If you’ve been working in a marketing team for a few years, you’ve probably heard people say “marketing teams are the creative ones that spend money.” However, this description is slowly losing its credibility — for the better.
The success of your marketing strategy demands more than just creative thinking and ingenuity. Sustainable decisions that secure your company's profits rely on regularly gathering and analyzing your data.
The data I’m referring to is marketing KPIs, which are indispensable to every marketer’s work. I would even go as far as to say that knowing your metrics is not just important to the marketing team, it’s essential for running a successful business. One of these vital KPIs is cost per lead (MQL, SQL, sale), a metric that will be explained thoroughly in this article.
Cost per lead (or CPL) tells you how much money you had to invest to acquire a lead, usually only accounting for ad spend and not including additional costs such as customer acquisition cost calculations. The same logic also applies to other cost per stage metrics such as cost per marketing qualified lead (MQL) or cost per sales qualified lead (SQL).
The average cost of generating a lead, an MQL or an SQL, gives you a trend in your KPI performance. An example that illustrates this use case is the longer sales cycle and lower availability of data in B2B SaaS businesses. Exploding costs are not immediately apparent if you only look at the cost per sale. Still, by tracking the average acquisition costs of the other stages, you will quickly establish whether you’re on the right track or if campaigns or processes along the way need to be fixed.
A one-dimensional approach is to take the ad spend of a specific channel, e.g. Google Ads, and divide it by the number of leads (or sales) from the same time frame. Often, these metrics are even pre-set as KPIs. But computing an average cost per lead across channels requires some additional effort — you will need to go into each tool, pull the relevant data, and then take the sum of all ad spends and divide it by the number of leads.
The formula is as follows:
This might look easy so far, but calculating the cost per MQL or SQL is where it gets tricky. For one, you need to extract even more data sources since the number of MQLs and SQLs are usually stored in a CRM platform. What’s more, you cannot use the same time range if you want to do marketing attribution correctly.
An example of this dilemma is a lead that is generated in January and turned into an MQL in February. If you’re going with a first-touch attribution model, it will not make sense to use February’s ad spend to determine the cost per MQL. There’s always the possibility that some marketing efforts from February influenced the conversion to MQL, but what you really want to know is on what date did the first touchpoint happen that nudged someone into becoming a lead.
In this specific instance, you would have to use just your ad spend from January as the base of your calculations. The same goes for cost per SQL. Gathering all relevant data from multiple sources can be a pain, but if the amount of data is limited, it is most likely doable. Taking attribution into account, however, is a complex task since you have to access and merge data from different time periods.
Cost per lead and cost per sale are often found in a marketing platform’s native analytics environment such as LinkedIn Ads, though they are titled differently as cost per result.
If you want to get a perspective across all channels and efforts, you need to integrate all campaign expenses from a specific time frame, e.g. from the “spent” column in LinkedIn Ads and invoices from your spend management platform. The number of MQLs and SQLs are in your CRM tool (such as Hubspot). Pulling these together will give you the result.
One important thing to remember when working with marketing data is to always put it into perspective. For instance, another metric that you should keep a close eye on, in addition to the cost per stage (lead, MQL, SQL, sale) metrics, is the revenue generated per stage (per channel). If, for example, your cost per lead on Facebook is much lower than your cost per lead on LinkedIn, does that mean that Facebook is automatically the better-performing channel? Not necessarily.
You need to check the quality of the leads, in other words, the average customer value (or ACV) and sales velocity for each channel. Let’s say that in this particular case, the leads from LinkedIn fit your ideal customer profile, and the average customer value (or revenue generated) is twice as much as the ACV generated from Facebook leads. In addition to that, the stats show that you can sell much faster to the leads from LinkedIn than from Facebook. So putting things into perspective is crucial if you want to get the right insights from this data.
The journey with marketing KPIs does not end when you know the ins and outs of CPL and its surrounding metrics. Rather, it’s where it begins.