Lead scoring models are fundamental in any solid sales strategy. They help you sort through the crowd and spot the golden leads who are silently (or sometimes obviously) waiting to convert into loyal customers.
This strategy gives you an unfair advantage over most sales teams that blindly chase every lead that pops up. The only thing a few people may not be too happy about is: there’s more than one way to do it.
With lead scoring, you have various options for your needs and business model. But no matter the options, the goal is the same: helping you separate the wheat from the chaff.
And today, we’ll share with you how we approach lead scoring.
Lead Scoring – What Is It and Why Does It Matter?
Lead scoring helps businesses sift through their leads and pinpoint the real winners. It ranks leads based on their engagement and conversion likelihood. Because not all leads are created equal!
Some are just window shopping, while others are ready to make a purchase. Sales team members will hear this in every other webinar and still go after the wrong leads.
That’s why the score exists: to avoid unnecessary detours and help you figure out which leads are worth your time.
The Criteria
Essentially, it assigns each lead a score based on criteria like…
Your leads’ demographics. Think job titles, age, and where they’re based. Demographic data is perfect for businesses with a clear vision of their ideal customer profile (ICP). Just note that it doesn’t capture a lead’s actual interactions with your brand, which could be valuable to you.
Their psychographics. Data like their preferences, pains, and beliefs when it comes to your product or service. It works best in industries where purchasing decisions depend on emotions or complex factors. It’s also effective for segmentation and personalization strategies.
Also, firmographic data, like what industry they’re in, how much revenue they’re bringing in, and what kind of tech they’re using. It’s particularly useful for lead scoring in B2B contexts.
Behavioral insights, like how often they’ve interacted with your website or social media, downloaded your content, engaged with your emails, etc.
There’s also predictive scoring. It uses machine learning algorithms to predict which leads are most likely to convert. It’s often accurate, taking into account several data points, from demographics to industry trends. But, it’s not the cheapest option of the bunch.
The above models give you a clearer picture of who’s likely to convert and who’s still just browsing.
How to Decide?
Start by teaming up with the marketing team and getting to know the crystal-clear definitions for what makes a lead qualified. Think about the traits, actions, and everything else that signals a lead is ready to close.
For example, if you know your target audience is enterprise-level clients looking for your SaaS solution, then an active lead with a CTO job title should be on your radar – and in your demographic data.
Now, different leads will interact with your website in different ways. Some will check out your pricing page, others will tune into your webinar, and some will download your eBook. Each action gives them points. (We’ll dive deeper into this)
Now, if that same CTO does all three – checks out the pricing, attends the webinar, and downloads the eBook – you’ve found yourself a four-leaf clover. It’s a clear sign they’re seriously interested in what you’re offering while showing all the signs of being a potential loyal customer.
So, naturally, you’d want to shower them with attention and nurture that relationship to fruition.
Where to Find the Data
To nail lead scoring, you ideally need to gather data from all corners. The more pieces you have, the clearer the picture. Here are a few tools you can use for that purpose:
Website Analytics Tools, like Google Analytics and Adobe Analytics, give you a better idea of who’s visiting your site, what they’re clicking on, and how long they’re sticking around.
Marketing Automation Platforms, like Marketo and HubSpot. These platforms track how leads are interacting with your emails, social media, and other marketing efforts, giving you a peek into their digital footprint.
Customer Relationship Management (CRM) Systems. They’re the brains of your operation, keeping track of all your interactions with leads and customers. Salesforce, of course, and Intercom are some big names.
If you weave together data from these sources, you can paint a full picture of each lead and score them based on their behavior.
A quick note: relying solely on demographic or behavioral data might leave you with just a surface-level view of your leads. Sure, you’ll know who they are and what they’re doing, but you might miss out on why they’re doing it. That’s where predictive scoring offers a clearer view of lead behavior and motivations.
Implementing Lead Scoring: What Else Should You Know?
First, your team needs to agree on the criteria for scoring leads. Otherwise, this strategy won’t work.
To keep everyone on the same page, create a lead-scoring playbook. It’ll be the guide to lay out all the criteria and definitions for scoring leads, making sure everyone’s reading from the same…well, playbook.
Once everyone agrees, it’s time to craft your scoring model and put it to work.
Let’s see an example in action.
Say your ideal customer is a mid-level manager in the technology industry, aged between 30-40, and located in urban areas. If you’re relying on demographic data, you could assign a score like so:
Age 25-35: +3 points
Job Title: Junior Staff: +4 points
Industry: Retail: +3 points
Rural Location (vs Urban): +3 points
The above isn’t an ideal lead. If we add up the scores, this lead would have a total of 13 points. Until this one comes along…
Age 30-40: +5 points
Job Title: Mid-level Manager: +7 points
Industry: Technology: +8 points
Urban Location (vs Rural): +6 points
And…bingo! This one’s a keeper.
Just remember, it’s not a one-and-done event. You need to keep that scoring model fresh by regularly fine-tuning it.
And don’t forget about lead decay, AKA “lead degradation” or “lead again”. Awful terms, we know. In a softer definition, if a lead hasn’t shown any love for a while, their score should reflect that.
Integrate with Marketing Automation
Now, to really supercharge your lead scoring system, you’ll want to connect it with your marketing and email automation platforms. That way, you can set up no-brainer workflows that kick in based on each lead’s score.
For example, if a lead hits a certain score threshold, they get a personalized email that reads like it was written just for them. (As long as you have your email marketing campaigns in place, of course!)
Ideally, you should be automatically syncing all this essential information to the tool you use every single day: Salesforce. Tools like Match My Email ensure that you don’t just store every email exchange or calendar appointment for a short time.
Instead, Match My Email permanently integrates this data into Salesforce, alongside other vital information – such as customer contact details, past interactions with the company, sales opportunities, customer preferences, purchase history…and any other relevant data that helps you manage and nurture your best leads.
No more manual data entry or hunting through scattered records. Everything you need to know about a lead is right there in Salesforce, every time, at your fingertips.
As a result, you have a more comprehensive view of lead behavior. Your lead scoring model gets a power-up, and so does your decision-making.
Beware of Data Quality Issues
If your data is unclean and messy, your scores will be too. This means wasted time and resources chasing leads that won’t pan out.
The fix is to tighten up your data quality processes. Regular audits of your data sources can help weed out errors and inconsistencies.
Golden tip! If your budget allows, lean on data enrichment tools like Clearbit to triangulate your existing information with technographic and intent data. With cleaner, more accurate data, your lead scoring model will be firing on all cylinders.
Adapt to Market Changes
Another curveball? “Keeping up with market shifts” is what’s called.
If your scoring model can’t keep up with the times, you’ll be left in the dust. That’s why you should regularly review and update your scoring model to reflect market shifts. Keep an eye on emerging trends and new competitors. Predictive analytics tools like Azure Machine Learning can be your edge here, helping you spot trends before they hit.
The Results Are Worth It
By focusing on the cream of the crop, you’ll see your conversion rates skyrocket and your sales team doing victory dances in the break room. Plus, you’ll learn who your customers really are and what they really need – so you can focus your marketing efforts to hit the bullseye every time.
