Marketing efforts can be difficult to track – when a contact comes through as an MQL (Marketing Qualified Lead) it’s never an exact science. As much as you can measure the content metrics, the online traffic, form submissions and social media analytics, there’s still so much we can’t put a figure on. Word of mouth, someone suddenly remembering that one social post from last week or going back to read a download of your report after a year of it sitting in your inbox are all reasonable reasons people come back as an MQL which may not have much to do with the marketing conducted at the time. 


But what you can do is qualify your leads on what you do know to give you an assumption of intention. And upon that intention you can send them off to sales to get qualified. 



Why is it Important to qualify an MQL with Sales?


When a contact becomes an MQL, it means they’ve engaged with you enough for you to assume some sort of intent from them. Based on that intent, you want to find out if that means they’re interested in starting the sales process. 


An SQL is only so if they have been qualified by sales to have some sort of interest in your brand. Converting an SQL to an MQL is a necessary step in determining whether your lead scoring and your wider marketing efforts are working. 



Why you’re not Converting MQLs into SQLs


  • MQLs are sitting around


Strike while the iron is hot. Being a sitting duck with your MQLs wastes valuable time and the longer you’ve waited to send these off to the sales team, the less chance your lead has of being interested. They could have been contacted by the competition, displeased with the lack of sales contact or they simply think that another solution is for them. Streamline your marketing relationship with sales and automate the movement of MQLs so they land at your sales team’s feet. 


  • You’re sending MQLs over too early


Labelling them as MQLs too early risks the sales team getting in touch early into their buyer journey. You may be experiencing the right lead, at the wrong time. What this does is it pressures the lead into making a decision before they’re ready. You could send these back into the marketing campaign efforts, but there’s a chance that they won’t be as receptive as they once were. 


  • Not bringing in the right audience


You haven’t narrowed down on a niche so there’s no way to know who your bringing in as an inbound lead. Being too broad can bring leads that simply won’t convert because your product isn’t the right fit for the market they’re in. To fix this, look toward your marketing and re-evaluate the buyer persona you’re trying to reel in. Making your marketing drill down on your ideal customer profile reduces the time sales teams have when approaching these MQLs. 


  • Your lead flow is too complex


If you have too many steps in place for a lead to flow down in order to be approached by sales, then there’s a higher chance that they will drop off. Minimise the chances of this happening by limiting the steps between MQL and SQL – that could be sending MQLs straight to specific sales team members, automating their movement or setting a deadline on when the sales conversation needs to happen. 


  • Your content isn’t solving challenges


Content is King. But only it’s only royalty when it’s targeted at the people you want to bring in as inbound leads. Your content doesn’t have to impress everyone – it has to impress your target audience by talking about and solving the challenges they have. That means that the people who download your content may either be: the wrong type of lead or they’re what you’d call a false lead. A false lead is when they’ve engaged with you but they don’t find use in your content. Try to eliminate this by creating content that solves challenges.


  • Your lead scoring isn’t accurate


When you build out your lead scoring system, you assign either points to certain actions like email opens and clicks, or you dedicate a percentage weighting. Regardless of how you assign leverage points to your triggers, you need to have them proportionate to one another. For example, a form submission requesting information on demos indicates a higher isolated intent than opening an email, so the former needs to have more points than the latter. If you’re not being intuitive with your scoring, then what actually comes through as an MQL may not actually be someone showing high intent.



Why you’re not Converting MQLs into SQLs


With immense detail on your ideal customer profile comes exceptional knowledge of what makes an MQL – it provides key information for your content and it gets your marketing activities in front of your key prospects, where they naturally are. 


Propensity modelling is an intelligence activity that marketing teams use to identify real businesses that match their ideal customer profile so they can understand the market challenges from real profiles and target specific audiences on social media. 


When you create propensity models you improve the accuracy of your marketing efforts. Targeting becomes more succinct and your content begins to align better with your audience. 

The Zint platform takes the guesswork out of finding the right audience. Propensity modelling is automated with sales intelligence to provide current lists of companies that fit your exact mould. And when you’re tailoring your marketing efforts to the right people, the rest follows. Conversion is key. Click here to learn more about propensity modelling for marketing teams.



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