As a marketer, one of the most important metrics to understand is lead source yield – the productivity and efficiency of the various lead generation channels you utilize. By calculating the lead source yield, cost per lead, and eventual customer acquisition cost for each marketing campaign and initiative, you gain crucial insights into where to focus your budget and efforts.
In this post, we’ll explore what lead source yield is, how to calculate it, and how to use it together with cost per lead and customer acquisition cost to optimize your marketing spending. We reference a sample dataset with lead and cost numbers across seven lead sources – PPC ads, Facebook ads, LinkedIn ads, sales farming, lead nurturing, organic search, and free trials.
Lead source yield is defined as the number of leads generated per dollar spent on a given lead source. The math is simple – take the total number of leads and divide it by the total spend. In our sample data, organic search produced a lead yield of 1.396 while Facebook ads only drove 0.127 leads for every dollar. The cost per lead metric helps understand the inverse of yield. High yield lead sources have low cost per lead.
By tracking lead source yield and cost per lead over time, you gain visibility into the true return on investment across lead gen activities. This allows better decision making around marketing budget and priorities. We explain how to take the analysis farther by connecting leads and spend to customers acquired.
So, what exactly is Lead Source Yield?
Lead source yield refers to the productivity of a lead source. This can be measured and reported as the number of leads received per dollar spent for a single lead source. It can also be measured by the reciprocal, cost per lead.
As an example, let us assume that we are using seven different lead sources to drive sales, and that the marketing spend and leads received for the year by lead source is as shown in the table following.
|PPC Ad Leads
|Facebook Ad Leads
|LinkedIn Ads Leads
|Sales Farming Leads
|Organic Search Leads
|Free Trial Leads
|Leads / Dollar Spend
|Cost Per Lead
We can chart this data to reveal our lead yield (leads per dollar) and the inverse, cost per lead. This data gives us some insight into where we might want to direct our marketing spend in the coming months.
This analysis can be greatly improved by tracking and filtering down to those leads that result in sales, so that we can calculate the customer acquisition cost by lead source, and even better statistic to use for planning marketing spend or for improving the marketing process.
As shown in the sample data above, lead source yield and cost per lead can vary significantly by channel. In this data, organic search provided the highest yield at 1.396 leads per dollar spent, followed by nurturing leads at 0.150. In contrast, Facebook ads only drove 0.127 leads per dollar spent.
When making marketing budget allocation decisions, marketers should dig deeper into the factors influencing lead source yield. For example, poor lead quality or irrelevant traffic can lower yield for paid channels like pay-per-click (PPC) ads. Lead nurturing and organic search yield may benefit from existing brand strength and site content.
Tracking lead source yield, cost per lead, and customer acquisition cost provides key insights to optimize budget for efficient customer growth. Continually testing new channels and messages allows marketers to double down on high performing lead sources over time.
Setting goals for cost per lead and lead source yield, then tracking progress at least monthly, will keep teams focused on the metrics that matter most for marketing ROI.
Get the Free Model
You can get an example leads pipeline model referenced here, and a PDF copy of “Guide to Leads Pipelines for the CMO”, by downloading the model (in Excel spreadsheet form) and guide from the landing page at here.
With the Excel spreadsheet, you can follow the example data shown here. You can also plug in your own data to build your own pipeline model and forecast your sales in any future month.