Wake any sales leader in the middle of the night and ask them “What do you need?” — 99% would say “More leads”.
The truth is, though, that most do not really need more, they need better leads. Even more accurately, they need a way to know who they need to focus on and who they don’t need to trouble themselves with.
Most sales and marketing teams already know this is the real challenge for them and are taking steps to do something about it.
And this is where the topic of lead qualification comes in.
What is lead qualification?
This is usually based on a) how lucrative they are for the company (i.e. how much revenue they can generate) and b) how likely they are to become customers.
Why is lead qualification important?
Most businesses that utilize a sales motion often see a common challenge: the sales team would try to work and close every lead that comes their way.
However, doing sales is an expensive endeavor. You need a system to make sure that they have enough high-value leads. You also need to make sure you are working only those deals that have the potential to repay the resources (time, money, etc.) put into them.
In addition, if your sales team is working and closing lower-value deals, it is also hurting your self-service funnel.
As a result, things suddenly start looking all over the place — sales are closing deals, but your revenue metrics (ARPA, LTV:CAV ratio, etc.) don’t look great. And your self-service funnel looks sluggish.
Qualifying your leads allows you to alleviate many of those problems and optimize how you’re using the limited resources of your team.
How are you thinking about your leads?
Even today, many teams still qualify leads manually. In practice, that usually looks like someone on the team going through all new leads and assigning a rank (or a score) to them based on how well they fit the pre-defined criteria of what a good lead looks like.
Traditionally, that criteria combine information from two distinct sources:
- demo- or firmographic data such as how big the team size of a company is, how much revenue it generates, and where the company is located;
- qualitative data from interviewing the customer using one of a number of popular customer discovery frameworks like BANT, MEDDIC, and so on.
These frameworks can help you understand if a particular customer has a true need for your solution, if they have the budget, and who else in the organization would be participating in the decision-making process.
Nowadays, teams consider it common sense to look for ways to automate at least some of the activities associated with qualifying leads. Using enrichment tools such as Clearbit to bring in data from other sources about the target organization and the people interacting with your brand is usually the first step in this process.
Forward-looking companies even outsource this whole process to a vendor who specializes in automating this through software.
With the emergence of trends like PLG, leads now come from many different sources — you have product-qualified leads, those coming from outreach and marketing, and so on.
When leads come from so many different places and have different patterns and data associated with them (how do you compare a lead who’s imported their data into your product with one who’s spoken to your sales team 3 times in a week?), you need a holistic system that combines all data points and informs your decision-making about how to spread your resources and what leads to focus on.
This is where the lead qualification model comes in hand.
Building your first model
The best way to start qualifying leads is to build a simple model and then add complexity gradually as you learn more about what makes the best leads for your company.
To do this, you have to have a good understanding of your model and be able to assess it over time and tweak it as necessary.
Start from known territory
The easiest way to start building a model is to start assigning values for the answers to the questions you’re asking when holding customer discovery interviews with customers.
For example, if you’re using BANT, your scores related to the customer budget can look something like this:
- Resources earmarked for a solution: 10 points
- Budget currently in approval: 5 pts
- Resources planned for next year: 1 point
- No budget planned for a solution like this: 0 points
The limit to this approach is that you don’t always have the luxury of doing a discovery call before you start working a lead.
In many cases, you need to know what your best leads are before you even speak to them. This is where enriching the model with demo- and firmographic data helps.
Bring in demo/firmographic data
Very often this comes as the first natural steps when teams start considering automating their lead management/qualification process.
However, adding the data to your model is just half the battle — you would still have to make decisions about how you’re going to use it. Sure, it’s fairly easy to assign values along data points such as where your lead is located to prioritize geographies that drive your highest-value customers, but that doesn’t always generate a sure-fire system.
I’ve seen a case where a company excluded all leads coming from India by default. However, that prevented them from working some really high-value customers who were a great fit for their product. In this particular case, the company switched to using MadKudu, which allowed them to identify and close a $500k deal.
Another thing to keep in mind is that usually there are multiple people who hide behind a single lead. You have those who are going to be using your product, but there are also one or more decision-makers who would be using it in a different way (or maybe not using it at all) and looking at different things.
Your lead qualification efforts also need to take this into account, especially when you bring behavioral and sales data into the picture.
Paint the full picture with behavioral and sales data
Every interaction a prospective customer has with your brand generates data — using the product, browsing the website, talking to the sales team.
Everything you learn about your customers in such interactions can be used to know when a lead is moving along the conversion path.
For example, when a trial user is logging into the product every day, browsing content on your blog, and reading your email sequences, every such action signifies they are more likely to become paying customers.
You can bring these into your qualification model by assigning points that tell you who the most engaged leads are and what is the best time to push to close the deal.
Tools of the trade
Using software adds a critical edge to any startup sales and marketing team tasked with identifying the best fit leads and closing as many of them as possible. Tools allow small teams to punch far above their weight when they have to compete with far larger and better-resourced teams.
What follows is an overview of the main categories of tools you need to consider.
The customer relationship management platform is the HQ of any sales team. It’s also where your lead scoring model lives.
Salesforce, Hubspot, Zendesk Sell, and most other popular tools allow you to assign scores and qualify leads within the software itself. (And some of them, like Hubspot, are also investing in creating predictive lead scoring products.)
I already mentioned tools like Clearbit that allow you to add more color to your leads by importing data such as company size, revenue, etc.
Enrichment is becoming ever more important in a world where the qualification process is driven excessively by machine learning. Algorithms allow you to use massive amounts of data without loss of productivity (i.e. there’s no point at which you have too much data).
Tools like MadKudu take all the hard work away from you and help you identify the best leads in your audience. Along the way, our platform pulls data from 3rd-party vendors such as Clearbit (see the previous section) to give you a fuller picture of who you’re talking to and whether they’re a good fit for your product.
Collecting data on how your leads are using your product is very important when it comes to identifying those who are the best fit.
Platforms such as Amplitude and Mixpanel have emerged as some of the leading solutions when it comes to this.
This is the last piece of the puzzle, which is still largely done manually. Your SDRs and AEs can (and should) record their learnings straight in the CRM, so they can be used in the lead qualification and sales processes alike.
You have a lead qualification model, now what?
You’ve put in all the work, added data from enrichment sources and different analytics tools and you now have a lead qualification model. Congrats!
Maybe you’ve even spent some time running the model and fine-tuning it. Maybe you even feel fairly confident about the results it is bearing for you.
But what good is the model on its own?
You can have the perfect model, but it’s not worth anything if it’s not guiding your activities. Here are some ideas about what you can do with your lead qualification model.
The most obvious way to use the qualification criteria you developed to decide what to do with each lead that enters your pipeline.
For example, you can split all leads into several categories such as:
- High: The most lucrative leads, those who have the highest propensity not just to convert, but to generate the highest revenue for your brand.
- Medium: Leads that are a good fit to become customers, but have limited potential to generate revenue for your brand.
- Junk: Those are the leads that are not a good fit for your product and/or are never likely to convert.
By splitting your leads in this way, you can decide what to do with each group, for example:
- High potential leads are assigned to an SDR who perform manual outreach and try to book a meeting with them for your account executives.
- Medium leads are placed in an automated sequence with little to no personalization. Unless they ask for a call/demo, no attempt is made by the team to book one.
- Junk leads receive no sales emails and only get regular onboarding and marketing messaging.
The primary function of a qualification model is to help in the process of managing your leads and guide how you use your limited resources. However, there are other ways in which it can help you get clarity over your pipeline.
Every sales leader is familiar with how much stress can come from the process of forecasting the sales pipeline.
There isn’t a “magic wand” that can solve this once and for all, but using your lead qualification model in this process can make it a little less stressful.
By looking at historical data (i.e. the leads you turned into customers in previous months), you can develop reliable knowledge about the probability each lead converts into a paying customer, thus making your forecasting model more accurate.
Start small, improve over time
Your model will never be 100% correct. Moreover, you’ll see it evolve over time as your product and your customers grow and change.
The earlier you start, the quicker you can start learning and improving the model you’re using.