Why returns cost more than you think – and how data can help prevent it
Imagine this situation: a customer opens a long-awaited package, only to find a damaged product inside. Their disappointment starts a costly return process for your company. Can this be avoided? Yes – by harnessing the power of data and modern technologies. Data analytics supports making informed business decisions and optimizing processes.
Return costs in e-commerce pose a significant challenge for almost every company operating in this industry. They impact profitability, reputation, and customer loyalty. Problems with return service quality, damage during transport, or product defects can generate costs far higher than just replacing the item.
Changing market conditions require e-commerce companies to be flexible and quickly adapt their strategies.
In this article, we will show how data analytics and machine learning (ML)-based tools can effectively help reduce these costs. Data analysis is crucial for gaining a competitive advantage. We will also present a client example where implementing these technologies brought measurable effects in process optimization and lowering return costs.
Return Costs – A Serious Challenge for Every E-Commerce Business
First, it is worth examining the sources of the problems to find the best solutions step by step.
High return costs result from many factors, such as:
- Damage during transport: Insufficient quality of logistics services, inadequate packaging, or wrong carrier choice.
- Production or packing errors: Issues at the production stage leading to defective products being delivered.
- Ease of filing returns: Free returns and simplified return procedures may encourage customers to file claims even for minor damages.
Knowing these causes, it is worth considering how they affect your business operations.
Based on cooperation with many clients, we distinguish two types of impact on business:
- Direct: Costs related to replacement, transport, and product repairs.
- Indirect: Decreased customer satisfaction, loss of loyalty, which translates into reduced revenue.
Moreover, handling returns engages company resources, diverting them from other strategic activities.
It is important to emphasize that e-commerce generates huge amounts of data related to returns. Effective data management is essential for organizing, storing, and maintaining this return-related data, ensuring its quality, security, and integration across systems. Utilizing appropriate data storage solutions, such as data warehouses or data lakes, enables companies to efficiently manage both structured and unstructured data generated by returns. Effective management of this data not only helps better identify problem sources but also optimizes return processes, resulting in lower costs and higher customer satisfaction.
How Data Analytics Helps Solve the Problem
Imagine your company has a detective who not only detects problems but also points out the best ways to solve them. That is exactly how data analytics works.
Data analysis allows obtaining essential information that supports making informed business decisions.
Such a detective does not operate in a vacuum – companies rely on data analysts who apply various techniques such as statistical analysis or machine learning to process data. By utilizing specialized tools and programming skills, data analysts efficiently process data, enabling effective interpretation of large data sets and drawing valuable conclusions to support decision-making.
Using a Data-Driven Approach
E-commerce companies can effectively reduce the number of returns by using historical data and analytical tools. Key actions we implement in cooperation with our clients include:
- Collecting data: Collecting data from various sources such as logistics, production, and sales systems is a crucial step in the analysis process. This enables comprehensive analysis of data related to transport processes (data about carriers, package types, delivery locations) and production processes (production stages, machines manufacturing a given product, product type).
- Analysis of damage causes: For example, identifying trends such as a high number of damages during transport to specific regions or problems with production machines, which may indicate issues at the material or final product production stage.
Before analysis, data cleansing is essential to remove errors, duplicates, and inconsistencies. This ensures the accuracy and reliability of the insights derived from the data.
Implementing Predictive Models Using Machine Learning
Machine learning allows predicting the risk of package damage based on historical data. It can be compared to having a “virtual advisor” indicating the riskiest transport routes and products requiring special attention.
Machine learning techniques have significant importance for optimizing e-commerce conversions, enabling trend prediction, service personalization, and business process automation.
With such knowledge, we can make operational and strategic decisions based on data. This translates into a range of benefits:
- Automation of logistics processes: Choosing a carrier based on damage risk analysis.
- Production improvement: Detecting weak points in the production process.
- Personalization: Tailoring services to individual customer needs. Recommendation systems use analysis of customers’ purchase history to predict their needs and suggest the most suitable products.
Artificial Intelligence in E-Commerce
Artificial intelligence (AI) is revolutionizing e-commerce by enabling online stores to precisely tailor offers to individual customer preferences. Thanks to advanced machine learning techniques, online stores can analyze customer behavior at every stage of the purchasing process – from browsing products, adding to the cart, to completing the transaction. This not only helps better understand consumer needs but also forecast their future buying behaviors.
Many AI and machine learning features are now integrated into popular ecommerce platforms, such as Shopify and Magento, allowing online stores to leverage advanced analytics and personalization tools directly within their chosen platform.
In practice, AI enables automatic recommendation of products that best match individual customer preferences, significantly increasing the chance of repeat purchases and building customer loyalty to the online store. AI also supports conversion optimization by analyzing which offer elements attract the most attention and how they can be better adapted to customer expectations. A high level of personalization translates into increased customer satisfaction and competitive advantage in the e-commerce industry.
Thanks to AI, online stores can not only automate processes but also dynamically adjust offers to changing consumer needs, which is crucial in an environment where customer loyalty and shopping experience are decisive for business success.
Analytics Platform and Tools
A robust analytics platform is a cornerstone of any data-driven e-commerce business. These platforms provide the essential tools needed to collect, process, and analyze raw data from a variety of sources, transforming it into actionable insights that drive business performance. Solutions like Google Analytics, Tableau, and Power BI empower businesses to perform comprehensive data analysis, create compelling data visualizations, and present findings in a way that supports informed decision-making.
With the right analytics platform, e-commerce businesses can easily identify trends and patterns in customer behavior, sales data, and marketing performance. Data visualization features allow teams to quickly spot opportunities and challenges, while data presentation tools make it easier to communicate insights across the organization. By integrating data from multiple sources, these platforms help businesses develop a holistic view of their operations and customer base.
Leveraging analytics platforms enables e-commerce businesses to optimize their marketing efforts, improve operational efficiency, and make data-driven decisions that support growth. Whether you’re tracking the effectiveness of marketing campaigns or monitoring changes in customer demand, a powerful analytics platform is essential for staying competitive in the fast-paced world of e-commerce.
Benefits for E-Commerce Companies from Using Data Analytics
Direct Benefits
- Cost optimization: Choosing a more expensive but more reliable carrier can reduce the number of returns, which in the long term improves profitability.
- Logistics improvement: Shortening the time of handling returns and shipping packages.
- Customer satisfaction: Higher service quality contributes to increased customer loyalty. Effective data analysis enables better management of customer service and processes, translating into higher satisfaction and loyalty of buyers.
Strategic Benefits
- Data-driven decisions: Better resource allocation and increased operational efficiency.
- Competitive advantage: Companies using data analytics outperform their competition more effectively.
- Better production management: Identifying problematic production stages, e.g., machines requiring repair due to frequent product returns.
User data analysis enables optimization of user experience, better understanding of their behaviors and preferences, and thus increased engagement and marketing effectiveness.
Challenges Related to Data Analytics
Although data analytics opens huge opportunities for e-commerce, its implementation involves several challenges. One of the most common problems is product non-compliance – a situation where the product does not meet customer expectations, leading to returns and the need to replace the item. This process is costly and time-consuming, and each return is not only an expense but also a risk of losing customer trust and loyalty. Using diagnostic analytics, businesses can better understand the reasons behind product returns and customer dissatisfaction by exploring underlying causes through techniques such as drill-downs and data mining.
For online stores, it is also crucial to respond quickly to changing market trends and flexibly adjust offers to current customer needs. Consumers expect individualized approaches and personalized offers, so effective customer segmentation and behavior analysis become necessary for building lasting relationships and increasing customer loyalty.
These challenges require not only advanced analytical tools but also skills in data interpretation, data modeling, and data mining to extract insights and structure data efficiently. Only then is it possible to effectively tailor offers to customer expectations, minimize the number of returns, and build competitive advantage in the dynamic world of e-commerce.
How to Get Started?
Problem Identification
At the beginning of cooperation with a client, we identify key areas requiring improvement, such as a high number of returns in specific product categories or delivery regions.
Technology Selection
We choose technologies best suited to the client’s needs, considering their preferences and currently used solutions. When selecting technology for analytics, we evaluate various data storage solutions such as data lakes, which can store both structured and unstructured data without predefined schemas; data lake architectures for flexible, large-scale storage; data warehouses, which are optimized for analyzing structured data and serve as a single source of truth; and relational databases, which use SQL for efficient storage, querying, and analysis of large datasets. One of the effective tools for implementing machine learning is BigQuery ML.
Collaboration with Experts
Cooperation with designated client-side personnel is crucial and includes:
- Data collection and organization: Analysis of existing data in collaboration with analysts.
- ML model implementation: Development and integration of models for risk prediction and process optimization.
Collaboration with experts allows better tailoring of services to user needs and increases implementation efficiency. Expertise in data science is especially valuable for implementing advanced analytics and machine learning models.
Case Study: Reducing Return Costs in a Furniture Company
Our client, an ecommerce brand, successfully implemented data analytics and ML models to support their growth and strengthen their presence in the digital marketplace. Here is how their journey to success went.
This case study demonstrates the benefits of analytics for online businesses, showing how data-driven decision-making can optimize operations and drive business expansion.
Implementation of data analytics allowed the company to better respond to changing market conditions, adjust offers to the needs of sellers and buyers, and significantly improve online sales performance.
Initial Situation
A custom furniture manufacturer struggled with a high number of returns caused by damage during transport. Customers often received damaged furniture, causing frustration. Data analysis identified users who most frequently made purchases and filed returns. Despite having advanced tools like BigQuery, the lack of an ML specialist hindered effective implementation.
Our Actions
- Data exploration: Detailed analysis of products most vulnerable to damage and transport routes generating the highest risk. Data exploration also included analysis of social media opinions and natural language processing to identify problems reported by customers. Additionally, transaction data was analyzed to identify patterns and prevent fraudulent returns.
- Model training: Testing various machine learning algorithms to select the best one for damage risk prediction.
- Implementation: Deploying the model within the BigQuery ML ecosystem, enabling data use without leaving SQL. The model supports the logistics department in making decisions about transport methods.
Achieved Results
- Reduced number of returns: The model achieved 75% accuracy in prediction, which helped limit returns and supported decisions in logistics, production, and operations departments.
- Logistics optimization: More efficient carrier selection and transport cost reduction.
- Scalability: The model can be easily applied in various processes without concerns about infrastructure or computation methods, thanks to BigQuery ML.
Implementing data analytics enabled more effective management of the online store and e-commerce business, as well as improved customer experience at every stage of the purchasing process.
Future Trends in Data Analytics
The future of data analytics in e-commerce involves even greater use of artificial intelligence and machine learning techniques to analyze customer behaviors and predict their needs. Machine learning algorithms will increasingly identify patterns in data, enabling online stores to precisely tailor offers to individual customer expectations and build long-term customer loyalty.
Another important trend will be integrating data from social media, which will become a valuable source of information about customer preferences, opinions, and behaviors. With the growing volume of unstructured data—such as social media content, IoT device data, and non-relational data from mobile apps—advanced data processing techniques are essential to efficiently convert, organize, and prepare this information for analysis. This will allow online stores to create even more personalized offers and respond faster to market changes.
Process automation, development of neural networks, and increasingly advanced data analysis tools will enable analyzing huge amounts of information in real time. This, in turn, will allow online stores not only to optimize offers but also to effectively build customer loyalty and competitive advantage in the e-commerce industry. In the coming years, data analytics will become an indispensable element of effective online store management and a key to understanding customer needs.
Summary
Data analytics and machine learning are not only tools for solving current problems but above all an investment in company development.
Analyzing purchasing behaviors and purchase history is crucial for effective online store management and increasing their competitiveness. It is up to you whether decisions in your company are made based on data or merely intuition and the popular saying “I think so.” If you want to reduce return costs in your e-commerce and get ahead of the competition, contact us, and we will help you take your analytics to the next level.
