How to Calculate the Value of Your Leads (MQLs and SQLs)
Has your sales department felt a little disconnected lately?
Up to 75% of those working in sales operations say they adopted new responsibilities at work over 2020.
Meanwhile, another 85% say the entire sales department has grown more strategic over the past year.
Despite all the sales strategy, planning, technology, and analysis, many companies still struggle to nail down concrete cost and value per lead.
The fluid nature of leads between MQL (Marketing Qualified Lead) and SQL (Sales Qualified Lead) put the burden on sales and marketing to cooperate over a definition alone — much less add metrics for elusive aspects like quality.
Below you’ll find a few solutions to calculate the value of leads — whether MQL or SQL —based on things like cost, quality, source, and more.
How to Calculate the Value of Your Leads
Calculating lead value, as opposed to cost, comes in handy for calculating marketing ROI.
Instead of starting with marketing costs and working backward, you can calculate the value of an average lead collected through a specific marketing tool.
Unlike cost, however, calculating the value of your leads poses an even greater challenge: How do you define value? Less than half of B2Bs track customer lifetime value so that removes one long-term metric from most marker’s equation.
Before jumping in, consider these tips to get everyone on the same page:
- Unify sales and marketing in a contact relationship management system (CRM).
- Integrate data on leads from every available source.
- Make sure lead data is up to date, complete, and correct.
You can’t calculate the value of leads if you aren’t working with clean and correct data. Data must come first. After cleaning and verifying data, you’ll want to integrate it into your CRM.
Using a CRM as a unified lead data dashboard, sales and marketing can discuss how to score leads and how to qualify the value of a lead over time, based on the available data.
Get Sales and Marketing on the Same Page with Lead Scoring
Most marketing and sales departments would no doubt agree that lead scoring is a lose-lose game. If that sounds familiar, start with marketing and sales defining the criteria for lead scoring such as:
- Behavior: What negative and positive actions should dictate a lead’s score? What actions should marketing take to move them along?
- Likelihood: What’s the strategy for judging whether a lead even needs your product or service?
Decide Whether to Calculate the Value of Leads by Quality, Quantity, or Both
Quantity is the easiest to measure, but it also doesn’t tell the full story because some leads are certainly more valuable than others.
Calculating average lead value based on quality alone, meanwhile, can dilute the impact of high-quality leads in a large sample size.
Instead, choose a combination of both quality and quantity.
Define Your Metrics for Quality and Quantity
For quantity, metrics are easy. Just choose a specific timeframe and calculate the number of leads.
Quality is another story. What would sales and marketing say are the best signifiers of a high-quality lead? This might include:
- Acquisition source
- Website engagement time
- Email open and click rates
- Free trial engagement
- Chat interaction
Calculate Your Average Cost Per Lead
Once you have metrics defined for judging quality and quantity, take a step back and calculate the average cost per lead across these same metrics for comparison later.
Calculate Your Average Value of a Lead-Based on Source
Do certain marketing campaigns deliver better quality leads than others? Start with those to calculate your average value of leads:
- Lead value = Sale $/# of Leads
- Lead value = Average Sale $ X Conversion rate
Calculate Your MQL to SQL Conversion Rate
Calculating your MQL to SQL conversion rate is easy:
# of SQLs / # of MQLs = # MQL to SQL Conversion Rate
How long does it take someone to go from SQL to a customer? Factor your average sales cycle length into your conversion rate here.
Calculate the Value of MQLs to Marketing ROI
It can be useful to compare the average value of your MQLs to your marketing ROI over a certain period. How will you track MQL value: converting to SQL or completing a sale and using the average dollar amount?
Once you have either a dollar amount attached to each MQL over a certain period, factor that into the marketing budget spent to acquire them.
Use Predictive Analytics to Track Future Leads and Marketing Budgets
Feeling frustrated with crunching numbers that never seem accurate anyways? Fortunately, the predictive analytics tools available can integrate your lead data and apply a specific value based on input.
Predictive analytics tools use artificial intelligence to consume massive historical datasets, apply algorithms, and make future judgments. They also improve the more they learn.
If lead value or other quality-based factors are important in your company operations, look into AI solutions for providing more accuracy than any human could.
Evaluate Your Metrics Quarterly or Monthly
Metrics alone make the case for integrating AI solutions into your lead cost/value calculations. Without artificial intelligence, you’ll need to dedicate time each month – or at least each quarter – for evaluating quality metrics, marketing ROI, and any other dynamic figures.
Calculating the value of your leads today is only one piece of the puzzle. Isolated, the value of a lead means little. You want to see how the value of a lead changes based on new marketing tools, sales operations, and other distinct tactics.
Calculating the Value of Your Leads Requires Consistent Communication
What is value to your company? Is it a dollar sign, referral, sustainable customer lifetime value?
No matter how unified sales and marketing become as they try to calculate the value of leads, they’re still restrained to the overall ambitions of the organization.
Keep lines of communication open. As your team starts evaluating lead value based on different data sets, you might realize “value” takes on a new meaning – whether from customer referrals or by contributing to data themselves.