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Indian D2C Playbook
A series breaking down analytics, metrics, and data truths for Indian D2C brands — sourced from the Datalyze website playbook.
Latest posts
D2C Playbook 7 -- Marketing Mix Modeling (MMM)
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: your influencer deals, your brand campaigns, the quick-commerce ad spend you can't cleanly isolate, offline. Marketing mix modeling is how you divide a whole budget across every channel at once, including the ones you can't turn off. It takes two ye
· 12 min read
D2C Playbook 6 -- Incrementality Testing
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.
· 10 min read
D2C Playbook 5 -- How to actually stitch website, Blinkit and Swiggy data
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.
· 10 min read
D2C Playbook 4 -- Event Taxonomy & Tool Stack Before You Scale Spend
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. Related: the free event tracking plan generator. A dev fires an event every time someone asks for a number, and a year later you're sitting on 200 events, half of them the same handful of actions recorded under different names, and no clean way to answer a funnel question. The fix is boring. Decide on 20 to 30 well-named events before you scale spen
· 10 min read
D2C Playbook 3-- Why your conversions look too good
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 made is the most expensive blind spot in your account. The fix isn't to make the numbers match. You can't. The fix is to take only your spend from the ad platforms, build attribution in-house, and treat your own model as the source
· 8 min read
D2C Playbook 2 -- Putting all your data in one place
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 you set up in Part 1, and it is the layer you own. The moment you start selling on Blinkit, Swiggy, and Amazon, that knowledge disappears. Those platforms hand you sales numbers, not people. You learn that 47 units of your 250ml bottle sold
· 10 min read
D2C Playbook 1 -- The Data Foundation: Fix Your Owned Layer Before You Scale Spend
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 and Google grade their own homework and, surprise, they're doing great. The one place you actually own the full picture is the website and app you built. So that's the layer you have to get right first. And most brands have it quietly broken. Not broken i
· 17 min readGet new posts by email
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