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Creating a Customer Profile and Persona System — with Examples

By January 15, 2018 December 29th, 2020 CCG Retail Marketing Blog

Learn how to create customer personas and view real-life examples used for targeting, messaging and more.

There are numerous tools in today’s marketing toolbox — and it’s becoming increasingly complex to determine which ones are worth your investment. Nonetheless, customer segmentation should be considered essential. It’s a proven marketing strategy that has stood the test of time.

To get the most out of customer segmentation, though, you need to build upon the basics and create a customer profile system. Our guide will help you get started on building robust customer profiles, or personas, that can form a basis for developing your customer targeting strategy.

Customer Segmentation versus Customer Personas

It’s important to understand the difference between customer segmentation, customer personas and other similar but distinct terms.

  • Customer segmentation is the practice of grouping different sets of similar people (customers or potential customers) based on distinct needs and/or characteristics.
  • Customer profiles include foundational demographic information collected through research with actual customers.
  • Customer personas are fictitious characters created by a retailer to mimic a real customer. They are usually based on customer profiles.
  • Aspirational personas take customer personas to the next stage. These are fictitious characters developed to mimic a desired customer that isn’t currently part of the customer base.

Why use customer personas?

Customer personas provide companies with a better understanding of core customer groups and help focus on opportunities with look-alikes and potential customers. They’re used in many industries, but retail marketers have traditionally used them to maximize the return on investment (ROI) of their sales and marketing activities.

In the past, that meant customer personas were primarily used for targeting and marketing. But in today’s digital, omni-channel environment, there are even more ways to leverage personas thanks to social network posts and groups, tracking browsing behavior, better understanding of offline purchase behavior and enhanced capture of customer data.

While it’s still a challenge to collect all of this data into a single profile, new tools are emerging that will help. For example, Facebook now offers profiles that combine third-party data providers’ information on customer offline purchase histories with Facebook’s custom audiences tool to match customer loyalty program IDs with Facebook users.

These new tools provide an added dimension to understanding the customer and will help retailers:

1. More intelligently push content and experiences to customers. The improved ability to map customer personas will allow brands to push out more personalized experiences and messages on their websites, in social media, on mobile and even in-store.

2. Improve real-time marketing efforts. Today’s real-time marketing efforts tend to be limited to mass broadcasting of generic social media messages during major events, such as the Super Bowl or music performances. The next generation of real-time efforts promises to be personalized and targeted to high-value customers.

3. Enhance customer lifetime value (CLV) by improving customer engagement. Because of the proliferation of channels, retailers will depend even more on maximizing customer lifetime value (CLV) to guide their marketing decisions and investments in pursuit of delivering the right message at the right time. The definition of CLV will expand as retailers gain a better understanding of the scope and depth of an individual’s social influence.

How to Use Customer Personas

We’ve already hit upon some of the ways to leverage customer personas in marketing. Below are some specific tactics to apply:

  • Target content
    • Messaging
    • Visuals
  • Target strategy
    • Acquisition
    • Growth
    • Retention
  • Promotions
    • Seasonal (Black Friday, Back-to-School, Easter, etc.)
    • Category-based (women’s, home, kid’s, etc.)
    • Clearance
  • Identify new or potential customers
    • New customers with limited transactional data can be identified by segment for targeting
    • Look-alike customers who may be transitioning from one cluster to another
    • Look-alikes for ad targeting

Although it can sound overwhelming, it’s a logical process that builds upon each stage.

Building Customer Personas

Let’s start with the basics. Customer data is the building block for any segmentation. But data is just information, and in retail that typically means it’s a collection of customer purchase histories. To begin to build insights from customer data, you need to develop a customer profile. Profiles help collect and organize the data to make it actionable. The goal is to collect the following types of data to help create customer profiles that are robust and actionable.

  • Behavioral. RFM — or recency, frequency, monetary value — is one of the basic building blocks for customer profiles. You can compile RFM from customer shopping history. With just this basic information you can begin to determine when a customer is at risk for lapsing based on their past frequency and recency.
  • Demographic/lifestyle. The next step is to collect and add demographics and other data about your customers. Typically, this is provided by a third-party data source and appended to your customer database. Now you can begin to build a picture of your customers. And many retailers begin segmenting customers at this stage to help direct messaging.
  • Attitudinal/Motivational/Aspirational. This is the key to building actionable customer personas — gathering attitudinal data through customer research.

For all other information that you need to gather, select a subsection of your customers (including best customers) to interview. A combination of qualitative and quantitative methodologies is effective for this type of research.

Quick tip: Learn more about research methodologies you can use to help capture customer motivations and aspirations.

In addition to combing through customer data and conducting interviews, retailers can also use tools like Google Analytics Audience reports to nail down the demographics of shoppers visiting your ecommerce site. Or use social media tools to define the core group interacting with your accounts, such as Facebook Insights and Twitter Analytics.

From there, you can start compiling this wealth of information into a comprehensive customer persona.

Select Your Methodology

Before you start building customer personas, be sure to define your business needs and objectives. What is the goal for your personas? This will determine which of the several available methodologies is best for your needs.

When you’ve finished that first step, keep those needs and objectives in mind as you consider these common approaches for building customer personas:

  • Cluster analysis
    • Involves grouping a set of data objects into clusters with similar objects grouped together
    • Clustering is unsupervised classification — no predefined classes
  • Criteria-based
    • The marketer determines key attributes that define the segments based on experience (i.e., demographics, purchases, RFM, lifestage, attitudes, purchase stage, etc.)
  • Propensity-modeled segments
    • Segments created by determining the statistical likelihood that a customer will take a particular action (i.e., make a purchase at Back-to-School)

More on Cluster Analysis

Cluster analysis is one of the most popular methodologies in retail marketing for building personas because it:

  • Allows the data to “speak” and determine the segments
  • Is neutral, non-biased
  • Provides statistical confidence

Cluster analysis can result in a significant number of individual personas, and you may want to group them into larger collections to make them easier to use. The rule of thumb is to aim for six to eight segments. But this is highly dependent on the data.

How you group the personas is up to you and should be based on your initial objectives. But some common types of groupings include:

  • Spending behavior
    • Spending
    • Margin
    • Percent that fall within a best customer tier
  • Lifestage
    • Families with young children
    • Families with teens
    • Non-family buyers
  • Shopper types
    • Necessity shoppers
    • Practical shoppers
    • Pleasure shoppers
Cluster Analysis for building personas

Retail Customer Persona Examples

No two retailers are exactly the same, so it follows that your customer personas will be unique. However, you can use the following examples of how customer personas are developed and used to help guide you through the process of compiling your data into successful personas that reflect your core customer groups.

Customer Persona Example 1: Changing the Promotional Cadence

CCG combined transactional, demographic, attitudinal and motivational data to develop customer personas for a retailer with nearly 500 stores and an online presence. During the process, a small but powerful segment was identified that accounted for a disproportionate percentage of sales and transactions.

These families reported spending the most in the past year of any profile and were the least likely to comparison shop or decide on style before shopping. Despite these positive indicators, this group had a low margin. Because of their high level of purchase activity, they were targeted for promotions at a high rate and were heavy users of discounts.

Customer Persona-Changing the Promotional Cadence

Understanding this customer persona led to changes in the promotional strategy and in development of best customer incentives that didn’t include discounts.

Customer Persona Example 2: Decreased Acquisition Costs and Improved Response Rates

CCG helped Proflowers, a direct-from-grower floral delivery company, use personas to better leverage acquisition channels and enhance email response.

Creation of detailed personas led to testing of different communication strategies and merchandising to these segments. For example, the online retailer tested excluding older customer segments from Mother’s Day promotions, which lowered the total deployment quantity, but the overall response rate from the campaign was slightly higher than previous Mother’s Day efforts. Using gender for Valentine’s Day produced similar results.

The type of purchase occasion (traditional holiday, birthday, etc.) was factored into the persona development to help better understand the customer motivations and attitudes and clarify messaging.

CCG also showed Proflowers how to incorporate results from the testing into a long-term strategy. This has resulted in a continued increase in response rates and decrease in acquisition costs.

Customer Persona Example 3: Exceeding a 5% Lift in Margin

Pet supply super store Petco used pet type as a key attribute in developing customer personas. This helped them create a high-value customer loyalty program and targeted messaging that resulted in a 3.3 percent lift in sales, 5.46 percent lift in margin and 3.9 percent lift in transactions over the control group.

Complementary Marketing Strategies

Developing customer personas is just one of the marketing tools available to hone your marketing plan. Below are some complementary tools you can use alongside personas to further enhance your results.

Customer Journeys

Develop customer journeys for each of your major customer personas. Understanding how customers research their purchases can provide critical insights for targeting and messaging.


You can use analytics to enhance your personas — from simple analyses, such as RFM, to more complex approaches, such as propensity modeling. See below for specific examples of customer analysis.

  • RFM. A method used for analyzing customer value. RFM stands for the three dimensions: recency (how recently did the customer purchase?), frequency (how often do they purchase?) and monetary value (how much do they spend?).
  • Propensity modeling. A predictive model that assigns a score to each customer indicating the degree to which the model predicts that customer will take a certain action.
  • Purchase Gap Analysis. By understanding the timing behind customer purchase pattern activity, marketing can better optimize the timing of future promotions to customers.
  • Market Basket. Identifies the combinations of items that customers are most likely to buy over time. Determines the probability that a customer will buy a particular item based on the purchase of other item(s).

Customer Value Scoring

Customer value scoring allows to you rank the value of each customer in your database. This helps you make the smartest decisions when allocating your marketing resources and efforts. In addition, this data mining tool allows you to identify customer attrition trends early and, just as important, to identify up-and-comers you may want to target with communications that push them toward the best customer level.

Looking Forward

Once you’ve built your customer personas, your work isn’t done, even if you’ve also tacked on complementary tools. As the market reacts to your products and brand, your customers and their personas will evolve. Continue to monitor your customer base over time and alter your personas and marketing plans accordingly, so you can stay in touch with who your customers are right now — and what you can do to win them and keep them.

Do you need help creating customer personas, leveraging data and analytics, or implementing customer retention programs? We can help. CCG’s retail marketing experts have been focused on building and maintaining profitable customer relationships for more than 40 years. Schedule a free consultation with CCG CEO and retail loyalty expert Sandra Gudat or call 303.986.3000 for assistance.

Sandra Gudat

Author Sandra Gudat

Sandra Gudat is CCG’s president & CEO. Considered a pioneer in the field of customer marketing, she has a diverse background in consulting, database marketing, advertising, retail and business management. She is a frequent speaker on customer loyalty marketing and developing customer-centric policies

More posts by Sandra Gudat

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