Anyone who’s been working with B2B sales processes in the past few years would tell you that they are getting more and more complex. If your sales team could achieve acceptable conversion rates with cold-calling before, today they need to effectively manage a whole array of metrics coming from MQL and SQL funnels.
The qualification and conversion processes themselves are becoming longer. Potential clients take time in evaluating multiple products on the market and are not always ready to be sold to the moment they discover a new company or offering.
Trying to sell to such leads can be a waste of your sales resources. Even worse, being too pushy might detract visitors who are early in their buyer journeys.
At the same time, marketing teams shouldn’t keep eager prospects at the nurturing stage for too long, or they will leave too.
So where’s the right balance between MQLs (marketing qualified leads) and SQLs (sales qualified leads)? How do you accurately qualify leads as they proceed through your marketing and sales funnel?
Start by identifying what strategic roles MQL and SQL play in your conversions.
How Does Marketing Influence MQLs?
Marketing qualified leads have more information about your product or service and, as a result, convert at a much higher rate than first-time visitors.
They follow your social media, subscribe to your marketing emails, read your content, download your reports, etc. However, that doesn’t mean that all of them are ready to do business with you.
How do you qualify and prioritize MQLs? Your business experience is the ultimate decision-making tool, but here are a few ideas to get you started:
- Fit with an ideal customer profile (ICP). Since you define your ICP beforehand, you can be more objective when comparing your marketing leads against it.
- Previous customers. Analyze closed deals in your CRM and take note of what kind of companies have been buying your solution before.
- Engagement. If you find certain leads consuming lots of your marketing content, it certainly moves them closer to being an MQL.
- Technology. Breakthroughs in AI and machine learning are allowing marketing teams new ways to decipher MQL quality. More on that later.
As leads go through your sales and marketing cycles, you get more data to refine your qualification criteria even further. You can then adjust your marketing content to reflect that.
MQLs that go through your nurturing process should at some point become SQLs and be delegated to your sales team.
When Should You Contact Sales Qualified Leads?
Sales qualified leads are prospects ready to move through the sales funnel. An exact moment when MQLs turn into SQLs varies for every company, but there are some general guidelines.
You should try to create tailored content for your MQLs and track their engagement. The most enthusiastic leads can definitely be upgraded to SQLs.
Any MQLs actively reaching out to you, whether with questions or to schedule demos, also exhibit what could be seen as high buyer intent, and should be prioritized by your sales team.
If MQLs reach out to you with a problem, you can instantly see how it corresponds to the solutions you have available. You can then initiate the sales process with a suitable offer.
A sense of urgency by MQLs is a good sign and could lead to a deal closing quickly. Try to accommodate urgent requests as efficiently as possible (e.g. through online chat).
What you have to be mindful about when moving MQLs to SQLs is whether they actually have the authority to buy from you. It’s easy to mistake an engaged professional with no say in procurement for a decision-maker and not spending your sales resources optimally as a result.
How to Evaluate Solutions for MQL to SQL Conversions
Every positive action, whether it’s coming from MQLs or SQLs, can be assigned a score and ranked. This process is called lead scoring and is absolutely foundational to any efficient sales team today.
If you have a lead scoring system in place, you can use it to move prospects through the different stages of your conversion funnel, including classifying them as MQLs and SQLs.
You can design a manual lead scoring process that involves as many custom criteria as you need. But making it effective will take a lot of time, and even then it will usually be subject to a large margin of error due to human bias and assumptions. That makes off-the-shelf lead scoring solutions that integrate first-party and third-party data a more compelling proposition.
How to Calculate MQL to SQL Conversion Rate
When you’re running two separate funnels — MQL and SQL — it becomes tricky to find the right point where you transfer a lead from one to the other.
The problem is that if you assign a lead as SQL too early, it will increase the MQL to SQL conversion rate, but it won’t produce better overall results and might overwhelm your sales team.
A more balanced approach is to tie the MQL to SQL conversion rate to the total funnel conversion rate.
To calculate the MQL to SQL conversion rate, divide the number of SQLs by the number of MQLs. Then adjust the MQL to SQL conversion point so that this number is not far off your overall conversion rate.
How to Improve MQL to SQL Conversion Rate
Businesses worldwide spend hundreds of thousands of dollars perfecting their funnels and making sure their MQL to SQL calculations are on the right trajectory.
But every business is different. For example, long B2B sales cycles definitely affect conversion rates. Where leads are coming from is equally important. Finally, who your BDRs choose to engage will make or break your sales results.
Lots of sales teams use various lead scoring tools. But most of them don’t go far enough to predict which leads actually want to buy your solution. For that, you need Lift AI.
Lift AI is a buyer intent solution powered by a unique machine-learning model. It accurately predicts the likelihood of every visitor on your website to convert to a paying customer, even if that visitor is not known to you (which is often up to 98% of the overall traffic) and not recorded in your CRM or other sales tools.
The machine learning model that runs Lift AI has been trained on billions of data points and more than 14 million live sales interactions. Lift AI uses this third-party data and integrates it with your own first-party data (e.g. the number of pages visited or time on page) in real time. As a result, Lift AI successfully identifies visitors with the highest buyer intent (which is usually around 9% of your overall traffic).
As soon as Lift AI spots a high-value visitor, it connects them directly to your BDRs through chat (using any chat platform you have installed). Visitors with lower scores get directed to a nurturing bot or a self-help guide instead to avoid overwhelming your sales team.
The magic of Lift AI starts working immediately. In just 90 days, customers have reported increasing their chat conversions anywhere from two to 10 times. PointClickCare, for example, grew conversions by 400%, whereas Formstack saw an 88% increase in its pipeline.
With Lift AI, you won’t have to constantly worry about getting the MQL to SQL conversions right — it will automatically surface the best leads for your sales team to engage. As a result, you can streamline your sales process and improve your conversions in no time.