What on Earth’s the Difference Between an MQL and an SQL Anyway?
By John Waldron
MQL vs. SQL. Marketing qualified leads versus sales qualified leads. What on earth’s the difference between these two categories? And, once you know the differences, what strategies should you use to target each cohort?
Let’s find out…
The key differences between an MQL and an SQL
What’s an MQL?
An MQL is simply a lead that your marketing team deems likely to eventually turn into a sale, and therefore qualifies for additional marketing, but isn’t yet ready to receive a sales call. An MQL does not represent a done deal – the prospect is generally not ready to buy as yet. Nonetheless, marketing qualified leads are more likely to turn into customers compared to regular leads.
Some factors that help identify MQLs are as follows:
- Lead requests information via email but doesn’t ask to be contacted.
- Lead has downloaded information from your website (ebooks, tip sheets, infographics, etc.)
- Lead has visited your website numerous times to look at relevant product or service pages.
And what’s an SQL?
In contrast, an SQL is a prospective customer that has been researched and vetted – first by your marketing team and then by your sales team – and is deemed ready for a direct sales push. Some of the factors which help identify sales qualified leads are as follows:
- Lead indicates that they want to be contacted.
- Lead has been identified as a decision maker or key stakeholder in the buying process at the company where they work.
- Lead has indicated they’re willing to invest time in a meeting to discuss your products/services.
The lead lifecycle stages – working through the stages to maximize sales
We’ve identified the key differences between MQLs and SQLs – one is ready for sales, the other needs more lead nurturing. But how do you work with your MQLs to turn them into SQLs? Well this comes down to the understanding of the lead lifecycle.
A simplified lead lifecycle looks something like this:
Generate leads through content and social media marketing. → Marketing and sales work together to identify which leads are MQLs and which are SQLs. → Marketing nurtures MQLs and converts them to SQLs. → Marketing passes SQLs to Sales. → SALE!
Let’s take a more detailed look at each stage in the lead lifecycle:
1. Generate leads through content and social media marketing.
The first stage is to generate leads through a variety of content marketing practices. Leads are generated at the point where they surrender an email address in exchange for access to content. See our “Beginner’s Guide to Lead Generation” for more on how to generate leads.
2. Identify which leads are MQLs and SQLs (and which are dead ends).
It’s vital for marketing and sales teams to work together to analyze where each lead is in the buyer’s journey. Your strategy at this point is essentially to determine which leads are already SQLs and can be passed on to the sales team, and which require further lead nurturing. To do this you can use lead scoring.
Lead scoring – a joint marketing and sales strategy
To identify whether you’re dealing with MQLs or SQLs, you’ll need to employ an effective lead intelligence strategy which includes lead scoring. Lead scoring is a vital process as it helps you determine which leads are ready for the sales team, and which need more development from the marketing team.
Lead scoring can maximize the number of MQLs you can convert to SQLs, and it’s most effective when sales and marketing work closely together. You assign a value to each lead based on levels of engagement with CTA triggers on your website, automated marketing emails, and on any other information you’ve gathered from your lead intelligence. Lead scoring is important for three reasons:
- To avoid your sales team bothering leads before they’re ready to buy
- To identify which leads require more lead nurturing from your marketing team
- To allow your sales team to more easily identify leads who are ready to buy
There are a number of ways you can score your leads – five of them below:
- Demographic information
- Company information
- Online behavior
- Email engagement and subscription status
- Social engagement
Once you’ve scored your leads and identified them as MQLs or SQLs, you can continue nurturing any MQLs further, and pass any firm SQLs directly on to the sales team.
3. Marketing nurtures MQLs and converts them to SQLs (using an appropriate marketing strategy).
In practice, research shows that as many as 90% of MQLs are not converted to SQLs, either because they were falsely identified as solid MQLs or because of faults in the marketing strategy at the MQL stage.
Nurture your leads carefully. The lead intelligence you collected should indicate which methods the potential customer is using to engage with your company. Target them using their preferred method – whether this be through marketing automation, social media interaction, or specific content and offers. Don’t apply a one-size-fits-all policy – use your lead intelligence to create the best possible marketing strategy for each lead.
4. Marketing pass leads to sales to close the deal.
Once you have converted your MQLs to SQLs, they can be passed onto the sales team. At this stage, the sales team can get in touch with the potential customer directly, confident that your marketing team has nurtured and educated them well, and that the customer is ready to begin the sales process.
The final stage is to let those sales whizzes do what they do best and seal the deal. And, when they do, you have a brand new customer. Congratulations!
Getting your marketing and sales departments to work more closely together is one of the most vital steps you can take to correctly identify whether it’s an MQL or an SQL that you’ve got on your hands. It’s an important distinction to make – leads are easily scared off by pushy salespeople when they’re not yet ready to buy. Therefore, the better the collaboration between the two departments, the stronger the SQLs that will come through – and generating sales qualified leads is the ultimate goal of marketing.
What are your MQL and SQL generation strategies? Let us know in the comments below.
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