How To Improve MQL to SQL Conversion Rate & Drive More Customers to Your SaaS?

How To Improve MQL to SQL Conversion Rate & Drive More Customers to Your SaaS? cover

Are you trying to improve MQL to SQL conversion rate to drive product growth?

This article will discuss 10 actionable strategies to help you drive more customers by improving MQL to SQL conversion rate.

Let’s dive in!


  • MQL is a potential customer fit for your business, reviewed by the marketing team.
  • SQL is a potential customer with a clear intent to buy in buying cycle’s final stages.
  • SQL is ready for direct sales follow-up and top-priority engagement, while MQL is not.
  • Understanding MQLs and SQLs can help you convert more leads.
  • Divide the number of MQLs by the number of SQLs and multiply by 100 to get the conversion rate.
  • Tracking MQL to SQL conversion rates can improve your marketing efforts, and conversion frequency, and thus drive business growth.
  • The average MQL to SQL conversion rate is 13%.
  • To improve MQL to SQL conversion rate, you can follow these actionable strategies:
  • Align your sales and marketing teams, set the right customer expectations, create case studies to showcase success stories, implement account-based marketing strategies to reach target accounts, collect customer feedback, create follow-up schedules, send personalized emails to nurture leads, ask for reviews, run fake door tests, and analyze marketing campaigns for future improvements.

What is a Marketing Qualified Lead (MQL)?

A marketing-qualified lead (MQL) is a potential customer who fits your buyer persona. The marketing team reviews and confirms the MQL before sending it to the sales team.

What is a Sales Qualified Lead (SQL)?

A sales-qualified lead (SQL) is a prospective customer with a clear and strong intent to buy your product. It is now in the final stages of the buying cycle after progressing through the earlier steps.

MQL vs. SQL: The key differences

Both MQL and SQL are qualified leads with potential customers. But the main difference between MQL and SQL is the customer’s intent to buy the product. A potential customer shows a more definite intent to purchase the product in SQL than in MQL.

An MQL is primarily a sales-ready contact but is not yet ready for direct, one-on-one attention from the sales team. It needs more effort from the marketing team since customers lack the intent to buy.

Contrarily, an SQL is ready for direct sales follow-up and should be given top priority to engage with personally. It is in the buying cycle’s last stages after going through the efforts of the marketing and sales teams.

Marketing & Sales funnel.

Why are both MQLs and SQLs important for sales and marketing teams?

MQL and SQL are essential for your marketing and sales teams to operate efficiently. They are both potential customers in different buyer journey stages, and knowing how to identify and classify them can help deliver the right message to each.

You can convert qualified leads into customers and brand advocates with the right qualification strategy. Understanding the differences and relevance of MQL and SQL helps marketers make the right decisions about engagement and lead content.

How to calculate MQL to SQL conversion rate?

To calculate MQL to SQL conversion rate, divide the total number of marketing-qualified leads by the total number of sales-qualified leads and multiply it by 100.

Suppose you generated 300 marketing-qualified leads and 500 sales-qualified leads last month. So your MQL to SQL conversion rate would be (300/500)*100 or 60%.

MQL to SQL conversion rate.

Why is tracking MQL to SQL conversion rate important?

Tracking MQL to SQL conversion rates can help you evaluate and improve marketing efforts to generate sales-ready leads for the sales team. Tracking it helps you –

  • Assess the effectiveness of marketing efforts.
  • Determine how often leads are being converted.
  • Evaluate the effectiveness of the sales team in generating qualified leads.
  • Evaluate the performance of individual B2B companies or teams.
  • Analyze and use the results to maintain, improve, or redesign strategies for improving conversions.
  • Compare the performance of your company to that of others.

MQL to SQL conversion rate benchmarks

According to Salesforce data, the average MQL to SQL conversion rate is 13%. Only 6% of such SQLs result in actual deals.

Websites, employee and customer referrals, webinars, and social media are the best-performing channels for conversion.

MQL to SQL conversion rate benchmark.

10 actionable strategies to boost the MQL to SQL conversion rate

Let’s go through 10 actionable strategies to improve your MQL to SQL conversion rate.

Ensure sales and marketing teams are aligned and there is no dark funnel

You can sometimes find yourself in a dark funnel where you lose potential customers because of not correctly tracking and sharing data among teams. Maybe the marketing team knows the data, but sales or another team doesn’t and leads clients down the dark funnel.

You must ensure the sales and marketing teams are working toward the same goal and have access to the same data. Then, you can create an effective sales and marketing strategy that will eliminate the dark funnels.

The dark iceberg.

Set the right expectations to avoid the customer gap

Customer expectation is what the customer thinks about your product or service before using it. Marketing messages, buzz around the company, and customer stories generally set expectations.

But the problem arises when there is a customer service gap because of a noticeable difference between customer expectations and real perceptions. In this case, a good rule of thumb would be to avoid over-promising things about your product in your marketing messaging.

The customer gap.

Create case studies to social proof the success of existing customers

Creating case studies to showcase client success can make you more trustworthy. You can share your products’ successes through them. Potential customers are more likely to trust you by seeing your products’ benefits.

Case studies help potential buyers understand your product’s uses and result conveniently. It helps customers realize how your solution matches their business and try your product.

Success stories for MQL to SQL conversion.

Implement an account-based marketing strategy and proactively reach out to target accounts

You should encourage your sales team to proactively reach out to leads via marketing strategies like AMB marketing and close them as soon as possible. Doing so will help you convert more MQLs into sales and lead generation.

You should also implement account-based marketing to create a more personalized buying experience for high-value clients. It focuses on advertising to specific target accounts rather than to a demographic.

To do this, you should first identify your high-value target accounts, research them, and develop and run campaigns. You can also try creating user personas for ABM campaigns.

User persona example.

Collect feedback from your existing customers

Understanding what motivates your existing customers to subscribe to your product is also essential. You can collect this data by gathering customer feedback and other information. This data can help you identify new potential customers and develop products or services.

You can use in-app microsurveys to collect feedback. Userpilot can be handy as it lets you create different microsurveys using the best customer feedback tools. You can also develop microsurveys by using its wide range of microsurvey templates.

Feedback collection.

Create a follow-up schedule for your sales team

Follow-ups are an integral aspect of the sales process, as they help to increase responsiveness and reduce pitch time. You should train your sales team on effective follow-ups with leads. Try to follow up with your leads within 5-7 days of the initial contact.

To streamline the follow-up process, you can create an easy-to-use and efficient schedule. You can also use automation tools to follow up with interested leads automatically.

Follow-up Schedule for Sales Team. Source: Cience.

Send personalized emails to nurture leads and convert them

Personalized lead nurturing campaigns can help you influence customer behavior and drive conversions. Email marketing campaigns usually have a high impact on potential customers. You can also easily automate contextual email marketing.

The most significant advantage of lead nurturing emails is you can offer potential customers personalized content, like demo content. As a result, potential customers are more likely to trust your product and make a purchase.

Let’s look at this example of Hubspot’s personalized emails.

Personalized emails.

Ask for reviews and maintain a good brand image

When a customer gives you a high score on your Net Promoter Score (NPS) survey, take the opportunity to ask them for a review. Encouraging your promoters to leave feedback helps boost your brand’s reputation and drive more referrals through word-of-mouth (WOM) marketing.

In-app modals can effectively ask for feedback from your customers at specific points in the customer journey. You can use it to let customers easily provide feedback and help you maintain a good brand image.

Asking for reviews.

Run fake door testing to see what customers actually want

Fake door testing can help you assess product or feature demand before developing it. It involves inviting customers to use an unfinished (maybe in development) feature to see their interests.

Its key benefits include validating your product or feature idea before committing to development, refining your pricing strategy before product launch, and enlisting beta testers. You can understand whether customers want that product or a specific feature so that you can build and market it. Your MQL to SQL conversion should be high too when there is high demand.

For example, let’s look at Buffer’s fake doors to test product demand. It created a landing page for its unfinished product and invited users to sign up.

A fake door test conducted by Buffer. Source: Hackermoon.

Analyze marketing campaigns and find weak points

Finally, analyze your marketing campaigns and understand why they are not working as planned. Try to find the weak points and fix them if you see they are the main reason for the low conversion rate.

There is also the possibility that your sales team is to blame. Sales processes and efforts may not be sufficient to close leads or care for them properly. You should train your sales team and prepare them to handle leads in that case.


A good MQL to SQL conversion rate is crucial to your SaaS business’s conversion and growth.

The best way to approach this is to use proven strategies to improve the MQL to SQL conversion rate. You should see what strategies work for you and use them to drive more customers to your SaaS.

Want to improve MQL to SQL conversion rate code-free? Book a demo call with our team and get started!

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