The Role of Interoperable Data in Enhancing the Retail Customer Experience

Chris: [00:00:00] All right, welcome to another data driven podcast. My name is Chris Detzel. Today's special guest is Venky Subramanian, and Venky is the Senior VP of Product at Reltio. How are you? How are you today?

Venki: I'm doing great, Chris. How are you? Good. And welcome back to the podcast. Absolutely. It's been a few weeks since we talked on this particular forum.
Venki: Great to be here again.

Chris: Yeah. It's great that you're here and today we wanted to talk a little bit about, the topic around operational data in the retail. Industry and what that really means. And one of the things that I know is that, there's a lot of data and a lot of systems that big retailers use from a B2C standpoint.
Chris: And so we're going to dive in to some of those things and have you answer some questions around that. How about that?

Venki: All right.

Chris: When you look at look at this, what are some of the key areas[00:01:00] that you hear from retail data and analytics leaders that are driving their priorities and investments in data?

Venki: Chris, retail is really no different from any other industry especially from a data and analytics perspective. The leaders that we talk to tell us that their focus areas, their priorities are basically driven by the company priorities, which focus around driving growth. Improving operational efficiency.

Venki: And managing risk and compliance in a cost effective manner. So these are the 3 broad themes that we keep hearing from our customers. But if you drive that dive down a little bit more specifically to retail growth, really is all about customer acquisition and increasing customer lifetime value.

Venki: Customers have a lot of choice in retail, right? And the concept of loyalty is not that strong. We would always go and price shop for goods based on the best price that we can get across multiple retailers. So brand loyalty aspect is becoming a challenge more and more in retail.[00:02:00]

Venki: And some of the more innovative retail companies are obviously trying to build a brand loyalty through different interesting means like loyalty programs or their digital experiences and things like that. We can all from personal experience, talk about companies like Amazon or Walmart or Costco and others who are doing an excellent job at this.

Venki: But most retailers really are focused on how do they acquire customers? How do they reduce the customer acquisition and increase the customer lifetime value? How do they make repeat purchases out of these these customers? And that all comes down to improving efficiency and targeted marketing in offering the best deals to customers and optimizing the price and attracting customers as much as possible and providing an omnichannel experience, omnichannel buying experience for customers, irrespective of whether they're walking into a store, going online or using a mobile application or anything else, right?

Venki: So those are all things that fall under growth. Retailers are also constantly under cost pressure. We know that the margins of retail industry typically are [00:03:00] very thin and they need to continuously optimize their supply chain and the price they offer to customers to be able to improve that. And then the other aspect of retail, especially with the.

Venki: Co companies with retailers that have a physical presence is the optimization on the store operations. Yeah. But even without that, the inventory management optimization and the areas like that are a top priority when it comes to the operational efficiency. Last but not least is retailers deal with a lot of customer data, right?

Venki: They're touching customers directly, as you talked about, b to C business to consumer that these are companies that deal directly with consumers like you and me we are sharing data and we expect these companies to manage our data in a responsible manner, respect our privacy and preferences and things like that.

Venki: So there is also constant challenge of how do they collect more and more data use that, but do this in a responsible manner. So these 3 broad teams, growth, efficiency and risk and compliance drive majority of. investments when it [00:04:00] comes to how do you use data in a, in a. In a smart and intelligent manner, but at the same time in a responsible manner as well.
Venki: No doubt and

Chris: I do know that just from my own experiences, customer experience is big the whole omni channel that you mentioned is big and just You know making that customer feel like they want to come back to that store If one you've got the in store kind of purchase experience Then two you have that digital online experience.

Chris: And how do you make them both come together. And that's a lot of data kind of stuff that you have to do on the backend to make that experience pretty amazing. And we'll talk a little bit about that and so thank you for sharing that.

Venki: Absolutely.

Chris: How do you, when think about data unification and management tech, such as like master data management, how does that integrate into the operational aspects of retail?

Chris: I

Venki: would 1st of all, start with, this whole notion of operational data. [00:05:00] Traditionally, there has been a difference differentiation made between operational data and, analytical data or use, use cases that are more analytics oriented versus operational? To be honest, I think that difference is going away, right?

Venki: There is a fusion happening that we can clearly see in the data landscapes where there is more and more of data that is being collected in unified in an interoperable manner that can support both operational and analytical use cases. And retail is no different. So if you look at data that retailers typically operate on.

Venki: Customer data, product data inventory data related to product the data from suppliers or vendors, the whole supply chain aspects of the data, any kind of data that they collect around their customers, suppliers, vendors, products. Stores and other kind of things and any number of places where they collect the data from, it all needs to come together to create a centralized set of data repository that they can use for driving any of the three things that we talked about driving growth, driving [00:06:00] operational efficiency while managing the data in a compliant manner.

Venki: The key areas that are driving data and use of data in retail. Are all the things that we just talked about, it's first and foremost is enabling using customer data to power omni channel digital experiences for customers to drive growth through that to optimize inventory supply chain broadly to do price optimization and things like that.

Venki: There's a lot of data being collected from point of sales systems. And again, that could relate to. Purchase buying preferences could be related to customer data and things like that. E commerce provides a huge funnel, from which a lot of data can be gathered and collected as well as data can be operationalized as well.

Venki: You can surface a lot of the insights back and optimize or personalize the experiences that people get. Through their digital experiences e commerce experiences, whether it's web or mobile and things like that. I think we touched upon a number of different areas. We can dive into a few of them specifically.

Venki: Yeah it's

Chris: funny because I was thinking about this question [00:07:00] and some of the things that you're saying is, when you go into a retailer, the in store kind of POS data system has. The place where they put their customers, maybe there's a reward program and everything else that they're trying to get.

Chris: So they get lots of really good customer data there. And then on the digital piece of it, online e commerce site is a different system, right? Then that is the POS system. And so connecting that data, because that customer. It's probably going to go online too to buy. So how do you connect all that?

Chris: And that's what kind of that data unification piece comes in. And we won't get into this necessarily, but one thing that is really interesting to me, and maybe this is a separate kind of thing, but is you mentioned product data. Think about all the products that retailers sell and think about how that goes out of date within months and then you get something new, right?

Chris: And they have certain manufacturers that they work with and everything else. And I think, [00:08:00] that's gotta be super complex, but just think about the problems that solves, at the end of the day. But I think that we could go on a deep dive at some point in, in that particular piece on product data and what that really means and things.

Venki: We can talk about that, Chris, and I think, back to the, where we started this discussion from how are retailers managing this data in an operable manner and using it for operational use cases, right? I think it all starts with identifying the right kind of use cases that you want to power in retail.

Venki: So we talked about some of them omni channel engagement customer care or self service. And things like that, and then working back from there to identify what are the core data domains that really matter. Obviously, we touched upon those 2 customer product store manufacturer, or sorry, supplier product.

Venki: And these are the areas and then for each 1 of them. How do you then identify the sources of data that you mentioned? And, is a huge source and you can get a lot of insights, but, the customer, the purchases and things like that. Yeah. How [00:09:00] do you tie that all back together? So how do you identify the sources of data?

Venki: How do you then bring the data into a unified layer? And then how do you then create the specific data products that can power those outcomes? So this is how I would expect any retail data leader to be looking at. How do they empower their businesses with data and how do they create those, the smarts or the intelligence.

Venki: From the data that then power better outcomes for the, for their businesses. Yeah,

Chris: I love that. How do you think retailers are using data to enhance the customer experience? And maybe you can give us a few examples or case studies

Venki: There are tons of examples that we can all think about from our personal lives, right?

Venki: So why don't we start with some of that? I think we talked about amazon earlier i'm a customer of costco and I use their mobile app. I use their online sources and things like that And if you look at how some of these companies were able to make better use of [00:10:00] data are able to personalize their offers are able to target the right customers with the right kind of products and things like that.

Venki: Obviously, that increases a higher propensity to buy right? But that really should depend on. Understanding the customer, understanding the segment, understanding the set of products or offers that are most relevant for that segment, and then really targeting those group of people. That's just one example. And that's probably the most common example that we can all think of as a consumer, as a customer ourselves. Right now, if I were to look at it from relatives perspective, we have worked with some customers where they have really made use of data in a really effective manner to drive their business.

Venki: And 1 of the examples for me is a high end fashion, retailer who at the beginning of the COVID pandemic. When all of the stores suddenly shut down, had to reinvent their business where they had to move to a, uh, clienteling mode where, they had to go and start reaching out to their high value customers and [00:11:00] work with them directly to help them to, first of all, understand their needs.

Venki: And Provide them with the products that they could utilize at that time. Which means basically means that, the whole model of business where you would have these people walk into the store, browse through your inventory, pick up the things that they need, come to a point of sale, come to the front desk register to check out and walk out completely stopped.

Venki: That model completely stopped. And now they had a hundred percent of their focus on enabling the same kind of business, but through digital channels. The only way the company can reinvent themselves in such a short time by switching to a new business model, if they have the right technology foundations in place and a big part of that technology foundation really is data.

Venki: Do you have the data to really understand who you are? High value customers are what are the typical products or product preferences they might have so that you can position the right products, personalize that experience for them so that you can actually incentivize them to [00:12:00] purchase goods from you and continue to do that right on a repeated basis.

Venki: So this is a great example of how I have directly seen 1 of our customers leverage data to their benefit. And switch literally overnight to a new business model and continue on their business without a major disruption, right? We've also seen other examples Chris, when I talk about, customers using data, I've seen another.

Venki: Company Lululemon. I think a lot, we've all heard about them in the public media. This is a company that has done extremely well through their in store online through different channels. But it's awesome. Yeah. At one point, they also wanted to know more about their customers and their other preferences, and they all started expanding into adjacent areas.

Venki: So Lululemon acquired a company, Mirror, had this smart mirror which you can have a subscription to, and it can actually look at provide various other aspects around workouts and lifestyle and things like that. And now you're able to stitch that all of the data together to create a richer profile of the customers that you're [00:13:00] working with, or, or people who you want to be converted to your customers.

Venki: And that provides. An advantage for them to target those customers and to service those customers in a better manner. Yeah,

Chris: that's good. And these customers are doing like you mentioned, Lululemon, you think about Thanksgiving time, Christmas, all the holidays and but can you talk about how real time data is used in retail decision making and the roles of interoperable data and facilitating

Venki: this?

Venki: Oh, absolutely. I think first let's talk about interoperable data itself. Yeah. I think how many different channels through which a retail company has to interface with their customers, but as a customer, you and I expect the company to know us, right? Irrespective of whether I walk into a store, whether I'm interacting through an online channel whether I call into a call center and ask for a, a replacement or a return of a product that I purchased or I might have a question about a product that I want to purchase.

Venki: Yeah. Any one of those touch points, [00:14:00] our expectation as a customer is that the company knows me, right? The company does not know me as a ticket number 345 when I call into a call center or loyalty number XYZ when I walk into a store. The company knows me. That's my expectation. So think of what that means.

Venki: That can only be possible. If the customer data is unified and interoperable across those various systems, various business processes, various touch points or engagement points across the company. That is a no brainer. Now you can extrapolate that from, customer to other kind of data, whether it's product data.

Venki: Again, you have to surface the product data through the mobile app, through your own e commerce sites, through the search engines that actually surface information about the product from your website and things like that. Again the key there is interoperability unified data that is available in an interoperable manner.

Venki: Yeah. The other aspect that you asked me about was real time. Again, think of interactions that we have with any of these companies. Our expectation [00:15:00] is, yeah, I can go to Google, I can search for a product, I can get the search results within milliseconds and I can click on it and I can go to a website and I can complete a transaction maybe the next two minutes.

Venki: And I'm doing all of this while probably I'm in transit, I'm on my way to work or I'm sitting at my doctor's office and I'm waiting for me to be called in. I've got five minutes, right? That's when these kinds of things are happening. Most of the retail transactions are actually happening on the move.

Venki: Majority of those are happening from mobile devices, and there is a reason why, right? Real time is the name of the game. There is no other, there is no other possibility. There is no other alternative. The old way of, you know Purchasing from a catalog, placing an order three days later, a confirmation email.

Venki: That's, I don't even remember which generation. 12 weeks later, get your

Chris: product.

Venki: Exactly. So I think I'm interoperable. The two keywords that you used are exactly the keywords that I think every data leader, every technology leader, retail company is focused on [00:16:00] right now.

Chris: It's funny. It's probably five to seven years ago, maybe it's longer, but Amazon really just changed the game of digital and real time and online and getting your packages to you within two days.

Chris: And now sometimes the same day. They really did. And now when you go to a retailer and. You have to wait even four days to get your, product. You're like, what the heck is going on? So I agree

Venki: with one of the aspects that Chris said we should not ignore, right? We talked about examples that I think we can all relate to easily because as customers, we see these things, but there are similar aspects that you can consider around that on this whole supply chain.

Venki: Typically detail, they are placing orders for their merchandise several months in advance. They have to be able to predict the demand way ahead. And that kind of demand prediction can only happen with a lot of data and insights that can be utilized with modern technologies like data science and AI to be able to predict demand in the most accurate manner.

Venki: The more accurate that demand prediction is, the better your supply [00:17:00] chain is essentially as essentially you're not either. You're not running out of inventory or you're not sitting on a lot of inventory that then you have to dump in the market at a huge discount. So there are other aspects of retail.

Venki: And again, I don't want to go into each 1 of them. Specifically, but I'm sure people listening to this podcast can appreciate the complexity of this industry and any part of that, where data plays a critical role, plays a huge

Chris: role. And this next question is 2 parts and 1 is. Can you discuss the role of predictive analytics in retail and how data management and unification tools are evolving to enable the capability?

Venki: Absolutely. In fact, we were just starting to touch upon that predictive analytics part, right? Unfortunately, this is a report that Gartner had published in 2022. So it's a little bit old. But this report basically said that retail respondents report lower reliance on data science platforms, right? So compared to other industries, it said that only about 10 [00:18:00] percent of respondents from the retail industry had used data science platforms.

Venki: And that 10%, just for comparison, it the same was the usage of data science and predictive analytics was at 28%. In digital technology and telecom, it was at 16 percent in healthcare, 15 percent in manufacturing, and I'm sure if you have, if you do the survey again today. These numbers would have gone up and I'm pretty sure the numbers in retail would also have gone up, but clearly retail was lagging in their use of data science and machine learning, but it's not for a lack of understanding or a lack of need or appreciation of what these technologies can provide in retail.

Venki: For example we have seen a lot of AI machine learning and predictive analytics that has tremendous possibilities, right? Just to name a few Chris, the whole edge AI using IOT for faster processing of inventory without [00:19:00] RFID tracking, things like that as just an, one example, and then how can that then lead into better inventory optimization overall.

Venki: That's just one area, one potential area of application of AI and edge AI specifically with IOT that is already being used in many retail companies. Computer vision is another example. Again, as a consumer, you might have seen examples of how you can do digital try ons, right?

Venki: Last time I actually bought my my, my specs, my spectacles I actually bought it from a retailer on online retailer. I did not go into an optometrist or, an actual store. I decided to try this online service, but I could just take a picture and I could try on different styles. And it will do it digitally and I can figure out which one fits my face the best and I can order it online and get delivered in in, in a week's time.

Venki: And the best part is the entire supply chain is optimized so that it's all just in time. They're not manufacturing and storing these frames and these specs and hoping them to be bought. They're doing it on demand based on what customers are [00:20:00] placing the order for, right? So computer vision has tremendous potential in those kinds of areas.

Venki: Visual, virtual agents, virtual assistants, especially today in the world of generative AI chatbots, chat GPT that we are living in that has tremendous potential in providing the right kind of advice to customers, helping them understand what is the right product for them, for their needs and things like that, or answering general questions about.

Venki: Any aspects of retail, which tremendously cuts down the support volumes for retailers to a large extent, right? In travel and hospitality, for example, an adjacent industry, there are virtual assistants that are doing entire travel planning for customers. Can I, think of a scenario in the future where I can actually describe the kind of event that I want to attend and the retail, a luxury, retailer could be able to recommend a wardrobe the entire set of, collection of things for me.

Venki: Absolutely. That's definitely a possibility. So there are tremendous areas of application, but all of this, if you look at it. Relies on the right application of [00:21:00] technology and data is the fuel for AI.

Chris: Yeah, it's funny is that was the second part of my question that you answered about emerging kind of AI, technologies and what that's going to do.

Chris: So I appreciate you answering that without asking, but that was great. Edge AI. I think it's going to be big. And the interesting. Piece that you mentioned, the advanced computer vision stuff. That's pretty cool. And how you described you buying your glasses online and, they're not carrying the inventory and they just do, that's, I think the, there's huge opportunity for the retail space.

Chris: Is there any emerging trends that you foresee for, in using SAS, like interoperable data for data analytics and retail, is there any? Thing there that you're thinking about.

Venki: I think in general the conversation that I have with data analytics or technology leaders in general, in retail and reading some of the reports from analysts and all, uh, one of the key terms that stands out for me is interoperability.

Venki: Basically, there is. [00:22:00] There's so much of technology available, which can be used in different parts of retail for different things. We just talked about even in the AI space, right? Computer vision, advanced computer vision or IOT and kind of these kind of things it is.

Venki: Impossible for any retail company to expect that all of these technologies will be put together in a completely integrated manner end to end from a single technology vendor or a handful of technology vendors. Retail is 1 of those places where you will see a lot of different technologies that needs to be put together in a manner that is specifically suited for the needs of that particular retailer, that particular company.

Venki: Yeah, so composable architectures is what everyone is working towards. Which means I should be able to take a piece of technology and another piece of technology and put it together in a manner that actually meets my business needs, right? So I can take e commerce, I can take computer vision, I can take my inventory management capabilities and integrate all of that together to provide an on demand, just [00:23:00] in time on, on, or on demand buying experience for my customer.

Venki: That involves a number of different technologies coming together. And that means you're looking at composable architectures being the. The norm in retail industry, just like it is in many other industries as well, but retail specifically, right? Because of the the different business models, different engagement channels, different parts of the business that they need to work together and things like that.

Venki: And basically composability is only possible when these systems can talk to each other with standard interfaces and contracts. And that means even the data needs to be interoperable. In terms of emerging trends the 1 of the main trends that I see, I foresee it's not entirely new, but it will continue on in the retail industry is.

Venki: The retail technology leaders having to create composable architectures, plug and play architectures by using the best of breed technology, which are suited for specific applications and stitching it together to create the technology foundations for the business. And the [00:24:00] interoperable data is going to be a huge part of that.

Venki: And that is the only way retail industry will continue to be agile and nimble so that they can change with the needs of the time. Yeah, I

Chris: love that. Is there any advice that you'd give like B2C data leaders who are just beginning to explore the use of these tools and want to become more unified with their data?

Venki: I think the probably the most obvious. Thing which I'm sure every leader is already aware of is to work back from business outcomes, right? Real industry, like I said, it's continuously under pressure to create differentiation to innovate their business to grow and to improve their operational efficiency and things like that.

Venki: So we touched upon the broad areas of growth. Efficiency, risk and compliance. But it is important for leaders to really look at what are the specific outcomes that the business is looking for within the next, let's say, 12 months, 18 months a time frame and identify [00:25:00] and prioritize the right investments that gets them to those outcomes.

Venki: It is really hard to start from a technology point of view and then say that if I build the best technology foundations, then everything else, all the other problems will get solved. We have to really invert that and then really start from, okay, what outcomes, what time frame, and then based on that, prioritize the right kind of investments and really start with bite sized investments.

Venki: One of the things that is, it's a no brainer. I don't know why I even have to say this, but, successful leaders always are able to show. Iterative value. They start with something, prove it out, get people to buy into their concepts of the vision create champions in other parts of the organization and then build on that success.

Venki: And that is no different in retail as well. So thinking of AI applications of AI, there are so many different areas that you can apply in. But a smart way to approach that is, again, let's talk about the virtual agent or the chat bot kind of example, right? Or generative AI, where would it make the most impact?

Venki: Is it in [00:26:00] my front end sort of eCommerce application, trying to guide and advise customers or is it in my contact center where it can reduce the burden on my support staff who are supporting calls from customers? Think of the time right now, right? This is interesting time. We are doing this recording.

Venki: Right after the holiday season, and I'm sure contact centers for most in retail companies are inundated with the calls regarding returns or, orders not getting delivered and things like that. How many of those things can be automated away and does that produce or provide a good ROI on the investments that they're making?

Venki: And if you're able to identify those kind of specific areas or use cases. and really build the solutions for that, prove the value. I think that is probably the best outcome that the data or a technology leader can create for a retail company today. Yeah, I love the

Chris: examples. One, he said, start small, find the business outcomes.

Chris: What are they? Maybe get with the, let's think about not just the business outcomes, [00:27:00] but, let's have a a case study or, a thing just to focus in on. I love that. So last question as we wrap this up what are some final thoughts or key takeaways?

Chris: Would you like to leave our listeners with regarding the future of data driven retail? I think

Venki: you asked quite a few very pertinent questions today, Chris. Let's just go and recap, right? Yeah. First was, the key growth drivers or, what are the key priorities that are actually focus areas that are actually driving priorities?

Venki: It falls into increasing growth improving operational efficiency and dealing with data, especially from a risk compliance perspective. In a responsible manner, right? We talked about how technology is a foundational as a foundational. I can enable a lot of that and how interoperable data is critical.

Venki: And for me, the biggest takeaway, at least the 1 thing that I want to highlight is really. Retail industry again, probably not very different from others as well, but really industry more. Is [00:28:00] reliant on composable architectures and interoperable data just because of the different aspects that they need to deal with all the way from customers on the on one side.

Venki: Through multiple channels, interacting with the company, the variety of products that they deal with and the supply chain that they need to optimize and the inventory management capabilities they need to put in place. So the, just look at the complexity of these companies, the businesses they run.

Venki: There's a lot of that which can only be enabled through composable technology with interoperable data at its foundation. So for me, the biggest thing is for technology leaders and data leaders to focus on those aspects. And the other part is. Efficiency improvements are going to happen and in leaps and bounds, especially with applications of AI.

Venki: So identifying the right areas where I can provide that 10 X return on investment and really prioritizing outcomes 1st, and really trying to focus on that as opposed to going and building or focusing on technology 1st is the need of the hour. Leaders and [00:29:00] companies were able to focus on that and provide a compelling experience for their customers, employees their suppliers and others they will actually.

Venki: Differentiate themselves from the rest of the crowd and they will be the ones to grow.

Chris: Thank you. Great stuff, man. I appreciate you coming on to the data driven podcast. So thank you everyone for tuning in to another data driven podcast. I'm Chris Detzel, and thank you Venky for coming on again.

Chris: And please don't forget to rate and

Venki: review us. Thank you, Chris. My pleasure. Thank you.