GOOGLE ADVERTISING

Google Rewards Intent
Most Brands Fund Interest Instead

Eva brings a decade of commerce expertise to Google, built on understanding what actually converts to purchase, not just what generates clicks.
Search, Shopping, P-Max, and YouTube structured for Shopify growth, with the measurement foundation to prove every dollar.

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The Problem

Most Google Agencies Optimise for Traffic
eCommerce Brands Need Profit

Google is the highest-intent search channel available to ecommerce brands. But intent is not the same as interest — and most agencies treat them identically. The result: budget poured into audiences who browse but don’t buy, measurement set up wrong so no one knows what converted, and landing pages that lose the sale after the ad wins the click.

Core Distinction

Intent vs. Interest:
the most important thing to understand about Google for ecommerce

Every Google channel serves a different buyer state. Treating them identically is the most common and most expensive mistake in ecommerce advertising. Eva structures every campaign by matching the channel to where the buyer actually is.

The Commerce Expertise Advantage

A Google Agency
That Knows What Actually Converts to Purchase

Generic Google agencies optimise for clicks and ROAS. Eva optimises for purchase — because we run Amazon advertising at scale and understand buyer behaviour across the full commerce funnel. That knowledge directly improves Google performance.

What Eva Manages

Four Capabilities
One Connected System

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Measurement Foundation

Before a dollar is spent, measurement is built correctly. Eva implements GA4 enhanced ecommerce tracking, Google Tag Manager configuration, Shopify purchase event tagging, and cross- device attribution — so every campaign is optimised on real conversion data, not estimated signals. This is the prerequisite most agencies skip or do poorly.
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Search & Shopping Architecture

Keyword strategy built on purchase intent, not traffic volume. Exact match isolation separates branded, category, and competitor terms into dedicated campaigns with independent bidding logic. Google Shopping feed managed as a continuous optimisation process — titles, attributes, pricing, and images refined to win the comparison moment. Bidding governed by Shopify contribution margin, not Google’s revenue targets.
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Performance
Max

P-Max’s effectiveness is determined almost entirely by the audience signals fed into it. Eva feeds Shopify customer purchase history, product affinity data, and high-LTV cohort lists as P-Max audience signals — making the AI significantly more accurate than campaigns running on generic Google signals alone. Asset groups structured by product category and margin tier, not by creative format.
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Landing Page
& Conversion

The ad wins the click. The page wins the sale. Eva optimises the Shopify landing pages that Google traffic lands on — PDP hierarchy, offer framing, trust placement, and checkout path — so conversion rate improves alongside traffic quality. Google spend and Shopify conversion rate are managed as one system, not two separate problems.

Eva vs Standard Agency

What Commerce Expertise Changes

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Capability

Eva Commerce

Standard Agency

Measurement

GA4 + GTM + Shopify purchase events — full ecommerce attribution

Basic conversion tracking

Keywords

Purchase intent-based — exact match isolation by campaign type

Volume-based, traffic-focused

Shopping Feed

Continuous optimisation — titles, attributes, pricing, images

Set up once, rarely optimised

P-Max Signals

Shopify customer lists, product affinity, high-LTV cohorts

Generic Google first-party data

Bidding Basis

Shopify contribution margin — profit-aware by product category

Google’s revenue / ROAS targets

Landing Pages

Shopify PDP and landing page optimisation — managed with ads

Not managed

Intent vs Interest

Search/Shopping separated from Display/YouTube by buyer state

Not differentiated — same budget logic

Commerce Context

Amazon + Shopify data informs Google keyword and audience strategy

Google channel only

Case Studies

View All Case Studies

Hear from the brands growing

Proven Results Across the Eva Growth System

Real outcomes from brands using Eva across Amazon, Shopify, and TikTok

Revenue Driven
Across Channels
$ 1 B+
Ad Spend Optimized
with Profit Focus
$ 0.3 B+
Avg. Profit Increase
in 6 Months
0 %
Reduction in
Operational Costs
0 %
Avg. Revenue Growth
in 6 Months
0 %

Based on aggregated performance across Eva-managed brands

Frequently Asked Questions

What is the difference between intent and interest in Google advertising?

Search captures buyers actively looking to purchase — high intent. Display and YouTube reach people who are interested in a category but not yet searching — interest. Eva builds separate campaign structures for each, with distinct bidding logic, budgets, and creative. Conflating the two is the most common way ecommerce brands waste Google budget.

Without GA4 enhanced ecommerce tracking and correct Shopify purchase event configuration, Google’s bidding algorithms optimise on incomplete or incorrect signals. That means P-Max and Smart Bidding campaigns are trained on bad data from day one. Eva builds the measurement foundation first — this is a prerequisite, not an add-on.

P-Max performance scales with audience signal quality. Eva feeds Shopify and Amazon-derived customer purchase history, product affinity data, and high-LTV cohort lists as audience signals. We also apply Amazon keyword intelligence — understanding which search terms and product attributes drive real purchase decisions — to Google keyword architecture.

Yes. Eva manages both ends of the conversion. Google traffic lands on Shopify — if the page isn’t built to convert, ad spend is wasted. Eva optimises PDP hierarchy, offer framing, and checkout path alongside the campaigns, so conversion rate and traffic quality improve together.

Get Started

Google is the highest-intent channel available to your Shopify store

Most brands underperform it because measurement is wrong,
campaigns aren’t structured by buyer state,
and the landing page is never optimised. We fix all three — before scaling spend.