A Marketer’s Guide by CirroLytix
Not all customers are created equal. Some are practically begging to be sold to, while others won’t give you the time of day even if their life depended on it.
Any seasoned marketer will agree.
In this regard, it’s absolutely important to prioritize—to focus fire on leads likely to enjoy your product and save yourself the trouble of convincing everyone else. Marketers call this lead scoring.
Lead scoring is the practice of ranking leads according to their likelihood to perform a favorable (or unfavorable) action, usually purchasing a product.
Marketers use it for setting priorities (read: budgeting).
Whatever the size of your budget, it’d be borderline criminal to waste any amount on low-priority leads when there are better alternatives available.
This article will walk you through the ins and outs of lead scoring and provide a clear instructional reference for practicing it yourself.
Traditional vs Predictive Lead Scoring
There are two common methods to lead scoring, each with its own strengths and weaknesses.
Traditional lead scoring is a manual effort done by marketers to sort their leads into two categories: those who qualify, and those who don’t. Qualified leads are those worth chasing—they get the attention, the slice of the budget, and with luck, the place among your closed sales.
They do this by identifying key factors that correlate with an inclination (or disinclination) to convert, and deriving scores based on the weights of those factors.
BiroLytix is a training service dedicated to helping stand-up comedians hone their craft.
They have a compilation of leads taken from various sources, and fine data on each of those leads such as their sex, age, professional background, and past training experience.
As per their lead scoring model, they assign people between the ages of 20 and 40 an extra +15 points, they give people in creative industries +10 points, and deduct 500 points from anyone working in data analytics.
Lead scoring is a matter of identifying your most and least promising leads, so it stands to reason that the more important a value, the higher the bonus or penalty that comes along with it.
Predictive lead scoring is functionally the same as traditional lead scoring, with one major difference: while it’d be a marketer’s job to calculate for scores in the traditional method, predictive scoring relies on algorithms to do the heavy lifting.
Good predictive lead scoring algorithms make use of statistical techniques like logistic regression and multivariate regression to rank leads. In English, these model likelihood by assigning weights to each factor depending on importance. Some advanced lead algorithms use machine learning to “learn” and improve as more leads pour in.
Building a Leads Database
You can’t perform lead scoring without data. How much data depends on your appetite for precision: on one hand, total precision is near impossible; on the other hand, a faulty model can confuse good leads and bad leads.
We suggest taking as deep of a dive as possible into your data sources, and gathering as much useful information as you can without getting creepy.
Start by doing an inventory of existing customer databases—your website, CRM, social media pages, etc. This will allow you to do some preliminary data analysis and clue you in on how much more data you need to collect.
This task is definitely easier than it sounds (for most cases). A simple Contact Us form, for example, can be the foundation for a customer database. That said, we suggest you squeeze content gates for all they’re worth by asking for specifics like age, sex, birthday, and occupation.
Once you are able to correlate that personal data with behavioral data such as on-page behavior, social media activity, and previous purchases, you’ll be well on your way to building your own lead scoring machine.
It bears mentioning at this point that lead scoring isn’t a solution for everyone. Naturally, you’d have to be managing a large number of leads for this to make sense. Moreover, predictive lead scoring is only possible with a large enough data set to feed into an algorithm –if you’ve got data but need to process it with intuition and observation, stick to traditional.
How Lead Scoring Ends in Sales
Lead scoring would be pointless if it didn’t contribute to greater ROI on marketing efforts. Needless to say, it does —by over 70% according to a report by Marketing Sherpa.
There are two likely reasons for this: a quicker response time to urgent leads, and a quicker time with trend-spotting.
Urgent leads who are closer to the purchase stage have done their homework on your solution, which means they’ve probably considered your competitors as well. Time is of the essence, so knowing which of your leads are worth your time can go a long way.
With big, shiny numbers, lead scoring points you to late-stage customers who are ripe for the attention, and who are at the biggest risk of falling into the clutches of a rival business.
Trend-spotting is also easier with lead scoring, because the mere process alone of refining a winning formula will lead you to great insight.
As mentioned, lead scoring depends on your ability to spot factors that influence the likelihood of closing a lead. Getting into the habit of identifying signs of a closer and weighing them against each other is invaluable practice in mindfulness when it comes to marketing.
In practice, lead scoring makes life significantly easier for any given marketing team. Targeted CTAs, emails, and alerts to schedule call can be set to fire for your most promising leads. All it takes is to determine a threshold, and program your automation software accordingly.
Neil Patel has an article covering a handful of automation techniques that align perfectly with a lead scoring strategy, and they’re worth looking into for inspiration as you write your own scoring playbook.
The cardinal rule for lead scoring is to follow the data. It can and will be tempting to abandon the method in favor of good, old fashioned intuition, but it pays to remember that numbers never lie. It may take some trial and error, but the persistence pays off when you’re closing leads like never before.
Predictive Lead Scoring on Solver
Not everyone has access to predictive lead scoring software—the best solutions don’t come cheap, and the cheap solutions can be a pain to work with.
Why not start with a spreadsheet?
Almost all laptops come with Microsoft Excel installed, and most people are already familiar with it. All you need to do is learn a few more functions, and you’ll be well on your way to being a data-driven marketer (it’s not as hard as it looks).
In the video below, we teach you how to use Solver to do predictive lead scoring.
A quick word on solver
Solver is an Excel add-in program that does what-if analysis, which is a fancy way of saying “it plays around with different values to find the best combo”.
It works by looking for the optimal (maximum or minimum) value for a formula in one cell (the objective cell) by manipulating its inputs (the variable cells) and subjecting those to limits and constraints.
How does Solver find the best value? Good old trial and error (using algorithms).
Here’s a good resource for the logic behind solver.
In plain English, you can use Solver to find the maximum or minimum value of one cell by changing other cells—like use the optimal combination of factors to find the best way to score leads, which we’ll be doing in the video.
Lead scoring is a powerful marketing technique that helps you turn a lead list into a conversion machine. It’s one of many tools worth learning, and we’ll be covering plenty more in our upcoming Business Analytics Masterclass series. If you like what you see so far, you’ll love what we keep in the back.
Enroll in a class to reserve your spot.