Analytics & Attribution Consulting
Know which ₹ of spend drove which deal.
GA4, GTM, server-side tracking, and multi-touch attribution built end-to-end so your data tells the truth.
Attribution is the most underdeveloped capability in most marketing stacks. Companies are making budget allocation decisions based on last-click platform data that over-credits paid channels, misses offline conversions, and cannot tell the difference between a channel that drove a customer and a channel that showed up at the moment of intent someone already had. I build attribution systems that are honest.
Why attribution is broken in most marketing stacks
Most marketing analytics stacks have the same structural problem: the data lives in disconnected systems that each report a different version of the same reality. Google Ads reports one conversion number. Meta reports a higher one. GA4 reports a third figure. The CRM shows a fourth count of leads from paid channels. Each number is technically correct from the perspective of the system that produced it, but they cannot all be simultaneously true because they use different attribution models, different lookback windows, and different identity resolution logic. The reconciliation failure means that every budget decision is made with inconsistent data, and no one in the business can answer the basic question of which channel actually produced which customer at what cost. The attribution system that solves this designates the CRM as the single source of truth, builds the measurement infrastructure so that every conversion event traces back to a CRM record, and produces a unified view where spend, pipeline, and closed revenue are visible in one place with consistent attribution logic.
Server-side tracking: the architecture that restores measurement accuracy
Server-side tracking routes conversion events through the business own infrastructure rather than the user browser. This architectural shift resolves the measurement degradation introduced by browser privacy restrictions, ad blockers, and iOS ATT. The browser-based pixel sends events using JavaScript executed on the user device, which means it is subject to every browser restriction, content blocker, and device privacy setting the user has in place. The server-side container sends events from the business server directly to the ad platform API, using first-party data that is available to the business regardless of browser-level constraints. The practical result is higher event match rates in Meta CAPI, more complete conversion coverage in Google Ads, and more accurate audience data across all platforms. The implementation requires a server-side GTM container, cloud hosting, and the configuration to route specific conversion events through the server path. A well-implemented server-side tracking layer typically recovers a meaningful proportion of the conversion events that were being lost through browser-only measurement.
From data infrastructure to weekly decisions that compound
Analytics infrastructure delivers value only when it changes the decisions being made. A GA4 configuration that the team does not know how to read, a Looker dashboard reviewed once per quarter, and a weekly report compiled manually from six different platform exports are all examples of analytics investment that exists without producing actionable insight. The reporting layer that makes analytics operationally useful is built around the specific decisions that need to be made on a weekly basis: which channels are producing pipeline at or below the target CAC and should receive additional budget, which campaigns are underperforming against the conversion objective and need restructuring, which audience segments are producing the highest customer lifetime value and should be scaled. Each metric in the weekly dashboard is tied to a decision threshold. When a metric falls below that threshold, a specific action is taken. This is the difference between analytics as a reporting function and analytics as a management system that drives consistent execution.
Full GA4 setup with custom event taxonomy, conversion events, user properties, and e-commerce or lead gen measurement configured correctly.
Tag manager container built with a clean trigger and variable layer, no spaghetti tags, no duplicate fires, no missing events.
Server-side GTM container for first-party data collection, bypassing browser restrictions and improving match rates across all ad platforms.
CRM transaction data fed back to Meta and Google so both platforms optimise for revenue signals rather than browser-only event data.
First-touch, last-touch, and linear attribution views in Looker so you can see demand generation credit versus conversion credit separately.
Looker Studio or Looker dashboard connecting GA4, ad platforms, and CRM into one view, CAC, pipeline, and ROAS by channel and campaign weekly.
- 01Tracking audit: audit every event firing in current GA4 and ad platforms, identify duplicates, missing events, and misattributed conversions.
- 02Taxonomy design: define a canonical event taxonomy before implementing anything, consistent naming across GA4, GTM, and ad platforms.
- 03Server-side build: deploy server container, route key events through server, and validate match rates before switching off browser events.
- 04Attribution model: build multi-touch model in Looker using CRM data as the source of truth, not ad platform data.
- 05Dashboard delivery: weekly Looker report with CAC, pipeline, ROAS, and cohort LTV by channel, reviewed with the team in a standing meeting.
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