The e-commerce industry has been growing rapidly, especially during the COVID-19 pandemic, as more consumers turn to online shopping. With the rise of online shopping, the amount of data generated has increased exponentially, providing a wealth of information that can be used to improve business performance. Data analysis has become a vital tool for e-commerce companies to remain competitive and meet the changing demands of their customers.
Although e-commerce has an early start in digitisation and already a high degree of maturity, companies continue to face unique challenges in managing data. Data integration issues often arise from large volume of data generated from various sources including customer transactions, website traffic, social media, and marketing campaigns. Such data is often unstructured and stored in different formats, making it difficult to analyse and gain insights. Not to mention, the possible false data contained in social media that needs to be screened and processed by companies. Due to the characteristics of e-commerce, companies are faced with massive real-time data, sales and order information must be captured and updated in time to make decisions quickly. Without the proper tools and processes in place, companies can miss out on valuable insights that could help them make better business decisions.
Maximising the power of data analytics for e-commerce success
Data-driven solutions can help e-commerce companies extract valuable insights from their data and improve business performance in several ways:
Personalising the customer experience
E-commerce companies can understand customer behaviour, preferences and needs by collecting customer data, such as purchase history, browsing history, click behaviour, search keywords and social media feedback, and processing them through data cleaning and integration, and data analysis. Using these analytics results, e-commerce companies can develop better marketing strategies, improve product design, and optimise customer experience, thereby increasing customer satisfaction and performance.
Optimising inventory management
Effective inventory management is critical to the success of any e-commerce business. By building prediction models and analysing historical sales data, companies can determine seasonal demands and trends for products and forecast future sales needs. Common prediction models include linear regression models, random forest models, etc. When conducting modeling analysis, in addition to sales data, other factors such as product life cycle, supplier delivery time and seasonal changes also need to be considered,. By comprehensively considering these factors, companies can formulate more effective inventory management strategies, thereby increasing inventory turnover and reducing inventory retention time, helping companies optimise inventory costs.
Improving website performance
E-commerce companies can use data analytics to monitor website performance and identify areas for improvement. By analysing website traffic, companies can identify patterns in user behaviour, such as the time users spend on the site, the pages they visit, and the categories of goods purchased. Through this data, companies can gain insight into users' needs and behaviours, and optimise website design to improve user experience. Through data analysis, companies can also conduct A/B tests to compare the effects of different design schemes, and choose the best scheme to optimise website design, and increase conversion rate and sales.
Enhancing fraud detection and prevention
E-commerce companies are vulnerable to various types of fraud, including payment fraud, account takeover, and identity theft. Data analytic technology helps e-commerce companies identify fraudulent behaviours early by analysing user behaviour patterns and identifying abnormal transaction behaviours. In addition, data analysis can establish a fraud detection model based on historical fraud data to realise automated fraud detection and prevention. Through data analysis, e-commerce companies can quickly respond to fraud, reduce losses, and protect customers' privacy and data security.
The above examples only enumerate some of the applications of data analysis in e-commerce companies. Additionally, combined with technologies such as RPA, cloud services and artificial intelligence, data analysis can also help enterprises drive business performance and maintain competitiveness in many ways:
- Marketing strategy optimisation
- Competitor analysis
- Supply chain management optimisation
- Price management
- Product development optimisation
How BDO Can Help
BDO can help your organisation turn data into insight and strategic value, addressing business needs and delivering results. Our business analytics and data team will work with you to identify pain points, develop data analysis solutions, and implement or advance your data analysis system for improved workflow throughout every level of your organisation.
We can also provide ongoing support and maintenance to keep your data analysis systems running smoothly with our suite of professional data analysis and visualisation tools, and big data platforms and cloud services.
Every company will face unique challenges from technological innovation and the continuous evolution of consumer formats. When you work with BDO, you can expect to receive customised solutions that meet your unique needs. We will help you unlock the potential of your data and guide your business to seize opportunities for growth and success.