Smart Bidding Pitfalls: Audit and Fix Campaign Performance
Many Google Ads campaigns are driven by Smart Bidding. This tool learns, optimises, and drives results automatically. If you don’t know how to properly use Smart Bidding, your campaign results may fluctuate more than you’d like. The reasoning behind this often comes from the signals, settings, and data it’s receiving.
In this guide, we will take you through some of the most common pitfalls when it comes to Smart Bidding. After reading this, you should be able to audit and fix this so that your campaign runs smoothly and delivers consistent results.
1. Conversion Tracking Issues
Smart Bidding is only as good as its training data. The number one cause of inconsistency is faulty conversion tracking.
Conversion Lag: A B2B sale might take 30 days. If the model optimises on a 7-day lookback, it’s acting on outdated signals, leading to erratic bidding. Fix: Adjust your attribution windows and use a long enough lookback period to train the model.
Micro-Conversions: Avoid overwhelming the algorithm with low-value, high-volume actions (such as "Page Scroll"). This dilutes the signal and confuses the bidding model. Fix: Include primary, high-intent conversions in the optimisation set. Use micro-conversions for observation only.
2. Ignoring Data Volume Requirements
Smart Bidding thrives on data. Campaigns with too few conversions or clicks create erratic, unpredictable results.
Insufficient Conversions: Target CPA or ROAS strategies need sufficient data to learn. Fix: Group similar campaigns, temporarily increase budget, or use Maximise Conversions until data thresholds are met.
Sparse Traffic Segments: If traffic is too thin, it prevents accurate algorithmic predictions. Fix: Expand targeting slightly or combine ad groups with similar intent to provide more learning opportunities.
3. Overcomplicating Campaigns
While automation is the goal, Smart Bidding still requires a clear, coherent campaign structure to succeed. Conflicting signals create confusion.
Keyword Intent Mismatch: Mixing highly informational keywords (low intent) with highly transactional keywords (high intent) in the same ad group confuses the algorithm. Fix: Isolate your commercial terms ("buy," "hire," "cost") into their own ad groups or campaigns. This allows the model to bid precisely based on the immediate purchase signal.
The Bidding and Traffic Segment: If a single campaign serves a niche audience on Search, a broad audience on Display, and a retargeting audience, the model receives conflicting signals. Fix: Segment your campaigns by Bidding Strategy and Traffic Temperature (e.g., Retargeting should be its own campaign with a high ROAS target).
Ad Copy Relevance: Even with Smart Bidding, a poor Quality Score will hold you back. Fix: Ensure your ad copy and landing page are hyper-relevant to the search query. High relevance tells the algorithm to bid confidently, leading to a higher Ad Rank at a lower price.
4. Not Adjusting for Seasonality or Market Changes
Historical trends are a primary guide for Smart Bidding. Sudden shifts, such as new PPC competitors, seasonal demand, or market disruptions, can instantly crash performance.
Seasonal Spikes and Promotions: Ignoring short-term, predictable changes leads to massive missed opportunities or panicked, wasted spend. The model often can't react fast enough to a one-week sale or a holiday rush. Fix: Use Google’s Seasonality Adjustments to temporarily increase or decrease bids during predictable peaks.
Market Shifts and Competitive Pressure: A new market entrant, a competitor’s aggressive price change, or an industry-wide event can instantly disrupt the algorithm’s learning curve. Fix: Review historical and real-time performance to adjust targets to match current conditions.
Campaign Flexibility: Static, fixed targets during unpredictable periods drastically reduce efficiency. Fix: Your team must maintain an agile mindset. When performance deviates from expectations, be ready to quickly make some adjustments to re-stabilise the algorithm’s learning path.
5. Neglecting Audience Signals and Context
You need to identify your ideal customers in order for Smart Bidding to prioritise effectively. Ignoring high-value audience segments, critical devices, or key geographies severely limits the algorithm’s ability to allocate spend.
Audience Segmentation and Value: Treating all users the same is the fastest way to confuse the algorithm. Fix: Layer Audience Signals onto your Search campaigns. Use Observation settings to identify which lists are converting best, and then switch to Targeting or use Bid Adjustments to aggressively bid up for those proven, high-value segments.
Device Performance: Conversion rates differ across devices. Fix: Review your conversion paths by device. If mobile has a low conversion rate but is critical for awareness, set appropriate bid adjustments by device to align with the true conversion pattern.
Geographic and Time Differences: Location affects both performance and cost. Bidding for an impression in Dublin is not the same as bidding in rural Irish villages. Fix: Adjust bids by location and time of day based on where your high-intent conversions actually occur. This allows Smart Bidding to optimise powerfully within the parameters you set, allocating the budget where the revenue potential is highest.
Smart Bidding can feel unpredictable, but most issues are fixable with a structured audit. Start by reviewing conversion tracking, data volume, campaign structure, seasonality, and audience signals. By addressing these pitfalls systematically, you’ll empower the algorithm to deliver consistent, predictable results for your business.
Let's audit your Smart Bidding setup for predictive performance.
