172: Ankur Kothari: A practical guide on implementing AI to improve retention and activation through personalization

Humans of Martech

What’s up everyone, today we have the pleasure of sitting down with Ankur Kothari, Adtech and Martech Consultant who’s worked with big tech names and finance/consulting firms like Salesforce, JPMorgan and McKinsey.

The views and opinions expressed by Ankur in this episode are his own and do not necessarily reflect the official position of his employer.

Summary: Ankur explains how most AI personalization flops cause teams ignore the basics. He helped a brand recover millions just by making the customer journey actually make sense, not by faking it with names in emails. It’s all about fixing broken flows first, using real behavior, and keeping things human even when it’s automated. Ankur is super sharp, he shares a practical maturity framework for AI personalization so you can assess where you currently fit and how you get to the next stage.

AI Personalization That Actually Increases Retention - Practical Example

Most AI personalization in marketing is either smoke, mirrors, or spam. People plug in a tool, slap a customer’s first name on a subject line, then act surprised when the retention numbers keep tanking. The tech isn't broken. The execution is lazy. That’s the part people don’t want to admit.

Ankur worked with a mid-sized e-commerce brand in the home goods space that was bleeding revenue; $2.3 million a year lost to customers who made one purchase and never returned. Their churn rate sat at 68 percent. Think about that. For every 10 new customers, almost 7 never came back. And they weren’t leaving because the product was bad or overpriced. They were leaving because the whole experience felt like a one-size-fits-all broadcast. No signal, no care, no relevance.

So he rewired their personalization from the ground up. No gimmicks. No guesswork. Just structured, behavior-based segmentation using first-party data. They looked at:

• Website interactions
• Purchase history
• Email engagement
• Customer service logs

Then they fed that data into machine learning models to predict what each customer might actually want to do next. From there, they built 27 personalized customer journeys. Not slides in a strategy deck. Actual, functioning sequences that shaped content delivery across the website, emails, and mobile app.

> “Effective AI personalization is only partly about the tech but more about creating genuinely helpful customer experiences that deliver value rather than just pushing products.”

The results were wild. Customer retention rose 42 percent. Lifetime value jumped from $127 to $203. Repeat purchase rate grew by 38 percent. Revenue climbed by $3.7 million. ROI hit 7 to 1. One customer who previously spent $45 on a single sustainable item went on to spend more than $600 in the following year after getting dropped into a relevant, well-timed, and non-annoying flow.

None of this happened because someone clicked "optimize" in a tool. It happened because someone actually gave a damn about what the customer experience felt like on the other side of the screen. The lesson isn’t that AI personalization works. The lesson is that it only works if you use it to solve real customer problems.

Key takeaway: AI personalization moves the needle when you stop using it as a buzzword and start using it to deliver context-aware, behavior-driven customer experiences. Focus on first-party data that shows how customers interact. Then build distinct journeys that respond to actual behavior, not imagined personas. That way you can increase retention, grow customer lifetime value, and stop lighting your acquisition budget on fire.

Why AI Personalization Fails Without Fixing Basic Automation First

Signing up for YouTube ads should have been a clean experience. A quick onboarding, maybe a personalized email congratulating you for launching your first campaign, a relevant tip about optimizing CPV. Instead, the email that landed was generic and mismatched—“Here’s how to get started”—despite the fact the account had already launched its first ad. This kind of sloppiness doesn’t just kill momentum. It exposes a bigger problem: teams chasing personalization before fixing basic logic.

Ankur saw this exact issue on a much more expensive stage. A retail bank had sunk $2.3 million into an AI-driven loan recommendation engine. Sophisticated architecture, tons of fanfare. Meanwhile, their onboarding emails were showing up late and recommending products users already had. That oversight translated to $3.7 million in missed annual cross-sell revenue. Not because the AI was bad, but because the foundational workflows were broken.

The failure came from three predictable sources:

Teams operated in silos. Innovation was off in its own corner, disconnected from marketing ops and customer experience.
The tech stack was split in two. Legacy systems handled core functions, but were too brittle to change. AI was layered on top, using modern platforms that didn’t integrate cleanly.
Leaders focused on innovation metrics, while no one owned the state of basic automation or email logic.

To fix it, Ankur froze the AI rollout for 120 days and focused on repair work. The team rebuilt the essential customer journeys, cleaned up logic gaps, and restructured automation to actually respond to user behavior. This work lifted product adoption by 28 percent and generated an additional $4.2 million in revenue. Once the base was strong, they reintroduced the AI engine. Its impact increased by 41 percent, not because the algorithm improved, but because the environment finally supported it.

> “The institutions that win with AI are the ones that execute flawlessly across all technology levels, from simple automation to cutting-edge applications.”

That lesson applies everywhere, including in companies far smaller than Google or JPMorgan. When you skip foundational work, every AI project becomes a band-aid over a broken funnel. It might look exciting, but it can’t hold.

Key takeaway: Stop using AI to compensate for broken customer journeys. Fix your onboarding logic, clean up your automation triggers, and connect your systems across teams. Once the fundamentals are working, you can layer AI on top of a system that supports it. That way you can generate measurable returns, instead of just spinning up another dashboard that looks good in a QBR.

Step by Step Approach to AI Personalization With a Maturity Framework - The First Steps You Can Take on The Path To AI Personalization

Most AI personalization projects start with a 50-slide vision deck, three vendors, and zero working use cases. Then teams wonder why things stall. What actually works is starting small and surgical. One product. One journey. Clear data. Clear upside.

Ankur advised a regional bank that had plenty of customer data but zero AI in play. No need for new tooling or a six-month roadmap. They focused on one friction-heavy opportunity with direct payoff: mortgage pre-approvals. Forget trying to personalize every touchpoint. They picked the one that mattered and did it well.

They built a clustering algorithm using transaction patterns, savings trends, and credit utilization to detect home-buying intent. From there, they pushed pre-approvals with tailored rates and terms. The bank already had the raw data in its core systems. No scraping, no extra collection, no “data enrichment” vendor needed.

That decision paid off fast:
The data already existed, so implementation moved quickly
The scope was limited to a single high-stakes journey
The impact landed hard: mortgage application rates jumped 31 percent and approval-to-close conversions climbed 24 percent within 60 days

> “Start with a high-value product journey where pers...