Forbes just released a study confirming what we're hearing from SaaS CMOs:
78% [of B2B Marketers] see B2B marketings’ role expanding from demand generation to deal acceleration.
In SaaS companies "deal acceleration" means arming the inside sales teams with better information about customers:
I'm covering all topics in our new course. In this post I'll tackle MQLs.
Do you measure the quality of Marketing Qualified Leads (MQLs)?Don’t worry, you’re not alone.Most SaaS CMOs don’t measure and track the effectiveness of their MQLs. In this post we'll show you how to use a single metric - the MQL Performance Score - to track MQL quality and grow your SaaS revenue.
When we interview our SaaS customers about their marketing and sales workflow we usually find sophisticated marketing automation systems and very basic MQL generation systems.For instance, a SaaS marketing team may “just tag every lead in Salesforce as ‘marketing qualified’ if the trial customer finishes signing up”. We usually discover the following problems:
The CMOs don’t know if sales treats MQL differently or even uses them at all. Some sales reps don’t even know what “marketing qualified” means - much less what to do about it.
Sales may use MQLs in ways marketing never expected.For instance, a rep may tag every MQL as a “Sales Accepted Lead” under an incorrect assumption that someone in marketing already reviewed them. The rep engages many leads who never buy and concludes MQLs “don’t convert”.
Should marketing send sales more MQLs? Fewer? Should marketing supplement Salesforce with key actions the customer took in the product? Did our latest update to the MQL scoring rules improve or reduce MQL quality?We suggest using a single metric - the MQL Performance Score - to track MQL quality.
Every day you run a set of business rules that identifies “Marketing Qualified” leads in your CRM (e.g. Nutshell, Salesforce, or Pipedrive...). Your sales team identifies those most likely to buy and close them.Your CRM also contains many other leads - what we call "non-MQL" leads - from trial customers, third-party sources, webinars, "contact" forms, etc.A percentage MQLs convert to paying customers and percentage non-MQLs convert to paying customers.In high-volume SaaS companies we expect (hope?) that MQLs convert at a higher percentage - if not, something is probably wrong.The easiest way to measure MQL performance is to calculate your MQL Performance Score:
Here’s how you do it.
If you can use Excel and know 5th-grade math you have all of the tools you need. The practical challenge is getting and cleaning up the data - especially since the data is in your CRM and not the marketing stack.
Breaking your data into cohorts helps identify trends and reduces the impact of data anomalies. We suggest starting with monthly cohorts - that is, collect all leads who signed up in a given month and track their progress through the sales funnel over the next several months.For each month gather the total number of MQL and non-MQL leads. Set up your spreadsheet as follows:
In October 17,000 new leads were added to Salesforce. We broke them into 2,000 MQL leads and 15,000 non-MQL leads which we entered into Column C.
Create a column for each step in your sales workflow and plug in the number of leads.
(click the image above to see a bigger one or download a copy)
Since your workflow is probably different I’ll walk through each column during October 2015 for the MQLs.In October of 2015 2,000 MQLs were added to the CRM. Sales accepted (SALs) 440 of these leads (Column E). Sales contacted 396 (Column H) of these leads and 71 of them responded (Column K). Sales qualified (SQL) 66 (Column N) as likely buyers and 46 (Column Q) bought the product.
Calculate the conversion rates for each column you created in Step 2.
In October 22% (Column F) of MQLs were accepted by Sales. We calculated by dividing SAL count (Column E) by new MQLs (Column C).Calculate this conversion percentage for Columns I, L, and O.
Calculate the percentage of MQLs and non-MQLs that convert into paying customers.
(Columns E-P are hidden)In October 2.3% (Column R) of MQLs converted to paying customers (Column Q/Column C).
Now calculate how much better MQLs performed relative to non-MQLs for each cohort.
In October an MQL was 3.8 (Column T) times likely to convert than an non-MQL (2.3% / .6%)
Looking at our complete spreadsheet above already raises some questions.
What happened in December? Did the sales and marketing team drink too much egg nog at the Holiday party? Marketing only generated 400 MQLs and sales only accepted 300 non-MQLs. This looks suspiciously like a data problem.Did November provide an example of how we can grow faster? It looks like the sales team paid more attention to MQLs in November. A higher percentage were accepted, contacted, and converted. Did we run a unique campaign? Did a particular sales rep choose to focus on MQLs? Further investigation is needed.
Tracking MQL Performance Score allows you to systematically test and measure changes to your campaigns, products, and scoring rules.
Unfortunately we don’t yet have enough data to give you a good benchmark - obviously there are tons of variables. An expensive, enterprise SaaS product will have a lower MQL Performance Score than one that sells for $10/month.For our high-volume SaaS customers we are seeing MQL Performance Scores of 3-6.
Seriously - just sign up for a free trial of MadKudu - we’ll calculate your MQL Performance Score and show you how to improve it.You have absolutely nothing to lose. You won’t have to pay us a dime until we prove how much more we can grow your SaaS revenue.Want to learn more? Sign up for our new course.
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