Unlocking Customer Delight: How This Product Manager Transformed Insights into a $250M Success

Sure, um, So, I mean, the first thing is how to identify those wins, right? Is you wanna develop sort of like a, a practice that you have the opportunity to grab those wherever possible. And I think the way that I um put, like, myself and my team in in the position to do that is, like, a very, very deep understanding of the customer, who they are, what they need, what's important to them, you know, what are their short-term and long-term goals, um. You know, if you're selling to a business, you know, what, what is organizationally, um, impacting in, like, decision making and things like that. Um, same with technology, so, right, with regard to engineering design, um, what's possible, how are things built, um, what's easy, what's hard, Obviously, that's always their call, but, um, you know, understanding the architecture, uh, well enough to To know how things are developed and understand, like, logically, where things can be added, uh, easily versus not, or, um, you know, where deficiencies are, what needs to be covered for that. So, um, yeah, and then, you know, also in the context of the long-term goals of the business and financial metrics and things like that and targets that we're looking to hit and why, um, and how that breaks down into the financial statement of like EBITA versus revenue and margins and things like that. Um, so, You know, I, I, uh, try overall to be incredibly knowledgeable about all of those things on an ongoing basis, um, so that when it comes time to making a decision that we have the opportunity to delight the customer, I can sort of make that call. So an example would be, um, when working most recently at UTech, you know, I, I led the product team there. I was responsible ultimately for around $250 million in revenue. I had a team of 9 people, we set the direction for another 75 in, uh, engineering, design, and QA. Um, and we built, uh, we had a retail technology platform that serves some of the largest, uh, retailers in the US, primarily focused around grocery. Um, so, this was a platform that, um, among other things, again, sort of, it was like B2B2C, um, where we had brands creating, um, like advertising, promotions, coupons, um, for Uh, application at various retailers. So, our, our retail customers were calling our APIs in order to get data for the promotions and displaying that on their website for their customers. Their shoppers were then logging in, you know, we were returning a relevancy algorithm in order to sort the promotion in the right order. The customer would click on, uh, let's say a coupon. That would be added to their loyalty card, they would go to the store. Fill their cart, go to the checkout. At checkout, the cashier would scan all the items, and then point of sale would call our platform APIs with the basket. We would return back what the promotions were and um apply them and what which ones should be applied to that basket. Within 50 milliseconds, um, because that's the, the requirement that our customers require in order to ensure, like a good customer experience within the lane. And then the customer checkout, they would get their discount, like 50 cents off milk or whatever. And, and then proceed on to, you know, go to the mobile app again and check for more promotions. So, all of that infrastructure

uh, just the next time you go back,

no, no, yeah, yeah, just the next time you go back. So, um, so the situation is, so prior to having this opportunity to build this feature, right, that's how our system worked, and, um, there is a huge desire of from brands, right, to be able to identify and target customers. In order to nudge them to purchase just a little bit more of their product. And the industry standard with regard to ability to to target these types of promotions, um, It was like lo fi, right? Like, it was not very elegant. It was very, um, you know, we'll look at data from a month ago or 6 months ago and identify, uh, this group of user in spreadsheets that will then, you know, remove segments from and then be able to sell this to a brand. They'll be able to OK it, purchase it next month, the data needs to be all run again to be updated for, you know, the, the past month, and then it'll be created in the system and flow through to the retailer. So that's what existed. Based on our architecture and our integrations point, we had the opportunity to create, uh, essentially, um, real-time targeting. So that rather than have to do all of this data analysis, which involved teams of people and was very static, um, in its ability to create and deploy, Um, We have the ability to read the data from the point of sale as the transaction completed. And then process it. And in near real time allow a brand to uh Essentially create a promotion. Uh, where users could drop into the targeted segment, like even throughout the month as they, they met the purchase requirement for that segment, um, and so, and I can just give an example of what that is if, if the kind of value wasn't clear, Essentially moving from I understand, I, I get it. OK, yeah, so obviously that increases both, um, the amount of money that brands wanna spend. And it increases the efficacy of their promotions, so it increases their ROI. And so, knowing this, uh, we created this feature. Um, of course, in this instance, right, uh, while the money is coming from brands for these promotions, our customers and, and the people that we're pitching are the retailers. Because they're the ones who like sign up with us as a technology partner and their sites call our API and their point of sale calls our platform and so no one asked for this feature, right? Everyone sort of said, oh, it would be like great if this could happen, but it's too hard of a technology problem to solve and da da da, um. But we were able to build it, um, and so, when we went in and pitched to retailers, this was a key differentiator in, you know, how we could enable them to create a promotions network and leverage brand dollars to the benefit of their customers. And so we went from You know, almost like winning, you know, 0% of sales, uh, it wasn't that low, it's like under 10% of, like, our at-bats, did we actually close the customer to, like, over 60% in the span of, like, 3 years. Um. And then, you know, we also got the opportunity, um, To pitch Walmart, who, uh, we closed from a Desire perspective, um, but I believe it's still tied up in legal for the last 6 months, but we were chosen as their preferred technology provider for this, uh, for this functionality in part based on that feature.

Yeah, so to clarify, the 50 millisecond requirement was the response time for um the callback. You send us the basket and we send you what promotions are applicable. Yep, I get it. Yeah, um, I do, yeah, so, uh, the, the processing of the data and the targeting, um. It it it happened not over 50 milliseconds, it happened in the background, um. And like I said, it was in sort of near real time but not meeting that 50 millisecond cause the point of sale didn't need that information, um, but the reason our customers, so the 50 millisecond was, um, provided as a requirement for one of our customers, and we rolled out across the board and made it an SLA for all of our customers, um, and the reason being is that you have customers that are waiting in the lane, right, in order to check out, and like their in-lane experience is a huge part of their grocery. Um, shopping, and if they have to wait in order to understand which coupons are gonna be applied, or like, which, um, Uh, You know, loyalty points they're getting credit for, then it creates a, a bad experience for everyone. Um, and if you look at the volume of, um, transactions that are being processed at a retailer's every hour, right, um, Even just an increase of a couple of seconds of waiting for a receipt to print is, it's gonna matter, like, at that volume, right? And so, um, it was a requirement that came from a customer, um, we met it for one customer, and then once we met it for that customer, we made sure that it was consistent across the board, and, um, yeah, I, I think it was a valid requirement.

Uh, sure. OK, so I can clarify a couple of things. Um, you know, for, for customers that didn't request it, um, we didn't update their SLAs in order to give it to them. It was a requirement of the system when, uh, it, it was like a performance requirement across the board of the system, um. That wasn't necessarily customer facing for existing customers. Um, the reason that it needed to be, uh, a performance requirement for the rest of the system, sorry, in, um, more detail is that, um, so the way that you, you know, you pitch, uh, retailers, or the way that we pitch retailers in order to sign up for their, um, to be their technology provider in this instance is, um, Essentially, they have, like, a checklist of requirements that, you know, they've developed, and then in some cases, they put out a formal RFP, in other cases, they invite you in to have a conversation, but they have, um, specific requirements. that are kind of created internally, um, where, you know, you need to meet in order to be considered for one of the technology partners that they're gonna, um, work with. So, Uh, You know, in this instance, being able to say that you have a 50 millisecond return, um, or that you have a return of, like, the, um, Commit call in this instance, um, within 50 millisecond SLA, it is one of the, um, Like check boxes that they're gonna evaluate on compared to other vendors, and it was industry leading, right? So, like, no other um No other technology provider in our space could deliver that type of performance, and the inlay performance is hugely important in winning sales, uh, and also, um, you know, it's, it's Well, it's a, it's a key part of the customer experience for, for these retailers, right? They, they think a lot about the inland experience and it's a big um It, it's something that a lot of attention is paid to, and so, We, yeah, I mean, we had to do it for one customer, um, and so then we, um, we included it in our pitch for other customers, and I think it was part of what helped us win sales.

Uh, this specific functionality or the 150 millisecond part?

Yeah, so I mean the retail space, um, when you're talking about medium to to large size grocery retailers, which was our sweet spot, uh, within the United States, you only have like a couple 100 right? And so, um, and their contracts only come up, uh, you know, depending, like, you know, every 1 to 3 years, um, so in any given year, you're only really talking to maybe like I don't know, let's just say 100, it's, it's probably more like 50 or 75 retailers, but, um, just for the sake of math, you're talking to like 100, right? So, um, You know, previously, prior to having this feature, we were getting at-bats, maybe like a couple times a year, um, invited into a conversation, asked to submit an RFP, um, you know, it was in the 10s, it was in like the low 10s of, of times we were getting invited, um, by You know, um, a couple of years later, we were, um, in most every conversation for retailers that were considering new service providers, um, and winning, you know, like I said, 60% of those. Um, the, I, I was actually in the, those conversations, um, for a large majority of them because I was doing, uh, demos of this functionality, and, and I can tell you that it was one of, you know, It was one of, for sure, in every conversation, it was one of the top 10 things that was discussed in detail and um a lot of focus was put on, and I would say, you know, in In, again, like the majority of his conversation, it was, it was maybe one of the top 3, Reasons why we were sort of selected and what they were most interested in taking advantage of when we onboarded them and rolled them out. Um, so it's, it's difficult to differentiate in this, in this space, um. Uh, because a lot of the functionality is very like Me Too, um, and building this and, and being able to, to speak to it, uh, competently and demo it and, and talk about where it was live in market was one of the ways that we, we were able to, to grab and hold a differentiator that no other competitor could touch, and so it was meaningful within those conversations, especially with, like, the larger retailers like Walmart.