Where paid media optimization should stop in long sales cycles

In long sales cycles, a lot of what happens after lead submission involves people. 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.

The common advice is to “optimize the full funnel” (i.e., track media spend to revenue, optimize campaigns to sales, etc.). But beyond lead capture, most of what drives sales has little to do with your paid media. It’s about who’s on the sales team, how busy they are, and dozens of other factors you can’t influence through targeting or creative.

When your sales team becomes the signal

I’ve spent over 15 years in financial services marketing, but this isn’t unique to mortgages or insurance. If your sales process relies heavily on people, you’ll recognize this immediately.

In most businesses, there’s someone like Dave. In my case, he’s a mortgage adviser, but in yours, he might be your top enterprise sales rep, your star business development manager, or your best project estimator.

He closes deals at twice the rate of his colleagues, not because he gets better leads, but because he’s naturally gifted at building rapport, asking the right questions, and guiding anxious customers through difficult decisions.

However, Dave isn’t always there. Sometimes he’s on vacation, sometimes he might leave the company for a better opportunity, or sometimes your business hires three more Daves.

The makeup of your sales team likely changes constantly. You might have more experienced closers one month, fewer the next, a recruitment drive that brought in several new starters, or Dave and two of his colleagues leaving within a month of each other. Sales rates can swing dramatically based purely on who’s in the office, regardless of lead quality.

This can lead to targeting problems. For example, when the conversion rate drops because Dave’s away and a junior team member is covering his accounts, the algorithm sees it as a targeting problem rather than a staffing issue. 

If you’ve set your campaigns to optimize for sales, it thinks, “Our targeting stopped working. These clicks are lower-quality for this conversion action now. We should shift spend away from these audiences.”

Eventually, this could result in keywords that were previously working well being turned off, audiences that were driving sales volume no longer being bid for, and, eventually, a decline in the entire account’s performance. But the leads haven’t changed, only the team has.

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Operational factors that distort your conversion data

It’s not just the sales team makeup either. Let’s say:

The team gets slammed in Q4 as everyone tries to close before year-end, response times stretch from two days to over a week, and customers get impatient and look elsewhere. 

Perhaps market conditions shift, and your most competitive product gets pulled. Or summer vacations mean the team is running short-handed, and some leads go cold before anyone contacts them. Then September comes and everything bounces back to normal.

It goes beyond the day-to-day. Budget approvals get delayed, product ranges change, and planning delays push projects back. The specific reason varies by business, but the effect on your conversion data is always the same.

The algorithm ends up thinking targeting got worse when, in fact, the team was just busy with leads from other sources.

When Dave becomes a superhuman: The Santa Claus Rally

The Santa Claus Rally, also known as the December Effect, is the best example I’ve seen of how human behavior can throw off algorithmic targeting.

Every December in financial services, something strange happens. In the third week of December, conversion rates from lead to sale spike dramatically. We’ve seen increases of up to 150% compared to normal weeks.

If campaigns are optimized for sales, the algorithm thinks, “Whatever we’re doing this week is working incredibly well!” Then the holiday week arrives, and everything crashes, with conversion rates plummeting to a fraction of normal levels.

None of it has anything to do with paid media. In week three, Dave and his colleagues are in target-hitting panic mode. End-of-year bonuses are on the line, and there’s one final push before the holiday break, so they’re calling leads faster, following up more aggressively, and closing deals they might typically have let simmer. Dave is working like a machine.

Then the holiday week arrives, and everyone’s mentally checked out, customers aren’t answering phones, and Dave has finally taken time off. The team that’s still at work is thinking more about family get-togethers and less about targets.

The lead quality, targeting, and ads haven’t changed. The team is just working at different levels of intensity due to seasonality. The algorithm overpays for normal performance and underbids for identical audiences, purely based on when Dave and his team take their vacations.

Dig deeper: How to analyze your marketing funnel and fix costly drop-offs

Where optimization should actually stop

So if optimizing for sales is being distorted by things outside your control, how should you draw the line? How can you balance this lead distortion and still drive the right type of leads?

The answer is your last point of control, which, for these kinds of sales, means at lead submission. But not just simply counting leads. Instead, value them based on both likelihood to convert and the commercial value of the end sale.

The other issue is that most high-value businesses only generate a handful of sales per month, which isn’t enough data for automated bidding to learn anything useful. Lead valuation also solves this issue by providing the platform with hundreds of conversion events rather than a few sales. 

This means automated bidding can actually function properly, campaign and audience testing can become meaningful, and the data stays reliable. You’re optimizing to lead quality before Dave and the sales team get involved.

To be clear, importing downstream conversion stages or revenue into ad platforms can be extremely powerful. But optimization to those signals only works when volume is sufficient, conversion lag is manageable, and the sales process is stable.

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How to build lead valuation

The starting point is your historical data, ideally 12 months of it, though you can work with six. You need to understand which leads actually closed, what they were worth, and what they had in common at the point of inquiry.

For financial services, it’s things like loan amount and term. For B2B, it might be company size or sector. For construction, it’s usually project size and urgency.

From there, it’s about grouping leads by their likelihood to close to a sale and by what a typical deal size looks like, and then assigning each group an expected revenue value.

The check to make sure it’s working as expected is simple. The total estimated value you assign to your leads over a period should roughly match the revenue they actually generated. If not, the model needs work. Ideally, you should revisit it at least quarterly as your campaigns and operational factors change.

As an example, you might end up with a high-likelihood lead worth $850, a mid-range lead at $420, and a lower-likelihood lead at $120.

Once you have that, set up your conversion tracking to pass the expected value back to the platform on your conversion action and use value-based bidding (target return on ad spend in Google Ads) to point the algorithm toward the leads that are actually worth chasing.

Dig deeper: How to make automation work for lead gen PPC

Optimize for what you can control

“Optimize the full funnel” sounds sensible until you realize how much of that funnel you don’t actually control.

You can influence the targeting, the creative, the landing page, and the experience that gets someone to submit a form. After that, it’s over to Dave and the sales team, and dozens of other factors that have nothing to do with your campaigns.

When you expect an algorithm to optimize for things it can’t see, it will start drawing the wrong conclusions, chasing the wrong audiences, and getting worse over time.

The answer isn’t to stop measuring what happens after lead submission. You absolutely should continue measuring, as those numbers can tell you a lot about what’s going well and what might need to be corrected for. Remember:

• When lead quality stays steady, but sales drop, that’s an operations issue, not a paid media one.
• When both drop at the same time, look at your campaigns.
• When sales spike, but lead quality is flat, that’s Dave having a great month, not your targeting.

That visibility is genuinely helpful, but it just shouldn’t be what you’re optimizing to.

Build lead valuation, feed expected values back to your platform, and let the algorithm do what it’s actually good at: finding people who look like your best leads. Leave the rest to Dave.

Know where your control ends, as that’s where optimization should stop.