Buildern had growth it couldn't explain. Datalyze rebuilt attribution and product tracking in PostHog, modelled their BigQuery data with dbt, and added a company-level health score, so the team can see which channels convert, what activation means, and which accounts are drifting toward churn.
The short version Incrementality testing told you whether one channel was real. You turned Meta off in a few cities and read the gap. But you can only test one channel at a time, and you can't turn off the ones that matter most for a growing brand:
Every ad platform you pay is graded on a test it writes, marks, and reports itself. Meta decides which sales to claim credit for, then hands you a ROAS built on that claim.
By now you have accepted the hard part. Your own site knows your customer. Blinkit, Swiggy and Amazon know a SKU and a sales number, never a person. Stitching sounds like one job. It is really three, and they are not equally trustworthy.
The short version Most D2C brands don't have a tracking problem because they track too little. They have one because they track too much, badly. A dev fires an event every time someone asks for a number, and a year later you're sitting on 200 events,
The short version You've scaled spend and you can finally see per-channel outcomes. Which channel drives trials, which trials convert to paid, which stick around. That's a real milestone, and most teams never get there. Here's the problem waiting on the other side
The short version: Your ad platform only sees the click, so it grades channels on signups. Join ad data to product and revenue data and the ranking flips: the channel with the cheapest signups often produces the users who never activate and never pay. Get off GA4, get ad spend
The short version You've hit product-market fit. Thousands of users. You've got a product analytics tool already, Mixpanel or PostHog or Amplitude, plus a CRM, an email tool, and two ad platforms. All of it wired up whenever someone needed a number, none of it
The short version: Pick one tool (PostHog if you want a warehouse bundled in, Mixpanel or Amplitude if you want the deepest pure product analytics). Write a tracking plan before anyone writes tracking code. Get proxy, server-side and identity stitching right at implementation. Then lay your numbers out as
The short version Every D2C brand is running ads on Meta and Google. If you're reading their conversion numbers and trusting them, this one's for you. Those numbers are inflated, in two separate ways, and the gap between what the platforms claim and what you actually
The short version Your marketing site and your product live on two subdomains, and most companies never join the data between them. So you can see cost per visitor by channel, but not which channel brings users who signup, activate, retain, and pay. That second number is the one that
The short version On your own website and app, you know who your customer is. You know that Priya came back three times before she bought, that she found you through an Instagram ad, that she abandoned a cart in March and came back in May. That is the layer
The short version: Analytics debt is the gap between the data you have and the data you'd need to answer the questions you're about to be asked. It accumulates in five predictable stages, and the damage peaks at Stage 3 — when the company believes it'
Indian D2C Playbook
The short version Here's the thing nobody tells you when you're about to scale spend. Every channel you're about to pour money into gives you data you can't fully trust. Quick commerce hands you almost nothing about who your customer is. Meta
Duolingo has 19 million daily active users. Foursquare, once held up as the gold standard of gamified apps with 50 million users at peak, quietly retired its own badges and points system before splitting into two separate apps. Both used streaks, badges, leaderboards but only one survived its own mechanics.
The short version: PostHog's pure product analytics is genuinely weaker than Mixpanel's or Amplitude's, and complex reports push you into SQL. I still recommend it to most founders, because it bundles session replay, feature flags, experiments and a real data warehouse into one tool
case-studies
In short: AnyIP didn't own its analytics and had little visibility into marketing performance. We cleaned and structured the raw data and built structured reporting on top. They gained visibility into marketing, improved CAC and spend efficiency. About AnyIP AnyIP is a high-performance proxy and networking infrastructure
case-studies
In short: FRAI had no clarity on which user segments converted or why others dropped off. We ran deep behavioural analysis, turned it into product recommendations, and designed the A/B tests. Paid conversion rate doubled, and they kept a repeatable experimentation process. About FRAI FRAI is an AI-driven
case-studies
In short: TermPlus had no clear analytics setup and fragmented data across the product. We ran a deep audit, wrote the tracking plan, and worked with their dev team to implement it. They ended up with a full analytics infrastructure and a unified user journey. About TermPlus TermPlus is a
case-studies
In short: Answering Agent had no visibility into the quality of its AI calls. We studied the product and industry benchmarks, defined a full event set, and built an AI-generated call score. They could finally measure call quality directly. About Answering Agent Answering Agent is an AI-powered calling
case-studies
In short: Sol is a complex product that had ended up with tracking to match. We defined core event categories instead of granular events and moved the complexity into properties. The result was a simple, accurate setup that scales. About Sol Sol is a powerful productivity and collaboration app designed
case-studies
In short: Wellness Coach was overtracking, with an inconsistent event structure that made the data unusable. We audited the setup and rebuilt a clean, simple tracking structure. The team could trust their numbers again. About Wellness Coach Wellness Coach is a B2B digital wellbeing platform that offers guided meditation, coaching
case-studies
In short: Speedyloans was sending millions of emails, driving up cost and putting domain reputation at risk, with 100+ user attributes and no prioritisation. We segmented and modelled it properly. Cost down 30%, engagement up 1%. About Speedyloans Speedyloans is an online lending platform that provides fast, accessible loan advances
case-studies
In short: Few zeroone users completed an artwork upload after onboarding. We moved uploads into onboarding as an optional step and cut unnecessary friction. Week 2 retention rose 15% and engagement 30%, with new tracking shipped alongside. About zeroone zeroone is a blockchain-powered social app where users upload, mint,