus on AI-driven personalization strategies that enhance retailer and customer interactions.

Thank you everyone and thank you for being here. Um I want to start with a joke. Usually when I presented this kind of events in other countries I get the slot just before lunch when people are without energy and they're waiting for their food. So very happy now I'm getting a full energized crowd just entering here and very curious to hear about what's this conference all about.

Um, I'm Karina Bulimar and I'm leading the business development for Mastercard service for retail and commerce across Southeast Europe. And I need to make this introduction because when everyone hears Mastercard, they think about the payments industry, which is true, but over the past 20 years, Mastercard has developed into being more than just a payments company. It developed into being a technology company. And it all started from

the data that we get from the payments. But we developed it by bringing in the best consultants in the world and developed loyalty programs. We developed personalization strategies and AB testing which I'm going to talk a little bit um a little bit later. Now before getting into how do we work with retailers, I want to show you some trends when it comes to the Bulgarian consumers. So

if you look at the categories, there's a huge rise in card payments. no matter the industry. But what's more important is that when it comes to ATV, which is the average transaction value, so the average basket to say it like that, this kind of stays flat, doesn't move as much, which means that we have shoppers trying to optimize their budget without increasing their basket, but going more

frequent to the stores, which can mean a challenge on loyalty, but also a good opportunity to catch them while we have their we have them there and this happens even in the e-commerce uh sector. But what's more important is that when we look at experiences, so accommodation and travel agencies and airlines, this basket is increasing. Bulgarian shoppers are spending more on experience and no matter uh there's

no uh surprise that you guys have so many concerts planned for this year with a lot of international names coming because there's a lot of potential there. Now if we are to look at the overall basket and how do does the shopper split it based on the industry as a weight of it. Again, the data we have here shows that there's more um allocation to appearance, to

travel, to experiences, and Bulgarians are diminishing a lot their essentials and the homes overall percentage they have spent in their basket. Should I point this one somewhere to like to move it? Okay. Um there's also another thing we looked at and by the way this is pure transactional data. So it's not what shoppers declare. It's not something we get from some statistics. It's exactly what we see

in the transactional data. So it seems that there's a huge inclination into spending on more premium when it comes to cosmetics versus jewelry into um uh hospitality, right? Going outside of our home and spending into traveling where there's a lot of tear down when it comes to uh apparel and food, groceries. Again, similar to what I was showing now, the data is here. So what I've tried

to bring at the Balcons conference a little bit of inspiration of how do we work with retailers across the globe into solving from some of some of the most common challenges and we said okay what are the top three things we look at the first one is that we understand we measure and we engage and personalize and I'll take them one by one when it comes to

understanding it goes back to the data and I would say that there's no one problem that fits all. It's also about the need that our retailers in Western Europe have. One is the need for market share, but the market share with a certain granularity like I need to understand how am I doing versus my competitors in a certain day in a certain time interval and the payments

data data can provide that or loyalty data for example which is there at point E. I do know everything about my shoppers based on the loyalty data, but I know what they're doing inside my store. I don't know what they're doing outside. So, with the help of Mastercard, I can kind of enhance this data and follow them outside of my industry. Understand what would be my best

marketing uh activities I can do it or propensity lookalike modeling, right? I can look at the behavior of the shoppers I have. try to understand what are their common things and how can I extrapolate them into an acquisition campaign and bring them uh bring them that's about data I just wanted to very touch uh to touch base very uh very briefly on that because I want to

move now on how do we measure right and I know everyone tests any idea any initiative they have in their business the question is all the How do we do it correctly? How do we choose those customers, those um products that we want to test on? How do we make sure that there's a test and a control that kind of sets separately? So this is how looking

around we found what was called advanced predictive technology at that time and we rebranded it into test and learn. We use it as a company to test our own initiatives, but we also partner with our retailers into trying to understand how we can help them. And how it goes is that before using test and learn, usually you are setting a test and the control group that were

kind of similar but not necessarily in the same pattern. Then you were introducing the test and it was very hard to measure the initiative. What this platform does that it identifies exactly and it tells you for example for a e-commerce retailer what should be the custom the custom control set and um the test uh the test group in order to have the same pattern and the moment

you test it to actually understand what would be the impact of an initiative eliminated all the other things that are happening in the same time and making sure then how to roll it out how to do it properly in order to actually have an impact on your business. I will go now to the third uh side of the story which is engaging and uh personalizing and again

I don't need a lot of introduction here because personalization is all around us right when you use Netflix everywhere we are serve the content that is of interest for us right everyone is talking about the big algorithms of these platforms So starting from the need again, Mastercard look around and they found dynamic yield which was um software as a service which was acquired by Mastercard because it

served the personalization in the easiest way possible. And how does it uh did it do it? It kind of creates all these affinity profiles as a bubbles. For example, here we have a female that it's interested in size small swimming whereas on the other side we have a size large a male which is interested in winter sports. How do we do that? By the way the shopper

is clicking on the website. There's that instant personalization that it's happening. It's also by the history of it. But there's also a lot of other data that is ingested like the location they are logging in, the website they're logging in, the platform and so on. And what's the result is that you kind of get the personalized experience. For you have the gear equipment on the landing page

serve to someone who has a big affinity to gear equipment and to men's wear. Whereas if I'm a woman, I will see a completely different thing, which is exactly what I'm expecting, right? So if buy the uh behavior I have, I don't own a garden and it's clear that I'm living in a flat. Why should I see gardening tools? Why should I see that? And I'm keeping

I keep being popped up with all these messages. Whereas this is very relevant and it's that simple. It also changes the menu. For example, I'm going to see here first woman here men and so on. And it goes like that in any other uh spaces like in the products the products recommendation. Uh I wish I had the time to explain you in detail what it does but

I'm going to I'm going to keep the topline details. Another interesting thing is that we do get some personalization. But what happens is that I'm looking for a phone for example, but the moment I bought it, the uh retailer doesn't necessarily understand that I bought it and it keeps popping me promotions on a phone whereas I don't need it. With this kind of platform, you kind of

predict the next purchase. So I bought the phone, my smartphone affinity profile, it's done. I'm gonna be ending in some other ones which are relevant and associated to it. So in this way I can kind of influence the shopper into the next um into the next purchase and not keep insisting on something that he has already bought. How do we do it with anonymized shoppers? For example,

there's this zip location code and we can allocate them to several criteria. For example, how's the weather? If the weather is rainy and if there's sunny in some other place, I can show you different content. For example, here we have uh understood that there's a specific zip code that had premium shoppers whereas the other one had like mainstream ones and we served the content different based on

the locations they were logging in again with more inclination to buy based on what they were seeing. And then the last one, but my favorite one, which shopping muse, which is AI integrating into that via search. And you can do it as I want to I'm looking for a dress similar to the one Angelina Jolie wore at Oscars in 2023. And then you kind of instantly get

the results of that specific or like the specific uh recommendation. Or you can do it via an image, right? you search on Pinterest, you find a look or you find a room for example that it's very relevant for you and then you search with that and it kind of prompt the the results on that. So that's about it from uh from myself. I told you I could

have spent hours telling you about the solutions and how we work in Western Europe with e-commerce partners to kind of help the bug the shopper immerse into the e-commerce business. Should you have further questions or do you want to to talk about it? Happy to happy to connect. Thank you. [Applause]