4 Ways to Model Lead Scoring for More & Better MQLs
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4 Ways to Model Lead Scoring for More & Better MQLs

When your sales team complains they’ve got too many leads to follow up on, it’s a nice problem to have. But make no mistake – it’s still a problem. Lead volume is not an indicator or success. It is quite the opposite if your close rate remains low and sales processes take far too long. Your sales team may have their hands full of leads, but most of them will do nothing but waste your time.

Why does this happen? Usually, as a result of sales and marketing teams failing to effectively collaborate on lead qualification and prioritization. This collaboration is critical as it gives you your best shot at a reliable, data-driven approach to identifying the leads most likely to convert. Unfortunately, many B2B companies are a long way away from “smarketing” that can bridge that gap. In fact, 43% of marketers don’t seek input from sales at all when they’re building lead scoring models.

Where to begin? One of the challenges of modeling lead scoring is that sales and marketing don’t always speak the same language. So before we can dive into the specifics of lead scoring and how to leverage it in your lead conversion strategy, let’s define some TLAs (three letter acronyms).

What is Lead Qualification?

At the most basic level, lead qualification is the process of determining whether a lead is worth pursuing or not. There are plenty of factors that, right off the bat, will indicate whether a lead is likely to make a purchase or not. For example, if you don’t ship overseas, then none of those lovely people with international IP addresses downloading your whitepapers are qualified leads.

That’s basic, but it quickly gets tricky. Is the CTO liking your posts on Twitter a better lead than the intern methodically reading through your knowledge base articles? As with so many of life’s biggest questions, the answer is “it depends.” You can (and should) create a buyer persona that matches your typical customer. You can then use it to qualify leads based on how similar they are to the persona. However, different personas may take center stage at different levels of the conversion funnel.

The low-level staffer doing initial research and the C-suite exec checking out your social media might both represent qualified leads, but they’re nowhere near each other in the funnel. In fact, they are located in different categories: Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs).

Marketing Qualified Leads (MQLs) are:

  • At the Awareness and Evaluation stages of the conversion funnel
  • Might convert, but aren’t ready to purchase yet
  • Have viewed your marketing materials, but have not yet been contacted by your sales team

Sales Qualified Leads (SQLs) are:

  • At the Purchase stage of the conversion funnel
  • Ready to convert, if your sales team can close the deal
  • Have been contacted by your sales team already, or have taken other specific actions identifying them as ready to purchase

Of course, “qualified” is a binary marker—the lead is either qualified or not. This is not nearly enough and we must go deeper. To prioritize your qualified leads, you’ll need a way to rank them according to other indicators. This is where lead scoring comes in.

What is Lead Scoring?

Lead scoring is the process of assigning points to various characteristics of a lead to create a “score” according to which the lead is prioritized in the pipeline. At the very base, the formula is simple – the more similar a lead is to your ideal buyer persona, the higher the score. Your sales team can then focus on the highest-scoring leads first, knowing they’re the ones most likely to convert. Assuming, of course, that your buyer personas and lead scoring model are based on reliable historical sales data.

There are three types of data you can use in scoring your leads:

  • Explicit, defined as information the lead has purposely provided to you, such as contact information and personal details submitted through a web form (like name, email address, employer, job title)
  • Implicit, defined as information extracted or inferred through the lead’s behavior when interacting with your web site and other marketing materials (like pages visited, products viewed, app downloaded)
  • Social, which is whatever you can learn about the lead from their social media profiles or their interactions with your company on various social media platforms

To devise a scoring system that delivers useful results, you need to be able to refer to past sales and identify the criteria that consistently generate conversions.

lead scoring example

Wait, Do I Really Need Lead Scoring?

Lead scoring can do great things for your sales team, but not every company is going to be positioned to take advantage of its benefits. In fact, 79% of B2B marketers do not use a lead scoring system—and it’s not necessarily because the idea never occurred to them.

Scoring leads is most effective when the volume of leads your sales team is dealing with is high enough to require some sort of filter or prioritization. For some small or specialized B2B companies, this will never be an issue. If you’re getting tons of leads and they’re all converting? Hire more sales staff. For businesses still working on lead generation sinking a lot of time into lead scoring doesn’t necessarily make sense.

Another reason to hold off on lead scoring is lack of data. If you don’t yet have enough data to put together accurate buyer personas, you will have a hard time building a model to help you identify your SQLs.

How Lead Scoring Increases Close Rates and Revenues

Customer acquisition costs keep rising, and 43% of sales and marketers say that the biggest challenge to aligning their teams was the lack of accurate lead data. Either because it wasn’t available, or because the other team wasn’t sharing it. Lead qualification and scoring systems build a bridge between sales and marketing that remedies this.

By qualifying and scoring MQLs before you pass them to sales, you can shorten the sales cycle and reduce the time your salespeople spend on low quality leads. This increase to your sales team’s productivity will improve sales forecasting as well. Shorter cycles (and higher conversion rates) will give you more accurate data to feed into forecasting algorithms.

lead scoring roi stats infographic

4 Components of a Successful Lead Scoring Model

You’ve got explicit, implicit, and social lead data. You’ve got a buyer persona based on historical sales data. How do you take all this and create an effective lead scoring model?

Companies and individual sales teams may have different needs in the level of complexity and detail in their lead scoring model. For some, a minimum qualifying score may be all that is needed. Other businesses can benefit from a highly detailed scoring system and assign MQLs to the salespeople best suited to close the specific deal.

To build a lead scoring model to increase close rates you should include variables of four main categories:

1. Company (firmographics)

In a B2B context, the most important thing to know is what company the lead is associated with. Is the industry or location correct for your buyer persona? Is your product an appropriate solution for their business needs? How big is the business and how significant will the payout be if a deal is closed?

Knowing as much as you can about the company whose representative you’re engaging is key to prioritizing and merging leads. This makes sure you don’t miss Google as a client while pursuing a lead from a dog-walking business, for example.

2. Persona(s)

The average B2B purchasing decision involves more than five people. This means that your buyer personas must consider the key stakeholders within the organizations you sell to. This includes the executives, the advisers, the users, and anyone else your salespeople are likely to communicate with. Implicit, explicit, and social demographic data can help you identify whether the lead is a decision-maker (or influencer), and score them accordingly.

3. Engagement

You can learn a lot about a lead from their engagement with your content on your website and on social media. Someone who only ever reads your blog posts might just be a fan of your content, whereas somebody who volunteers their contact information to download a whitepaper will receive a higher score. 

Prior sales data can help you assign values here. For instance, if you know that leads who sign up for a particular webinar on your site have a high tendency to convert? You can assign a higher point value to that interaction.

4. Intent

Engagement can be misleading. Plenty of people will poke around product and pricing pages because they’re doing research with no intent to buy. The true signs of intent to purchase should carry the highest point values. But how can you tell what they are?

You can survey sales staff and customers to get a better idea of what indicators of intent to look for. That’s not very accurate or scalable. The best approach is to apply a co-dynamic lead scoring model. One that considers brand sentiment and engagement alongside other variables (like firmographics). This can be tricky to do manually, but marketing automation solutions often feature AI-based lead scoring systems that can do this for you.

Leads make sales, and lead scoring simply makes your sales teams work on the leads most likely to bring the most value. So it’s no wonder that more and more businesses are implementing lead scoring in their lead qualification processes.

To take the next step, implement lead scoring automation to save on labor and resources by leaving the math to computers. Machine learning technologies can refine and improve lead scoring models over time, taking the guesswork out of divining your leads’ intentions.

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