Host:
Hey, welcome everyone to another podcast episode with Clear Ads. I’m so excited today because we have an absolute legend joining us: Sreenath from Intentwise. He’s been a tidal wave in the Amazon Marketing Cloud space, a thought leader from the very beginning, and it’s just so good to have him here.
Sreenath:
Thanks so much, George. Excited to be here.
Host:
Brilliant. Before we dive in, I want to let the audience know how I came across Intentwise. I’d gone down a rabbit hole on Amazon Marketing Cloud about a year ago, trying to understand how it could bridge DSP and PPC. When I looked for content on YouTube, you were the only person consistently putting out valuable material, and you broke it down in a simple way that really helped me.
What sealed the deal for me was your SQL learning tool. I kept failing the AMC exam because I had no SQL experience, but your platform had this amazing feature where anyone could learn the basics and run queries. I wasn’t even a client, but when I reached out for access, your team gave it to me for free, answered my questions, and left a really positive impression. Later, we met at conferences, and here we are.
For listeners who don’t know Intentwise or your work, can you give us some background on the company and what you do?
Sreenath:
Absolutely. First of all, I love that story about you coming to us to learn SQL. Education has always been our way of building brand awareness, so I’m glad you benefited from it.
At Intentwise, we think of the problem like this: if you’re a brand or agency in the Amazon or Walmart space, you’re flooded with data. It’s abundant but fragmented, and making sense of it is hard. Our mission is to bridge that gap and turn fragmented data into actionable insights.
We’re, at our core, an analytics company. Our platform has three main solutions:
Ad Optimization Platform – our first product, helping brands optimize campaigns.
Data Foundation Layer – we fully automate the collection of Amazon and Walmart data, giving brands ownership of their data, along with reporting and visualization (including white-label solutions for agencies).
Amazon Marketing Cloud Solution – co-developed with Amazon to make AMC more accessible. It simplifies queries, offers custom audiences, and provides education so brands and agencies can extract value much faster.
These solutions can work together or independently. We partner closely with agencies like yours, and that’s been critical to our growth.
Host:
That’s great context. Let’s go back to the beginning—what did you want to be when you were younger, and how did you end up here?
Sreenath:
I grew up in a lower-middle-class family in South India. My dad worked in government, and entrepreneurship was considered too risky. As a kid, I loved math and science and thought I’d be a physicist. I ended up studying engineering and always had a fascination with data.
Before Intentwise, I spent nearly 10 years at a public company managing data teams and running online marketing with close to $100 million in spend. That experience gave me plenty of battle scars and shaped my understanding of data and marketing.
I’d tried starting businesses before—one during the dot-com boom, which failed. Later, I worked in steady jobs, got married, had kids, but the entrepreneurial bug never went away. At one point, I quit my job, initially thinking I’d help hotels with digital marketing. Quickly, I realized it was too commoditized.
Then I stumbled into Google AdWords consulting. While analyzing a client’s search term report, I noticed they were paying 10% of spend to agencies. That was my lightbulb moment—I started a small AdWords agency. Business was fine, but my co-founder and I were product people, not service people.
We noticed inefficiencies in matching search intent to ads, which inspired the name “Intentwise.” For example, people searching “hotels near Wrigley Field” would just get generic Chicago hotel ads. That mismatch sparked our idea.
Then one client said, “I’m moving my budget to Amazon ads.” My reaction was: “Amazon runs ads?” That was our pivot point. We quickly realized there was a huge product opportunity, pivoted away from Google, raised some funding, and focused on Amazon. That was mid-2018, and that’s how Intentwise began.
Host:
That’s such a fascinating journey. Let’s talk about today. Amazon’s data landscape has exploded—AMC, search query performance, marketing stream. For brands listening, the big question is: how can I actually use this data to make smarter campaign decisions?
Sreenath:
Great question. A few years ago, the only data you had came from sponsored ads, DSP, and NWS. Today, you have real-time signals, granular shopper-level data, and AMC has become the center of all ad measurement.
The key is this: the value you get from AMC depends on the quality of the questions you ask. So first, I always recommend people get AMC-certified through Amazon’s Learning Console so they understand what datasets exist. Second, create a “roadmap” of questions that matter most to your business—things you couldn’t answer before.
For example:
How much of my brand search traffic comes from people who first did a non-brand search?
What’s the true lifetime value of my customers, now that AMC can provide up to five years of purchase data?
How are DSP exposures influencing sponsored ad performance?
By consistently asking and testing questions like these, you unlock insights that directly shape your strategy.
Host:
That makes sense. Can you share some concrete examples of how brands have used AMC successfully?
Sreenath:
Sure. A publishing client with 2,400 products wanted more new-to-brand customers. AMC revealed that 12 of their products were far better at acquiring new customers. They reallocated budgets, even tolerating higher ACoS, and significantly increased new-to-brand acquisition.
Another example is creating high-value customer audiences—people who’ve spent a certain amount or purchased multiple times in the last year. You can target them with DSP or boost bids in sponsored ads.
We’ve also seen supplement brands upload their DTC customer data into AMC. They can then identify overlap with Amazon shoppers, upsell based on last purchase, or exclude those customers from ads to avoid wasted spend.
These examples show how flexible AMC is. Some use cases are universal, while others are highly specific to each business.
Host:
Brilliant. A question I often hear is: “I don’t see incremental growth when I scale DSP. How does AMC help with that?”
Sreenath:
Two ways: execution and measurement.
On the execution side, AMC lets you see your penetration within in-market audiences and exclude people you’ve already reached, so you target net new users. You can also analyze ad exposure frequency to set frequency caps based on data rather than guesswork.
On the measurement side, AMC shows whether DSP-exposed users are driving organic sales, how DSP and sponsored ads overlap, and even brand lift metrics (e.g., shoppers who’ve only been exposed to DSP but start searching for your brand).
It also enables custom audiences—for example, targeting people who added a product to a wishlist but didn’t purchase. That level of control wasn’t possible before.
Host:
This is gold. Let’s pivot to AI for a moment. With so much buzz around ChatGPT and generative AI, where do you see this going in our industry?
Sreenath:
AI isn’t new—machine learning has long powered bidding and forecasting. What changed with ChatGPT was its ability to handle unstructured data: text, images, audio, video. That opened up massive new applications.
One important distinction: these models don’t actually reason. They detect patterns. So for tasks where accuracy is less critical—like summarization, content generation, translation—they’re amazing. For accuracy-critical tasks, you need to validate outputs.
What’s exciting is how this changes software. For 30 years, users clicked buttons and menus. Now, software understands language. Soon, instead of logging into Intentwise, you might just message a bot in Slack: “Audit this account” or “Show me changes in sponsored products last week.” The system will respond with full reports.
The future of software is conversational, and that’s where we’re headed.
Host:
That’s powerful. Final question—what should listeners keep an eye on in the future?
Sreenath:
Two things:
The GenAI Tidal Wave. The pace of advancement is staggering. Either you prepare to ride it, or you risk being blindsided.
Commerce Evolution. Quick commerce in places like India already delivers groceries in under 10 minutes. Meanwhile, AI shopping agents like OpenAI’s Operator or Amazon’s Rufus are changing how consumers discover and purchase.
The combination of quick commerce, AI-driven shopping, and social commerce will transform retail faster than most expect. Anyone in this ecosystem needs to keep one eye firmly on that future.
Host:
Amazing. This has been such a valuable conversation. Thank you so much for joining us and sharing your journey, insights, and predictions.
Sreenath:
Thank you, George. I really enjoyed it and look forward to catching up at the next conference.
