D2C conversion rate benchmarks in India: what we can actually show

This post was meant to be a benchmark table. We stopped, because after excluding the figures we cannot stand behind, we were left with three published conversion rates and exactly one complete before-and-after pair. Three numbers across incomparable price points is not a benchmark. Publishing it as one would make us the thing this series exists to argue against.

So this is the honest version: what we can show, why we will not average it, and the part that actually helps, which is how to measure conversion properly and what to fix in what order.

Where these numbers come from

Our own client list, not a market study. The Shizz has published 21 client engagements as case studies. The figures below come from those pages and link to them. The sample is self-selected, being brands that hired a performance agency, written up by the agency they hired.

Three published cases are excluded from every figure here because their numbers are not confirmed to our satisfaction. One of those exclusions is precisely what shrank this post from a table to an argument, and we would rather tell you that than quietly pad the set.

The conversion figures we publish

A complete before-and-after pair. n=1.

• Soothys, 3 months: conversion up from 1.3% to 6%, with revenue up 208% and order volume doubled. Testing also surfaced two hero products that now drive over 60% of sales. Read the case.

An endpoint with no published starting rate. n=1.

• Vediko Origins, 8 months: conversion rose to 2.8%, alongside CAC down 60% and sales up 1321%. Read the case.

A relative improvement with no absolute rate. n=1.

• Pure Whites, 8 months: conversion improved 350% through the funnel, with click-through rates up 200% and sessions up 3060%. Read the case.

That is the whole usable set. One pair, one endpoint, one multiple. We are not going to average a chocolate brand's rate against an organic-staples brand's rate and call it an Indian D2C benchmark, because those two numbers were never measuring the same thing. Soothys sells an impulse product on a crowded shelf. Vediko sells replenishable staples. A 6% and a 2.8% from those two businesses tell you about the businesses, not about a market.

We also have engagements where conversion clearly improved and no figure was published, and we are listing them as qualitative rather than promoting them into data. Lal Sweets: website conversions improved significantly after the CRO and content revamp. Blume Life: a funnel that converted more of the traffic already being paid for. Kroslo: a checkout step that finally held the people the brand had already paid for.

Why site-wide conversion rate is the wrong number to benchmark

It is a ratio of two things you control separately

Conversion rate is orders divided by sessions. You can move it by improving the site or by narrowing the traffic, and those look identical on the dashboard. A brand that cuts a badly-targeted campaign will see conversion rate jump without a single change to the store. A brand that scales into cold audiences will see it fall while the business gets bigger.

Pure Whites is the clean illustration. Sessions rose 3060% over the engagement, and conversion improved 350% at the same time. Those normally pull against each other. Getting both means the traffic and the site were fixed together, which is the only version of the number worth quoting.

Blended rates hide everything useful

Before comparing yourself to anyone, split the number by device, channel, campaign intent, and new versus returning. Mobile and desktop rarely behave alike in India. Branded search and cold prospecting are different populations. A single site-wide figure averages a very low cold-traffic segment against a much higher branded one, and tells you nothing about either.

The measurement is often broken before the site is

Bots, misdirected sessions, tracking that double-counts, and analytics that disagree with the platform's own reporting are all common. If your session count is wrong, your conversion rate is wrong, and every test you run on top of it is measuring noise.

This is also where the omnichannel problem bites. Inc42 reports that Renée Cosmetics now takes 65% of sales offline, and of the online remainder only about a quarter comes through its own website (Inc42, 21 July 2026). For a brand shaped like that, site conversion rate is a real metric measuring a small slice of a real business. Treating it as the health of the company is a category error.

What to fix, in order

1. Stability, before anything else

You cannot optimise a site that intermittently fails, and you certainly cannot A/B test on one, because the variance in your test will be dominated by whether the page worked that day. Butterfly Ayurveda is our clearest example of this: the engagement was purely CRO and web, and the job was removing technical roadblocks that were capping demand the brand was already receiving. The gain there was not extra visitors, it was a larger share of the existing ones completing their order. Read the case.

For a brand where demand was never the missing piece, clearing the blockage was the entire job. Nobody should be running a headline test while that is true of their site.

2. Mobile speed and the checkout path

In Indian D2C the overwhelming majority of sessions are mobile, often on variable connections. The reliable wins are unglamorous: page weight, sticky buy buttons, UPI-first checkout, guest checkout, honest delivery estimates, and COD guardrails that stop cancellations eating the gain. Kroslo's turnaround included a checkout step that stopped losing people the brand had already paid for, and that alone contributes to a doubled ROAS in 25 days.

3. The product page, then the offer

Most conversion problems on Indian food and nutrition sites are trust problems wearing a UX costume: unclear sourcing, no reviews near the buy button, no answer to the obvious objection. Soothys is instructive here because the testing did two jobs at once. Conversion moved from 1.3% to 6%, and the same testing surfaced two hero products that now carry over 60% of sales. Finding out what people actually want to buy is a conversion intervention, not just a merchandising one.

4. Only then, tests

Structured testing needs enough traffic to reach a readable result and a stable baseline to test against. Below that volume, run the known fixes rather than pretending to run experiments. A test that cannot clear its own noise floor is a decision made by coin flip with extra steps.

Why this is a CAC lever, not a vanity metric

Every point of conversion improvement makes the traffic you are already buying cheaper per order. A store moving from 1.2% to 2% has cut its effective acquisition cost by roughly 40% without touching a bid. That is why the conversion work and the media work belong to the same conversation, and why Vediko Origins' CAC fell 60% in the same engagement where conversion rose to 2.8%.

The same logic runs the other way. Scaling media into a leaky site is the most expensive mistake in D2C, because you pay full price for traffic that a fixable problem then discards.

A conversion rate you cannot segment is not a metric. It is an average of two arguments you have not had yet.

What a useful conversion benchmark would need

If anyone publishes one, ask for these before believing it. Sample size and whether the brands are comparable in price point and category. Whether the figure is site-wide or segmented. Whether sessions are cleaned of bots. Whether it counts orders or paid orders, which in a COD-heavy market are very different. And what the traffic mix was, because a brand with heavy branded search will beat a brand doing cold prospecting on identical stores.

We could not clear that bar with three figures, so we did not try. When we have enough verified pairs to publish a real range, this page will carry it.

The work itself is our CRO and web practice, usually running alongside performance marketing, mostly for food and beverage and nutrition brands. There is a practical companion piece on Shopify CRO for Indian stores if you want the fix list rather than the argument.