eCommerce Data Collection: 10 Best Practices & Examples

As online shopping grows and customer expectations shift, eCommerce businesses face increasing pressure to stay competitive. Real-world data is key to making faster, smarter decisions.

eCommerce data collection offers actionable insights into customer behavior, market trends, and operations.

Businesses need a clear strategy to harness data’s full potential. Here are 10 best practices—supported by real-world examples—to help eCommerce leaders use data more effectively.

Best practices for eCommerce data collection

1. Prioritize customer data collection

Central to the success of any eCommerce business is understanding its customers. Therefore, eCommerce customer data collection should be a top priority. The types of customer data include: 

• Demographic data
• Buying behavioral data
• Preferences
• Historical data on purchases
• Search patterns and more

Such data can be used to segment customers and provide a more personalized shopping experience. 

Real-life example:

Amazon’s product recommendation system personalizes shopping using collaborative filtering, content-based filtering, and machine learning.

• Collaborative filtering suggests items based on shared preferences among users or relationships between items (e.g., frequently bought together).
• Content-based filtering recommends items similar to those a user likes, based on product attributes like genre or category.
• Machine learning uncovers hidden patterns in user behavior and predicts preferences using advanced algorithms.

By combining these methods and analyzing real-time data, Amazon delivers dynamic, accurate, and personalized recommendations.¹

2. Utilize automated data collection tools

The vast amount of eCommerce data generated online can be challenging to manage manually. Automated tools like web scrapers and web crawlers simplify data collection by extracting relevant data from competitor websites and other sources in real time:

• These tools gather information such as pricing strategies, promotional campaigns, and key metrics on competitor performance.
• This valuable data helps eCommerce businesses optimize their own marketing strategies, improve pricing models, and refine marketing campaigns.

Additionally, combining automated tools with analytics platforms increases the ability to measure business performance and track customer journeys across the eCommerce store.

3. Engage in social media listening

With the rise of social media platforms, online conversations became essential for eCommerce companies looking to collect customer data and monitor customer sentiment. Using social media analysis tools, businesses can:

• Analyze social media interactions and engagement data to identify emerging trends and understand how customers perceive their brand.
• Track customer preferences, reactions to marketing campaigns, and competitor activity.

These insights support targeted campaigns and product decisions, while ensuring a more personalized shopping experience and improved customer satisfaction.

Real-life example:

Nike leverages social listening to monitor conversations and trends on social media platforms to enable the brand to stay relevant and responsive. This strategy helps Nike to:

• Understand customer interests: Identify emerging trends and customer preferences to refine marketing strategies and product offerings.
• Address issues quickly: Detect and respond to complaints or concerns in real time to improve customer satisfaction and trust.
• Gain valuable insights: Analyze engagement data to tailor marketing campaigns and predict future trends.
• Strengthen connections: Engage authentically with customers while fostering loyalty and a sense of community.
• Benchmark competitors: Monitor competitors’ activities to maintain a competitive edge.

By leveraging social media monitoring, Nike enhances its customer experience and adapts to emerging trends.²

4. Deploy advanced analytics

Simply collecting customer data is not enough; it must be analyzed to uncover actionable insights. Advanced analytics tools enable eCommerce businesses to:

• Identify hidden patterns and predict future trends using predictive analytics.
• Analyze behavioral data, such as purchase history and customer interactions, to improve marketing efforts and boost sales.
• Optimize inventory and logistics by studying transactional data for efficient resource allocation.

For example, a fashion retailer can use predictive analytics to forecast product demand while ensuring they maintain an optimal inventory and reduce warehouse costs.

eCommerce analytics tools usually offer combined features for market data extraction and analytics.

5. Invest in a CRM system

A Customer Relationship Management (CRM) system is crucial for managing eCommerce customer data collection effectively. CRMs provide a unified view of a customer’s interactions with the brand, allowing businesses to:

• Track customer journeys and analyze engagement data for a deeper understanding of their customer base.
• Deliver personalized shopping experiences based on demographic and behavioral data.
• Use marketing analytics to optimize email marketing campaigns and loyalty programs.

Real-life example:

Perfumes & Companhia, a Portuguese beauty retailer, partnered with Salesforce to enhance customer experiences and manage operations.

By integrating Service Cloud as its CRM with Marketing Cloud, Commerce Cloud, and existing systems, the company established a unified technology infrastructure to support data and process alignment.³

6. Ensure data security

The increasing volume of eCommerce data collected also increases the need for strong security measures. eCommerce companies must prioritize data quality and protection by implementing:

• Regular security audits to identify vulnerabilities.
• Ensure encryption and secure storage for first-party data.
• Compliance with privacy standards to safeguard customer trust.

By protecting customer data, businesses can avoid legal issues and also build loyalty and trust within their customer base.

Real-life example:

Shopify has implemented new data protection measures to enhance customer privacy and security. As of API version 2022-10, customer personal data is redacted by default, and apps must request access to specific data fields necessary for their functionality.

This approach helps ensure that developers comply with data minimization practices by accessing only the data essential for their applications. Developers are required to inform about data usage, obtain customer consent, and comply with opt-out requests.

These measures align with global privacy regulations and reinforce Shopify’s commitment to safeguarding customer information.⁴

Check out to learn more about data security best practices.

7. Respect privacy regulations

Finally, online businesses must comply with global privacy regulations, such as GDPR in Europe and CCPA in California. This includes informing customers about data collection practices, seeking consent where required, and respecting data rights.

Data privacy-related issues can lead to expensive and damaging lawsuits between companies. Therefore, it is important to consider all data protection regulations of the state in which your business operates.

8. Leverage zero-party data through interactive experiences

Unlike first-party data (observed behavior), zero-party data is information customers intentionally and proactively share—often through surveys, quizzes, preference centers, or wishlists. This type of data is valuable because it’s:

• Voluntarily provided by the user.
• Specific to their preferences, needs, or intentions.
• Typically more accurate and trust-based.

Real-life example:

Sephora’s AI-powered Shade Finder⁵ tools help users discover their ideal foundation or concealer match. Users are guided through steps like:

• Uploading a photo or using live camera input.
• Selecting how their skin reacts to the sun.
• Choosing visible undertones (cool, warm, neutral).

These tools collect zero-party data—data that customers voluntarily provide—which is then used to deliver personalized recommendations that feel curated just for them.

This approach reduces returns and improves customer satisfaction by helping users find accurate matches online—no physical sampling needed.

9. Integrate omnichannel data sources

eCommerce businesses should unify data across all online and offline customer touchpoints—website, mobile app, email, chat, customer support, and even physical retail (if applicable). This holistic approach ensures a 360-degree customer view.

Here are some of the benefits of omnichannel integration:

• Provides a consistent shopping experience.
• Helps track behavior across platforms (e.g., browsing on mobile, purchasing on desktop).
• Improves attribution modeling and campaign effectiveness analysis.

Real-life example:

The Starbucks Rewards program has significantly boosted the company’s performance. This points-based loyalty program allows members to earn “stars” for purchases, which can be redeemed for free drinks and food items.

Repeat visits from members generate 40% of its UK revenue and have contributed to a 7% sales increase since its 2019 introduction.

The app enhances user engagement by enabling members to track their star balance, explore the menu, place orders ahead of time, preload funds onto a digital Starbucks Card for in-store payments, and send gift cards.⁶

10. Continuously test and optimize data collection methods

Data collection should not be static; it must evolve based on performance, user feedback, and changing behaviors. Businesses should apply A/B testing and multivariate testing to optimize:

• Form placement and length (to reduce drop-off).
• Consent request language (to increase opt-in rates).
• On-site data prompts (like pop-ups, banners, or exit intent offers).

Why is data collection important for eCommerce businesses?

Surviving in the highly competitive eCommerce landscape depends on collecting and analyzing market data. This data includes trends in consumer behavior, industry standards, competitor strategies, technological developments, and regulatory updates.

Here are some ways online businesses leverage market data:

1. Identifying trends

Market data enables businesses to spot and predict emerging consumer trends. By analyzing patterns in purchasing behaviors, seasonal demands, and preferences, businesses can tailor their product offerings, marketing campaigns, and website experiences. For instance:

• A sudden increase in demand for eco-friendly products may prompt businesses to adjust their inventory and emphasize sustainable practices in their branding.
• By monitoring shifts in customer preferences, businesses can adapt to evolving demands rather than fall behind their competitors.

2. Competitor analysis

Studying competitors’ actions allows businesses to differentiate themselves effectively. Through market data, businesses can assess:

• Website functionality: What features make competitors’ platforms user-friendly and engaging?
• Customer experience: How do competitors handle customer support, delivery, and returns?
• Marketing strategies: Which advertising channels and campaigns drive the most engagement?

By leveraging these insights, businesses can refine their own strategies to address gaps or capitalize on competitors’ weaknesses.

3. Regulatory compliance and technological adoption

Staying updated on regulatory changes ensures that businesses remain compliant and avoid penalties. Similarly, awareness of technological advancements allows businesses to stay ahead by adopting innovations. Examples include:

• Secure payment gateways: Building trust and ensuring seamless transactions for customers.
• Data protection measures: Maintaining compliance with regulations like GDPR while protecting customer information.
• Fulfillment technologies: Enhancing supply chain efficiency with tools like real-time inventory tracking and automated warehouse management.

4. Strategic planning

Market data plays a crucial role in crafting effective strategies and optimizing resources:

• Digital advertising: eCommerce data reveals which platforms generate the highest ROI to enable businesses to allocate advertising budgets effectively.
• Online store optimization: Identifying pain points in the user journey helps in improving navigation, page loading times, and checkout processes.
• Product development: Insights into customer preferences help businesses design products that meet market demand.
• Pricing strategies: Analyzing competitors’ pricing and customer willingness to pay ensures businesses set competitive and profitable prices.

7 methods for collecting eCommerce data

Since the types of data generated and used in the online retail sector have various forms, different methods can be used to gather it. Below, find these methods differentiated into 2 categories: automated methods and alternative methods.

Automated methods

Automated eCommerce data collection methods rely on technology to gather data efficiently and at scale, also minimizing manual effort.

1. Web scraping

Web scraping is an automated technique to extract data from websites. Web scraping tools or bots crawl online platforms to collect data such as:

• Product pricing and availability.
• Customer behavior patterns, preferences, and trends.
• Competitor product descriptions and features

eCommerce scraping helps companies track customer preferences, monitor competitor activities, and identify emerging market trends to inform business strategies.

2. Cookie tracking

Cookies are small files that are downloaded onto a device when users visit certain websites. These cookies allow eCommerce sites to track:

• User behavior, such as the pages visited and the time spent on the site.
• Items added to a shopping cart.

The tracked data can provide valuable insights into user habits and preferences, therefore enabling tailored marketing efforts and user experiences.

However, stricter regulations like GDPR and the phasing out of third-party cookies require businesses to adapt their data collection practices. Check out to learn more about the changes in cookie collection regulations.

3. Social media analytics

Social media platforms are a rich source of user data. Analytics tools can track:

• Demographics and interests of followers.
• Customer interactions with posts, ads, or product pages.
• Sentiments expressed in comments, shares, or hashtags.

This data helps businesses understand product popularity, customer preferences, and potential demand for specific items. Advanced tools, like social media scraping, can also provide detailed insights.

4. Natural language processing (NLP)

NLP uses AI to analyze human language in text or speech. In eCommerce, it can be applied to:

• Customer reviews: Identifying recurring themes or sentiments about products.
• Chat transcripts: Improving customer service through better understanding of user queries.
• Social mentions: Gaining insights into how people perceive the brand or specific products.

NLP-driven insights help improve customer experiences, enhance marketing strategies, and offer more relevant product recommendations.

Alternative methods

Collecting customer data with alternative methods involves traditional or less automated approaches to complement or fill gaps in automated methods.

5. Customer reviews

Customer reviews are a direct line to what customers think about a product or service. By analyzing these reviews, online retailers can gain insights into product strengths and weaknesses, customer satisfaction, and areas for improvement.

This user-generated content can also help identify new opportunities or market trends. However, analyzing these reviews manually can be difficult with large product lines.

6. Surveys

Surveys provide a more structured approach to gathering data. eCommerce businesses can conduct customer surveys to gain specific insights into their experiences, preferences, and satisfaction levels.

While these may require more effort from customers to fill out, they can yield valuable data that isn’t available through automated methods.

7. Sign-up and email preference forms

When customers sign up to an eCommerce website or fill in their email preference forms, they provide explicit information about their interests and shopping preferences.

This data can be used to personalize the customer experience and email marketing campaigns. It also helps businesses segment their customer base for more targeted marketing.

Further reading

• Top 10 E-commerce Analytics Tools & Selection Guide
• Overcoming Top 10 eCommerce Challenges
• 12 Best Practices of eCommerce Market Intelligence

External Links

• 1. Amazon Product Recommendation System: How Does Amazon’s Algorithm Work?. Stratoflow
• 2. Nike’s Social Media Strategy: Campaigns & Statistics. Keyhole
• 3. New ecommerce and data platform transforms Perfumes & Companhia’s customer journey - Salesforce.
• 4. New Requirements for Shopify Apps that Use Customer Personal Data - Shopify.
• 5. Shade Finder.
• 6. Starbucks Rewards: A Loyalty Program Case Study | LoyaltyLion.