E-commerce data analysis methodology: using data to drive business decisions

"E-commerce data empowers decision-making, and methodology leads growth." In the fiercely competitive environment of e-commerce, data is like a treasure. How can we tap its value and help the business take off?

In the era of data explosion, how e-commerce companies extract valuable information from massive data and use this information to guide business decisions has become the key to their success.

Brother Yuan will introduce the commonly used methodologies for data analysis, such as the AARRR model, the funnel model, etc., and explain how to use these methodologies to conduct data analysis and ultimately achieve data-driven business decisions.

1. Overview of Data Analysis Methodology

Data analysis methodology provides a structured framework for e-commerce companies to help them systematically collect, process and analyze data, ultimately extract valuable insights and guide business decisions. Commonly used data analysis methodologies include:

AARRR model: This is a user growth model that breaks down the user growth process into five key stages: Acquisition, Activation, Retention, Revenue, and Referral. By analyzing the key indicators of each stage, you can identify bottlenecks in the user growth process and develop corresponding improvement measures.

For example, an e-commerce platform found that its user activation rate was low. Through AARRR model analysis, it was found that new users did not receive timely guidance after registration, resulting in their loss. Therefore, they optimized the new user guidance process and improved the user activation rate.

Funnel model: This is a user conversion model that breaks down the user conversion process into multiple stages, such as browsing products, adding to shopping carts, submitting orders, and paying. By analyzing the conversion rates of each stage, the bottlenecks in the user conversion process can be identified and corresponding improvement measures can be formulated.

For example, an e-commerce platform found that its shopping cart abandonment rate was high. Through funnel model analysis, it was found that the payment process was too complicated, causing users to abandon their orders. Therefore, they simplified the payment process and reduced the shopping cart abandonment rate.

User Lifetime Value Model (LTV): This is a user value model that calculates the expected total consumption amount of a user throughout his or her life cycle. Through the LTV model, high-value users can be identified and corresponding maintenance strategies can be formulated.

For example, an e-commerce platform found that some of its users had low LTV. By analyzing the behavioral characteristics of these users, it was found that they had low loyalty to the platform and were prone to churn. Therefore, they launched strategies such as membership system and personalized recommendation to improve users' LTV.

User group analysis: This is a user segmentation model that divides users into different groups based on different characteristics (such as demographic characteristics, behavioral characteristics, interests and hobbies, etc.) in order to formulate more precise marketing strategies.

For example, e-commerce platforms divide users into three groups: high-value users, medium-value users, and low-value users, and formulate different marketing strategies for different groups. For example, exclusive services are provided to high-value users, and promotional activities are pushed to low-value users.

Cohort analysis: Analysis based on user groups. For example, users can be divided into groups based on user registration date, first purchase date, etc., and the changing trends of user behavior in different groups can be observed to find user behavior patterns and rules. This helps to understand the user life cycle.

2. Data Analysis Tools and Methods

To analyze e-commerce data, we need to use some data analysis tools and methods:

Data analysis tools: Google Analytics, Baidu Statistics, Zhuge io, etc. Choosing the right tool depends on your data volume, analysis needs and budget.

Data visualization: Convert complex data into intuitive charts (e.g. bar charts, line charts, funnel charts, etc.) to facilitate understanding and decision-making.

Statistical analysis methods: For example: descriptive statistics, inferential statistics, regression analysis, variance analysis, cluster analysis, etc. The choice of appropriate statistical methods depends on your analysis goals.

Steps to Data-Driven Decision Making

Using data to drive business decisions generally requires the following steps:

Define your goals: Make it clear what your business goals are, for example: improving conversion rate, increasing average order value, increasing repurchase rate, etc.

Collect data: Collect data related to business goals, such as user behavior data, product data, market data, etc.

Analyze data: Use appropriate data analysis methods and tools to analyze data and identify key factors that affect business goals.

Develop strategies: Develop corresponding improvement measures based on the results of data analysis.

Implementation strategy: Implement the formulated strategy.

Evaluate the effect: Evaluate the effect of the strategy after implementation and make continuous optimization based on the evaluation results.

The e-commerce data analysis methodology provides e-commerce companies with a data-driven decision-making framework. By using methods such as the AARRR model, funnel model, LTV model, user segmentation analysis, and combining data analysis tools and methods, it can help e-commerce companies better understand user behavior, optimize operational strategies, and ultimately achieve business growth.

Continuous data monitoring and analysis, as well as continuous improvement of data-driven decisions, are key to e-commerce companies remaining competitive.