What FunnelFreaks Does Differently From Every Other CRO Agency

There are excellent CRO agencies specialising in D2C ecommerce. SplitBase works with high-revenue DTC brands on Shopify Plus, with a portfolio spanning over 1,000 optimised landing pages and product pages. Invesp brings 18 years and tens of thousands of experiments to research-led programmes. Speero, The Good, and Conversion Rate Experts all run rigorous, methodologically sound experimentation for large ecommerce operations.

If you're an established Western beauty brand with a card-payment checkout and low return rates, several of them are a genuinely good fit.

Two things separate FunnelFreaks from all of them, and neither is a claim about being better at experimentation. They're structural differences in what we do first and which market we're built for.

Difference 1: We Validate the Data Before We Test Anything

Nearly every CRO agency begins an engagement by analysing your funnel and building a hypothesis backlog. The analytics data feeding that analysis is treated as an input, not something to be verified.

That assumption breaks more often than the industry acknowledges. On Shopify stores specifically, three failure modes are common enough that we find at least one in most audits:

• Duplicate purchase events from post-purchase apps or confirmation page reloads, inflating reported conversion rate by 20–30%

• Missing begin_checkout on mobile after theme updates, making checkout appear catastrophically broken when the real leak is elsewhere

• Attribution collapse at payment gateways when Razorpay, Cashfree, or PayU domains aren't excluded, sending paid conversions into direct / none

None of these produce an error message. They produce plausible-looking data that leads confidently in the wrong direction.

What we do instead: before any hypothesis is written, we build or fix the data layer; GA4 ecommerce events, GTM configuration, data layer architecture, checkout and cross-domain tracking, consent mode and reconcile GA4 revenue against Shopify order data. If those numbers don't match within an acceptable margin, nothing else starts.

This isn't a preliminary step we rush through in week one. It's the phase the agency is built around, because every decision made afterward inherits whatever errors exist in the measurement.

That's what "every decision is data-backed" means in practice. It's a claim that only holds if the data has been verified first.

Difference 2: We're Built for Indian D2C, Not Adapted to It

The international specialists optimise for a market with card payments, negligible return-to-origin rates, and a single consent framework. Their playbooks are excellent for that market. They don't transfer cleanly.

Four realities that Indian D2C brands face and Western CRO frameworks have no concept of:

Cash on delivery as a parallel funnel. COD still accounts for a substantial share of Indian D2C orders. COD buyers have different intent at checkout, different abandonment triggers, and different post-purchase outcomes. Standard GA4 setups don't fire add_payment_info for COD orders, which means your payment step shows artificially high completion because only prepaid users are being counted at it. Half your buyers move through a funnel your analytics can't see.

UPI redirects breaking session continuity. Payment app redirects break the session unless gateway domains are on the referral exclusion list. The purchase then gets attributed to the gateway rather than the campaign that drove the visit.

RTO making purchase a misleading success metric. In GA4, purchase is the terminal event. In Indian D2C, a completed purchase isn't a completed transaction. Optimise checkout for maximum completion and you can raise reported conversion rate while increasing returns; the metric improves, the business gets worse. We treat purchase as an intermediate event and measure against delivered orders.

DPDP consent requirements. India's Digital Personal Data Protection Rules take effect through late 2026 and into 2027. Consent Mode configuration with modelling is what preserves measurement continuity as opt-in rates settle.

Our post on the Indian D2C analytics problem covers each of these in technical detail.

What This Means Practically

The two differences compound. Because we build the data layer, we can segment funnels by payment method, city tier, and order type; the cuts that actually explain how Indian D2C funnels behave. An agency that inherits a standard GA4 setup can't run those cuts, because the dimensions don't exist in the data.

A concrete example. An Indian D2C skincare brand comes to us with a 68% cart-to-checkout drop-off and a plan to redesign the cart page.

• Standard approach: analyse the cart page, run session recordings, form hypotheses about trust signals and shipping transparency, test.

• What we do: first check whether begin_checkout fires on mobile which carries the majority of Indian D2C traffic. If it doesn't, and often it doesn't, that 68% is largely an artefact. The real leak is somewhere else entirely, and the cart redesign would have consumed a quarter of the testing budget solving a problem that didn't exist.

We documented a full version of this in our account of A/B testing on broken GA4 data, where a quarter of declared "winning" tests turned out to be tracking artefacts.

What We Actually Do

The full scope, so this reads as description rather than positioning:

Analytics infrastructure : GA4 setup and auditing, GTM and server-side GTM implementation, ecommerce event tracking, custom event and parameter tracking, data layer planning, cross-domain and checkout tracking, attribution and campaign tracking, consent-mode compliance, tracking validation and QA.

Conversion rate optimisation : CRO and UX audits, funnel analysis, landing page and product page optimisation, checkout and cart optimisation, A/B testing strategy and analysis, hypothesis development and prioritisation, user journey analysis, post-test implementation and monitoring.

Reporting and BI : Looker Studio dashboards, ecommerce and conversion reporting, funnel and customer journey dashboards, BigQuery-based analysis, automated reporting.

MarTech consulting : stack audits, tool selection, platform integration planning, tag and script performance audits, technical documentation.

These sit under one team rather than split across an analytics vendor and a CRO vendor. That integration is the point, the team that validates the data is the team forming hypotheses from it.

Who We're Not the Right Fit For

Being direct about this, since misaligned engagements help nobody:

• Brands with validated tracking who want pure experimentation velocity. If your GA4 is clean and you need someone running high-frequency tests, an experimentation specialist is the better fit.

• Enterprise operations needing large embedded teams. The international specialists have deeper bench strength for that scale.

• Stores without enough traffic for statistical testing. Tests won't reach significance on a practical timeline. You need traffic first — though a one-time audit to fix tracking and identify structural issues is still worthwhile at any volume.

• Brands wanting a site redesign. A redesign isn't CRO, and it destroys the baseline needed to measure whether anything worked.

The Question That Reveals the Difference

If you're evaluating CRO agencies, ask each one: "Will you audit our GA4 tracking before building a test roadmap?"

Most will say they'll work with your existing setup. That's not incompetence, analytics implementation is a genuinely different discipline from experimentation, involving GTM configuration, data layer architecture, and platform-specific behaviour. Most CRO agencies are experimentation specialists, and that's a legitimate specialisation.

It just means the foundation their work rests on is one nobody has checked.

Our breakdown of CRO agencies for D2C brands in India covers who specialises in what, and our 10 questions to ask when hiring a CRO agency gives you the full evaluation checklist, including for us.

The Ten-Minute Check

Before hiring anyone, run this: pull your GA4 purchase event count and your Shopify order count for the same 30-day window.

Within 5–10% is healthy. Over 20% needs fixing before any testing begins. If GA4 is higher than Shopify, you have duplicate events and every conversion rate you've been working from is inflated.

That comparison tells you whether the CRO investment you're about to make is measurable at all. Our guide to why GA4 and Shopify numbers don't match explains what each gap size means.

Running a D2C brand in India and want CRO built on data you can trust? Talk to FunnelFreaks, we start by finding out whether your current numbers are real.