ICP Persona · AI-powered personas for personalized offers
Turn a million customer profiles into personas your marketing team can actually use
Aryng's AI customer segmentation uses unsupervised machine learning to find the customer groups hiding in your preference, behavioral and demographic data, then profiles each one as a persona, so every segment gets its own messaging, offers and next best offer instead of the same campaign sent to everyone.
Proven with a beauty e-commerce brand
The problem
Customers expect personalization, and they notice when you get it wrong
of consumers expect companies to deliver personalized interactions
get frustrated when that personalization doesn't happen
are more likely to repurchase from brands that personalize
more revenue from personalization at faster-growing companies than at their slower peers
Personalization at scale starts with knowing which kinds of customers you actually have.
Most brands segment on assumptions, such as age bands or a few marketer-drawn buckets, and then wonder why the personalized campaign performs like the generic one. AI-powered customer segmentation lets the segments emerge from the data itself, which gives every message, offer and product recommendation a real audience to speak to.
Source: McKinsey & Company, The value of getting personalization right, or wrong, is multiplying (Next in Personalization 2021 report).
What is AI customer segmentation?
Segments that come from your data, not from a whiteboard
AI customer segmentation applies unsupervised machine learning, typically clustering algorithms such as k-means or k-median, to group customers by how similar they really are across hundreds of variables at once. Each resulting cluster is profiled into a customer persona with a name, a share of the base and a clear set of traits, which turns a statistical output into something product, marketing and CRM teams can build personalized experiences around.
Data-driven personas power personalization across the business
Personalize messaging
Write to each persona in the language and tone that matches what they care about.
Choose the next best offer
Match offers, bundles and discounts to each segment's affinity for value or brand.
Build products around real needs
Give product teams a shared, company-wide view of who they are designing for.
Sharpen acquisition
Target prospects who look like your most valuable personas across every channel.
Spend where it lands
Allocate campaign budget by persona and measure lift segment by segment.
How it works
Aryng's ICP Persona Roadmap
Aryng's SWAT data science team runs every engagement on BADIR, its proprietary, recipe-based Data-to-Decisions framework, which keeps the unsupervised learning work anchored to a marketing decision from the first stakeholder session to the final persona deck.
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B
Business question
We use the Business Question framework to pin down the real problem and how the resulting segments will be used, whether that is messaging, offers, products or all three.
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A
Analysis plan
We brainstorm hypothesized segments with your product and marketing teams and agree on the data, hypotheses and clustering methodology before any model is built.
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D
Data collection
We write the data specification and pull preference, behavioral and demographic data for every customer in scope, checked for gaps and irregularities.
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I
Insights
Data preparation, sampling, variable reduction and several clustering models with different cluster counts, with the optimal model chosen on Dunn Index or Silhouette score.
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Recommendations
Profiled personas and the cluster solution we recommend, chosen for how actionable it is for marketing and not only for statistical separation.
Case study · Beauty e-commerce
How Aryng used machine learning to segment a million beauty subscribers into five personas in nine weeks
About the client
Our client runs a monthly subscription service that ships a makeup bag of deluxe cosmetics samples, including skincare, perfumes, nail and skin products, and makeup. Each bag is curated from a detailed preference questionnaire that customers fill in when they sign up, covering skin tone, hair color, preferred products and channels, makeup comfort and color preferences.
The client wanted its entire customer base segmented using that questionnaire along with age and household income (HHI), so it could personalize its messaging and offers. The age and HHI data came from an external data vendor, matched to customers through their social profiles.
The real business question
Use preference questionnaire data to create self-emerging customer clusters that become company-wide personas for customized experiences and products.
The segments had to be usable by marketing, product and the merchandising team that builds every bag.
The analysis plan
Stakeholder alignment
We brainstormed with the product and marketing teams to narrow down hypotheses on activation and profitability, and narrowed the candidate marketing offers based on historical campaign performance.
Hypothesized segments
Agreed with stakeholders before modeling began:
- Color-obsessed
- Beauty-obsessed
- Value-driven
- Care-obsessed
- Millennials
Analysis plan
Drafted with the consensus of every stakeholder, setting out the required data, the hypotheses and the unsupervised learning methodology.
The data
150+ questionnaire data points plus age and HHI for more than a million customers, covering every account created in the previous year.
Why we hypothesize before we cluster
Many data scientists skip hypothesized segments when using unsupervised learning, however, they lose much of the context stakeholders can offer at this stage, context that builds better segments much faster.
Why this data was hard to cluster
We ran the insights stage on the BADIR recipe for unsupervised learning, and the preference data pushed back at almost every step.
- Preference data is far less cluster-friendly than behavioral or demographic data.
- The clustering data mixed continuous variables with 150+ binary (0/1) categorical ones, and binary data does not work well with Euclidean distances.
- Answer options such as "sometimes", "often" and "rarely" were highly correlated with each other.
- Principal component analysis (PCA) alone did not reduce the variables usefully.
How we solved it
What the models found
| Model | Variance captured by PC1 + PC2 | Result |
|---|---|---|
| 11 clusters · appearance variables included | About 31% | Skin tone, hair color and eye color were the most separating features, so including them alongside the combined variables produced 11 clusters. Separation was low and the clusters overlapped heavily. |
| 5 clusters · appearance variables excluded | About 60% | The best separation of any model tested. Brand preference, makeup comfort, preferred channel, age, hair and skin concerns, makeup affinity and fragrance scores did the work. Recommended. |
We recommended the five-cluster model because appearance variables are not very actionable, whereas relative differences in preference for products and value can drive marketing across every channel.
Five personas, each with its own messaging and offers
The final model produced five well-defined segments that showed distinct affinities: value versus brand, beauty-obsessed versus minimalist, and makeup-savvy versus not. The client used them to create five distinct sets of personalized messaging and offerings.
Minimalist Megan
- Not very comfortable with makeup
- Relatively older, average age 39
- Low brand preference, buys fewer makeup products
- Uses less makeup
Casual Casey
- Not comfortable with makeup
- Average age 28
Frugal Fatima
- Very comfortable with makeup
- Average age 35
- Few brand preferences, buys from few channels
- Few concerns, uses less makeup
Beauty-obsessed Beth
- Very comfortable with makeup
- Average age 28
- Prefers lots of brands, buys across channels
- Uses lots of makeup
Shopper Susan
- Not very comfortable with makeup
- Brand-obsessed, likes lots of brands
- Buys from lots of places
- Likes doing makeup and uses it
Percentages show each persona's share of the segmented customer base.
How BADIR turned 150+ survey answers into five personas marketing could act on
Unsupervised learning on preference data can easily produce clusters that are statistically clean and commercially useless. BADIR prevented that by bringing the product and marketing teams in before modeling, so their hypothesized segments shaped the feature engineering, and by judging the final model on actionability as well as cluster separation.
The result was a five-persona segmentation, delivered within nine weeks of kickoff, that gave the client a shared language for its customers and a clear basis for deciding which message, offer and product mix each subscriber should see next.
Free download
Get the complete customer segmentation case study
The full PDF walks through every step of the BADIR engagement, including the hypothesized segments, the clustering challenges, the model comparison charts and the persona profiles.
Outcomes
What to expect from AI customer segmentation
Personas grounded in data
Segments that emerge from your own customer data through machine learning, each profiled with its size, traits and defining variables.
Segments built to be used
A recommended cluster solution chosen for marketing actionability, not only for the tidiest statistical split.
A basis for next best offer
A clear map of which messages, offers and products fit each persona, ready for your CRM, email and personalization engine.
Explainable clusters
Profiles that show which variables separate one persona from another, so stakeholders trust the segmentation and know how to act on it.
A validated model
Multiple distance measures and cluster counts tested and scored on Dunn Index or Silhouette, with the reasoning behind the final choice.
Pair it with LTV
Combine personas with the LTV Predictor to see which segments are worth the most, and the CAC Optimizer to see what each one costs to acquire.
Frequently asked questions
What is AI customer segmentation?
AI customer segmentation uses unsupervised machine learning, such as k-means or k-median clustering, to group customers by similarity across many variables at once. Unlike rule-based segments drawn up by hand, the clusters emerge from the data, and each is then profiled into a persona your teams can act on.
Why hypothesize segments if the model finds them anyway?
Unsupervised learning will always return clusters, however, it cannot tell which ones matter to your business. Hypothesized segments from product and marketing stakeholders guide feature engineering and model selection, which gets you to usable personas much faster.
How does segmentation support next best offer personalization?
A next best offer strategy needs to know what each customer values before it can choose what to show them. Personas give you that foundation, telling you which customers respond to value, which to brand, and which to product breadth, so offers and recommendations can be tailored segment by segment.
Can you cluster survey or preference data?
Yes, though it takes more care than behavioral data. Binary and highly correlated survey answers need feature engineering and the right distance measure, which is exactly the problem solved in the beauty e-commerce case study on this page.
How does this work with the LTV Predictor?
Segmentation tells you who your customers are and predicted LTV tells you what each one is worth, so together they show which personas deserve the richest offers, the most retention effort and the largest share of your marketing budget.
Find out who your customers really are
Talk to Aryng's data science team about building AI-powered customer personas on your own data.
About Aryng
Aryng is a data science and AI consulting, training and advising company. Aryng's SWAT data science team solves complex business problems, builds each company's Data DNA through data literacy programs, and delivers rapid ROI using machine learning, deep learning and AI. Clients include Google, Box, HERE, Applied Materials, Abbott Labs and GE.
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