The goal of this article is to provide marketers with a framework for setting budget caps and lead cost. Whether your presenting this to a CMO or CFO, the model below approaches marketing budgets from a corporate finance perspective. Using the calculations below, you can to determine the maximum price you should pay for leads, and the appropriate budget.
- Average Customer Lifetime Value (CLV) - this is the average projected gross profit a company will receive from new customer.
- Conversion Rates for MQLs, SQLs, and SALs
- Target Return on Ad Spend (ROAS) - this is the minimum return from ad spend needed to run a profitable campaign.
- Budget - the maximum ad budget
We look at lead conversion rates as a matrix as shown in this example. The three conversion rates we look at are MQL to SQL, SQL to SAL and SAL to Win. Based on these conversion rates we can see the rate at which we convert MQLs, SQLs, and SALs to wins. From here we just need to know the CLV and we can determine what we should be paying for leads.
If we have an average customer lifetime value of $7,500, we can calculate the projected profit from each type of lead. Here is how that works out in our scenario.
- Projected Gross Profit per MQL: $7500 * 0.68% = $50.63
- Projected Gross Profit per SQL: $7,500 * 4.50% = $337.50
- Projected Gross Profit per SAL: $7,500 * 15% = $1,125.00
For this example, let's use 1.3 or 130% as our goal ROAS. That means that if we spend $100,000, we need to generate $130,000 in gross profit to be profitable.
Maximum Average Cost-Per-Lead
Now we can calculate the maximum average cost-per-lead for MQLs, SQLs and SALs. This can be calculated by taking the average lead profit and dividing by the target ROAS.
- Max lead cost for MQLs: $50.63 / 130% = $38.94
- Max lead cost for SQLs $337.50 / 130% = $259.62
- Max lead cost for SALs: $1,125.00 / 130% = $865.38
At this point, setting the budget is simple. Assuming our conversion rates remain constant, you should buy every MQL, SQL and SAL you can at our below the max costs calculated above.
Enhancing The Model
Once this model is in place, it can also be calculated by channel. This is recommended since conversion rates can vary by lead source. You would just need to know your conversion rates for the channel you're analyzing.
You can also improve the model by making customer lifetime value a variable. CLV can often be projected based on customer attributes such as number of employees, or revenue. Larger customers usually have a higher projected CLV than smaller customers.