Where paid media development should stop is long sales cycles

In long sales cycles, much of what happens after a lead is sent involves people. When you’re setting up campaigns for the last sale, you’re teaching the ad platform to respond to how well the sales team did that month rather than lead quality, and that’s a problem no campaign changes will fix.

Common advice is to “optimize the full funnel” (ie, track media usage for revenue, optimize campaigns for sales, etc.). But beyond lead capture, most of what drives you has little to do with your paid media. It’s about who’s on the sales team, how busy they are, and many other factors that you can’t influence by targeting or creativity.

When your sales team becomes a signal

I have spent over 15 years in financial services marketing, but this is not limited to mortgages or insurance. If your sales process relies heavily on people, you will see this quickly.

In most businesses, there is someone like Dave. In my case, he’s a mortgage consultant, but in yours, he might be your best business salesperson, your star business development manager, or your best project estimator.

He closes deals at twice the rate of his colleagues, not because he earns better, but because he is naturally gifted at building relationships, asking the right questions, and guiding anxious clients through difficult decisions.

However, Dave is not 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 composition of your sales team is probably constantly changing. You may have more experienced associates one month, fewer the next, a recruitment drive that brought in a few new recruits, or Dave and two of his colleagues going off on their own during the month. Sales rates can change dramatically based on who is in the office, regardless of the quality of the lead.

This can lead to targeting problems. For example, if the conversion rate drops because Dave is away and a small team member covers his accounts, the algorithm sees it as a targeting problem rather than a staffing problem.

If you’ve set up your campaigns to optimize sales, it’s thinking, “Our targeting has stopped working. These clicks are low quality for this conversion action now. We should remove spending from this audience.”

Ultimately, this can result in previously effective keywords being shut down, audiences that were driving sales volume no longer being bid on, and, ultimately, a decline in overall account performance. But the lead hasn’t changed, only the team.

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Functionality that distorts your conversion data

It’s not just about building a sales team. Let’s say:

The team receives criticism in Q4 as everyone tries to close before the end of the year, response times go from two days to more than a week, and customers get impatient and look elsewhere.

Perhaps market conditions are changing, and your most competitive product is being pulled. Or the summer holidays mean the team runs slow, and some leads get cold before anyone can touch them. Then September comes and everything goes back to normal.

It goes beyond day to day. Budget approvals are delayed, product scopes change, and planning delays push projects back. The exact reason varies by business, but the impact on your conversion data is always the same.

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

When Dave becomes 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 guidance.

Every December in financial services, something unusual happens. In the third week of December, conversion rates from lead to sale skyrocket. We have seen an increase of up to 150% compared to normal weeks.

When campaigns are made for sales, the algorithm thinks, “Whatever we’re doing this week is working great!” Then the holiday week comes, and everything crashes, conversion rates drop to half of normal rates.

None of it has anything to do with paid media. In the third week, Dave and his colleagues are in a state of panic directed at them. Year-end bonuses are on the line, and there’s a last-ditch push before the holiday break, so they’re calling leads quickly, aggressively following up, and closing deals they might have let languish. Dave works like a machine.

Then comes the holiday week, everyone gets a mental health check, customers don’t pick up the phones, and Dave finally takes a break. The working group thinks more about family gatherings and less about goals.

The quality of leads, targeting, and ads has not changed. The team just works at different levels of intensity because of the season. The algorithm overpays for general performance and underbids for similar audiences, based solely on when Dave and his team take their vacations.

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Where the preparation should really stop

So if your sales preparation is derailed by factors outside of your control, how should you draw the line? How can you balance this lead distortion and continue to drive the right kind of leads?

Feedback is your final point of control, which, in these types of marketing, means lead submission. But not just counting clues. Instead, value them based on both the likelihood of conversion and the commercial value of the final sale.

Another problem is that many high-volume businesses only generate a few sales per month, which isn’t enough data for automated bidding to learn anything useful. Lead measurement also solves this problem by providing a platform for hundreds of conversion events rather than a few sales.

This means that automated bidding can be efficient, campaign and audience testing can be meaningful, and data remains reliable. He prepares to lead quality before Dave and the sales team get involved.

To be clear, importing conversion streams or revenue into ad platforms can be very powerful. But doing well on those signals only works if the volume is sufficient, the conversion lag is manageable, and the sales process is stable.

How to generate lead value

The starting point is your historical data, which is 12 months of it, although you can work with six. You need to understand which leads were really closed, what they were important for, and what they had in common during the investigation.

With financial services, it’s things like loan amount and term. In B2B, it may be company size or industry. For construction, there is often a project size and urgency.

From there, it’s about gathering leads by finding the opportunity to close the sale and what a typical deal size looks like, then assigning each group an expected revenue amount.

A check to make sure it works as expected is simple. The total amount you give your earners over a period of time should roughly match the income they generate. If not, the model needs work. Ideally, you should revisit it at least quarterly as your campaigns and performance characteristics change.

As an example, you might end up with a high potential lead worth $850, a mid-range lead of $420, and a potential lead of $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 (targeted return on ad spend in Google Ads) to point the algorithm to sources worth chasing.

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Prepare for what you can control

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

You can influence the targeting, creativity, landing page, and information that prompts someone to submit a form. After that, it’s over to Dave and the sales team, as well as many other aspects that have nothing to do with your campaigns.

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

The answer is not to stop measuring what happens after the lead is delivered. You should definitely keep measuring, as those numbers can tell you a lot about what’s going well and what needs fixing. Remember:

• If lead quality remains strong, but sales are down, that’s a performance problem, not paid media.
• If both are down at the same time, look at your campaigns.
• If sales are up, but lead quality is flat, that’s Dave having a good month, not your targeting.

That seems really useful, but it shouldn’t be what you’re preparing for.

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

Know where your control ends, as this is where the optimization should stop.