MarTech Interview with Kevin Laymoun, Chief Customer Officer & Chief Revenue Officer at Constructor

Welcome to MarTech Cube, Kevin. We’re thrilled to have you with us to explore the evolving landscape of ecommerce product discovery, AI advancements and retail media innovation. Let’s dive right in.

To start off, could you please share a bit about your professional journey and what led you to your dual role as chief customer officer and chief revenue officer at Constructor?

Thanks for having me here, and sure. My career started in the infrastructure-as-a-service space. I joined a startup (Apigee) early and saw it IPO and then eventually get acquired by Google. That experience was rewarding, and though the company was massively successful, working in API management — with so many layers of complexity away from bottom or topline revenue for the customers we helped — showed me I wanted to work in a space where the proximity and impact to top-line business outcomes was direct and measurable.

Soon after, I got the opportunity to do just that. I met Constructor’s founders, Eli Finkelshteyn and Dan McCormick, and found that we shared a vision: that product discovery in ecommerce — or helping shoppers find what they need and love — could be radically improved with AI and clickstream data at the core. Clickstream data refers to online shoppers’ behavioral signals as they browse, search, add to cart, purchase and more.

I’ve been with Constructor since 2017, before we even launched our search platform. From the beginning, I focused on building our go-to-market engine around the customer buying journey — not just a sales motion.

That philosophy evolved naturally into us as a company viewing revenue and customer experience as a unified concept. We believe those two experiences should be tightly connected: The best way to drive growth is to show customers exactly how a solution will help them be successful with the platform… and then keep proving it after the contract is signed.

We believe that when revenue and customer experience stay aligned (in my role, and across the organization) it ensures that every stage — from first conversation to long-term value — is connected, consistent, and focused on measurable outcomes around the customer’s business.

How have you seen product discovery trends evolve in ecommerce over the past few years, and what do you think is driving the most change today?

The biggest shift is toward context-aware product discovery: understanding not just what a shopper searched for, but when, why and in what context. This helps us know how to deliver something they’re actually likely to buy. That means capturing the relationships between products and other products, and between customers and other customers — and then matching the relationship between the customer and the right product, at the right time.

Clickstream data lies at the center of all of that. And emerging AI like multimodal large language models (MLLMs) and transformers are making it possible to interpret and act on those signals in real time.

We’re also seeing a new wave of interfaces — like AI shopping agents — that collect “zero-party” data (that is, data willingly provided by users).

The retailers who will win are the ones embracing this richer signal set to deliver results that aren’t just relevant (e.g., shirts when someone searches for “shirts”) but attractive — shirts in the color, size, style, brand, price point, etc., that appeal to the individual shopper and get them to convert with satisfaction.

AI-powered shopping agents are gaining traction. How do you see their role shaping the future of online retail experiences?

AI shopping agents represent a fundamental UX shift. Instead of shoppers having to adapt to a static site structure or formulate search queries with terse keywords, the site begins to adapt to them — answering their questions, narrowing down products, and guiding discovery in a much more human way.

We see early success with tools like Constructor’s AI Shopping Agent. I think the bridging factor between agents today and a future of broad AI engagement is the UI that companies bring to bear.

Over time, these agents will become even better at understanding intent, capturing zero-party data and processing clickstream behavior to streamline the entire shopper journey. As that context compounds, it unlocks discovery experiences that increase engagement and satisfaction, reduce return rates, and feel built just for you.

With the growing influence of GenAI, what are some of the most exciting opportunities or challenges you’re seeing in search and product discovery?

The most exciting opportunity with GenAI is also its biggest challenge: contextual data.

GenAI models are getting better at understanding natural language — but without access to the right data, they fall short of delivering meaningful results. For example, someone looking for information on me, Kevin Laymoun, would have a much more successful time looking me up on LinkedIn than asking ChatGPT. This is in large part because ChatGPT doesn’t have access to LinkedIn. This is the same problem in shopping — all the verified product data lives with the retailers or their close partners like us.

In retail, the brands that succeed will be the ones that can feed their agents not just general language understanding but deep, structured context from their product data and actual shopper behavior. That’s why many retailers are looking to AI companies like ours that combine GenAI with their own onsite behavioral data for meaningful shopping experiences.

We’ve been investing in this space long before GenAI hit the mainstream — building models that use behavioral signals, shopper inputs and product relationships to optimize discovery in real time. What’s changing now is the interface: Agentic AI gives retailers a way to surface that intelligence in a more conversational, intuitive format. And the more context these agents collect, the more valuable and differentiating they become.

Constructor recently made moves in the retail media space. Could you tell us more about the offering and what differentiates it in terms of personalization and performance?

Retail media, or putting paid product listings or ads directly on a retailer’s website or app, is a natural extension of what we already do: helping retailers show the right products to the right shopper at the right time. Our new Retail Media Suite is natively integrated with Constructor’s product discovery platform, which means retailers can optimize both sponsored and organic results in one coordinated system — without compromising the shopper experience. That’s unheard of, and it benefits shoppers, advertisers and retailers alike.

Here’s how it works: For every placement (or slot to display a product), our system can intelligently decide whether to show an ad or an organic result based on what will drive the greatest total revenue for the retailer. It means brands perform better, shoppers get a better experience, and retailers grow ad revenue without cannibalizing conversions.

Our system also addresses a persistent problem with ads on retail sites: They’re not typically personalized. This creates a jarring experience when shoppers see products that don’t match their tastes or intent. For example, a shopper might search for “shoes” — expecting running shoes — but see a sponsored ad for high-end fashion heels. Technically footwear, but not what they’re looking for. Or they might see a product that’s out of their size or price range.

Constructor solves for that by personalizing sponsored listings using the same behavioral signals, context and preferences that drive organic discovery. Ads become both more attractive and more effective.

As retailers invest more in sponsored listings and retail media, how can they balance monetization with relevance and user experience?

The key, again, is context. Not every ad is the right ad to show. Showing the wrong sponsored product to a shopper can derail an otherwise successful shopping journey.

That’s why balancing monetization with user experience isn’t a matter of placement volume; it’s about knowing when an ad adds value versus when it distracts or cannibalizes revenue.

Retailers need systems that can evaluate that tradeoff intelligently, in real time. Constructor’s approach is to treat sponsored and organic results as part of a single, cohesive ranking — so every slot is optimized for total revenue and a strong shopper experience. That means sometimes showing an ad and sometimes holding back, depending on what will perform best overall.

Done right, retail media isn’t intrusive but, rather, enhances the shopping journey.

What role do you believe data and user behavior signals play in unlocking more effective AI-driven personalization in ecommerce?

Clickstream data is so powerful precisely because it gives us real-time feedback about what shoppers actually want.

At Constructor, we’ve always believed that great AI starts with great behavioral data. Our models are trained not just on past purchases or static attributes, but on how shoppers interact with products, moment to moment. The more shoppers engage with our platform, the more we learn, and the better their experiences become. And shoppers are engaging a lot: Last year, our platform powered 250 billion interactions — in other words, delivering 8,000 personalized experiences per second.

The goal is simple: to show each person products they’re more likely to love, in the moments they’re most likely to buy. Each interaction is a moment of learning. Think about how many consumer AIs exist: DeepSeek, Perplexity, ChatGPT, Anthropic’s Claude, Llama, etc. The difference comes down to the dataset. This is why when I see new AI companies, I applaud the enterprising spirit — because I’ve always been a startup guy — but in this era, I always question their dataset because that is so central to how you compete in this era of commoditized AI.

On a strategic level, how do you personally approach aligning customer success with revenue growth in a space that’s rapidly innovating?

To us, there’s no real revenue growth without customer success… and no lasting customer success without growth. The two have to be deeply aligned. If revenue teams operate separately from success teams, it creates a disjointed experience that ultimately hurts the customer. That’s why Constructor has kept those functions connected from Day One.

None of our customers, whether they’re small or large, has to submit a support ticket to talk to us. Every customer has a shared Slack or Microsoft Teams channel with our team, and that channel includes not just support engineers, but product managers and data scientists. That direct line builds trust, encourages real-time feedback, and helps us build the next version of our platform.

As I like to say, we early folks at Constructor thought up about 15% of the platform you see today. The other 85% has come directly from customer feedback.

That philosophy is reflected in how we invest our resources, too. Around 70% of our team is in engineering, data science or product… a huge deviation from the typical Series B SaaS company that’s 70% sales and marketing. But it means we’re able to move fast on new ideas, solve real problems, and keep innovation rooted in what our customers are asking for. It works, and we’re proud to have a customer retention rate of 97%.

What advice would you give to ecommerce leaders looking to future-proof their product discovery and retail media strategies?

Invest in your data and your technology partners. The more context you can record and deliver to your partners, the deeper you plant yourself at the center of this shopping evolution.

At the same time, be thoughtful about how you evaluate new technologies. GenAI, agentic interfaces, behavioral data models… these are all powerful tools, but only if they’re used to win A/B tests and drive uplifts. Focus less on what’s shiny and more on what drives actual value: better shopper experiences, stronger monetization, faster insights and clearer ROI. And whether you’re serving up a sponsored or organic placement, it has to feel natural and map to what the shopper wants to see.

Keep in mind, too, that future-proofing isn’t about chasing every trend. Bet on the ones that align with how your customers want to shop and test.

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