When 'Dave' Distorts Your PPC: Use Lead Valuation — Not Sales — to Fix Bidding Signals

When ‘Dave’ Distorts Your PPC: Use Lead Valuation — Not Sales — to Fix Bidding Signals

Edward Newman’s recent piece on Search Engine Land makes a clear call for rethinking how paid media is optimized for businesses with long sales cycles. As Newman writes, “When you optimize campaigns to final sales, you’re teaching the ad platform to respond to how well the sales team performed that month rather than lead quality, and that’s a problem no amount of campaign changes will fix.” (Edward Newman, Search Engine Land)

For many B2B and high-value B2C advertisers, the customer journey continues long after the form submit. Newman’s point is simple but powerful: the part of the funnel you can’t control — the variability inside the sales team and downstream operations — can create false signals for automated bidding systems. That leads to over- or under-investment in audiences and keywords based on temporary, human-driven performance swings.

Why sales-based signals can mislead automated bidding

Sales outcomes are influenced by staffing, seasonality, product availability, internal processes and sudden operational shifts. Newman illustrates this with a recurring pattern he calls the “Santa Claus Rally,” a December spike in sales-team performance that causes algorithms to overvalue specific audiences, only for performance to collapse afterward when the team takes holiday leave. The result: bidding systems learn the wrong lesson.

Rather than treating final sales as the single source of truth for optimization, marketers should identify the last reliable point of control — typically lead submission — and then enrich that signal so it carries meaningful business value.

Lead valuation: the practical alternative

Lead valuation assigns a monetary or proxy value to each lead type based on historical conversion likelihood and revenue potential. Instead of sending a raw “lead” conversion to your ad platform, you return a value that reflects expected revenue or lead quality. This supplies algorithms with hundreds of meaningful events and stable signals rather than a handful of noisy, downstream sales.

Google’s own guidance supports this approach: “Value-based bidding enables you to maximize the total value of conversions generated by your campaigns. Google AI optimizes bids in real time to reach people who are likely to bring more value to your business.” (Google Ads Help) Using value-based bidding (Maximize conversion value or tROAS) lets you optimize toward economic outcomes the platform can learn from, provided you feed it accurate values.

How to build and operationalize lead valuation

• Collect historical data: Pull 6–12 months of lead-to-sale history from your CRM and identify the characteristics of leads that closed (company size, product, form fields, source, etc.).
• Group and score leads: Create buckets (high, medium, low) by likelihood-to-convert and average deal size. Assign expected revenue values or lead scores to each bucket.
• Feed values back to ad platforms: Use conversion tags, enhanced conversions, or offline conversion uploads to pass expected values to Google Ads daily.
• Test value-based bidding: Use experiments and holdout campaigns. Start with limited budgets but ensure you give algorithms at least 2–3 conversion cycles (or two weeks) to stabilize.
• Revisit and recalibrate: Recompute values quarterly or after major product/market changes to keep the model aligned with reality.

Tracking and technical considerations

Robust analytics are the backbone of this approach. Common pitfalls include missing CRM attribution, delayed offline uploads, and inconsistent tagging. Google Ads recommends selecting a single stage of the lead-to-sale funnel with a relatively short conversion delay and at least 15 monthly conversions for reliable bidding behavior. If you cannot meet those thresholds, use lead valuation to create higher-volume signals for the bidding engine.

Also consider conversion adjustments and enhanced conversions for leads to make sure your offline sales data maps back to the original ad click. Where precise revenue isn’t available, proxy values (lead scores or expected ticket size) are acceptable — better a consistent proxy than no signal at all.

When to still use sales-based signals

Optimizing to final sales remains appropriate when: you have high-volume sales, short lag times, and stable operations. In these cases, downstream data is reliable enough for automated bidding to learn meaningful patterns. For most high-value, low-volume businesses, however, optimizing at the lead submission stage with valuation is a safer, more controllable choice.

Read this and act

Newman’s prescription is actionable: know where your control ends and optimize to that boundary. Build lead valuation, feed expected values into your ad platforms, and rely on value-based bidding where appropriate. Those steps will reduce algorithmic churn and align media spend with economic outcomes.

Original article: Where paid media optimization should stop in long sales cycles — Edward Newman, Search Engine Land.