Who are your most profitable customers? Customer scoring models take a data-driven approach to identifying the people who generate the most value for your business, so you can focus your sales efforts accordingly.
Every customer matters, but not all contribute equally. In a study of more than 159,000 customers across 17 direct-to-consumer (DTC) brands, the top 20% of customers generated 77.8% of revenue. The top 1% alone accounted for 25%. With scoring models built on your own data, you can pinpoint your most valuable customers and sell to them more effectively.
Here’s what customer scoring models are, the types that ecommerce brands use today, and how to build one using your own customer data and scoring criteria.
What are customer scoring models?
Customer scoring models are data-driven frameworks that businesses use to quantify and rank how much value or revenue a customer generates. That value is based on a constellation of data points that include buying behavior, demographic segmentation, and engagement.
Customer scoring models assign a numerical score to each customer based on their potential value—whether that’s projected lifetime revenue in dollars or a 1-to-5 rating based on purchasing and referral behavior.
Marketing and sales teams use these scores to focus their efforts where they’ll have the most impact. For example, you can use these scores to segment customers into groups, then target high-value groups with tailored social ads and emails.
Goals of customer scoring
You can use Shopify or other scoring software like HubSpot or Pipedrive to build models that:
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Understand your customer base. Scoring gives you a clearer picture of who your current customers are, how they behave, and what they’re worth to your business.
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Identify loyal customers. By assigning a value to consumers, you can identify those who make repeat purchases, engage with marketing campaigns, and refer new customers.
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Segment customers. Customer scoring is a precursor to segmentation, allowing your marketing team to focus on high-value customers by grouping them together based on their score. With customer scores calculated, you can segment customers based on potential revenue value.
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Tailor messaging. Focus messaging at top-scoring shoppers who drive the most revenue rather than chasing low-potential ones.
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Increase customer lifetime value (CLV). With customers grouped into segments based on their scores, you can optimize your marketing and sales strategy toward the highest-value groups. Sales teams can fine-tune their messaging, find upsell opportunities, and improve overall CLV.
Customer scoring vs. lead scoring
Both aim to answer the same question: How much is this person worth to my company over the course of the relationship? The difference is where each person sits in the funnel.
Customer scoring focuses on people who have already made a purchase, taking into account data like average order volume (AOV) and Net Promoter Score (NPS), depending on what the company is looking to track. Because it’s based on actual behavior—sales, referrals, repeat purchases—it offers a more concrete picture of future revenue.
Lead scoring focuses on people who’ve shown interest, but haven’t yet made a purchase. It evaluates potential value based on actions like signing up for an email newsletter, starting a free trial, or requesting a quote. Someone who clicked an Instagram ad for one of your premium products, for example, would likely score higher than someone who signed up for a free newsletter from your homepage.
Types of customer scoring
There are a few different customer scoring models you can use to assess value and guide your marketing efforts. Here are the most common:
Lifetime value scoring
Lifetime value scoring estimates the total revenue a customer will generate over time. The resulting metric is known as customer lifetime value. Here’s how to calculate CLV:
Customer Lifetime Value = (Average Purchase Value × Purchase Frequency) × Average Customer Lifespan
Let’s say the average customer lifespan for your company is three years. If a customer’s average purchase value is $150 and average purchase frequency is four times per year, their CLV is $1,800.
Understanding CLV helps you grow repeat sales and revenue, improve your customer acquisition cost ratio, and build customer loyalty.
Neil Hoyne, Google’s chief data and measurement strategist, says on Shopify Masters that CLV clearly lays out what each customer is potentially worth going forward. “It’s not to say you only focus on the very top 5% or 10% of your customers,” Neil says. “You just get this understanding to say, these are the people your business gets along with. These are the people that aren’t as interested, and you can start adjusting your marketing plans accordingly.”
Shopify’s customer reports feature calculates lifetime value, letting you quickly identify which customers drive the most revenue.
Recency, frequency, monetary (RFM)
Recency, frequency, monetary (RFM) scoring assesses customers on three criteria: how recently they purchased, how often they buy, and how much they spend. Retailers use RFM scoring to measure engagement and identify customers who are at risk of churning (ending their relationship with your business).
RFM scores each dimension on a scale of 1 (least) to 5 (most), producing a combined three-digit code from 111 (an infrequent, low-spend customer) to 555 (the ideal customer, who spends big and often). Those in the top 20% of each calculated value get a 5, and those in the bottom 20% get a 1. For example, if a customer’s last purchase was more recent than 80% of other customers, their purchase frequency is around the median, and their total spend is in the 60th to 80th percentile, their RFM score would be 524.
Shopify features built-in RFM analysis that automatically scores each metric and segments customers into groups like Champions, At-Risk, and Dormant.
Net Promoter Score
Net Promoter Score (NPS) measures customer satisfaction and loyalty. Its primary goal is to assess if shoppers are happy enough with the customer experience to refer your business to others. To calculate NPS, ask customers, “From 0 to 10, how likely are you to recommend our business to a friend or colleague?” Responses fall into three groups:
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Promoters (9–10). Loyal customers who are highly likely to recommend your business.
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Passives (7–8). Satisfied but indifferent customers who may switch to a competitor.
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Detractors (0–6). Unhappy customers who are unlikely to recommend and may discourage others.
To calculate NPS, subtract the percentage of detractors (those who gave a score of 0 to 6) from the percentage of promoters (scores 9 or 10), resulting in a score ranging from -100 to 100.
Tom Hassell, president of positivity-focused clothing brand Life Is Good, calls NPS “the ultimate report card” on Shopify Masters. “There is no greater predictor of a company’s future success or failure than their NPS score,” Tom says. Customers freely recommending your brand signals trust and value, and predicts they’ll return for future purchases.
Customer scoring examples
You can use customer scoring models to identify your most valuable customer segments or find out if you’re providing a strong enough value proposition for people to recommend your company. Here are a couple of examples of how customer scoring helped businesses grow:
Odd Bunch: NPS
Odd Bunch is a Canadian subscription service that delivers imperfect produce instead of letting it go to waste. Founder Divy Ojha uses NPS to measure growth and subscriber retention, specifically tracking whether new subscribers refer others within their first four weeks. High referral scores are a strong indicator of long-term retention.
“If someone feels like they’ve now become a champion for the brand, or have convinced someone to do something that they themselves started doing,” Divy says on Shopify Masters, “there would be that extra stickiness.”
Odd Bunch’s subscriber base grew from 87 in 2017 to six figures in 2025, with a 53% retention rate. This is far above the 20% to 30% industry standard, Divy says.
BIOHM Health: RFM scoring
BIOHM Health is a wellness brand focused on digestive health. As cofounder Afif Ghannoum says on Shopify Masters, BIOHM Health uses RFM scoring within its ambassador program to identify its most valuable and enthusiastic customers.
Afif found that top customers were worth 20 times more than others. RFM analysis helps the team identify these power customers and reward them with free products, cash prizes, social media shout-outs, and guest spots on the company’s podcast.
BIOHM also uses RFM to stay in regular contact with its highest-value customers, learning what keeps them coming back. “By understanding our customers, we were able to really optimize our retention,” Afif says. “We saw our customer lifetime value double and then some over 12 months when we started paying attention to retention.”
How to build a customer scoring model
Here’s how you can start using customer scoring models in your business today, regardless of the model you choose:
1. Define your objective. Ask yourself whether you’re looking to lower your churn rate and improve your messaging to those likely to bounce, or tailor marketing toward the segments that drive the most revenue. Like BIOHM Health, your goal might be identifying your most passionate advocates or pinpointing the customers who spend the most each year.
2 Build your ideal customer profile. Before you start segmenting customers, have an idea of what makes your ideal customer. Is it someone who purchases frequently? Someone who spends big but doesn’t shop too often? A frequent low-spender might be your best ambassador, recommending your brand to others.
3. Assign weights to customer scoring values based on your objective. For example, if you’re looking for customers who refer new business to you, you can assign higher values to referrals and those who are rated as “promoters” in the NPS model.
4. Score the customers. You can use a customer data platform like Tealium to automatically calculate customer scores, or do it yourself in a spreadsheet based on your customer data.
5. Segment your customer data. Use an ecommerce platform like Shopify to immediately group customers according to values like demographics, behavior, or purchase history. This should line up with the traits of your ideal customer profile.
6. Develop marketing messaging targeting ideal customer segments. Target high-value customers with email and advertising messages based on their history and preferences. That could mean exclusive flash sales emailed only to your highest spenders, or behind-the-scenes content about how you create or source their favorite products, all tailored to their preferences and purchase history.
Customer scoring models FAQ
What are scoring models?
Scoring models are formulas that quantify how much value or revenue a customer generates for a business, measuring metrics like purchase size, frequency, and referrals.
What are the two most widely used scoring models?
Two of the most widely used scoring models are lifetime value scoring and recency, frequency, monetary (RFM) scoring.
How do scoring models work?
Scoring models work by assigning values to customer traits, such as how often they’ve bought from you, average order value, and how likely they are to recommend your business to others. You can then build actionable customer segments based on these values.
