eCommerce Data Collection: 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.

Failing to collect and utilize data properly can result in missed sales, inefficient operations, and poor customer retention. eCommerce data collection provides actionable insights into customer behavior, market trends, and operational efficiency.

Businesses need a clear strategy to harness the full potential of data. 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. Use 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, including 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 have become 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

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, including purchase history and customer interactions, to enhance marketing efforts and drive sales growth.
• 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

7. Comply with evolving privacy regulations

eCommerce businesses operate in a rapidly changing global privacy environment. Beyond the well-known GDPR (EU) and CCPA (California), businesses must now comply with a growing set of regional laws:

Key frameworks as of 2025:

• CPRA (California Privacy Rights Act): Expands CCPA rights and includes stricter opt-out rules.
• UCPA (Utah), CPA (Colorado), VCDPA (Virginia): Add state-level compliance complexity in the U.S.
• Digital Markets Act (DMA) & Digital Services Act (DSA): Reshape data transparency and gatekeeper obligations in the EU.
• India’s DPDP Act: Introduces granular consent and localization mandates for Indian users.
• Quebec’s Law 25: Enhances consent standards for Canadian customers.
• Brazil’s LGPD: Continues to enforce robust data protection in Latin America.

Recommendation: Implement a global compliance strategy, using automation to manage consent preferences, data deletion requests, and policy disclosures tailored to each region’s laws.

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, eliminating the need for physical sampling.

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. Test and improve data collection

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 remains a valuable tool for collecting publicly available eCommerce data (e.g., competitor pricing, descriptions, and stock levels). However, as of 2025, legal and ethical constraints have tightened significantly:

Key considerations:

• Legal challenges: In light of recent rulings and the EU’s Data Act, scraping must respect website terms of service and intellectual property rights.
• Ethical responsibility: Businesses should avoid aggressive or opaque scraping that may overload servers or compromise the integrity of the platform.
• Anti-bot defenses: Many sites deploy bot-blocking measures and dynamic content loading to deter unauthorized data extraction.

Safer alternatives:

• Use official APIs, partner data feeds, or data marketplaces.
• Ensure compliance with robots.txt directives and clearly documented fair use policies.
• Consider licensed commercial scraping platforms with built-in compliance frameworks.

Recommendation: Use scraping responsibly, prioritize legal compliance, consider ethical implications, and explore structured, permissioned data access options wherever possible.

2. Cookie tracking

Cookies help eCommerce websites track and understand user behavior, such as visited pages, time spent, and abandoned carts. However, third-party cookies are now deprecated across all major browsers, including Google Chrome (as of 2024), Firefox, and Safari. This marks a significant shift toward privacy-centric data practices.

What’s changed:

• Third-party cookies are no longer reliable for cross-site tracking or behavioral advertising.
• Consent-based first-party cookies and server-side tracking have become standard for respecting user privacy and maintaining data accuracy.
• Platforms like Google’s Privacy Sandbox and Meta’s Conversions API offer compliant alternatives for tracking.

eCommerce Implications:

• Transition to first-party data strategies (e.g., authenticated sessions, user accounts).
• Use Consent Management Platforms (CMPs) to collect explicit opt-ins for all tracking.
• Leverage zero-party data (data users voluntarily share) to offset data loss.

Recommendation: Businesses must prioritize transparent data practices, phase out dependency on third-party cookie tech, and adopt privacy-first analytics tools.

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 (Now Powered by LLMs)

Traditional NLP allowed eCommerce platforms to extract insights from customer reviews, chat transcripts, and social media. As of 2025, large language models (LLMs) like GPT-4, Claude, and Gemini have redefined the standard.

Recent use cases with LLMs:

• Semantic review analysis: Understand nuanced sentiment across millions of customer reviews with precision.
• AI-powered support chat: Deploy LLMs for multilingual, real-time customer assistance and intent resolution.
• Voice-of-the-customer intelligence: Summarize themes from surveys, reviews, support tickets, and returns feedback.
• Product Q&A automation: Generate innovative, conversational responses to product inquiries using fine-tuned LLMs.

These models are often integrated via APIs or directly embedded in commerce platforms, such as Shopify, Salesforce Commerce Cloud, or bespoke headless CMS setups.

Recommendation: Move beyond basic keyword matching, invest in AI-native tools that use generative LLMs to automate, understand, and act on natural language at scale.

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.