Customer data analysis helps businesses gain a deeper understanding of their customers, including their behaviors, needs, and purchasing decisions. This insight can be used to develop targeted marketing strategies, increase online sales, and manage marketing costs more efficiently.

In this article, we’ll discuss what customer data analysis entails, its importance, how to collect data, and the key metrics required. We’ll also explore how businesses can use their analyses to improve online sales and introduce solutions that help businesses gain better insight into their customers.

What’s in this article?

• What is customer data analysis?
  
• The importance of customer data analysis
  
• What data is necessary for customer data analysis?
  
• What metrics are necessary for customer data analysis?
  
• How to increase online sales using customer data analysis
  

What is customer data analysis?

Customer data analysis is the process of collecting, organizing, and interpreting customer information in order to gain insight into purchasing behavior, needs, and satisfaction levels. It also helps businesses understand the factors that are influencing customer decision-making.

The main goal of customer data analysis is to use data to plan marketing more accurately, increase sales, and help the business grow steadily.

The importance of customer data analysis

Customer data analysis often involves using data to formulate marketing strategies. The information obtained is valuable and can be leveraged in several ways.

Planning marketing strategies effectively

The data obtained from customer analysis can be used to improve sales strategies and enhance communication to align with the genuine needs of customers. This data-driven decision-making leads to more accurate business planning and adaptation.

Increasing sales

Analyzing customer purchasing behavior can help businesses present related or more interesting products at the right time through upselling and cross-selling. These are key strategies for increasing customer value, and they can help boost revenue with minimal cost. Businesses can use data insights to offer packages or accessories, or recommend products that are often bought together.

Reducing marketing costs

Using data to inform marketing strategies can help businesses advertise and promote more effectively. For example, customer data analysis might help a business select target groups that are more likely to make a purchase. This reduces the amount of broad advertising the business has to do, thereby lowering advertising costs and increasing its return on marketing investment.

Segmenting customers more accurately

Customer data analysis helps businesses better understand the needs and interests of customers in each segment, delving into purchasing behaviors, interests, lifestyles, value per purchase, and the time taken before making a purchase decision. This enables businesses to divide customers based on their behaviors—also known as customer segmentation. It also helps them design offers and promotions that precisely meet the needs of each customer group.

Using personalized marketing

Personalized marketing—which is made possible only by studying customer data—can be used to offer products, promotions, and content that align with individual interests, or that otherwise feel unique to the customer. For instance, a business might offer birthday discount coupons or special promotions during periods that customers usually make purchases. This creates a positive personalized experience, which makes customers feel special and valued, and often increases the likelihood of closing a sale.

Increasing customer satisfaction

Data insights help businesses improve communication and services to meet customer needs, which has a major effect on the likelihood of repeat purchases and long-term customer satisfaction. Customer data analysis enables businesses to design targeted loyalty programs, boost sales, and enhance brand loyalty.

Improving customer retention rates

Customer retention is at the center of any business, as retaining existing customers costs much less than acquiring new ones. Customer data—including purchase frequency, average spending, and preferred products—helps businesses retain customers in the long run through strategies such as loyalty point systems, exclusive benefits for regular customers, and personalized marketing. Businesses can adjust or tailor their communications to build long-term customer relationships.

Predicting future behavior

Businesses can use customer data to help identify trends and predict future purchasing behaviors, leading to better business decisions. For example, analysis helps businesses understand the times when customers are most likely to make purchases, the types of products that are popular, the channels that customers pay the most attention to, the times when customers are likely to make repeat purchases, and which products will sell well during an upcoming season.

What data is necessary for customer data analysis?

It’s necessary to gather specific data if you want to use customer data analysis to increase sales and develop your business. You must also keep in mind the proper methods of collecting and managing customer data in accordance with the Personal Data Protection Act (PDPA) law. Here’s the information you’ll need:

• Demographic data: This is basic information that helps identify customer characteristics—such as age, gender, occupation, income, education level, marital status, and address or region—to better understand who the business’s main target group is and to plan marketing accordingly.
  
• Behavioral data: This is information about purchasing behavior for products or services, such as purchase frequency, average purchase value per transaction, preferred days and times for buying, channels used for ordering (e.g., online, in-store, app), types of products frequently bought or interested in, website visit or app usage history, and use of promotions or coupons. Businesses can use this data to improve promotions, product presentations, and communication with customers to better meet their needs. They can also integrate their customer relationship management (CRM) systems with their payment systems to enhance data analysis and create comprehensive customer profiles.
  
• Psychographic data: This entails behavioral, psychological, and lifestyle data, including interests, values, hobbies, and attitudes towards brands and products. This data is used to create campaigns that customers want to participate in and connect with based on their feelings.
  
• Contact and engagement data: This includes information and channels for contacting and interacting with customers, such as email addresses, phone numbers, email opens, link clicks or replies, social media engagement, interest or interaction with advertisements, survey responses, product reviews, and satisfaction ratings. It’s used for effective communication and CRM.
  
• Transactional data: Information about payments—such as order details, payment history, accumulated spending, frequently purchased products, and returns and exchanges—is used to analyze customer payment behavior and calculate their customer lifetime value (CLTV). The resulting information is then used for marketing and financial management to grow the business. Stripe Checkout and Payment Links are among the options businesses can use to collect customer transaction data accurately and systematically.
  
• Segmentation data: This is when customers are segmented based on behavior or value (e.g., new versus existing customers, high-value versus regular customers, and inactive customers). The data is used for different marketing strategies, such as encouraging repeat purchases, running retargeting campaigns for customers who have shown interest but not bought anything, and offering special discounts or early trials of new products.
  

What metrics are necessary for customer data analysis?

The appropriate metrics are key in helping a business gain a clear overview and understanding of its growth potential and sales. The metrics necessary for customer data analysis can be divided into four main types.

Sales metrics

These metrics relate to purchases and customer value, and they include:

• Average order value (AOV): Knowing the average value of each order and the average spending per order helps businesses analyze the effectiveness of promotions, cross-selling, and upselling.
  
• Customer lifetime value (CLTV): Also called “LTV,” this is the value that a single customer generates for the business for as long as they remain a customer. If the CLTV is higher than the cost of acquiring customers, it means the business has the potential for long-term profitability.
  
• Conversion rate: The conversion rate—the rate at which website visitors become actual customers—can be used to measure the effectiveness of web pages, promotions, or sales funnels.
  
• Repeat purchase rate: The number of times customers make repeat purchases is an indicator of their satisfaction and the value they receive from products and services.
  
• Cart abandonment rate: An ecommerce business’s shopping cart, or basket, abandonment rate is used to identify problems that occur in the purchasing and checkout process, such as overly high shipping costs, complicated checkout steps, or system errors.
  

Customer behavior metrics

These metrics generally measure how interested customers are in a business. They include:

• Customer engagement: This involves email open rates, frequently viewed product categories, the number of pages a customer visits each time they’re on the website, scrolling and clicking behavior, and more. This metric helps indicate which communications are effective and what topics customers are particularly interested in. It can be used to put customers into different segments, such as those interested in a particular type of product (e.g., clothing), those looking at promotional products, or those who visit often but do not make purchases.

• Time on site and time on page: The amount of time a customer spends on a website or a specific page is an indicator of their interest in seeking information and possibly making a purchase. If a customer stays on a page for a long time, it usually means they are considering the product or have a genuine interest in it. Businesses can use the time on page metric to segment customers into groups, such as those who read the product details and those who just browse briefly.

• Click-through rate (CTR): The click-through rate from ads, emails, and links measures how interested customers are in the content or offer. This information can be used to evaluate the performance of content or design, and to develop campaigns that target audiences with a high CTR.

Marketing metrics

Businesses need the following metrics to employ an effective marketing strategy:

• Customer acquisition cost (CAC): This is the average cost to acquire a new customer and can include advertising expenses, campaign costs, and sales team commissions. This metric helps businesses evaluate whether their marketing strategies aimed at customer acquisition are worthwhile.
  
• Return on marketing investment (ROMI): This measures the return on marketing investment (i.e., whether the marketing is generating revenue or a profit), assesses the value of marketing activities, compares the effectiveness and success of different campaigns, and helps businesses use resources more efficiently.
  
• Email open and click rate: The email open and click rates for various campaigns can indicate how effective the email subject lines and sending times are. These rates also measure the engagement of email content. Emails with high open rates can be used to improve communication strategies.
  
• Source of traffic: Examining where website visitors come from is important to understand which channels attract the most viewers, such as searches from Google Ads or Facebook Ads, direct searches by typing a URL, referral links, social media (e.g., Facebook, Instagram, LINE, X), or email links. This metric allows businesses to adjust their SEO and advertising strategies precisely, increasing budgets on channels with high conversion rates.
  

Customer satisfaction and loyalty metrics

These metrics reflect how much users are enjoying a business’s product and are likely to buy again. They include:

• Net promoter score (NPS): This score measures customer satisfaction and loyalty, and is based on a survey that asks whether the customer would recommend the product to others. A high score indicates that a customer had a good experience and is likely to support the brand.
  
• Customer satisfaction score (CSAT): This is a score that represents the customer’s satisfaction level with a product, service, or experience with a brand, and it’s usually collected via a short survey after a purchase or customer service interaction. For example, a customer might be asked how satisfied they were with the service they received and their answer options would be “very satisfied,” “satisfied, “neutral,” “dissatisfied,” or “very dissatisfied.” Or, they might need to answer using a 1–5 or 1–10 scale. CSATs can be analyzed and used to improve the quality of products and services.
  
• Customer retention rate: The customer retention rate (CRR) is an indicator of satisfaction and brand loyalty. The more customers a business retains, the lower its marketing costs. The rate also reflects the quality of products and services. Retaining existing customers costs less than acquiring new ones, and existing customers are more likely to make repeat purchases and refer friends.
  
• Churn rate: This is the proportion of customers who’ve stopped using a business’s services over a certain period, which can be a result of dissatisfaction, turning to competitors’ services, or no longer needing the service. Churn rate can indicate the overall health of a business, and if too high, can lead to continuously declining revenue.
  

How to increase online sales using customer data analysis

It’s possible to increase your business’s online sales by applying the insights you’ve gained via customer data analysis and the various metrics involved. Here’s how.

Reduce marketing costs by focusing on the channels with the highest response

After it’s clear which campaigns are successful and generate new customers most cost-effectively, consider shifting your budget to the channels with the highest response. You’ll want to end campaigns with high CAC but low conversion rates. Then, you can test ad copy using A/B testing, opting for the messages that have higher click rates to increase leads and boost sales.

Encourage customers to make repeat purchases

You can send promotions to customers based on repeat purchase periods. For example, you can send discounts in the 35 days post-purchase, provide product recommendations that complement previously purchased items, or send promotions to loyal customers to encourage repeat purchases. You can also run campaigns that target long-term inactive customers, offering them discounts so that they re-engage with your brand and potentially make a purchase.

Boost revenue per order through cross-selling or upselling

Use order data thoughtfully. You can increase order amounts by offering products that match the customer’s purchasing behavior, by suggesting premium or larger quantity products (i.e., upselling) or recommending complementary products (i.e., cross-selling).

Target customers with high LTV

When you’ve identified customers with high cumulative purchase value, you can use targeted promotions to encourage them to join a membership program, along with offering them special promotions—such as discount coupons or exclusive early access to new products—to strengthen brand loyalty. You can also start using a system such as Stripe Billing to support automated payment processing and help manage your customer base systematically. This can support long-term increases in CLTV.

Focus on turning site visitors into customers

Analyze conversion rates and identify successful sales channels. Then, you can adjust communication and the user experience (UX) to be simpler. You can increase your business’s credibility with product or service reviews, which can assist in customer decision-making. Additionally, you’ll need to develop your customer service so that it is easily accessible and reduce the number of checkout steps to close sales and convert visitors into customers more quickly.

Aim for a higher retention rate

Your retention rate can be improved by creating attractive membership programs and enhancing the quality of your after-sales services. Data from your CRM system can be integrated with payment solutions such as Stripe Payments to track transaction information and respond to customer needs accurately. This can help reduce marketing costs and positively impact long-term sales retention.

Use NPS to better understand your customers

Your Net Promoter Score (NPS) helps you understand how willing customers are to recommend a product or service. You can ask for reviews from customers who had positive experiences—and are therefore likely to give high scores. Use high-quality reviews in advertising or testimonials, and implement a referral program to encourage customers to refer others. New customers gained via referrals usually have a higher conversion rate than those gained via advertisements.

Reduce cart abandonment rates by solving checkout issues

You’ll need to figure out the reasons customers are abandoning their carts, such as shipping costs that are too high or errors on the checkout page. Once identified, the issues need to be addressed appropriately. For example, you might need to improve the checkout process so that sales can be completed faster. Or you might want to send reminders and special offers via email—or to the customer’s primary communication channel—to make it easier for them to complete their purchase.

Use engagement metrics for better marketing

Customer engagement rates help brands understand what customers want and what type of content will attract their interest. Additionally, personalized product recommendations and targeted advertising can increase a brand’s reach to customers, boost purchase likelihood, and ultimately impact sales conversion.