Customer Analytics in E-commerce: Optimizing the Online Shopping Experience  

Key Metrics in E-commerce Analytics   

According to a McKinsey survey on the impact of COVID-19 on consumer behavior, buyers shifted to online channels. Respondents were three times more likely than before the crisis to say that at least 80% of their customer interactions are digital. 

Effective utilization of e-commerce customer data and analytics enables businesses to personalize the shopping experience. This trend allows marketers to monitor the entire customer lifecycle with fewer hurdles. On this basis, they can more easily convey individually tailored messages in every customer journey phase. E-commerce customer analytics lifecycle is based on data from the following sources: 

• Website analytics tools and platforms like Google Analytics, Adobe Analytics, or custom systems, tailored individually for a concrete company. They provide detailed insights into website traffic, user behavior, and conversion rates, including customer segmentation in e-commerce. Such platforms can display KPIs such as unique visitors, page views, average session duration, and goal completion rate. 
• Sales and order management systems gather data from sales departments. These systems show sales volume, average order value, customer acquisition cost, order frequency, and other order details, complementing e-commerce customer analytics. 
• Customer relationship management (CRM) software collects customer behavior insights like real customer engagement score and tracks customer interactions and purchase history. Customer lifetime value (CLV) analysis also aids customer segmentation and targeting. 
• Inventory management systems track KPIs like inventory turnover rate, stock-out rate, and inventory holding cost help retailers, for example, reduce warehousing costs. 
• Payment processing platforms, including Stripe and PayPal, assist in real-time customer tracking to provide data on transaction volumes, payment methods, and checkout abandonment rates. 
• Social media like Facebook, YouTube, TikTok, and others count e-commerce customer engagement metrics like content performance, subscriber growth rate, and watch time to measure engagement and conversion rates. 

By leveraging data from these sources, e-commerce businesses can track key performance indicators and make informed decisions. Based on this data, online retailers can, for example, weigh up opportunities and risks, jump on trends, or counteract undesirable developments early. Let’s compare two situations to see the difference. 

1. An online retailer relied solely on website analytics to inform their marketing strategies during the high peak season. They noticed a decline in website traffic but failed to understand the underlying reasons because they did not consider data from other sources like the company’s social media or customer surveys. This was where the negative feedback from customers was concentrated, as they tried in vain to contact the company in the hope of finding out when the goods would be delivered. As a result, they continued to invest in ineffective marketing campaigns, wasting budgets to nowhere – and losing customer loyalty. 
2. Another retailer implemented an omnichannel approach that integrated e-commerce customer data and analytics from various touchpoints, including website analytics, mobile app usage, in-store transactions, and social media interactions. They analyzed the data and discovered that customers often browse products on their website and then visit physical stores to make purchases. Thanks to omnichannel analytics integration, they personalized their marketing efforts both online and offline. Customer analytics solutions allowed them to deliver seamless shopping experiences across channels. As a result, they have noticed an increase in online sales conversions. 

As you can see, the more data sources e-commerce customer analytics relies on, the more companies can take proper action on time. 

Utilizing Customer Analytics for Personalization  

The well-known grocery store customer A likes to buy fresh fruit. Also, customer B is happy about the latest magazines in the same store – and it won’t be too difficult for the seller to cater to their interests. However, with over 450,000 active customers in e-commerce, it is impossible to know every customer personally and at all times what their needs and interests are. All of these questions can be answered: 

The data sources we’ve listed above provide the keys to answering these questions: a personalized approach for each customer, whether they are 450,000 or a few million. 

According to the latest surveys, 76% of consumers are more willing to buy from a brand that offers personalized experiences. As a full-cycle software development company, we cover all the technical and marketing aspects of personalization and follow a robust predefined strategy. Here are the main steps we take when implementing e-commerce customer analytics: 

Step 1: Define use cases for personalized customer experience 

We identify specific use cases aimed at leveraging e-commerce consumer analytics for personalized content delivery. This involves analyzing customer data and anticipating future needs to derive technical requirements. Considering scalability and flexibility requirements, we determine whether redesigning existing systems or a greenfield approach is most suitable. 

Step 2: Conduct analyses for user experience optimization  

Next, the Lightpoint team performs a comprehensive gap and shopping journey analysis to assess its software systems’ capabilities and identify areas for improvement. Existing functionalities are documented, and unused or unavailable features that could enhance customer engagement through personalization are identified. This analysis informs strategic decisions and validates the chosen approach. 

Step 3: Design the target architecture for personalization 

We design a target architecture that supports personalized shopping experiences, prioritizing use cases and identifying dependencies. An overall architecture is developed, outlining individual solution components and ensuring alignment with customer needs and expectations. 

Step 4: Select and evaluate tools for implementation 

We select and evaluate an appropriate tech stack for implementing personalized customer experiences. Emphasis is placed on scalability, usability, and integration capabilities to collect and process more e-commerce customer data and analytics. The selected technologies undergo A/B testing and experimentation to ensure they meet customer requirements and deliver expected benefits.  

Step 5: Implement and operationalize e-commerce customer journey analytics 

We implement e-commerce customer analytics, starting with the most impactful use cases. Regular implementation status and target architecture reviews are conducted to ensure project success. We implement change management processes to train stakeholders and facilitate the adoption of the cross-channel customer analytics solution. We also consider continuous monitoring and optimization of predictive analytics for e-commerce as a key for maintaining relevance and effectiveness in personalized content delivery. 

Improving Customer Retention and Loyalty  

There are many reasons why customers leave a company. In general, one can distinguish between the following reasons: 

• Market competition: better price-performance ratio among competitors, active poaching, attractive switching offers, etc. 
• Company-related reasons: poor service, negative image, too high prices, etc. 
• Customer-related reasons: moving, unemployment, desire for change, retirement, etc. 

Inadequate service and the resulting negative customer experience are often the reason for customer churn. Customer satisfaction decreases due to incorrect processes, poor communication, or poor customer service in general.   

However, a combination of different reasons is usually the trigger for churn. For example, if a customer experiences a financial bottleneck, a product or service may become too expensive for them. To prevent customer retention and win their loyalty back, we suggest using the tips below: 

1. Develop barriers to exit. Regardless of whether it is technical, time-related, or financial, you increase the hurdles for customers to churn. This can be achieved, for example, through longer-term contracts. However, barriers to exit can also be created through the personalized experiences we discussed above. 
2. Increase customer loyalty through incentives. Small gestures maintain relationships, a crucial strategy in the business world to combat customer churn. These incentives, such as discounts, special offers, or event invitations, foster loyalty and engagement. However, targeted use is key; indiscriminate distribution of incentives can be costly and ineffective. Leveraging churn prediction helps identify at-risk customers, allowing focused efforts to retain them. 
3. Identify customer service growth points. Data from Microsoft, as reported by Hubspot, reveals that 90% of American shoppers consider customer service a pivotal factor in their business decisions. The constant search for room for improvement in customer service is vital in e-commerce as it builds customer trust, satisfaction, and loyalty. The measures taken were repeat purchases and positive word-of-mouth referrals, ultimately driving business growth. 

With the help of churn management and determining the churn rate, at-risk customers can be identified, and their termination can be prevented with the help of individually tailored measures.    

Customer Analytics Implementation Case Studies  

Let’s look at a few successful cases in e-commerce where implementing in-depth analytics has led companies to success. 

Case 1: Automotive retailer 

Challenge 

France’s largest online tire retailer faced the challenge of identifying the elusive «frequent traveler» segment among their website visitors, a group crucial for maximizing sales. Manual segmentation proved insufficient, leaving potential high-value customers unidentified. 

Solution 

Utilizing predictive analytics, the retailer implemented an algorithm that analyzed both hot data (clickstream, brand preferences, tire specifications) and cold data (buyer profiles, vehicle types) to accurately identify the «frequent traveler» segment. By leveraging advanced data analysis techniques, they overcame the limitations of manual segmentation. 

Result 

As a result of this data-driven approach, Allopneus successfully identified 48% more frequent travelers among their website visitors. This led to a remarkable increase in the average shopping cart value by over 15%, showcasing the tangible benefits of e-commerce customer data analysis in driving business success. 

Case 2: Subscription-based business 

Challenge 

A subscription-based meal delivery service aimed to reduce churn rates by identifying key factors leading to customer attrition. Understanding why subscribers were canceling their memberships was crucial for improving retention. 

Solution 

Employing advanced predictive analytics, the meal delivery service analyzed various data points, including customer demographics, order history, feedback, and usage patterns. By integrating this data, they developed a churn prediction model to identify subscribers at risk of cancellation. 

Result 

Through proactive churn management enabled by data analytics, the meal delivery service significantly reduced churn rates by 30%. Implementing a targeted retention strategy for identified at-risk subscribers that included personalized offers enhanced overall customer satisfaction and loyalty. 

• Lack of data literacy. This challenge includes distrust in data, inadequate employee skills for data utilization, uncertainty about profitable data use, and a desire to understand the customer journey better. Overcoming this challenge necessitates comprehensive training and fostering a culture of data-driven decision-making within organizations. 
• Poor data security. Issues such as data loss from browsers and ad blockers, ineffective tracking setups, and data protection concerns undermine the collected data’s reliability and utility. Also, ensuring accurate tracking during website relaunches adds complexity. Businesses often grapple with the decision of whether to implement server-side Google Tag Manager to address these challenges effectively. Resolving these issues requires proactive measures to enhance data integrity and compliance while optimizing tracking mechanisms for accurate insights. 
• Problems in website & campaign optimization. Retailers’ concerns arise regarding the inability to convert high website traffic into leads or sales and the ambiguity surrounding target audience definition and effective targeting strategies, particularly with broad target groups. In addition, businesses grapple with the dilemma of increasing sales without sacrificing margins or engaging in price wars with competitors. Unclear customer satisfaction measurement and insufficient campaign ROI compound these challenges, necessitating strategic adjustments and data-driven insights to drive successful optimization efforts. 
• Unclear business goals. Many e-commerce businesses struggle with defining clear objectives such as increasing leads, sales, or hiring employees, and often lack guidance in setting meaningful KPIs to track progress. Additionally, they seek strategic partners for tactical and strategic advice to foster sustainable growth. Another obstacle is understanding which KPIs are relevant and meaningful for their business context.  

There is no one-size-fits-all solution for the success of an online business, but there are data and contexts that every company should know. Therefore, navigating the challenges above requires a strategic approach supported by actionable insights from custom e-commerce software development solutions.

Conclusion  

When modernizing the e-commerce customer analytics lifecycle, you should always start from your specific use cases. No question: modern architecture and suitable technologies play an important role. However, the technology change should not be an end in itself, but rather should be geared primarily to the new requirements of the departments. With e-commerce customer analytics, you can get the following benefits relevant to your shop:  

• Segment your visitors based on browsing behavior and personalize the user journey depending on the pages previously viewed. 
• Analyze customer data to target specific segments with tailored marketing messages, increasing campaign effectiveness and ROI. 
• Utilize browsing and purchase history data to suggest relevant products to customers, enhancing cross-selling and upselling opportunities. 
• Identify factors contributing to cart abandonment through analytics, allowing for targeted interventions to increase conversion rates, minimize lost sales, and more. 

A customer data analysis always makes sense because it shows optimization potential and customer needs, especially in the highly volatile e-commerce market. If you would like to develop tailored software solutions for customer analytics, we would be happy to help you — just get in touch with us!