Rahul Wankhede is a seasoned marketing and analytics leader who turns complex data into clear business wins. He brings over a decade of experience in AI-based personalization, unified measurement, and advanced modeling to help brands deliver offers that genuinely matter.
In a conversation with Rahul, he discussed how lookalikes and propensity scores make your ads smarter, why blending first-party and third-party data still respects your privacy, and how real-time bidding and dynamic content optimization keep messages timely and relevant. Along the way, he unpacked common misfires in AI-driven targeting, explored the human touch behind automated campaigns, and shared tips for taking control of your personalization settings.
Grit Daily: How do AI-powered models like lookalikes and propensity scores help consumers see offers that truly match their interests?
Rahul Wankhede: AI models like lookalikes and propensity scores are AI systems that find new people whose behavior and traits closely match an existing audience. They analyze a mix of behavioral, demographic, and contextual signals, such as your past purchases, browsing patterns, location, and even time of day, to predict what you’re most likely to care about next. Instead of bombarding you with irrelevant ads, they help brands serve offers that actually make sense, like showing lease deals on electric vehicles if you’ve been comparing hybrid models online. Advanced techniques, such as uplift modeling, take it a step further by identifying not just who’s interested but who’s likely to respond to the marketing itself, making outreach smarter and more respectful of your attention. And behind the scenes, this is all done using anonymized or aggregated data, so your personal information stays protected.
Grit Daily: What can consumers expect when marketers blend first-party and third-party data to deliver more relevant messages while protecting their privacy?
Rahul Wankhede: When brands combine first-party data, like what you do on their website or app, with third-party data from trusted partners, they gain a fuller, more contextual view of your needs. That means the messages you see become not just personalized, but precisely timed, like getting a furniture promotion after you’ve moved, not weeks before. Or receiving travel insurance offers right after booking a flight, rather than when you’re still browsing. These experiences feel more relevant because the models can stitch together your intent across touchpoints. And behind it all, privacy remains a priority. Data is anonymized or linked through secure methods like hashed identifiers and clean rooms, so your personal information stays protected while still delivering value.
Grit Daily: In your view, how does real-time bidding ensure the ads I see online feel timely and tailored to what I care about?
Rahul Wankhede: Real-time bidding (RTB) works behind the scenes in milliseconds, evaluating thousands of signals like the website you’re visiting, your device’s time of day, and recent browsing behavior to decide which ad is most relevant to show you in that exact moment. Think of it like a digital auction happening every time a page loads, advertisers bid to show you an ad, and the one with the best combination of relevance and value wins. What makes this even more powerful is dynamic creative optimization, where the actual content of the ad, like the car model, price point, or call-to-action, is customized based on those same signals. That’s why you might see a specific SUV lease deal while reading auto reviews, or a banner with weekend flight prices while browsing travel blogs. It’s all designed to match your context and interests in real time.
Grit Daily: From a consumer standpoint, what frustrations arise when AI-based targeting misses the mark, and how can brands fix that?
Rahul Wankhede: When AI-based targeting misses, it can feel off-putting, like being shown baby ads long after your child’s grown, or seeing repeated promos for something you already bought. These misfires break the sense of relevance and can damage trust. Often, the issue is outdated inputs or overly generic models. Brands can course-correct by applying exclusion logic, suppressing ads post-purchase or based on life-stage changes, and retraining models with fresher behavioral and contextual signals. But the fix is just technical. The art of marketing still matters: understanding your customer, your business context, and the nuances that data can miss. Human judgment plays a critical role in setting model guardrails, reviewing edge cases, and interpreting soft signals that AI may overlook. Ultimately, the best systems are not just automated — they’re informed by marketers who deeply understand their audience and use AI as an accelerator, not a replacement.
Grit Daily: Can you share an example where dynamic content optimization made your online experience feel more personal and engaging?
Rahul Wankhede: Definitely, I was shopping online for sneakers recently, and what caught my attention wasn’t just the product but how the content adapted in real time. As I filtered by brand and style, the ads I saw later started reflecting the exact models I had engaged with, even highlighting nearby inventory availability and flash sales at a local store. That’s dynamic content optimization in action, tailors not just who gets the message, but what version of the message they get, based on behavior, preferences, and context.
Another example is location-based dynamic targeting. While I was walking through an outdoor retail center, I received a push notification with an exclusive in-store discount just as I entered a retailer’s geofenced zone. The timing, offer, and channel were all dynamically aligned with where I was and what I was likely to be interested in. When done right, these experiences feel intuitive and helpful, not forced, which is what personalization should aim to deliver.
Grit Daily: How do brands balance AI-driven personalization with a human touch so campaigns still feel authentic to consumers?
Rahul Wankhede: You can leverage all the models in the world, but at the end of the day, the connection with your customer drives impact. AI helps scale personalization. deciding when to engage, on what channel, with what offer, but it’s human creativity and brand empathy that make those moments feel real. One of the key ideas from algorithmic marketing is that while machines can automate signal detection and delivery, it’s the marketer who ensures the message aligns with emotion, intent, and trust.
For example, a retention model might flag a high-risk customer, but it’s a well-timed message acknowledging their past loyalty, maybe even referencing their specific experience, that makes the difference. When brands combine algorithmic precision with human storytelling, the experience feels like a relationship, not just a transaction.
Grit Daily: As privacy rules change, what should consumers know about how AI is used behind the scenes to customize their shopping and browsing experiences?
Rahul Wankhede: AI-driven personalization in marketing has been evolving for years to work within stricter privacy standards, not just recently. Most modern systems don’t rely on directly identifiable data; instead, they use aggregated behavioral patterns, hashed identifiers, and probabilistic models to infer preferences. These techniques have long allowed brands to customize experiences, like relevant product recommendations or timely offers, without needing to know precisely who you are.
Technologies like clean rooms, federated learning, and differential privacy help brands analyze trends and activate insights without exposing individual-level data. AI now focuses less on tracking specific users and more on understanding cohorts and contexts. So while the experience may feel personal, it’s often powered by models that optimize for relevance within secure, privacy-safe frameworks.
Grit Daily: How can consumers take control of their personalization settings to ensure they only receive offers they actually want?
Rahul Wankhede: Most platforms today offer preference centers, privacy dashboards, and ad settings that let consumers control how their data is used for personalization. Whether opting out of interest-based ads, limiting tracking across apps, or adjusting content preferences, these tools give users more control.
Beyond toggles and settings, consumers also shape experiences through feedback, like completing a post-purchase survey, skipping irrelevant ads, or even clicking “not interested.” These actions serve as real-time signals that help models recalibrate. Many brands are also making feedback loops more explicit, asking consumers directly what they want to see more or less of.
At the end of the day, it’s the consumer who drives the direction of personalization. AI can optimize experiences, but it’s user choices, preferences, and earned trust that set the boundaries. The best personalization happens when consumers are not just passive recipients but active participants in shaping how they engage with brands.
Grit Daily: What impact does AI-driven personalized pricing have on consumer trust and perceptions of fairness in online shopping?
Rahul Wankhede: To estimate price elasticity, AI-driven pricing uses data like purchase history, location, and browsing behavior. You will likely convert at a given price and adjust offers accordingly. Models like contextual pricing or reinforcement learning optimize for margin or conversion in real time. But trust can erode fast if prices feel arbitrary. It’s like two shoppers grabbing the same product, only to find different prices at checkout. It feels unfair if the rationale isn’t clear. Consumers are open to dynamic pricing when it’s transparent and value-driven, like rewarding loyalty or timing promotions to incentivize, but push back when it feels exploitative.
To get it right, brands need more than just smart models. They need pricing strategies that feel consistent, explainable, and earned.
