Second-level optimisation: how to optimise your campaigns taking different lead qualities into account

Analysing the quality of the leads each acquisition source brings, and taking it into account, is essential to maximising your campaigns’ profit. Bring those variables into your optimisation and your results will lift immediately.

When you set up a performance marketing campaign, the aim is to maximise its profit — defined as the margin generated less the marketing costs. If the campaigns generate customers directly, rather than prospects, maximising profit is a matter of optimising the campaigns on the customer data passed back by the various tracking pixels, as that article describes.

For many companies, however, the campaigns’ initial objective is to acquire leads. Those leads then have to be worked on, by a sales team or by marketing automation, so that as many as possible turn into customers. In that case the pixels pass back the number of leads acquired, but they cannot pass back the number of customers — nor the revenue or margin those customers generate — because either the delay between generating the lead and the moment it becomes a customer is too long, or the final sale calls for “offline” action, which the pixels do not track. Solutions exist (gclid, importing offline conversions), but they mean waiting for the lead to become a customer, which can take a long time, and that penalises the day-to-day optimisation of the campaigns.

Setting a specific CPL for each source.

The solution most companies choose, so as to be able to optimise their campaigns day to day nonetheless, is to set a cost per lead (CPL) they are willing to pay, identical across every channel and every campaign. They then optimise their campaign against that CPL.

But looking more closely, that choice proves extremely costly in performance: reality shows that the value of leads — their ability to turn into customers — is usually very different from one channel to the next and from one campaign to the next. Using an average CPL leads to paying too much for some leads, and conversely to missing opportunities by setting a CPL too low for leads of very high quality. Surveying a representative number of campaigns, we found differences in lead quality of up to tenfold, depending on the acquisition channel.

It is therefore imperative to put a so-called “second-level” optimisation in place, so as to maximise the profit the marketing campaigns generate. At Everlife, the campaigns are steered this way, bringing in the second-level data that shows what becomes of the leads.

That means analysing the leads’ ability to turn into customers, against defined criteria — acquisition channel, campaign, keyword — in other words the lead-to-customer conversion rate. And then optimising the campaigns against those multiple values.

Concretely, why does second-level optimisation improve your results so much?

Picture the following situation:

• Channel 1 brings you leads worth 20
• Channel 2 brings you leads worth 40

Before doing your second-level optimisation, you had set a maximum CPL of 30 for all channels, which means:

• you were paying too much for your leads on channel 1, and part of your budget was wasted
• you were not paying enough for your leads on channel 2, and you were leaving considerable money on the table — you could have had more quality leads by investing more

So by moving part of your budget from channel 1 to channel 2, appropriately, you will make an enormous leap in profit.

Three essential steps to put second-level optimisation in place

1. Record the UTMs, to follow offline conversions

Because conversion delays are too long, or because offline action is needed, the pixels cannot pass back the number of customers or the revenue to the various platforms (Google Ads, Facebook Business Manager and the rest), nor at the level of campaigns, keywords and so on.

One of the best ways to tie a customer to a lead and then to a campaign is to record the UTMs — correctly configured beforehand on your campaigns — in your internal database. Here is an example of what that might look like:

2. Calculate the conversion rate per source
Once the UTMs are recorded, you can calculate the lead-to-customer conversion rate per source, by this formula:

(1) Conversion rate of source X = number of customers from source X / number of leads from source X × 100

3. Calculate the value of a lead per source
The last step is to calculate the value of a lead per source. Assuming every customer brings the same, whatever the source, the formula is:

(2) Average value of a lead from source X = average value of a customer × conversion rate of source X

If every customer brings 100 and the conversion rate of source X is 20%, then a lead from that source is worth 20. Each lead brings 20, because you need five leads to get one customer, who brings 100.

In reality, a customer’s value can vary with the source. That too has to be taken into account when valuing a lead. In that case the formula becomes:

(3) Average value of a lead from source X = average value of a customer from source X × conversion rate of source X

Maximising profit through second-level optimisation
Once you know the value of a lead for an acquisition source, you know the maximum price you are willing to pay to acquire it — a price that will vary from one acquisition source to the next, from one keyword to the next, and so on.
You can then optimise your campaigns accordingly, by the method described in that article, and your profit will certainly be lifted. Six years later, the lesson still holds with AI Max: optimised on a top-of-funnel objective, the algorithm generates micro-conversions, not customers.

Would you like to put an equivalent disruptive strategy in place quickly for your company? Get in touch