What if the biggest threat to your brand’s profitability isn’t the next tariff or supply chain disruption, but an outdated playbook that forces you to choose between raising prices on loyal customers or sacrificing your margins?
Agility requires more than just reacting quickly to market changes; it requires the intelligence to anticipate them and automate the optimal response.
Today, we’re going to talk about how leading retail brands are navigating complex economic pressures like tariffs and inflation—not by resorting to the old tactics of deep discounts or across-the-board price hikes, but by deploying AI to create a more resilient and intelligent operation. We’ll explore how AI is helping brands maintain pricing stability, turn insights from major shopping events into real-time strategy, and fundamentally shift teams from staring at dashboards to taking automated, margin-protecting actions.
To help me discuss this topic, I’d like to welcome, Sai Koppala, CMO at CommerceIQ.
About Sai Koppala
Sai brings over 20 years of marketing and strategy experience. Before CommerceIQ, he was Chief Marketing & Strategy Officer at SheerID and held leadership roles at Apigee (acquired by Google) and SAP. He holds an MBA from the Kellogg School of Management and a Master’s in Electrical Engineering from Arizona State University.
Sai Koppala on LinkedIn: https://www.linkedin.com/in/koppala/
Resources
CommerceIQ: https://www.commerceiq.com
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Catch the future of e-commerce at eTail Palm Springs, Feb 23-26 in Palm Springs, CA. Go here for more details: https://etailwest.wbresearch.com/
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Transcript
Greg Kihlstrom (00:00)
What if the biggest threat to your brand’s profitability isn’t the next tariff or supply chain disruption, but an outdated playbook that forces you to choose between raising prices on loyal customers or sacrificing your margins? Agility requires more than just reacting quickly to market changes. It requires the intelligence to anticipate them and automate the optimal response. Today, we’re going to talk about how leading retail brands are navigating complex economic pressures like tariffs and inflation not by resorting to the old tactics of deep discounts or across the board price hikes, but by deploying AI to create a more resilient and intelligent operation. We’re going to explore how AI is helping brands maintain pricing stability, turn insights from major shopping events into real-time strategy, and fundamentally shift teams from staring at dashboards to taking automated margin-protecting actions. To help me discuss this topic, I’d like to welcome Sai Coppola, CMO at commerceIQ
Sai, welcome to the show.
Sai Koppola (00:56)
Thanks Greg for having me on.
Greg Kihlstrom (00:57)
Yeah, looking forward to talking about this with you before we dive in though. Why don’t you give a little background on yourself and your role at commerceIQ?
Sai Koppola (01:03)
Sure, I joined a commerceIQ a little over a year ago. I run all of marketing for commerceIQ. Prior to that, for 20 years I worked in a variety of technology companies running marketing functions.
Greg Kihlstrom (01:15)
Great. So yeah, let’s let’s dive in here. Want to start with the strategic view of this and touch on what I talked about a little bit in the intro is moving beyond purely reactive measures. And certainly this is this is tough. know, when brands are faced with tariff driven cost pressures, other type of economic concerns, the default playbook often involves layoffs, passing costs directly to consumers, things like that. How is AI fundamentally changing that C-suite conversation offering maybe a third more sustainable path forward?
Sai Koppola (01:50)
Sure. If you actually look at what’s happening over the last year, we’re actually seeing brands rewrite the P &L playbook using AI. it’s not just how can you find profit with precision and not necessarily just price hikes. So if we take a step back, what lot of the leading brands have been able to do is combine a variety of data, They’re working with retailers, sales operations, media data.
And then figure out, what are the places where we can actually increase prices and where we cannot afford to increase prices? That’s one piece on the revenue side, right? So we’ve seen this, by the way, throughout the year. If you look at when we did analysis over by price bands, we looked at any category, like let’s say toys. We did three different price bands. What we actually seen is
the higher price bands, the prices have gone up like 10 % plus over the last year. So significant price increase. But on the lower end, you are not seeing a very flat pricing. There’s no pricing because we know the consumer is having difficulty at the bottom end. So therefore, the prices have not gone up as much. So that’s one aspect of it. On the cost side, like the example of retail media.
Brands are much more sophisticated now, looking at, how am I doing organically on the retailer’s page? And if I’m doing really well, let’s not go spend too much money on that particular search term. Let me invert. So brands are using AI to both optimize on, what shoppers can I, there’s price elasticity, so I can increase my price, so I improve my revenue. At the same time, where can I reduce my investment, be more efficient with my investment? So that’s what we’re seeing brands do.
Greg Kihlstrom (03:32)
Yeah. So, you know, in talking about moving away from purely reactive discounting and other things like that, how does using a eye to manage pricing not only protect margins, but also build and maintain consumer trust? Because I think that’s the other aspect here is obviously we need to think about profitability and and internal operations, but we also can’t lose the consumers through this through this journey. So how does, how can AI help there? You know, especially when shoppers are more price sensitive than ever.
Sai Koppola (04:05)
Yeah. This is all in the context of over the last few years, brands have engaged in essentially shrink-flation, right? Which actually hasn’t really helped with trust. Right. So I think shrink-flation has stopped right now. So the brands, where the focus a lot is on, as I was talking about, especially now with our shopper data, which is all the click-through signals, the sales data and promo data to figure out I’m able to do micro-targeting by segments and identify, hey, which SKUs need price support? On which SKUs there’s some elasticity that I can afford to not erode my margins. And that helps brands maintain a pricing strategy which still meets where the consumer is, and in many ways, frankly, being transparent about price increases. We are actually seeing.
Couple of the retailers have come out publicly and said, hey, because of tariffs, we are trying to absorb as much of the cost as possible. And we’ve seen margins also go down. At the same time, they are passing costs onto consumers, especially where there’s price elasticity.
Greg Kihlstrom (05:11)
Yeah, and so another kind of shift in consumer behavior, you know, major events like recent prime days, for instance, reveals some other shifts in consumer behavior, like splitting between premium purchases and bulk buying essentials. Can you walk us through maybe a practical example of how AI helps a brand not just spot a trend like this, but pivot its media pricing, inventory strategies, all of those things in real time, if not near real time.
Sai Koppola (05:44)
Let me walk you through a couple of examples here. We have a few leading brands who use 50 plus real-time data signals. That would be like CPC, share of shelf, and inventory data to kind of identify, what’s the optimal demand curve? And make media investments based on that, right? So during Prime Day, 10 plus of our customer brands actually saw
like 100%, 140 % plus increase in incremental ROAS, incremental ROA, is essentially is how much incremental sales I’m actually driving with this media spend, while also seeing the CPCs drop. And the reason they’re able to do that is essentially they are able to optimize now in real time based on ready-of-signals, say, where is the opportunity for me to invest on the right keywords
for the right SKUs I have based on inventory, and we’re already not doing as well in organic search. So we can be a lot more precise in where to make my media investments. And that’s been very valuable for lot of retailers where, yes, you’re spending a lot of retail media dollars, but you’re able to stretch your dollars lot more. And one of the examples, one of the auto care brands we work with, optimize their traffic by, you
by growing 3X in traffic and 200 % increase in sales while not having to increase the retail media spend. So there’s lot more efficiencies that can be gained. If you take a step back, if I’m a brand with 500 plus cues and I’m selling this across 20 different retailers in the US, it is manually impossible for me to optimize each and every aspect of it.
That’s where AI steps in, right? The manual, the execution of it, where the scale is so much, I can’t just hide so many analysts and hands on keys, is where we see AI really being impactful.
Greg Kihlstrom (07:36)
Yeah, and another part of this is not only is that impossible to do all that manual work and just have the resources to do it in any reasonable way. Looking at dashboards and reacting to the there’s a time lag, right? I mean, you can you can look at the most beautifully designed dashboard with all the information that you could possibly have added, but you’re still reacting to information and need to take that process it versus the idea of an AI teammate, for instance, that is a little more proactive and things. So maybe could you talk a little bit about what does something like that look like in, in practice and for a marketing or operations leader, what is, what is transitioning to a, an approach like that actually look like?
Sai Koppola (08:26)
When we launched earlier this year, early beginning of this year, when we launched Ali, which is essentially our AI teammates for commerce, the whole, we spent a lot of time actually thinking what is this? There’s an AI agent, there’s a teammate, what is it? The reality is the best, from a business impact perspective, yes, AI can give you recommendations based on a variety of data. You still need human in the loop for some decisions.
So what we’re seeing more and more is in the teams are not spending as much time chasing data and putting the data together and cleaning the data and you know analyzing the data, but they’re more focused on strategic activity as addition making right so you know for example, know when it comes to Auto stocks, right? You know, we can automatically quickly tell you hey here are a set of skews that are out of stock So let’s not invest retail media dollars on those
The way we build the systems is AI makes the recommendations. The human can then approve those recommendations to automate that. So we give the flexibility for brands to make the right decisions because I still believe you need, in many cases, human in the loop. But take the grant work out of having to do those things.
Greg Kihlstrom (09:40)
And so in terms of measuring success of all of this, obviously there’s some metrics that are, mean, sales are revenue. You know, there’s certain things that are not going to change regardless of the tactics or even the strategies used. But as brands move towards this, let’s just call it more AI driven stability versus more rapid fluctuations, promotion, heavy strategies, what KPIs do matter most, also perhaps change.
Sai Koppola (10:08)
I think for the longest time, focus has been most companies are focused on growth, growth, growth. Look, it definitely focuses now on more profitable growth, not just volume. That means in the media side, shifting from just ROAS to incremental ROAS, net PPM, contribution margin. Those are the factors end of the day. I’m sure you’ve seen the recent McKinsey report or the prior MIT report where they said, hey, lot of companies are focused on
AI pilots, but how many of them are actually seeing real business impact? In many ways, our approach to AI has been that, let’s pick a couple of use case and go deep, right? Whether it’s in content, by way, another interesting thing is on the content side, if you look at it in several categories, 30 % 40%, 30 % can be all digital sales, or at least digitally influenced sales, right?
In those cases, and once again, going back to the example I told you, if I’m a brand with 500 SKUs across 10 plus retailers, and yes, I have the ideal how my brand should show up, how the PDP should show up in terms of the imagery and the text and everything like that, but now, how am going to make sure that’s all consistent across all those retailers and also be able to adjust it? You suddenly realize, hey, increasingly customers are searching for a specific keyword in in pet food. It may be, you know, picking an example, like, you know, maybe protein is a big kind of keyword people are searching by. How do you know incorporate that? It’s extremely manual process today. We are able to automate with AI. We can see, what keywords are trending and based on that, how do you improve your product page on a given retailer and have your marketing approve it and automatically updates it, right? Things like that we’re seeing more and more where, the focus has to be more. And that, of course, improves conversion. So that’s where it’s all about efficiencies and profitability. Either it’s improved conversion, reduced cost, or being very strategic or very increased prices is all the things we’re seeing brands do increasingly.
Greg Kihlstrom (12:20)
Yeah. And looking at from the customer standpoint, know, customer loyalty, retention, lifetime value, even what role does transparency play in whether that’s transparency and pricing or other other ways? Like how can brands measure the impact of of greater transparency on things like customer loyalty and retention?
Sai Koppola (12:44)
Yeah, look, in a market that is defined by uncertainty, trust is essentially the new currency of loyalty, So we can correlate transparent, driven messaging with changes in sales, ASP, and sentiment across retailers. What we are seeing is ⁓ brands are increasingly being public about Walmart, think, couple of months ago said that, we’ll keep prices as low as possible. But the reality is, you know, we have to increase where needed because we are already on very thin retail margins. P &G also announced recently that, hey, we’re going to increase prices by 25 % of their portfolio across the board because of increased tariff costs. So in many ways, being transparent with a customer, the end consumer about the need to increase prices where we need to at the same time
same time willing to absorb some of the costs is the way you would build some trust.
Greg Kihlstrom (13:38)
Yeah, yeah. So I to talk a little bit about some some of the future of AI powered commerce. And certainly one of the topics that comes up a lot these days, agentic AI, it’s becoming more more integrated into retail operations. But as as this continues automating, you know, decisions across media sales supply chain, what becomes the new core competency for the human teams in this equation and what should leaders be thinking about when looking at things like talent development and other things.
Sai Koppola (14:08)
Look, AI is taking over some of the day-to-day manual execution of it, right? Reality is the human in the loop is still critical. The human is moving to move into the deciding and the collaboration aspect of it. If you take a step back today, if you look at on one side of retailers going increasingly algorithmic, right? Like if you look at what Walmart and Amazon are doing. So brands need to keep up with that.
If you look today, typically a joint business planning between a brand and a retailer, it’s extremely manual process. There’s lots of time spent on the brand side by the category management teams and the sales strategy teams building together a whole plan for the joint business plan. Then there’s a negotiation going on with the retailer. And then on execution side, on a monthly basis and a weekly basis, you’re literally sitting face to face with the retailer figuring out what’s working well, what’s not working well.
Today, a lot of that stuff is still done on PowerPoints and Excel sheets. All we are saying is AI can simplify that aspect of it, where it can streamline getting data and analysis. But you still need the human to make additions and collaborate with other humans on the other side to drive results. So there is lot of gloom and doom over, hey, AI is going to replace jobs.
I firmly believe AI is an extremely powerful force that can streamline a lot of the work, helping humans transition to more of the strategic and the collaboration kind of Yeah. And decision making, right?
Greg Kihlstrom (15:36)
Yeah. So thinking ahead a little bit here, you know, thinking about whether it’s the next major frontier for AI and retail or other things, you know, if we were having this interview a year from now, what is one thing that we would definitely be talking about?
Sai Koppola (15:51)
What we will see is the collaboration between retailers and brands would become more streamlined because of AI. As I saying earlier today is extremely manual and in a slow process. What’s going to happen is I foresee a world where that collaboration becomes ⁓ much faster and streamlined where you have agents on the retailer side, you have agents on the brand side, and they will work together. Let me give you a concrete example. I was talking about digital shelf. We see a world where when the AI on the brand side will identify here the 10 things you need to fix to improve your conversion on the retailer website. The brand side, the person in the responsible for the commerce team would approve those recommendations based on the recommender. They’ll review the recommendations once they’re approved. It automatically hits the retailer site. On the retailer site, the retailer agent goes and updates the website. That’s where we’re headed, right? I’m very bullish in where this can go. Frankly, if you look at the value chain, retailer margins are so low, right? The only way to increase profits without having to increase prices is to be lot more efficient that means personalization, better conversion, all of that comes into play. One of the things we haven’t talked about is sharper behavior. Now I can combine sharper behavior, which includes sales clicks and all these things with my SKU data and everything, as well as a shelf data. We can do a lot more personalization, which improves conversion and more sales. So lots of interesting stuff. I’m excited about coming over the next year.
Greg Kihlstrom (17:31)
Yeah, yeah, we’ll have to have you back on in a year and we can we can talk about all that. Definitely, definitely interesting stuff. what? So thanks so much for joining today. One last question before we wrap up, though. What do you do to stay agile in your role and how do you find a way to do it consistently?
Sai Koppola (17:47)
What I found more for my teams is because there’s so much change, you need to have a culture of psychological safety within your teams that allows people to experiment, do A-B testing, try things out without worry about that role. ⁓ Because we are in a time of uncertainty. And that’s where it also gives you the most opportunities.
But you need to let your teams feel free to try, test, experiment on things so that way they can find something that really works for their business. That’s one. The second one is, when I’m recruiting now, I’m looking for people with a growth mindset. People are willing to say that, I haven’t done this way before, but I’m open to look at from first principles and see if this is something that makes sense. Let me try it out. Those are the things I’m looking at when I’m building my teams. And even personally, yesterday, for example, I was trying to build an automated workflow within marketing on how I can go and look at what are leading thought leaders on LinkedIn saying about this specific topic. How can I automate it using an A10, right? So even I’m getting my, as a marketing leaders, how to get their hands dirty to understand how AI can help their teams.
