Emily 0:00:05.2:

Welcoming Lindsay DiPietro, senior director of product management at Anaplan, and Alex Khan, VP of sales at Syrup, for changing the retail planning paradigm, winning with Agentic AI. 

Lindsay DiPietro 0:00:18.0:

Hi, everybody. Like Emily mentioned, my name is Lindsay DiPietro. I lead our product management function for retail, and when I mean retail, I mean retail products, so think merchandise, financial planning, assortment planning, allocation replenishment, pricing and promotions, that's kind of my remit of responsibility. I'll let Alex introduce himself. 

Alex Khan 0:00:37.1: 

I don't even know how to introduce myself at this point. We were just acquired by Anaplan 22, 23 days ago, something like that, so my title was VP of sales. I'm going to continue going with that until someone tells me otherwise, but we'll talk a little bit about Syrup and how we think we're going to fit into the Anaplan ecosystem. 

Lindsay DiPietro 0:00:57.6:

Obviously, retail is super-complex. It's relentless; it changes every day. It depends who's in office. Maybe tariffs are a problem today, and tomorrow, and the next day. You guys know how it is. You're also trying to deal with your consumer. Their buying behaviors have changed, with the advent of e-commerce and the explosion through COVID. What channels are important to you has evolved over time, and how much is e-commerce versus in-stores, and also the swing back to how many people are actually shopping in stores now, and having to deal with that relentless change. So that's one hand dealing with the supply disruptions, your consumer, and then on the other hand, internally trying to deal with the copious amounts of data that you ingest in, that you get from your transactional data from the stores, and dealing with inadequate systems, to be honest. I was an allocator and a planner back in my time, and we definitely had a lot of systems that didn't work really well, weren't connected, decisions were made in siloes. So, you guys are very familiar with the pain points that the teams that you work with, or you yourselves, are dealing with on a daily basis. 

Lindsay DiPietro 0:02:14.7:

There's a big cost to that, right? These aren't the only two costs. There's also your employee cost, your satisfaction of your employees, and how they actually manage their day jobs, and how tactical it is now, and SEM strategic. You don't have time to think anymore. There's real cost too. There's the cost of stock-outs, not having the right product in the right place at the right time, like we like to say, and there's some pretty big numbers up here, so that also means there's huge opportunity on the table. Anaplan, as a whole, we have a number of different verticals within Anaplan, as it relates to retail. We're actually joking about this. We're only allowed to use certain logos on the screen, but we, obviously, work with a number of retailers, tons of luxury brands. I can't name a couple of other ones that are, obviously, in the room, that use Anaplan. I also came from Lululemon, who uses Anaplan, and so we have quite the number of clients specifically in retail, and it is our fastest growing vertical within Anaplan, so supply chain and retail planning.  

Lindsay DiPietro 0:03:31.8:

So, what are we doing in retail planning? Well, I'm here to transform retail planning. That is why I moved to Anaplan, because there's huge opportunity. One, integrating all of that data together, connecting it on one platform; ensuring that you can use it to your advantage, it's not a detriment. So, it's not always about making really detailed decisions; sometimes it's about pulling back and saying, 'What decisions do we actually need to make?' I think that's a question that you should constantly be asking yourselves. If you're trying to transform your retail practices within your organization, I think a lot of the time what happens is people just want to keep repeating the same things. They want to keep doing the same thing that they were doing a year ago, or two years ago, or three years ago, and that just doesn't work. Things are changing, and we need to start asking ourselves the question, 'Well, what decision are you actually making? Why do you need to make that decision, and what information do you need to actually make it?' Sometimes you'll find that it actually eliminates a number of decisions that you could actually automate, or present a recommendation, instead of fully automating it, and taking people out of trying to stitch together themselves what the answer should be. So, moving people from that very tactical execution to a more strategic planning role can be quite transformational, and also quite challenging on the change management side.  

Lindsay DiPietro 0:05:05.0:

This is what we're trying to drive in retail planning, and part of that strategy, like you heard on stage this morning, is the application strategy. So that's why I joined Anaplan, was to build out the application specifically for retailers. That includes merchandise financial planning, assortment planning, which is coming in Q1, and also allocation replenishment, which strategically acquired some horsepower there with Syrup. Really, applications are meant to get the time to value. It's meant to be indicative of the best practices within the industry, that you can then take and mold to what it is that you need. Every retailer likes to think they've got a little bit of secret sauce. You all do, it's true. The best part about Anaplan is that you get the flexibility of the platform, but you also have out-of-the-box best practices that cover probably 60 to 80 per cent of what you're going to need. The intention is not to cover 100 per cent, because one customer might do something in one particular way, and another might do it in a different way, or you want to actually incorporate that really secret sauce, so it's not meant to be all-encompassing, but our flexibility of platform allows us to make those changes quite easily. 

Lindsay DiPietro 0:06:28.9:

So that's what we're working on, and that's the evolution for us. When we walk into customers', you'd actually be - I don't know if you wouldn't be surprised - but I'm still surprised that we often look like the left. A lot of retailers are still using Excel sheets for so many of their processes, it sometimes is mind-blowing. It's like, 'Okay, we do MFP on Excel.' All right, I can understand that. It's a big financial cashflow type statement. Cool. That's fine. 'Oh, we also do assortment planning in Excel.' I'm like, 'Okay, and you're a global retailer that operates in EMEA, and operates in APAC, and operates in North America, and how do you ever see a rollup of your whole plan?' 'Well, we don't.' 'Well, you're a $10 billion company.' 'Well, we don't.' I'm like, 'Okay. All, right, cool.' So there still a ton of opportunity. Like I said, you'd be very surprised at how many people are still in that. We're trying to move maturing approaches, 'Hey, let's connect within product itself. Let's connect MFP, assortment and allocation replenishment. Can you do that?' That, in and of itself, is a huge evolution from these things being either in disparate systems or in Excel. Great. We're moving in the right direction, like the three core - like the stool. Maybe there's a fourth leg there, but let's just say there's three legs of the stool. We've got those things on lock, and we're maturing our business, we're bringing best practices in, but where we're ultimately trying to drive is this last one, which is connecting not only within your product, but connecting upstream into finance, connecting downstream into supply chain partners, and having that driven by an analytical engine.  

Lindsay DiPietro 0:08:28.9:

This is a big reason why Anaplan acquired Syrup, because they're best in class in retail analytics, and we plan on leveraging that throughout our apps that we have today, and into our broader supply chain apps, and we'll talk a little bit about that, but what you ideally want is to have this brain that feeds different parts of this process. So that's the maturing approach. I would love to add another one. Eventually, maybe I don't need this legacy approach anymore. We've got enough people moved, maybe, and the middle one becomes the legacy approach, and the last one really becomes, well, once you're fully connected on one platform, I have all of your information. It's driven by best-in-class science. Great. Well, now, how do I get visibility into the end-to-end, because, guess what, I'm in merch planning, but I have a supply chain partner, and we report to different people, and we have different objectives. That's great, but I want to know if I can chase this product tomorrow and get it in six months from now, and how do I do that. That's where Agentic is really going to take us from just having analytics that support each one of these individual processes, to being able to go across the entire planning platform, and provide you recommendations on what's possible, address the gaps that in your assortment, find the opportunities. Even find risks in your plan. Great. That's ultimately what you want. So that's where we're headed. 

Lindsay DiPietro 0:10:06.6:

Anaplan has made a huge investment in the product side. You heard it this morning; we think over five years we're investing $500 million into our product, into our platform itself, and the capabilities that our platform offers. The other thing, the other big part of that, is AI, like how do we bring that to market, how do you have access to it. Anaplan's been in that for a really long time, since 2019 was really the start of Anaplan's AI journey. That's great. We didn't maybe do a great job explaining that we've been in AI for a really long time, but we're really moving along quite nicely here. Where we had Optimizer, truly an optimization engine that you can plug in, having Anaplan forecaster, which was previously called PlanIQ. Moving to phase two, which is kind of last year when we introduced CoPlanner, so this is conversational AI within the application itself, so the ability to ask, 'Hey, what were my sales in Q4 of 2023? Which classes are performing the best to plan?' as an example. That's really what CoPlanner's intended to do, is be this conversational AI within the platform, within our applications. Where we are now is 2025, where we're starting to introduce agents, so think about having a planning agent that can maybe reforecast your plan. That would be nice, right?  

Lindsay DiPietro 0:11:47.3:

So, these are some of the things that we're introducing now. Also, when you see CoModeler, so when you think about the development of the application itself, or the development of your model, so in the case where you're building your own model and not buying an application, we've introduced something called CoModeler, which is really like, thinking about ChatGPT, it would be like, 'Hey, can you build me a hierarchy that includes this, this and this?' and it will actually build the model itself. So, it's really a tool for our developers, our builders, and it's only going to get better with time, and we're going to be moving more towards this side, so into autonomous agents, and then like an agent studio, so where you can actually program the agents, and you'll have multiple agents. I might have a planning agent, and in supply chain we might have a supply chain agent, and in finance we'll have a finance agent, and they all talk together. So that's where we're moving with our whole intelligence, and a big part of our investment is, like I said, in AI, and being best in class in that, and with that, comes Syrup. 

Alex Khan 0:12:59.1:

Over to me. 

Lindsay DiPietro 0:12:59.8:

Yes. 

Alex Khan 0:13:01.3:

Does this mean we're phase six? Is that how that works, it's phase five, and then Syrup is right there? 

Lindsay DiPietro 0:13:05.5:

Yes, we should plug it in there somewhere. 

Alex Khan 0:13:08.6:

It's amazing to be here, amazing to be part of the Anaplan community. The acquisition, actually, just closed, as this slide indicates, on September 9th, but we knew Anaplan... 

Lindsay DiPietro 0:13:19.7:

Thank you. 

Alex Khan 0:13:20.5:

Congratulations, yes. We knew Anaplan for quite some time before that, as is often the case. In fact, what was happening is, retailer after retailer, we kept walking up and finding that, hey, Anaplan is already there from a planning standpoint, and we're bringing best-of-class demand forecasting, which powers allocation replenishment, workflows, buying workflows, and so there was an opportunity we found, and obviously that's been validated now, to partner up in such a way that, again, you combine that best-in-class forecasting, that best-in-class AI, with best-in-class planning from Anaplan. So, we're only a few weeks in, but, again, the relationship has been going on for some time now, and we think the sky's the limit. We think this is really a wonderful, complementary opportunity, and an opportunity where it's really one-plus-one equals three. So, lots to look forward to. I'm going to give a little bit of background. The high-level goal is to infuse intelligence and decision support at every step of the supply chain. So, again, using workflows and applications from Anaplan, and infusing the AI in the demand forecasting, in the intelligence of Syrup every step of the way. Now, I'm much newer, obviously, to the organization relative to Lindsay, so I may have to turn around. I thought you were very good, by the way... 

Lindsay DiPietro 0:14:39.4:

Oh, thank you.  

Alex Khan 0:14:40.0:

...being able to deliver the slides without turning. This is, again, week three, so I may have to do a little bit of this. In terms of the background of why Syrup came to be in the first place, we were founded five years ago, and the original hypothesis was retailers are sitting on terabytes and terabytes of data. They've got all this wonderful transaction data, inventory data, customer data, you name it. There's no shortage of data within a retail environment. There aren't that many retailers that are really maximizing the opportunity within that data, taking that data, working it through AI models or engines, and coming out with tactical, tangible recommendations on how to make better decisions, how to act smarter and faster within the organization. So, our founders, with the proliferation of these AI models and the availability of data - many retailers are moving their data to things like Snowflake and Databricks, and so on - so it's easier in many cases to get at the data. We've got these AI models that we've built. We should be able to help retailers make better sense of that data, and really leverage it strategically. So that's what we've been doing for the past five years, is really helping retailers, primarily, around buying more intelligently, and then once they've got that inventory, allocating it, replenishing it, more intelligently using it, a stronger sense of demand through the AI-driven demand forecast models that we've built. 

Alex Khan 0:16:01.7:

There were three things that really stood out, in terms of our ability to go to market, how we competed. I think what brought retailers to Syrup, and what kept them renewing once they were on board with us - those are the three things that you see outlined on the right-hand side of the screen - first and foremost, it was the strength of the models. The whole value proposition really centered around building a better demand forecast. If you can forecast demand more precisely, especially in apparel retail, where you've got a lot of newness, new items that the world has never seen before, so how do you forecast demand for that? If you can forecast the demand in terms of the timing by which we're going to need to replenish these products into what stores, at the SKU level. This is something that wasn't really being done in retail prior to the arrival of Syrup at those store SKU week level, so at a super-granular level, and the models themselves are more flexible than anything was available on the market in that. We're able to pull in all kinds of disparate data sources, so not just that data that existed within the four walls of the retailer, but also external data - it could be weather data, it could be local events data, it could be competitive data, social medial data, you name it - the models were able to ingest all of that, and again, spit out very tactical, tangible recommendations on what to do with your inventory. 

Alex Khan 0:17:24.8:

That second piece is the actionability, if you will, of the insights. Like, if somebody just comes and dumps a forecast in your inbox, it's really difficult to make sense of what to do with that. It's saying we need so many of such-and-such a SKU at such-and-such a store next week. Well, how much of that SKU do we already have in the store, how long does it take to get inventory to that store, and so on and so forth. So, unless you can build out these tactical recommendations like that, a forecast in and of itself is great, but it's just a starting point, and so we cracked the code on actually making these very tactical recommendations. Then, lastly, we're always able, basically, in near-real time to show our customers exactly what kind of value they were deriving from Syrup, so show the pre-situation, the post-Syrup situation, and align the value that we were driving for, with the KPIs that mattered most to an individual retailer. So doing a lot of QBRs and dashboards and reporting, and so on, that allowed them to ensure that they were getting maximum value from Syrup.  

Alex Khan 0:18:27.7:

Now, this one, there's just so much going on that I may have to turn again. At the end of the day, what we're doing - I've kind of touched on this already - the idea that we're taking internal data, so that transactional data, that inventory data, etc., and external data, so weather data, social media, whatever matters most to an individual retailer, taking all that data, running it through the engine, but we're also taking the business constraints that a retailer uses to run their business. So, you've got, I don't know, visual merchandising requirements, and lead times, and target weeks of supply, and some stores have a back room, and some do not, so you've got different inventory capacity constraints in each store. All that information will now reside in Anaplan. So, we're still taking the models and the intelligence from Syrup, taking these guardrails around the forecasts that live in Anaplan, running that through the engine, and then infusing every step of the supply chain process with that intelligence. Lindsay and I had a really good conversation about this earlier, so I wouldn't mind you, Lindsay, chiming in on what those use cases might look like. The application of our intelligence in, say, FP&A, in MFP, for instance, this is new to us - We were focused on buying allocation replenishment, previously - so I would love it if you could touch on some of the use cases. 

Lindsay DiPietro 0:19:48.3:

For sure. There are obvious MFP use cases, just at the top, your high-level forecasting. Can you get to infuse that with better recommendations for mark-down promotions, things like that? Can you take that into consideration and forecast those elements out for MFP, which is pretty high-level? My holy grail lives in assortment planning. For those that do assortment planning, there's just so many decisions that you're trying to make. So, I'm going to tell you where I think the white space lives, and maybe you'll agree with me or maybe you won't, and if you don't, I'd love to hear, because I'm going to run with this big time. So, I imagine being a planner and a merchant, like the POD. I imagine being a POD, and I'm sitting in today, and I'm trading my business today. All I care about is how am I going to manage my Q4 plan. Then, I'm also worried about how the heck am I going to buy Q3 of 2026 now. I'm living in today and trading today, and I'm living in the most future season that I'm buying. What lives in the middle is out of sight, out of mind. I planned it a year ago. I will deal with it when I get to my allocation replenishment. I'm not going to deal with it now; I'm going to deal with it when it's Q1 and I have to relook at my plan, maybe do some reforecasting, or whatever.  

Lindsay DiPietro 0:21:26.2:

So, to me, in the sweet spot, where you're going to find all of the opportunity and the juicy nuggets is, well, I know what's happening today, and I can trend that out, and we can leverage Syrup to do demand sensing and predicting, all the way down to the SKU store day. They can say, 'Okay, well, how are you doing? Let's project that forward,' and guess what, Anaplan has your plan. Anaplan has today's plan, it has that Q1 plan, it has that Q2 plan, and it can look out and say, 'Oh, you know what, maybe we do have an opportunity.' The example I like to use - and it's probably dated now, so maybe someone can give me a new one - but I remember when straight-legged pants - like denim - just blew up out of nowhere. It was like, everything was a jegging or skinny leg, and then all of a sudden it was straight, and everybody was like, 'Oh, no, straight.' Like, whoa, the big shift. I remember being in a position where we did not plan enough straight-legged pants to meet the demand, but it's because I'm living today and I'm actioning today and I have a trend, and then I'm trying to buy my future season being like, 'Did I buy enough?' and I'm not looking at the in-between being like, 'How do I reposition the mix of my business, knowing what I know today?' so that I can either cancel purchase orders, or redirect things and maybe chase into a product, and so you can right-size your assortment in the in-between.  

Lindsay DiPietro 0:22:58.4:

Once you get to allocation replenishment, you've already bought it. It's about optimizing where it goes, and making sure that you have enough stock. Cool. You still have time in the in-between to change your assortment and maneuver. Sometimes. Hopefully, a little bit more nowadays. I think that's where the real opportunity exists, and the use case that we'll be really going after. Because we'll have the plans, we'll be able to, with Syrup, look at what it looks like in between, before we actually get to buying, incorporating that into your assortment plan in the future, so that you're making the best possible decisions about the trends that you see today, and whether or not you want to apply it to a future season. So that is really the secret sauce. 

Alex Khan 0:23:48.1:

This is a big part of why we're so excited, because we might have got there eventually on our own, at some point, but we were a 35-person series A startup. Now, partnered with Anaplan, we can accelerate that roadmap and start building out Lindsay's dream in just a few quarters, I'm sure. We've been doing a lot around insights and analytics that were part and parcel with our offering from day one, so things like forecasting for newness, which I touched on, is always a challenge for fashion apparel retailers, early warning on stock-outs. So if we think that you've got a hot seller and you're about to stock-out of a product in a given store, or a given region, or what have you, we can identify that early, make sure that you're making the inventory moves to capture that and avoid that stock-out situation, size-curve optimization, and on and on. All of these types of analytics, the types of work that we've been doing since day one at Syrup, will be surfaced within the Anaplan applications in a built-in in-app analytics offering. So, last to look forward to is Lindsay builds out her roadmap. MFP is available today, assortment planning in Q1, and then can we…? 

Lindsay DiPietro 0:25:03.5:

Allocation replenishment sometime in Q1. 

Alex Khan 0:25:06.4:

Okay. We're taking orders now, by the way. 

Lindsay DiPietro 0:25:08.3:

Yes, sometime in Q1. 

Alex Khan 0:25:11.6:

We'll close the Syrup component with just a couple of minutes on one of our case studies. So, this is a $4 billion footwear apparel retailer, with a couple of hundred stores across the US. They came to us to work on a proof-of-concept, in which they took, basically, half of the stores and used Syrup's demand forecasting allocation replenishment capabilities against 50 per cent control stores, so their current in-house-built legacy system. We were up and running in eight weeks, something like that. It was a very quick time to value, which was really standard practice with Syrup. That was part of our value proposition, frankly, is how fast we were up and running, and within three months you see the results that we were able to drive for this retailer. If and when the time is right, we'll connect you with them. Unfortunately, we can't name names in this case, but they have since moved from a POC into a long-term contract with us. I won't go through all the numbers here, but I think the main thing to point out is the fact - and this is what we do for all of our retail customers, frankly - is we help them improve service levels.  

Alex Khan 0:26:18.6:

So, in this case, in-stock rates were up 12 per cent, while reducing on-hand inventory. So, they're actually providing a better customer experience. They're selling more by having the right products in the right store at the right time, while carrying less inventory. So, you're driving top line, you're reducing bottom line, and everybody's happy as a result. So, lots of wonderful case studies like this that we'd love to introduce you to more when the time is right. 

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