What Kind of Customer Data Do Companies Capture?

“Half the money I spend on advertising is wasted; the trouble is I don't know which half.”
— John Wanamaker
This famous quote by John Wanamaker perfectly captures one of the oldest and most persistent dilemmas in marketing. For decades, companies have struggled to identify which marketing efforts truly drive results and which simply drain budgets without impact.
At its core, marketing has always been about answering a few fundamental questions:
Who is the right customer?
What is the right product for them?
When is the right time to engage?
Which channel is most effective?
In traditional brick-and-mortar retail, marketers largely relied on mass or bulk marketing strategies. Advertisements were broadcast to an entire population with limited ability to differentiate between customer needs. As a result, large portions of advertising budgets were inevitably wasted. For instance, advertising diapers to 12-year-old children or promoting baby products to teenagers clearly represents irrelevant targeting — and thus, wasted spend.
While such inefficiencies were once unavoidable, today’s marketing environment demands far greater precision. Marketing budgets are increasingly constrained, customers are overwhelmed with choices, and attention spans are shrinking. Under these conditions, relevance is no longer optional — it is essential.

Why E-commerce Changed the Rules of Marketing
Compared to physical retail, e-commerce provides a fundamentally different opportunity. Online businesses can engage customers with a level of precision that was unimaginable in traditional retail environments.
One of the biggest reasons for this shift is data availability. Advances in hardware commoditization and software scalability have made it cost-effective for companies to collect, store, and analyze vast amounts of customer data. Every digital interaction leaves a trace — from browsing behavior to purchase decisions.
Over the last decade, the global e-commerce market has grown at an unprecedented pace. According to Statista:
Global retail e-commerce sales were USD 1.86 trillion in 2016 and are projected to reach USD 4.5 trillion by 2021.
In China, nearly 19% of all retail sales were online in 2016.
In Japan, online retail penetration stood at 6.7%.
In India, e-commerce sales reached USD 20 billion in 2017 and are expected to grow to USD 52 billion by 2021.
This rapid growth is largely driven by customers migrating from physical stores to online platforms. In doing so, customers willingly share personal and behavioral data — enabling companies to gain deeper insights into preferences, intent, and purchasing patterns.

What Kind of Customer Data Do Companies Capture?
Modern e-commerce platforms capture customer data across the entire customer lifecycle, often through multiple devices and channels. Some of the most commonly captured data points include:
Demographic data (age, gender, location)
Socio-economic indicators
Browsing behavior (pages viewed, time spent, search queries)
Purchase history (frequency, categories, basket size)
Time-based patterns (seasonality, time of day, day of week)
Payment behavior (COD vs cards, wallet usage, discount sensitivity)
Returns and refunds behavior
This data is collected at multiple touchpoints — websites, mobile apps, email campaigns, push notifications, and even customer support interactions.
But collecting data alone does not create value.

Why Customer Segmentation Matters
Companies invest heavily in data infrastructure and analytics for a reason: to turn raw data into actionable insights. One of the most impactful applications of customer data is customer segmentation.
Rather than treating all customers as a single homogeneous group, companies divide customers into smaller, more meaningful segments — often referred to as micro-segments. These segments allow marketers to design personalized campaigns, reduce acquisition costs, and improve customer lifetime value.
A well-known example of advanced segmentation is Netflix. Netflix reportedly maintains over 76,000 micro-genres for its content, including extremely specific categories such as Indian_Mother_Son_Love_1980s. This level of granularity allows Netflix to recommend content with remarkable accuracy, driving engagement and retention.

Business Benefits of Micro-Segmentation
Effective customer segmentation delivers measurable business benefits, including:
Reduced marketing spend and wastage
Lower customer acquisition costs
Improved customer retention
Higher customer satisfaction and engagement
Increased cross-selling and up-selling opportunities
Higher average order value and purchase frequency
Improved Net Promoter Score (NPS)
Early identification of dissatisfied customers
Reduced customer churn
Better targeting for new product launches
In essence, segmentation allows companies to do more with less — fewer messages, better timing, and higher relevance.

A Practical E-commerce Segmentation Example
Consider an e-commerce company selling a wide range of products — electronics, apparel, baby products, books, and home appliances. The platform attracts hundreds of thousands of visitors daily, including:
New and returning customers
Users from rural and urban regions
Shoppers using smartphones, tablets, laptops, and desktops
Visitors browsing at different times and days
The objective is to create micro-segments that help personalize both marketing communication and the website experience.
Key Segmentation Dimensions
Some important segmentation categories include:

1. Old vs New Customers Existing customers already have a relationship with the brand. Their past behavior can guide recommendations, discounts, and messaging. New customers, on the other hand, require exploration based on contextual and behavioral signals.
2. Customer Objective Understanding why a customer visits the website is critical. Are they browsing, comparing prices, or ready to purchase? Signals like conversion ratios, time spent, and product views can help infer intent.
3. Device Used The device used for browsing can indicate socio-economic status and preferences. For example, users browsing on premium smartphones may have higher purchasing power.
4. Date of the Month Some customers shop immediately after salary credit dates. Identifying such patterns helps optimize campaign timing.
5. Day of the Week If a customer consistently purchases on weekends, sending reminders or offers on weekdays may be ineffective.
6. Time of Day A user browsing late in the evening may be a working professional, making evening communication more effective.
7. Discount Sensitivity Understanding which customers respond to discounts — and how much — helps balance promotions with profitability.

Creating a Micro-Segment in Action
Now, let’s combine multiple attributes to create a detailed micro-segment.
Customer Profile:
Type: Existing customer
Objective: High purchase intent (conversion rate above 10%)
Device: iPhone
Time pattern: Active mostly on weekends, between 8 PM – 10 PM
Product interest: Gadgets (40% of total spend)
Recent purchase: iPhone 7
Payment behavior: Credit card during offers, otherwise COD
Returns rate: 4%
This profile provides a highly granular view of the customer.
How Should Marketing Engage This Customer?
An effective email or push notification strategy would:
Highlight premium laptops and gadgets
Be sent during weekends, between 8 PM and 10 PM
Include relevant credit card discount offers
Avoid excessive discounting on unrelated categories
Additionally, since this customer is a gadget enthusiast, they can be targeted for new technology launches, accessories, or extended warranties.

Final Thoughts
Customer segmentation — particularly micro-segmentation — has become the backbone of modern e-commerce strategy. In a competitive digital marketplace, success depends on understanding customers deeply and engaging them with relevance and precision.
Targeting the right customers, acquiring them efficiently, and retaining them over time are the foundations of sustainable e-commerce growth. Data-driven segmentation enables companies to move beyond intuition and build meaningful, long-lasting customer relationships.

Author Bio
Perceptive Analytics provides data analytics, data visualization, business intelligence, and reporting services to e-commerce, retail, healthcare, and pharmaceutical industries. Our clients include Fortune 500 and NYSE-listed companies across the USA and India.
At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. Our services include working with leading tableau consulting companies and serving as a trusted Power BI consulting company, turning data into strategic insight. We would love to talk to you. Do reach out to us.