Client Profile
The client operates a product-based eCommerce business in the UK, offering custom merchandising and printing solutions to clothing labels, promotional merchandise companies, and corporate buyers. Their catalog spans Direct-to-Film (DTF) transfers, 3D UV stickers, and a range of made-to-order print products.
The client’s primary sales channels are their own eCommerce website and an Amazon store. Both operate in a competitive, specialist niche where buyers have high product specificity and pricing pressure is constant.
Scope Of Work
The client initially engaged Data4eCom to manage SEO for their eCommerce website. Following measurable early gains, the scope expanded to multi-channel paid advertising. The requirement included:
Challenges Identified
Despite an average daily budget of approximately £40 across platforms, the campaigns were generating low conversion volume and declining margins. An audit across Google, Meta, TikTok, and Amazon identified four major challenges:
All campaigns lacked a clear distinction between awareness, prospecting, and conversion goals. The budget was distributed without understanding the buyers’ intent and their purchase journey. The result was ad spend consistently generating impressions and clicks without converting them into sales, because conversion-stage messaging was shown to people with no prior brand exposure.
Keywords were not organized by match type, leading to ads appearing for queries unrelated to the products being sold. Audience strategy faced a similar issue—retargeting pixels were inactive, CRM-based custom audiences were nonexistent, and lookalike segments were not created. The campaigns were reaching cold, unqualified users with bottom-funnel messaging that had no chance of converting them.
Bids remained static regardless of device, time of day, location, or audience performance signals. In a competitive print and merchandise niche—where auction prices shift rapidly on high-intent keywords—the lack of structured bid management led to a consistent pattern of overbidding on low-value clicks and underbidding on high-converting opportunities.
Although certain campaigns succeeded in generating clicks, conversion performance remained limited due to two core backend issues. First, the landing pages lacked sufficient alignment with the ad messaging and keyword intent, negatively affecting Google Quality Scores and reducing the probability of conversion. Second, unresolved product feed errors in Google Merchant Center — particularly in the structuring of product types and attributes — restricted the reach and eligibility of Shopping campaigns.
Our Approach
Instead of handling each channel separately, we built a unified eCommerce PPC and SEO strategy in which keyword data was shared across both channels—organic research informed the paid campaign structure, and PPC search term data was used directly in SEO targeting. A dedicated team of four specialists — one SEO expert and three PPC specialists — was assigned to the project.
We began with a technical audit and addressed the site’s crawlability issues first. This was followed by on-page optimization, with meta tags, page titles, and heading structures rewritten around commercial-intent keywords relevant to the UK custom print market. The internal linking structure was also redesigned to direct authority toward the highest-value product and category pages.
Keyword research identified priority terms across DTF transfers, custom stickers, and heat press prints, all filtered for commercial intent, realistic ranking potential, and UK market relevance.
The Google Ads account was restructured, with each campaign type assigned a defined role in the funnel—Performance Max for brand awareness and reach, and manual Search campaigns for conversion targeting at the keyword level.
Keywords were segmented by match type — separate campaigns for broad, phrase, and exact match queries, each with its own bid strategy. Responsive Search Ads were built with multiple headline and description combinations and tested to improve ad relevance and Quality Score.
Audience targeting was layered across in-market segments, custom intent audiences, and remarketing lists for search ads, with device and location bid adjustments applied based on conversion data. The Google Merchant Center product feed was audited and restructured to resolve eligibility issues that were affecting Shopping campaign reach.
| Campaign Type | Bid Strategy | Audience & Targeting |
|---|---|---|
| Performance Max | Maximize Clicks | Brand, In-Market, and Interest Signals |
| Search | Maximize Conversions | Broad Match — Traffic Volume Expansion |
| Search | Maximize Conversion Value | Exact / Phrase Match — High-Intent Queries |
| Search | Target CPA | Affinity & Custom Intent Audiences |
| Search | Target ROAS | RLSA + Device, Location & Demographic Adjustments |
We configured the Meta Pixel to track conversion events for key actions, such as purchases, add-to-carts, and page views.
We built custom audiences from three sources—website visitor data, CRM lists, and engagement history. Additionally, we modeled lookalike audiences (1%–3%) on existing buyer data to reach new users with similar characteristics. For UK-based buyers, we leveraged geographic targeting.
We ran campaigns across awareness, engagement, and conversion objectives. Our PPC experts applied Bid Cap and Target Cost strategies to conversion campaigns to control cost per acquisition.
We designed dedicated retargeting campaigns to re-engage cart abandoners and past visitors, using pixel-based audience segments, which refreshed continuously from live traffic data.
| Campaign Type | Bid Strategy | Audience & Targeting |
|---|---|---|
| Awareness | Maximize Conversion Value | Custom Audiences — Visitors, CRM & Engagement |
| Sales | Bid Cap | UK Geographic & Radius Targeting |
| Engagement | Maximize Conversions | Demographics & Behavior (Age, Gender, Interests) |
| Sales | Target Cost | Lookalike Audiences (1%–3%) — Buyer Profiles |
| Sales | Pixel Retargeting | Cart Abandoners & Past Site Visitors |
TikTok was introduced as a dedicated acquisition and awareness channel after performance had stabilized across Google and Meta. The work began with pixel implementation and full-funnel conversion event mapping to ensure accurate data from the outset supported campaign optimization.
Creative testing followed a parallel-launch approach, with multiple user-generated content (UGC)-style and in-feed video variants launched simultaneously across ad sets. We monitored hook retention rates to identify where viewer drop-off occurred, and used those insights to refine the opening three seconds of underperforming creatives. Additionally, we tried multiple creative assets to prevent ad fatigue and cost per mille (CPM) inflation.
High-performing organic posts were then amplified through Spark Ads. We managed budget scaling through Cost Cap bidding to protect cost-per-acquisition (CPA) performance as spend increased gradually. Our team monitored ad campaigns daily throughout the learning phase.
| Campaign Objective | Bid Strategy | Audience & Targeting |
|---|---|---|
| Awareness | Reach Maximization | Broad + Interest — Print, Craft, Small Business |
| Consideration | Click Optimization | Custom Engagement + Device & Location (UK Mobile) |
| Sales | Complete Payment | Custom Intent + Pixel Retargeting (Cart Abandoners) |
| Scaling | Value Optimization | Lookalike (1%–3%) — Existing Purchaser Profiles |
We created Amazon ad campaigns from scratch, with clear separation between broad, phrase, and exact-match keywords. This prevented budget cannibalization and gave us direct control over bid levels by intent.
Our team reviewed Search term reports on a fixed weekly cadence. High-performing terms were identified to increase spend, while irrelevant queries were added as negative keywords to stop wasted spend.
We adjusted bids at the keyword level based on conversion data rather than impressions or click volume. Our team defined the Advertising cost of sale (ACoS) targets individually for each stock-keeping unit (SKU) based on product-level margins, rather than averaging across the catalog. The underperforming keywords were paused before losses escalated.
We increased Top-of-Search placement bids for campaigns that consistently delivered conversions. Product targeting campaigns were also introduced to secure visibility for competitor and complementary product listings, helping capture buyers who are already in a purchase mindset.
The project was completed within eight weeks, delivering a production-ready taxonomy that was imported directly into the client's backend system. Key outcomes included the following:
| Month | Conversions | Revenue | ROAS |
|---|---|---|---|
| Dec 2025 | 176.14 | £7,949.72 | 8.20 |
| Jan 2026 | 543.40 | £14,913.39 | 8.20 |
| Feb 2026 | 585.06 | £13,469.27 | 8.20 |
| Total | 1,304.60 | £36,332.38 | 8.20 |
| Change % | 255.37% | 76.37% | 33.66% |
Conversions increased by 255.37%, and revenue grew by 76.37% within three months of the campaign restructuring. ROAS improved 33.66% over the same period.
| Month | Orders | Revenue |
|---|---|---|
| Oct 2025 | 678 | £16,391.93 |
| Nov 2025 | 683 | £21,437.99 |
| Dec 2025 | 705 | £24,174.06 |
| Jan 2026 | 637 | £21,293.10 |
| Feb 2026 | 559 | £18,895.85 |
| Mar 2026 | 622 | £17,470.70 |
Revenue grew from £16,391.93 in July 2024 to £24,174.06 in October 2025 — a 47.5% increase across the peak growth window. Total revenue across six months (Oct–March 2026) reached £119,663.63.
Even in the non-peak months of November and December, monthly revenue stayed above £17,470, indicating stable audience retention.
| Month | Sales | Performance (£) | ROAS |
|---|---|---|---|
| Dec 2025 | 451 | £9,845.44 | 2.70 |
| Jan 2026 | 581 | £12,852.62 | 3.26 |
| Feb 2026 | 663 | £14,929.86 | 3.61 |
| Total | 1,695 | £37,627.92 | 9.57 |
| Change % | 47.01% | 51.64% | 33.70% |
Sales grew 47.01%, and revenue grew 51.64% between Dec 2025 and Feb 2026. ROAS improved 33.70% — from 2.70 to 3.61 — exceeding the client's stated performance benchmarks.
| Attribute | Oct–Dec 2025 | Jan-March 2026 | % Change |
|---|---|---|---|
| Order Product Sales | £44,246.53 | £48,786.25 | ↑ 10.26% |
| Avg. Sales / Order Item | £12.80 | £14.84 | ↑ 15.94% |
| ROAS | 2.87 | 3.15 | ↑ 9.76% |
| Sessions | 53,730 | 57,098 | ↑6.27% |
| ACoS (%) | 42.14 | 38.47 | ↓ 8.71% |
Comparing the two periods, order product sales grew by 10.26%, average revenue per order item rose by 15.94%, and sessions increased by 6.27%. ROAS improved 9.76%. ACoS declined 8.71%, from 42.14 to 38.47, reflecting the impact of tighter keyword segmentation and per-SKU bid controls.
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