To help you build more personalized customer journeys, here are some of the most common variables grouped into the four primary customer segmentation models.
Demographic segmentation (the identity markers)
Gender: This segmentation allows for nuanced creative orchestration. By tailoring everything from visual aesthetics to the specific verbiage of a call to action (CTA), they ensure the experience resonates with each group’s unique buying traits.
Age: While a foundational metric, age is most powerful when layered. A 20-year-old male in New York has different needs than a 65-year-old female in Miami. Apparel brands also use this to segment their 'youth' vs. 'adult' sections, showing models that reflect the age range of either the shopper or, in the case of children's clothes, the parents making the purchase.
Occupation: This serves a dual purpose: identifying interests and making inferences about income. By knowing a customer's profession, you can run hyper-targeted ads on professional sites or during specific broadcasts (for example, targeting blue-collar workers during local sports games).
Marital status: Family situations directly impact disposable income. A video game developer might use this to target families specifically, such as creating ads that show the joy of a child opening a new game while parents watch, rather than using the same creative for a single user.
Income: This indicates spending power, but context is key. A single person earning $150k would have more disposable income than a family of four at the same level. Use this to set thresholds for luxury goods ( for example, targeting those making $80k+) while ensuring you don't ignore high-performing segments just below that line.
Birthdays: This is a win-win for loyalty. By using automated mailing lists to send a 'special day' discount code or a small gift, you stay personally relevant and provide a reason for the customer to return to your shop annually.
Geographic segmentation (the local lens)
Location: Whether you are an international brand grouping by country or a local shop targeting a specific county, location is vital for expansion. If a San Diego business wants to move into San Francisco, they may create a 'Golden City' segment to build local awareness and relevance before the first door even opens.
Behavioral segmentation (the action triggers)
Device type: If 75% of your traffic is mobile, your strategy must pivot. This data tells you where to optimize content and where to spend your ad budget, such as shifting funds toward mobile-centric platforms like Instagram rather than desktop-heavy channels.
Cart abandonment: This is a high-intent segment. If 20% of users leave at checkout, you can isolate them for automated retargeting. Adding an incentive like free shipping in a follow-up email is a proven way to recover that lost revenue.
New customers: These users are in the nurturing phase. Instead of a hard sell, they need top-of-funnel content that introduces your values and brand story. This builds the brand awareness necessary to move them toward their first purchase.
Repeat customers: These individuals already trust you. You can skip the introductions and jump straight to your value proposition. Use their purchase history to send concise, purposeful ads or 'thank you' discounts for their continued loyalty.
Top purchasers: These are your VIPs with the highest lifetime value. They deserve exclusive offers, early access to new releases, or a rewards program that gamifies their loyalty by unlocking higher discounts at certain spending thresholds.
Source (referral channel): This is about budget efficiency. If Instagram ads drive 40% of your traffic while Facebook drives only 5% for the same cost, source segmentation allows you to reallocate funds to the platform where your audience is active.
Re-engaging active customers: Proactive marketing to current users can boost average order value (AOV). For example, suggesting a sale on shirts to a customer who is currently adding jeans to their cart creates a seamless upsell during the checkout process.
Most viewed products: Use browsing history to personalize retargeting. If a cosmetic brand sees a user frequently viewing 'eyeliner', they can serve ads specifically for that category rather than a generic brand message, significantly increasing engagement.
Coupon lovers: Some shoppers are motivated by finding good deals. Identifying this segment allows you to use tactics like 'spend $100 to get 10% off.' These users will often add more to their cart just to unlock the savings.
Psychographic segmentation (the belief system)
It’s important to note that some brands may not have this data and may need to rely on third-party partners.
Lifestyle: This combines demographics with behavioral inferences. A luxury car brand might target 'top earners' (demographic) but specifically those whose lifestyle data shows they attend high-end events and value prestige.
Values: Consumers choose brands that reflect their beliefs. If your brand uses recycled plastics, segmenting for 'sustainability' allows you to share specific stats on plastic waste reduction with the audience most likely to care about eco-conscious manufacturing.
Interests: Look for adjacent interests to expand your reach. If your healthy snack bar customers also drive SUVs and enjoy hiking, showing your product on a trail (instead of just in a kitchen) makes your ad instantly more relatable to their lifestyle.
Preferred language and tone: This applies to both literal translation for international markets and tonal shifts. A business might use professional, formal syntax for their B2B decision-makers while switching to a casual, informal tone for their B2C social media audience.
Religion: Respecting cultural and religious milestones, like the surge in shopping leading up to Christmas or Hanukkah, allows brands to stay relevant during peak Q4 gift-giving seasons while demonstrating an understanding of their customers' values.
