Common Issues with Google Ads Optimisation and Solutions

Why addressing common issues with Google Ads optimisation and solutions matters

Google Ads remains a primary acquisition channel for US eCommerce, B2B, and service businesses, but suboptimal setups turn ad spend into noise rather than revenue. This guide walks through the most common issues with Google Ads optimisation and solutions you can apply immediately - focusing on attribution accuracy, funnel alignment, and measurable revenue impact rather than vanity metrics.

Top symptoms to watch for

• High spend with low incremental revenue ($ figures in US examples below represent estimated ranges).
• Platform-reported conversions that don’t match backend orders or CRM leads.
• Large variance between GA4 and Google Ads conversion counts.
• Poor conversion rates at the bottom of funnel despite healthy clicks at top of funnel.

Core root causes

Most optimisation problems fall into three buckets: tracking & attribution error, mismatched funnel strategy, and inefficient campaign structure. Below we map each common issue to practical solutions you can test and measure.

Conversion tracking and attribution issues

Inaccurate tracking drives bad decisions. Typical problems include duplicate tags, client-side losses from ad blockers or ITP, and mismatched attribution windows between systems. Start by verifying the end-to-end event pipeline: ad click → landing page → client-side event → server-side receipt → backend order/CRM.

A practical next step is to reconcile a 30-day sample of conversions between Google Ads, GA4, and your order system. Expect differences - if variance exceeds ~20-30% in typical US eCommerce cases, you have a measurable tracking problem that affects bidding and budget allocation.

For implementation patterns and services that align with this technical-first approach, see our Services Overview and our agency Homepage to understand how tracking and attribution fit into recurring growth retainers.

Funnel misalignment and campaign structure

Another common issue with Google Ads optimisation and solutions is mixing funnel stages inside a single campaign or bidding strategy. TOF traffic (discovery, prospecting) requires different creative, metrics, and attribution than BOF retargeting. Use structured campaign design: separate TOF, MOF, and BOF with tailored creatives and KPIs.

TOF → MOF → BOF breakdown

• TOF (Top of Funnel): awareness and reach, metric = view-throughs and assisted conversions.
• MOF (Middle of Funnel): consideration, metric = engagement and lead events.
• BOF (Bottom of Funnel): purchase intent and conversions, metric = orders and LTV.

When TOF and BOF are evaluated on the same conversion, Google’s automated bidding may overvalue broad traffic that doesn’t convert downstream. Split campaigns and align CPA/ROAS targets to the funnel stage to avoid this mismatch.

Bid strategies, budgets, and automation pitfalls

Automated bidding is powerful but depends on clean signals. If your conversions are undercounted or noisy, automated smart bids will learn the wrong patterns. Common fixes include using conversion imports from your backend, adjusting conversion value rules, and limiting automated bids until tracking quality improves.

Practical checklist to stabilise bidding

• Import server-side conversions and revenue ($ examples: test with a $50-$200 sample order range).
• Set appropriate attribution windows that reflect typical US buyer cycles (e.g., 7-30 days for transactional offers).
• Use value-based bidding only after verifying revenue accuracy in your data warehouse or CRM.

Testing, reporting, and decision frameworks

A repeatable experimentation framework reduces guesswork. Use a strategy → build → test → scale → report loop. For example, test a landing page variant tied to a single campaign for 2-3 weeks, measure incremental revenue versus control, then scale winning variants. When designing tests, ensure your tracking supports experiment-level attribution.

Example experiment structure

• Hypothesis: A simplified checkout reduces dropoffs and increases conversion rate by 10-15% for returning visitors.
• Metric: incremental revenue per 1,000 clicks ($ scale shown in US dollars).
• Dataset: campaign A (control) vs campaign B (variant) for 30 days, tracking via server-side events.

Operationally, store gclid or click_id in the checkout flow and order DB to enable precise joins with Google Ads. This supports clear reporting and long-term attribution modeling, reducing overreliance on platform-reported conversions.

Learn more about our technical approach and team experience on the About page. If you want an actionable diagnostic, review setup against our checklist and, when ready, start a tracking diagnostic with your team.

Compliance and US-specific considerations

Privacy and consent affect data capture in the US. Common pitfalls include improper cookie consent banners and failing to persist click identifiers post-consent. Make sure your consent flows allow necessary marketing signals where legally permissible and implement server-side fallbacks for browsers restricting third-party cookies.

Quick remediation steps

• Audit consent banners to ensure essential marketing signals are documented and stored after user consent.
• Implement server-side GTM to capture events that client-side scripts may lose.
• Document your attribution rules and communicate them to stakeholders so ROAS and MER are interpreted consistently.

Summary: prioritised action plan

• Audit tracking end-to-end and reconcile Google Ads, GA4, and backend orders for a representative US sample.
• Separate campaigns by funnel stage and align bidding to funnel goals.
• Stabilise automated bids only after importing accurate conversion revenue.
• Run controlled experiments tied to server-side tracking and revenue lifts.

Sources

• Google Ads conversion tracking guide
• GA4 measurement and events overview
• IAB overview of US privacy considerations (CCPA)
• Shopify guidance on passing gclid through checkout