eCommerce Data Collection: 7 Methods & Best Practices in 2024

Currently, the ecommerce sector is encountering a multitude of challenges (Figure 1). The rise of numerous online shopping platforms, ever-changing consumer expectations, and the volatile business environment has forced ecommerce companies to be more strategic, proactive, and innovative.

Amid these disruptions, data emerges as a potent tool. eCommerce data collection is fueling businesses with the resources required to become more aware and resilient.

To harness this power, however, businesses must follow some best practices. This article outlines the top 7 such practices, with examples and case studies to further clarify concepts for ecommerce leaders.

Figure 1. Ecommerce industry disruptions and revenue decline in 2022

Why is data collection important for eCommerce businesses?

Surviving in the highly competitive ecommerce landscape hinges upon the collection and analysis of market data. This data encapsulates trends in consumer behavior, industry benchmarks, competitor strategies, technological advancements, and regulatory changes. Here are some ways of how online businesses leverage market data:

1. Identifying trends

Market data helps in predicting emerging consumer trends, allowing online businesses to adapt their product offerings, website design, and marketing strategies accordingly.

2. Competitor analysis

Understanding competitors’ strategies enables online businesses to differentiate themselves effectively and learn from others’ successes and failures in terms of website functionality, user experience, customer service, and digital marketing tactics.

3. Regulatory compliance and technological adoption

Awareness of new regulations and tech advancements prevents businesses from falling foul of the law and missing out on innovative opportunities. Technologies such as:

• Secure payment gateways
• Data protection measures,
• Fulfillment technologies etc.

4. Strategic planning

Market data informs strategic decision-making, such as resource allocation for digital advertising, online store optimization, product development based on customer preferences, and pricing strategies that align with market realities and online shoppers.

7 methods of collecting ecommerce data

Since the types of data generated and used in the online retail sector has various forms, different methods can be used to gather it. This section explain some of those methods differentiated in 2 categories; automated methods and alternative methods.

Automated Methods

1. Web scraping

This is a technique used to extract large volumes of data from websites. Web scraping tools can be used to track customer behavior patterns, preferences, and trends on online stores. This information is crucial for ecommerce companies in shaping their marketing strategies.

Click here to learn more about web scraping.

Sponsored

Bright Data offers an automated ecommerce data collection and analytics platform called Bright Insights. The web data platform offers:

The platform:

• Capture data from competitors
• Data analytics of multiple competitors
• Track competitor pricing for their products and services
• Monitor competitor product catalogs to help you improve yours
• Monitor consumer sentiment toward your brand

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, the amount of time spent on the site, and items added to a shopping cart. The tracked data can provide valuable insights into user habits and preferences, enabling tailored marketing efforts and user experiences.

However, this practice is changing. Click here to learn more about the changes in cookie collection regulations.

3. Social media analytics

Ecommerce companies can utilize social media analytics tools to gather data from social media platforms. This can include information about user demographics, behaviors, interactions, and sentiments towards products or the brand. The data can be used to gauge product popularity, customer satisfaction, and potential market trends.

Social media scraping tools can be leveraged for this purpose.

4. Natural language processing (NLP)

NLP, a subset of AI, involves machines “understanding” human language. For ecommerce, it can be used to analyze customer reviews, comments, and questions to extract insights about customer sentiment, preferences, and needs. NLP can also help in providing personalized product recommendations and improving customer service.

Alternative Methods

5. Customer reviews

Customer reviews are a direct line to what customers think about a product or service. By analyzing these reviews, onlines retailers can gain insights into:

• product strengths and weaknesses
• customer satisfaction
• areas for improvement.

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

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 make the customer experience and email marketing campaigns more personalized. It also helps businesses to segment their customer base for more targeted marketing.

7 best practices to consider while collecting eCommerce data with case studies

This section highlights some of the best practices that online retailers can consider while collecting market data.

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. 

The algorithm of Amazon¹, for instance, learns from customer interactions with the website, offering relevant product recommendations and thereby improving both the customer experience and conversion rates.

2. Utilize automated data collection tools

The vastness of data in the ecommerce sphere and its collection can be overwhelming. Automated data collection tools can streamline the data collection process, offering real-time information with high accuracy. They can be used to gather data from competitor websites. The collected data provides insights into pricing strategies, promotional campaigns, and product offerings.

You can also check our data-driven list of automated web data collection tools (web crawlers and scrapers) to find the right option for your online business.

3. Engage in social media listening

With the digital age, customers’ voices are louder and more influential than ever before and one way to hear that voice is social media monitoring. Businesses can leverage social media analysis tools to monitor, listen and analyze these online conversations. This can provide insights into the latest trends, consumer sentiment, and the strategies of competitors. 

For example, Nike² engages in social listening to keep up with trending sportswear designs and influential figures, enabling them to create successful marketing campaigns.

4. Deploy advanced analytics

The collection of data alone isn’t enough; online retailers must also be able to analyze and interpret this data. Advanced analytics can offer deep insights, identify hidden patterns, predict future trends, and support data-driven decision-making. A fashion retailer can use predictive analytics to forecast product demand, 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

CRM systems are indispensable tools for managing customer data effectively. They provide a comprehensive view of customer interactions, aiding in understanding customer journeys, identifying patterns, and improving customer service. 

Adidas³, for example, uses Salesforce CRM to gain a holistic view of its customers, leading to personalized marketing strategies and improved customer relationships.

6. Ensure data security

With the increasing amount of data collected, data security becomes paramount. Businesses must ensure they have robust security and data protection measures in place, including encryption, secure storage, and regular security audits. 

Shopify⁴ prioritizes data security, using several layers of protection to ensure the security of both merchant and customer data.

Check out this quick read 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.

Further reading

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

If you need help finding a vendor or have any questions, feel free to contact us:

References

1. Krysik, Arkadiusz (2021). Amazon’s Product Recommendation System In 2021: How Does The Algorithm Of The eCommerce Giant Work? Recostream. Accessed. 13/July/2023
2. Keyhole (2023). Nike’s Social Media Strategy: A Deep Dive Into Campaigns & Statistics. Accessed 14/July/2023.
3. Hollensen, Bergitte (2018). Adidas: Trailblazing Personalization At Scale. Accessed. 13/July/2023
4. Bryson, Emma (2022). New Requirements for Shopify Apps that Use Customer Personal Data. Shopify. Accessed 13/July/2023