Predictive Lead Scoring 101

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.

Traditional Lead Scoring Sample Table
Borrowed from SalesForce

Example: BiroLytix

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.

Predictive Lead Scoring on MS Excel

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.

Conversion Funnel by Moz
Conversion Funnel
Image borrowed from Moz

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.

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.

Customer Segmentation 101

Customer Segmentation 101: A Marketer’s Guide by CirroLytix

The secret to effective marketing is to know your audience inside and out. When you know what your market base wants, needs, and fears, capturing them through targeted marketing is simple.

This guide will teach the fundamentals of customer segmentation: how to sort your prospects into categories that make it possible to run targeted marketing campaigns based on shared similarities like age, purchase history, or even price sensitivity.

What is Customer Segmentation?

Before we take a deep dive into the how-to’s, we’ve got to get on the same page regarding the what is—specifically, what is customer segmentation?

As we’ve mentioned in a previous article, customer segmentation is the process of sorting leads and existing customers into useful categories.

It’s a subset of the wider process of segmentation. You can segment a lead list, and you can even segment your whole market base.

For the purposes of this article, we’ll talk about segmenting customers: the set of people who’ve already made a decision to spend on your business.

We’ll be discussing lead and market segmentation during our Business Analytics Masterclass series later this year. If you’re interested in our full playbook, you can download our primer or sign up here.

Finally, customer segmentation works for businesses of all kinds. It doesn’t matter if you offer products or services, and it works just as well for B2B marketing as it does for B2C.

Data Sources

Customer segmentation is an exercise in data analytics, meaning you’ll need to peek into the numbers behind your sales activity and customer acquisition. For basic segmentation, you won’t need a sprawling data infrastructure; basic information like age, gender, or home industry should do the trick.

One premise whenever a business wants to perform customer segmentation is that they have existing pools of data from which they can draw.

If you’re a business operating in the 21st Century, you’re more than likely to be sitting on some measure of data about your past customers, and the leads you’re currently chasing.

For example, if you’re into B2C, then you probably have data on customers including their age, sex, location, place or industry of work, and a list of their past purchases. In this case, a point of sale (POS) system or customer-relationship management (CRM) software is always a good place to start.

If you’re a B2B firm, then you probably store data on your customers’ locations, industries, purchasing power, and marketing cycles (i.e. the amount of time from capturing a lead to conversion). So long as this data is digital, you can already do basic segmentation.

Naturally, more data lends itself to better insight—-but don’t let your current situation dissuade you from exploring analytics. Learn enough, and you should already be able to make a stellar case for why your outfit should invest in data down the line.


The end product of segmentation is to create a reliable set of marketing personas: profiles of imaginary people who perfectly represent the different segments you’ll be targeting.

Personas aggregate the characteristics of your segments that matter most when it comes to actual marketing. These are things like what they need out of your product or service, what marketing tactics get their attention, and what habits they’ve expressed in their purchases.

Now, there’s a tendency among marketers to base the personas they use on gut feel and surface memory. This is a good start, but far from optimal.

Data-driven marketing expects a more precise method behind persona generation. At the very least, you should have some idea of what sex or age group lends the most to your market share, and a sense of what geographic factors work in your favor (ex. Your best customers come from X, or Your strongest location is at Y.)

You can start your segmentation efforts by assessing the personas you already use, or start with your goals and strategies, building personas as you perform segmentation.


CirroKnittix is a business that sells custom sweaters. At the broadest level, their market is, “Anyone who would buy a custom sweater.” Today, they want to take a data-driven approach and make smarter marketing decisions.

In their 4 years of doing business, they’ve noticed that their top 3 most frequent customers are the following:

  1. People who frequently travel to countries with cooler weather,
  2. People who work office jobs with a casual dress code policy, and
  3. People who put in orders for novelty sweaters to be given out as gifts.

CirroKnittix examines their data and discovers the following personas:

Persona A, a.k.a. The Traveler is a young man with a fortune in frequent flier miles. He shops at, or right before, peak travel season (May-June) and prefers to shop online. He spends an average PHP 4,000.00 per order, and is best attracted using print ads.

Persona B, a.k.a. The Employee is a young to middle-aged woman who believes in expressing herself through her fashion choices. She shops at various points throughout the year, and prefers to shop in-store. She spends an average PHP 2,000.00 per order, and is best attracted using Instagram ads.

Persona C, a.k.a. The Joker is a young man who enjoys giving gifts that leave an impression. He shops at various points throughout the year (mostly during the holidays) and prefers to shop online. He spends an average PHP 8,000.00 per order, and is best attracted using Facebook ads.

Segmentation Characteristics

There are four types of characteristics used in profiling customers:

  1. Demographic – Who your customers are
  2. Psychographic – How your customers think
  3. Behavioral – What your customers do (and their habits)
  4. Environmental – Where your customers are
Segmentation characteristics

These characteristics vary depending on whether you’re selling to a B2B or B2C market.

At a glance, you can see how these characteristics can be useful in marketing. They give you an idea of your clientele’s needs, habits, purchasing power, and preferences.

When segmenting your customers, these characteristics will form the variables that you’ll be examining.

In the next few paragraphs, we’ll discuss two popular segmentation methods. Both of these are great starting points for marketers looking to use data for better targeting.

RFM Segmentation

One of the most straightforward ways to segment customers is to study their purchasing patterns. RFM segmentation looks at purchases according to their Recency, Frequency, and Monetary value.

Customers who purchase more recently, frequently, and with larger monetary value are usually more valuable, while those who do the opposite are usually less valuable to a brand. There are also dozens of others types in between.

Let’s use the CirroKnittix example again.

If we were to rank Personas A, B, and C according to RFM segmentation, it would look like this for the month of May, 3 being the highest score and 1 being the lowest:


Recency score

Frequency score

Monetary value score













However, the table above is just the tip of the iceberg. Once we start going deeper into the data, more detailed segments should emerge. Below are actual RFM segments we did for a retail company. These are based off of POS data, and include some recommendations for each segment.

Customer SegmentActivityActionable Tip
ChampionsBought recently, buy often and spend the most!Reward them. Can be early adopters for new products. Will promote your brand.
Loyal CustomersSpend good money with us often. Responsive to promotions.Upsell higher value products. Ask for reviews. Engage them.
Potential LoyalistRecent customers, but spent a good amount and bought more than once.Offer membership / loyalty program, recommend other products.
Recent CustomersBought most recently, but not often.Provide on-boarding support, give them early success, start building relationship.
PromisingRecent shoppers, but haven’t spent much.Create brand awareness, offer free trials
Customers Needing AttentionAbove average recency, frequency and monetary values. May not have bought very recently though.Make limited time offers, Recommend based on past purchases. Reactivate them.
About To SleepBelow average recency, frequency and monetary values. Will lose them if not reactivated.Share valuable resources, recommend popular products / renewals at discount, reconnect with them.
At RiskSpent big money and purchased often. But long time ago. Need to bring them back!Send personalized emails to reconnect, offer renewals, provide helpful resources.
Can’t Lose ThemMade biggest purchases, and often. But haven’t returned for a long time.Win them back via renewals or newer products, don’t lose them to competition, talk to them.
HibernatingLast purchase was long back, low spenders and low number of orders.Offer other relevant products and special discounts. Recreate brand value.
LostLowest recency, frequency and monetary scores.Revive interest with reach out campaign, ignore otherwise.

RFM segmentation will allow you to prioritize your marketing activity. It should help you identify your most valuable (and least valuable) segments using data so that you can budget your efforts and funding accordingly.

Correlation Clustering

While RFM segmentation analysis is a great place to start, it falls short on two counts: (1) it only looks at three factors, and (2) it’s only useful for segmenting customers that are already engaged with your brand (i.e. those with existing transaction records).

What if you want to predict purchase size based on gender, age, and location, or find out which characteristics your most valuable customers share?

Then you’ll need to cross-examine, cross-tabulate, and analyze more factors.

Correlation clustering groups data points according to their many similarities—that is, by examining all existing factors and figuring out which data points are most alike.

This is done by identifying centroids (think of these as the reference points for each cluster), and grouping data points according to their closest centroids or “nearest neighbors”. A centroid is identified by measuring the distances between data points. The point with the smallest aggregate distance (i.e. the one closest to its surrounding points is usually the centroid We illustrate this in the charts below.

Data points and centroids

Data points clustered according to nearest neighbors

Although the chart appears flat, in reality, clustering can involve multiple dimensions. As a matter of fact, many clusters involve far more dimensions than the three we’re used to seeing. It really depends how many fields you want to analyze.

Correlation clustering can tell you which factors tend to go together—and the results may surprise you. Remember beer and diapers?

A Quick Example (Segmentation Using Pivot Tables)

This isa simple customer segmentation exercise using the Pivot Table function on MS Excel. For this exercise, we’ll use the table below:

Our objectives are to find out Average Spend for the following segments:

  • Age
  • Gender
  • Age & Gender

Check the video below for a step-by-step guide.

Ready to do some analysis of your own? Download the Excel file here and let us know what you find in the comments below.


Customer segmentation is one of many tools that data-driven marketers use to monitor, predict, and optimize their work.

Once you’ve discovered and understood key similarities among your customer base through segmentation, it becomes much easier to reach your most valuable audiences with targeted ads and campaigns.

We’ll be exploring more marketing analytics techniques in the weeks leading up to our Business Analytics Masterclass this coming April. Check back regularly, or subscribe to our mailing list for live updates.