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A Marketing Qualified Lead (MQL) to Sales Qualified Lead (SQL) Conversion Rate is something that every B2B marketing team would want to know. It helps to assess the effectiveness of their marketing and sales efforts.
They are crucial metrics for measuring the effectiveness of your B2B lead generation campaigns.
So, how does one know the conversion rate between MQLs and SQLs?
First of all, let’s see what MQLs and SQLs are. To understand this, you need to go through their definitions first.
So here they are:
MQLs are leads that meet specific criteria and can be used by sales teams right away. A lead is qualified when it meets all the set criteria for a marketing campaign. For example, if you have run a campaign to target small businesses in America who use PPC Marketing (Google Ads) or social networking sites like Facebook, people interested in your product fall under this category.
Sales Qualified Leads (SQL) are ones that have been pre‐qualified by the sales team. The sales team uses SQLs as a lead to gauge their market of interest and criteria such as revenue, seniority level of the target companies, etc.
These leads do not need to go through any close screening process as they are already pre-qualified.
Now, you know what an MQL and an SQL are. You also have a clear idea of how these conversions work.
So let’s see how to compute the conversion rate between MQLs and SQLs.
There are multiple ways to calculate the MQL to SQL Conversion Rate:
The simplest way is to divide the number of SQLs by the total number of MQLs
Another method is to take the number of SQLs generated in a month and divide it by the total number of leads generated during that period. This is called the MQL to SQL Conversion Rate.
Yet another method is to divide the number of leads converted into sales by the total number of leads generated during that period. This is called the Lead-to-SQL Conversion Ratio.
The MQL to SQL Conversion Rate can be used in multiple ways.
MQL to SQL Ratio and Lead-to-SQL converting Rate are two terms you often come across while reading articles or talking about lead generation strategies. Both terms might seem similar, but they have different meaning and usage.
MQL to SQL Conversion Rate is the ratio between the number of SQLs and MQLs whereas Lead-to-SQL Conversion Rate is the number of leads converted into sales divided by total number of leads generated. This rate is also called Sales Conversion Ratio or Leads-to-Sales Ratio.
MQL to SQL Conversion Rate is a very effective tool that can be used by companies to measure the performance of their sales team. It also helps in evaluating the effectiveness of your marketing efforts and improving conversions.
There are many strategies and tools which can help your business to penetrate in the market. Through Ansoff matrix you can determine the market and product development but for lead generation and converting to loyal customer you need to go in a new way. MQL to SQL Ratio or Lead-to-SQL Conversion rate not only helps you measure the effectiveness of your marketing efforts but it also helps you in improving conversions.
The MQL to SQL Conversion Rate or Lead-to-SQL Conversion Ratio can be computed by using the following formula:
MQL / SQL = Total Number of Leads Converted into Sales / Total Number of Leads Generated
MQL to SQL Conversion Rate Formula
SQLs generated / MQLs Generated * 100 = MQL to SQL Conversion Rate
Calculating the MQL to SQL Conversion Rate is easy. Just follow these simple instructions:
Identify the number of MQLs and SQLs generated during a particular period. For example, if 100 leads were generated in a month-long sales campaign, 40 of which successfully converted into sales, then your MQL to SQL rate is 40/100 = 0.4 or 40%. This means that on average, 4 leads out of 10 leads become sales.
Calculate the number of leads generated during the same period. For example, if 100 leads were generated in a month-long sales campaign, then your lead generation figure would be 100.
Divide the number of SQLs generated by the number of MQLs generated and multiply it by 100 to express as a percentage. For example, 40 out of 100 SQLs became sales which means that your MQL to SQL conversion rate is 40/100 x 100 = 40%.
The calculation method you choose depends on the information you have. The simplest method is to divide the number of SQLs by the total number of MQLs generated.
However, this does not provide a proper estimate as it ignores one very important factor – time. For example, if 100 MQLs are generated in December and only 40 converted into sales within that month; then your MQL to SQL rate is 40%. However, if only 20 MQLs were generated in January and all of them converted into sales; your MQL to SQL Conversion Rate would be 80%.
This clearly shows that the report showing 40% conversion was created at a time when the leads had less time to convert. To provide you with an accurate estimate of your MQL to SQL rate, it’s advisable that you use the second or third method which takes time into consideration.
There ideal MQL to SQL Conversion Rate benchmark is around 13%. This means that each marketing campaign should generate at least 13 successful SQLs out of 100 MQLs.
There are multiple reasons for this:
MQL to SQL Conversion Rate gives you an insight into how effective your marketing campaigns are. It helps you understand why some campaigns perform better than others and whether your sales team is equipped with the necessary skills to convert leads into customers.
If for example, your MQL to SQL rate is lower than average, it means that either your target market is not responding to your campaign or that your sales team is unable to convert leads into customers. You should then review both these areas and work on them to improve conversions.
Using MQL to SQL Conversion Rate helps you determine if a marketing campaign is successful and whether you need to make any changes before launching it again.
There are several ways in which you can visualize MQL to SQL Conversion Rate. For example, you can use a bar chart where the x-axis represents the number of leads generated and the Y-axis shows the corresponding number of successful conversions.
Another way is using a pie chart where the size of each slice represents the number of leads converted into sales, and the percentage of each slice represents the percentage of total leads.
You can also use a line chart where the y-axis shows the number of SQLs and the X-axis represents time. This would help you visualize how your MQL to SQL Conversion Rate changes over time. The visualization is not limited to the simple charts, if you have a good visualization library you can use any chart to show your data which may give ease in understanding to the stakeholders.
As you are not bound to stick with simple visualization, let’s have some awesome data visualizations created by ChartExpo. You can use these to represent your data in a meaningful way.
Below chart is an example of Group Column Chart. You can see the data side by side and have a good comparison.
Visualization Source: ChartExpo
So why not use another visualization which can show you bar and lines simultaneously to track the MQL to SQL conversion rate.
Visualization Source: ChartExpo
Same data can be shown with another visualization so that you can compare it side by side.
Visualization Source: ChartExpo
There is another beautiful visualization i.e Radar Chart or Polar Chart which you can use to represent your MQL to SQL conversion rate
Visualization Source: ChartExpo
So on all these visualizations are on same dataset, it’s up to you which one you need to present you results.
If your MQL to SQL Conversion rate is too low, then you need to increase the number of leads generated. You can do that by following the steps below:
In order to increase MQL to SQL Conversion Rate, you must first identify the reasons behind the low number of leads generated. You can ask yourself a few questions in order to identify the problem:
Where to find information about why MQLs are not converting into sales:
You can look at past campaigns and analyze campaign performance.
You should compare it with the performance of other similar campaigns. This should give you an idea about why your MQLs are not converting into sales.
You can also ask yourself these questions while reviewing MQLs, by looking at their profile and determining what would make them buy. You can also look at website traffic statistics to see if they are coming from sources with a high conversion rate.
Based on the reasons identified in Step 1, you can then focus on specific areas within your business that need to be addressed to increase MQL to SQL Conversion Rate.
For example, suppose you have identified that one of the main reasons for low conversions is low-quality lead generation sources. In that case, you should hire an agency that generates high-quality leads.
After fixing the problems identified in Step 2, launch a new marketing campaign to increase MQL to SQL Conversion Rate using the same offer/product or introducing a different offer/product that has better chances of being purchased.
After the new marketing campaign launches, you should monitor conversion rate after a certain period (a week or two) to check if it has increased. The aim of this step is to analyze results obtained from the previous steps and make data-driven decisions.
You should encourage your sales team to proactively reach out to leads and close them as soon as possible. This will result in a higher number of MQLs converted into sales and lead generation for your business.
Know more about your competitors’ strategies and work on improving your performance in the eyes of your prospects. This will lead to higher conversions and enable you to compete with other businesses that are already operating in the market.
You should also employ the use of personalized lead nurturing emails that are sent to prospects who show an interest in your products, services, or brand. These emails must be informative and interesting enough for prospects to convert into sales.
It is also important to understand why your existing customers bought from you. By gathering their feedback and other information – you can use the data to identify possible new target audiences and develop products or services that your existing customers would buy.
You should educate your sales team on effective follow-ups. Strive to follow up with your leads within 5-7 days after the initial contact.
To do this, you should create a follow-up schedule that is easy to follow and efficient for you and your team. Follow-ups are an integral part of the sales process, as it increases responsiveness and decreases pitch time.
Staying in contact with leads and customers is important for customer retention. If you want to stay competitive, it’s essential that you implement a marketing automation strategy.
You can set up an email drip campaign using one of the many email automation tools available.
Strive to form a relationship with your customers by sending personalized emails and communicating frequently. Customers will feel valued, which is likely to increase the probability of them purchasing from you again in the future.
Utilize the internet, especially social media platforms like Facebook and Twitter, to connect with your customers. Maintain a good brand image by regularly updating your current content with exciting blog posts related to your industry. Also include promotional offers that will be beneficial for your customers.
Find out what your target audience wants by utilizing tools like Google Trends. This way, you can focus on developing products and services your customers want.
You should also discover what products or services are popular with other people in your industry by visiting forums and Q&A sites like Quora. This will help you gain insight into what your potential customers may be searching for, allowing you to develop effective marketing strategies.
Don’t mirror existing market leaders when it comes to the design of your website. You should always strive to develop an effective marketing strategy that sets you apart from your competitors, even though it may take some time and effort.
Data driven analysis a vital process in any business or marketing growth. If you are ignoring the data you are missing lot of opportunities or may go in loss too. For example you may have full focus on conversions but you are not aware that your cost per conversion is also increasing.
Many growth opportunities are hidden in data. You need to go some extra mile to discover what could be achieved more and in different way if you want to find new customers and increase sale.
Now you have got the idea and even detail knowledge on MQL to SQL Conversion Rate. So now the question is what if you are managing lots of PPC campaigns and you want to track them properly, efficiently and in less time, which tool you should use? The answer is PPC Signal.
The above image is a screenshot of this tool. You can see different tiles which are representing different signals. You can filter the results based on many parameters. Let’s filter out the results based on impressions and conversions.
All the signals will be updated based on these metrics. You can explore in detail by clicking on the explore button.
The above image showing the detail trend of this signal based on Conversion metrics. You can add another metric simultaneously and see the comparison of trend side by side. Let’s add impressions and see the results.
So the impressions are increasing and your conversions are going down. So here you got the idea to analyze your MQL to SQL conversion rate. So using intelligent tool is one of the ways to increase sale through data analysis and then make strategies.
Now just imagine if you have 100s of campaigns with lots of keywords you can’t even manually calculate how many combinations can be created to analyze your data. How your campaigns running on mobile on different time of day on different times and the list goes on.
PPC Signal handles such scenario in no time. You just link your account with this tool and you are good to go. So why not try trial version of this tool to explore your results.
B2B MQL to SQL Conversion Rate benchmark varies depending on the Lead source for B2B. Website Leads, for example, have a 31.3% MQL to SQL Conversion Rate benchmark.
Customer/Employee Referral, Webinars, Events, and Lead Lists have 24.7%, 17.8%, 4.2%, and 2.5% MQL to SQL Conversion Rate benchmarks respectively. Email Campaigns have a 0.9% MQL to SQL Conversion Rate benchmark.
Number of SQLs / Number of MQLs = MQL to SQL Conversion Rate
In your sales funnel, this will provide you with percentage conversion rate.
MQL to SQL conversion rate is a metric used to measure marketing campaigns’ effectiveness. It helps with effective communication between sales and marketing teams, allowing them to work together to attain goals such as increased revenue and higher customer satisfaction levels. You cannot just stick with market strategy blindly you need to track your marketing to sales qualified lead cycles as well.
MQL to SQL Conversion Rate can vary depending on several factors, including industry, company size, market conditions, and more. When you know what factors contribute to your MQL to SQL Conversion Rate, you can implement different strategies to boost it.
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