Drive to Reimagine

MQLs v/s SQLs - What's the difference, dammit?

Jeremy DSouza
.
Oct 15
.
5-6 mins

Table of contents

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MQLs and SQLs

You know that lead generation is one of your primary goals as a marketer. But not all leads are equal. There’s a big difference between unqualified leads, marketing qualified leads (MQLs), and sales qualified leads (SQLs) and how valuable they are. MQLs and SQLs help you figure out which of your leads are most ready to have conversations with your team.

So, let's learn more about the most valuable types of leads here.

What are MQLs and SQLs?

MQLs (Marketing Qualified Leads)

Marketing Qualified leads or MQLs are prospects who have an interaction with your brand, displayed interest in your offerings, and have shared their information with you. These leads have a greater likelihood of converting into customers than the rest of your leads. An MQL has displayed interest in your offerings but isn’t ready to make a purchase just yet.

SQLs

Sales qualified leads are further down the sales funnel. These are leads which your sales representatives have identified to be suitable for a direct sales follow up. Such prospects tend to have made direct sales inquires or might have even responded to a bottom of funnel offer. They might even be MQLs who went on to interact with your sales team in a manner that displayed real and immediate buying potential. Your marketing team would generally research and vet these leads thoroughly before handing them over to your sales team.

There also are two other types of leads

Product Qualified Lead (PQL)

Product qualified leads are leads who have tested your offerings via a free trail and have demonstrated that they would like to make a purchase and do business with you. These could also be prospects who have used a free version of your product and want to upgrade to a paid plan. 

Service Qualified Lead

Service qualified leads are customers who are already doing business with you but have expressed interest in using a higher, upgraded version of your products or services. These are customers who you can make upsell pitches to with a greater chance of success.

Why do MQLs and SQLs matter?

MQLs and SQLs matter because understanding these types of leads classifying them correctly allows your marketing team to nurture the leads which are most likely to convert and enables your sales team to focus on pursuing the prospects that have the greatest potential to make purchases.

Your teams get to concentrate their efforts on the most qualified prospects, which allows them to have more meaningful conversations and increases your conversion rates.

It even gives your marketing and sales teams insights into what’s working - it shows them what’s bringing leads in, what hooks them, what they’re most interested in, and what makes your leads most likely to convert.

MQLs and SQLs help you figure out which of your leads are most ready to have conversations with your team.

How to Classify MQLs and SQLs in Inbound Marketing

To classify MQLs and SQLs, you will have to engage in lead scoring. This process involves assigning a numerical value to each contact in your lead generation database. The scores assigned to leads are dependent on a range of factors. 

There are two main types of lead scoring:

Unidimensional lead scoring

All the leads are assigned a single score, which would generally range from 1 to 100. These scores can be assigned retrospectively according to the data they have provided and the actions they have taken or in a prospective manner according to an analysis of their behavior that determines the probability of them performing the action you are looking for.

Multidimensional lead scoring

Instead of assigning a single score to every lead, several different variables are used at the same time. As an example, it would be possible to independently assess how much a user corresponds to your buyer personas. You could also check their level of brand awareness and the stage of the buying process that they are in. It’s more complex lead scoring technique, but it lets you be more precise and personalize your interactions to a greater degree.

Some of the factors that are taken into consideration while building your lead scoring algorithm include:

Demographic information

This helps you figure out whether the lead matches your buyer persona. You can find this information through the leads’ responses in your lead generation form or the conversations that they have with your lead generation chatbot. This data is more important for brands that focus on very specific, niche audiences. 

Company information

This data is particularly valuable for B2B companies. They need to have information about the size of the company, the lead’s position and role in the company, and other information like funding raised (which woud help them determine the company’s buying power).

Online interactions

The leads interactions with your brand across various touch points online are a very important factor. You need to evaluate these interactions across your website, chatbot, social media, and other touchpoints. You can figure out which interactions are most important by looking closely at the behaviour of leads who eventually turn into customers. When you analyze their behavior retrospectively, you can figure out what behavior displayed by your current and future leads would be most important. Keep an eye on the pages they visit, the content that they download, the social networks that they follow your brand on and the way in which they interact with your brand on these social media platforms.

Responses to outreach messages

The responses to your outreach messages via email, social media, and your chatbot will help you assess the level of interest that your leads have in doing business with you. Even their actions like opens, clicks, etc. can help you make decisions about the scores to assign to your leads.

How to generate better quality MQLs?

Here are three steps that you can take inorder to generate higher-value MQLs:

Understand your sales cycle

Sure, generating MQLs is your marketing team’s job. But you do need to have conversations with your sales team and understand what your sales funnel and sales cycle looks like. Doing that helps you qualify leads in a better way, because it helps you understand what makes prospects buy your offerings, so it becomes easier for you to identify qualified leads.

Make sure that your marketing goals are in alignment with your sales goals

Your marketing team’s job is to bring your sales team leads that are easier to close. For this to work out well, your marketing team’s goals need to align with those of your sales team. 

Pick the right lead generation channels

Picking the right channels to convey a message to your customers can increase the effectiveness of the message and drive them to share their contact information with you. The best way to generate leads would be to opt for an omnichannel approach in which you continue interacts with your customers across all their preferred channels, without making them start from scratch every time they switch channels.

How to turn MQLs into SQLs?

To turn an MQL into an SQL, you’d have to engage in lead nurturing. This would involve preparing, guiding, and accompanying the leads throughout the buying journey. These lead nurturing strategies help move customers from MQLs to SQLs and finally to customers.

Here's a good article highlighting the tips to generate higher MQLs.

Lead nurturing involves sending a series of communications that start by providing them with value and then go on to share offers that might interest these leads.

Emails are the medium traditionally used for this, but there’s a better medium with a higher open rate. 

You can use a WhatsApp chatbot to nurture your leads at scale, with a 99% open rate… far higher than anything you could get on an email campaign.

Jeremy DSouza

Jeremy is a marketer at Engati with an interest in marketing psychology and consumer neuroscience. Over the last year he has interviewed many of the world's brightest CX, AI, Marketing, and Tech thought leaders for Engati CX.

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