Imagine the situation: a customer opens his long-awaited package, and there - a damaged product. His frustration becomes the beginning of a costly claims process for your company. Can this be prevented? Yes, using the power of data and technology.
E-commerce complaint costs are a challenge that affects almost every company in the industry. They affect profitability, reputation and customer loyalty. Poor handling of returns, damage in transit or product quality issues can cost a company much more than the mere cost of replacing goods.
From this article, you will learn how data analytics and machine learning (ML) tools can effectively help reduce these costs. We'll also look at an example from one of our clients - a company where we successfully implemented these technologies to optimize processes and reduce claims costs.
Complaint costs - a no small challenge for any e-commerce business
At the outset, it is worth considering what the sources of the problem might be, so that, like a thread to a knot, we can arrive at an optimal solution.
High complaint costs are generated by a number of factors, such as:
- Damage during transportation: Insufficient quality of logistics services, inadequate packaging or the wrong choice of carrier.
- Manufacturing or packaging errors: Problems at the manufacturing stage leading to the delivery of defective goods.
- Ease of claiming: Free returns and simple complaint processes can prompt customers to file complaints even with minimal product damage.
Having already known the issues that cause claims costs to be significant, let's now look at how they affect your business.
Working with many customers, we can distinguish two types of business impact:
- Direct: Replacement, transportation and product repair costs.
- Indirect: Decrease in customer satisfaction, loss of loyalty and, consequently, decrease in revenue.
In addition, the complaint handling process engages the company's resources, pulling them away from other strategic activities.
How does data analytics help solve a problem?
Imagine that your company has an investigator who not only identifies problems, but also points out the best ways to solve them. That's what data analytics is.
Using a data-driven approach
E-commerce companies can effectively reduce complaints by using historical data and analytical tools. The key steps we take when working with our clients are:
- Data collection: Gather information about the transportation process (information about carriers, package types, delivery locations) and about the production process (production stages, machines producing a product, type of product)
- Root cause analysis: For example, detecting trends, such as a high number of shipping defects to specific regions or problems with production machinery. Which could mean that the problem already lies at the root i.e. production of materials or final products.
Implementing predictive models
Machine learning makes it possible to predict the risk of parcel damage based on historical data. Imagine having a “virtual advisor” that pinpoints the riskiest shipping routes and products that need extra attention.
With this knowledge, we are able to make operational and strategic decisions based on the data. This, in turn, translates into a number of benefits:
- Automation of logistics processes: Carrier selection based on damage risk analysis.
- Production improvement: Identification of weak points in the manufacturing process.
- Personalization: Customization of services to meet individual customer needs.
Benefits for e-commerce companies of using data analytics
Direct effects
- Cost optimization: Choosing a more expensive but reliable carrier can reduce claims, which improves profitability in the long run.
- Streamlining logistics: Shorter turnaround times for returns and parcel shipment.
- Customer satisfaction: Higher quality service builds loyalty.
Strategic benefits
- Data-driven decisions: Better allocation of resources and higher efficiency of operations.
- Competitive advantage: Companies that use data effectively are ahead of the competition.
- Better production management: Identification of problematic production processes, such as machines in need of repair due to frequent complaints for products made on them.
Where to start?
Identification of problems
At the outset, we start together with the customer by identifying key areas for improvement, such as the high number of complaints in specific product categories or delivery regions.
Technology selection
We select technologies that are optimal from the perspective of our client: his preferences, currently used technologies, or depending on what requirements the solution to the problem itself can expect. One of the solutions that enable efficient ML implementation is BigQuery ML.
Cooperation with experts
It will be crucial to work with our experts of designated people on the client side, we distinguish 2 steps here:
- Data collection and organization: Analysis of existing data in collaboration with analysts.
- Implementation of ML models: Development and integration of models for risk forecasting and process optimization.
Case study: How we reduced claims costs for a furniture client
Our client is an example of a company where we successfully implemented data analytics along with ML models. What was their path to success like?
Baseline situation
Our client, an innovative manufacturer of custom furniture, was facing a high number of complaints due to damage to packages during shipping. Imagine their customers' frustration: their dream furniture was arriving in poor condition. Despite having advanced tools such as BigQuery, the lack of an ML specialist prevented the effective implementation of a solution
Our solution
- Data mining: We started with an in-depth analysis. Which products are most vulnerable to damage? Which transportation routes generate the greatest risk?
- Model training: We compared different Machine Learning algorithms to choose the one that best predicts damage risk.
- Implementation: Implementing the model in the BigQuery ML ecosystem allowed us to make full use of the data without going beyond SQL. In addition, the algorithm is being used by the logistics department as a decision-support component on what form of transportation to choose.
Results
- Reduction in complaints: The model's effectiveness of 75% has reduced the number of returns, supporting accurate decisions by logistics, production and operations departments.
- Logistics optimization: More efficient selection of carriers and reduced transportation costs.
- Scalability: The model can easily be used in different processes without worrying about infrastructure and calculation. All thanks to the use of BigQuery ML.
Summary
Data analytics and machine learning are not just tools for solving operational problems. Above all, they are an investment in the future of your company.
It's up to you to make decisions in your company based on data, or perhaps intuition and the rather popular statement “it seems to me.” If you want to reduce claims costs in your E-commerce and get ahead of the competition, write to us and we will help you get to the next level of analytics.
