Janie and Jack: Turning retail tech into business growth

[00:00:00] Madhav Kondle: Uptime was never one of the key metrics that I discussed with business because, okay, what's the purpose of having a 99.99 up if conversion is down? So we always talk about business metrics more than tech metrics with business. When I talk to the business, the tech metrics are within the engineering team. But when I discuss the business, it's primarily how do we increase the traffic? How do we increase the conversion?

[00:00:25] Kailin Noivo: On today's episode, we're gonna dive into some of the tips from one of the best CIOs with a ton of experience that I've ever spoken to before. Someone who has a ton of experience with taking brands into a company, taking them out, and building a single pane of glass across multiple teams in a complicated way, so everyone's working off the same data and really turning tech into profit. So really excited for you guys to hear that today.

[00:00:52] Kailin Noivo: Welcome to another episode of the Ecommerce Toolbox Expert's Perspective. Joining us today, we have Madhav, who's the CIO of Janie and Jack. Welcome to the show.

[00:01:02] Madhav Kondle: Thank you, Kailin. Thanks for having me.

[00:01:04] Kailin Noivo: Yeah. I always like to start off with a bit of an introduction. So why don't you take us through your career journey? You actually joined Janie and Jack at a really interesting time when it was being spun out from the Gap. So maybe talk to us a bit about your career journey and how you ended up in your role.

[00:01:19] Madhav Kondle: Yeah. Actually, I was part of Janie and Jack even before that. So I started my career as a software engineer, always in within retail. Worked for JCPenney, Cisco Foods, Walmart, and Safeway before joining Gymboree, the original company of Janie and Jack, in 2013. Till that time, I was part of the services industry, joined the industry in 2013; Gymboree, Crazy 8, and Janie and Jack were three brands. In 2019, we went through the bankruptcy process. Gap acquired Janie and Jack. As part of that transaction, I joined Gap, stayed back in Gap doing something else for Gap, and again joined Janie and Jack a couple of years later. So, Janie and Jack has been one of the best premium children's brands, and there's a lot of brand value for that, and I liked working for Gymboree, so that was one of the reasons I came back to Gap, from Gap to Janie and Jack. And recently, we acquired one more brand, Hatch Collections Maternity. So we are trying to build more premium brands for the mom.

[00:02:33] Kailin Noivo: I love it. And maybe talk to us a bit about the spinning-out process. How was that from, like, a technical standpoint, high-level? What kind of, if someone's listening here that may have to go through something similar, do you have any kind of high-level piece of advice there?

[00:02:51] Madhav Kondle: Yes. So when Gap bought us from Gymboree, the idea is to integrate with Gap ecosystems because if you think of all retailers, more or less, whether they're big or small, they have similar size type of technology needs right from merchandising, inventory management, various management, transportation, ecommerce, point of sale, right? So there'll be a lot of overlap. So the idea was to slowly pick and choose and see which tools will integrate with Gap and which tools will remain as is, and at that time, Gap was going through their own diversification plan of splitting out Gap and Old Navy, which did not pan out. And then they've decided to offload Janie and Jack again. So we started that integration to Gap systems, but luckily, not much of the integration happened. So we were able to retain what originally Janie and Jack had, but then we started to rebuild the pieces again. So, for example, we are rebuilding the ERP, we are putting a new PLM system, so during any transition, identifying the systems that overlap and then picking the right tool for the new integrated company would be the key when you're going through that transitions under the data conversions, and how do we make sure the data gets moved over from one company to the other company structure.

[00:04:24] Kailin Noivo: That's very cool. And maybe talk to us a bit about, you had to kinda do a pretty big tech overhaul. What did you do? Why? How did you kinda line up the road map to be able to hit your goals?

[00:04:36] Madhav Kondle: Yeah. So in 2017, Gimboree restarted investing in technology. So in 2022, 2023, we were already five years back from a technology perspective, and to support the business growth that we were experiencing from the pandemic and, with ecommerce growing, we had to revamp certain tools, primarily all the customer-facing, right? We had to put up a new order management tool and a new point of sale, because the older point of sale was a legacy, register-based system where the store associate was always standing behind the counter and not able to help the customer on the floor. So we had to go to a mobile-friendly POS. So we had put a Salesforce now Salesforce, earlier request PredicSpring, and then we revamped our order management system to help us grow wholesale and then all the omni-channel capabilities. Then commerce cloud, we were on the older side, SiteGenesis architecture from 2017, so we had to upgrade to the new SFRA structure. We had put a new ERP system, we had put in Dynamics 365, and we had put a new PLM system because our older PLM system was almost a 17-year-old system, so we had to replace it with that. So there was a combination of technology that is almost at the end of life or already end of life in the case of PLM. And, also, to support the business growth we are experiencing, we had to make smarter chances, to make newer technologies that will be easy for a smaller-scale company to manage and maintain without larger engineering teams. And we are to move from a legacy data warehouse system to a Snowflake system. So we did make a lot of changes in the last two years.

[00:06:30] Kailin Noivo: That's a huge, huge road map. How are you now thinking about moving forward? Like, is it optimization time? Do you feel like you guys still need to invest in more foundational stuff? Like, how are you thinking about that moving forward?

[00:06:44] Madhav Kondle: I think we are 90% there on the foundational stuff. We are growing our wholesale business, so we are trying to find out what additional system we need to add from a foundational basis to support wholesale growth. But now it is a time to optimize and drive value from all the tech investments in the last two years, right, or two plus years. So how do we utilize and deliver more value from the platforms, work with business, and see how better they can utilize the tools that are already in play, and then drive business growth from those tools? I think we are at a stage where we need to up our value realization from the investments.

[00:07:27] Kailin Noivo: Very cool. And from your perspective, like, what are some of the types of KPIs that CIOs should be reporting on in retail? Like, is it kinda your traditional tech KPIs where it's uptime, through point of data, things like that? Or do you think they need to start kinda blending into business KPIs, like conversion rates and things like that? Like, how are you thinking about that?

[00:07:52] Madhav Kondle: In the last three years at Janie and Jack, uptime was never one of the key metrics that I discussed with business, right? Because, okay, what's the purpose of having a 99.99 up if conversion is down, right? So we always talk about business metrics more than tech metrics with business. When I talk to the business, the tech metrics are within the engineering team. But when I discuss with business, it's primarily, what are we doing that can help them with the traffic improvement, right? How do I increase the traffic? Then, once the traffic is there on the side, how do we increase the conversion? When the conversion is there, how do we increase our basket size? How do we increase our average transaction value? How do we then look at, what's our return percentage, right? So, primarily, our discussion in business is always business metrics and how tech is playing to help those business metrics. And being a small team, it's always challenging to keep adding new capabilities, right? So we look at the ones that really drive business value and add them. So we don't just go ahead and keep doing things for tech reasons if there's no real business value.

[00:09:13] Kailin Noivo: No. That makes a lot of sense. And, obviously, you're managing; you guys have a lot of stores as well. Talk to us a bit about your data strategy from a high level. How are you kinda leveraging in-store POS data, your ERP data, your online data? Like, were you spending a lot of time manually kinda fixing that with the data pipelines before you did those projects? Maybe talk to me a bit about that from a high level.

[00:09:39] Madhav Kondle: Yeah. So from a data perspective, again, as part of the last two years, technology road map, we moved to Snowflake. Earlier, we were in a legacy data platform, so we moved to Snowflake. And we have built real-time data pipelines from order management. So one of the reasons why we went for Salesforce order management is to make sure that we have one single place where I'm pulling both in-store purchases and online purchases to the same order management database. So all our POS transactions and ecommerce transactions are in Salesforce OMS, and I do a real-time data pipeline using Salesforce data pipelines to push the data to Snowflake. So we have that real-time data available in Snowflake from transactions that are happening in the stores and in the ecommerce. From a customer data perspective, we have built our customer 360 recently on Snowflake, again, pulling both POS data and ecommerce data together and merging it with the transactional data and inventory data. So we have a solid foundation where we have all channels' data along with customer data in one place, and we are building on the same data platform to pull in other data that doesn't exist at enterprise level today. For example, we do not deep dive into Google Analytics data and combine it with marketing in real-time. So we wanna bring Google Analytics data into the same data platform and then use it for marketing, right? So we try to keep the architecture as simple as possible, and make sure that all the data sources are connected to a single place, and we are trying to build one single data platform in Snowflake.

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[00:11:45] Kailin Noivo: You sound like you have a pretty deep understanding of, like, functional KPIs, like how marketing interacts with tech. I have the benefit of speaking with a lot of different CIOs, especially at scale. And yeah, like, candidly, sometimes I find folks lack the functional context that you're describing here. Like, maybe talk to us a bit about, how did you get in the weeds there? Like, what are some of your tactics to actually get that type of knowledge? Because yeah, I mean, if especially if you're a technologist, it doesn't always necessarily come.

[00:12:16] Madhav Kondle: Yeah. I think it's how you grew up in your career and what roles you've been playing, right? So though I started as a developer, I then moved on to doing solution architecture. So my strength is building solutions and solution architecture, right, where having an end-to-end understanding of the entire business process, so that's easy to meet with business, understand their needs, so that we can come up with solutions that are really solving business problems. So, I get involved in all functional technical solution design sessions. So that's why I keep myself close to the solution that we are building. So I know exactly how the data flows from one system to another system and how the data is being used per business, and what different business processes are being run in the company. and what processes are being used for, and what tools are being used. So I stay involved with business very closely, so that way, I really understand how we are using or how we are enabling business to deliver their KPIs.

[00:13:28] Kailin Noivo: Very cool. It sounds like you've put a lot of thought and effort into data architecture, data flow. Do you feel like your team is kind of unified across different organizations, from marketing to technology? Are they looking at the same data sources, or do you find that there's still some data silos where it's like, hey, I'm seeing this, and your team's seeing that, and it doesn't exactly line up in all cases?

[00:13:52] Madhav Kondle: 95% both the business teams and the IT team are looking at the same data sources, but there are a few areas where business may have other sources; that's what we are trying to identify all the sources and bring them in. For example, when they're looking with marketing, right, nside marketing, and they're working with different advertisers or third party, things, sometimes I may not exactly have the data that they are working with, but when they come to us and ask for details, so we try to understand what how are they going to use the enterprise data for that purpose, and what are they combining with, or what are they adding to that so that we can at least help them, how enterprise data can be consumed in those discussions.

[00:14:48] Kailin Noivo: That's interesting. I just did a LinkedIn post on this, and it was, I'm starting to see more and more, and it's definitely in mid-market and scaled businesses like you guys, kind of more on the enterprise. A lot of internal time is spent, almost aligning on what the right KPI, like, there's these different systems, and it sounds like, obviously, you guys are not really in this camp where you have this dashboard for conversion, and then you have this dashboard for conversion, and this one includes returns, but that one doesn't sometimes, and then this. And you end up just honestly arguing over who has the right data across different teams.

[00:15:28] Madhav Kondle: Yes. That's the biggest challenge. So that's what we try to bring back everything to enterprise KPI and then see it, for example, I'll take, we use Google Analytics, right? Google Analytics may have some traffic number, and Salesforce Commerce Cloud will try its own traffic number, right? And some of the orders or the conversion to the metrics will differ, but when I do my KPI reporting, which is more of an audited sales, which go through all your financial auditing needs and filters out fraud orders or other things and then report it out, it will be a different number. So we make sure that what is our base that we wanna use. And we have always said that whatever is in enterprise, that becomes your base. And we always try to compare it with that, and then not worry and try to avoid looking at some numbers from other systems because they may give you a different number, like in case of this conversion, because he's counting extra two orders, which are fraud orders, which I will not be counting in the enterprise-wide system. So we do that. And we have those discussions on having that understanding helps when we have meetings with the marketing team who comes, and says, "Oh, my tool says this. Why are you saying it is different," right? So having that understanding helps us to ground the discussions. And, yes, we do have those situations, but we are able to come to conclusions faster because we understand the different data sources.

[00:17:07] Kailin Noivo: And now that everyone's kind of trying to build their own, we'll call it agentic interface on analytics, right? That magic experience of, "Madhav, what do I do today to double my conversion on mobile," right? Like, talk to me a bit about how critical you think data structure, single source of truth, data cleansing, like, is it more of a data problem in your perspective than it is an AI problem, or do you still think the foundational models are too far away from being able to do this?

[00:17:40] Madhav Kondle: I think it will primarily be the data problem. When I say data problem, making sure that you are providing a consistent and reliable source for your Gen AI on which you're trying to ask, right? If you ask Gen AI and point it to a data source that is inaccurate, it's going to give you strategies based on that inaccurate data. So, ensuring where you are applying your Gen AI becomes very critical because you don't wanna apply it on some data source that is wrong, right? And understanding the semantic layer so that you speak the same metric in all places is critical. So getting the semantic layer that is common across different platforms and getting the data right becomes distinct. But I think GenAI helps you to 60 or 70% of your productivity gain, but doesn't take you to 100%. Still, you need to make sure how you take it from that to the endpoint before you make any decisions on that. It'll help your discovery faster. It'll help you cut down on your repeated analysis. But I don't think that you can ask until it tells you the exact strategy that you can just blindly implement, right? It may, it'll give you, maybe, a different insight that you didn't think about on how to improve conversion. But I don't think that you can ask a Gen AI tool, and it says that, "Oh, send a new campaign with extra 10% with this targeted audience." If you try to just execute it, I don't think you'll get the result. You'll like to, oh, it's asking you to do this, that means it may be adding a new flavor of identifying a new audience set, or it's asking to do a new tactic. I don't think they are there that can tell you everything, but it can; it'll speed up your discovery and analysis.

[00:19:38] Kailin Noivo: I think you're bang on. I call that the magic machine. People are trying to build the magic machine of, like, you press a few buttons, and it's like the perfect outcome. You know what I mean? And I think data is a part of it, but I think you're right. I think it's closer to analyzing and investigating than action, if that makes sense, and recommendation.

[00:19:56] Madhav Kondle: Yes. It'll help you because you may be overlooking certain common themes in the data that you may not even see, and it may help you identify those.

[00:20:05] Kailin Noivo: Makes sense. And as we look to wrap up, like, what's kinda top of mind for you going into F26 planning? Like, what's keeping you up? What's top of mind?

[00:20:14] Madhav Kondle: So, for us, we are doing an international expansion. So Janie and Jack, as I said, is a premium brand, and we want to appeal to the international customers. We do a lot of international shipments from the U.S, but we wanna grow and establish our own presence internationally. So that's our focus for next year. So we are opening our first store in London, and then we wanna grow our wholesale business. So those are the two priorities. And the third thing is from an investor perspective, they want to keep looking and buy premium brands that serve the premium mother. So our mission, our vision is to be a service provider for different products, a premium mom is looking for, right? So the investors are constantly looking to buy other brands and other businesses that serve in the same space. So my work is to see how I'm building a platform that I can easily integrate. Going back to your first question, when I buy a new brand, how do I integrate? Say, as I said, we bought Hatch Collection, which is a maternity brand, last year, and I was able to easily integrate with Janie and Jack platform.

[00:21:27] Kailin Noivo: That's really cool. I mean, it makes a lot of sense. I wanted to thank you for taking your time. This was awesome. Great episode. Thank you so much for hopping on.

[00:21:39] Madhav Kondle: Thanks for having me, and I enjoyed chatting with you.

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