Ten Q&A on Data Analysis Models

1. Q: What are the commonly used data analysis models?

A: We have summarized the most frequently used data analysis models in enterprises, including: Event Analysis, Funnel Analysis, Retention Analysis, Attribution Analysis, Distribution Analysis, User Path Analysis, LTV (Lifetime Value) Analysis, Interval Analysis, Session Analysis, User Segmentation, Heatmap Analysis, and User Attribute Analysis. Each model serves different analytical purposes and provides unique insights into user behavior and business performance.

2. Q: If an e-commerce platform wants to analyze why conversion rates were low during a recent promotional event and identify the root cause, which data analysis model should be used?

A: Funnel Analysis. The e-commerce platform can analyze users based on specific dimensions, such as geographic location (e.g., province-level segmentation), to compare conversion rates across different regions. By further analyzing factors like product availability, logistics status, product popularity across regions, and conversion rates for different payment methods, businesses can pinpoint optimization opportunities.

Additionally, the platform can examine user drop-offs at a specific funnel stage, generate a list of users who abandoned their purchase, and implement targeted marketing strategies. By incorporating user path analysis, the platform can track subsequent actions of lost users, hypothesize potential reasons for drop-offs, and develop corrective strategies accordingly.

3. Q: How is user retention analysis conducted?

A: For instance, if a product manager wants to understand the impact of product iterations on new user retention, they can use retention analysis by segmenting new user 7-day or 30-day retention rates by application version. This allows for a direct comparison of retention rate differences between different versions and helps assess whether a new version positively impacts user engagement.

4. Q: How does retention analysis benefit businesses?

A: Retention is a core metric for most businesses. Retention analysis, as a widely applicable and highly valuable model, enables companies to quickly assess whether users return after their initial interaction and whether they engage in key business activities.

By comparing retention rates among different user groups, businesses can identify more loyal customer segments. Whether conducting promotional campaigns or user research, companies can prioritize high-retention users, ensuring higher engagement and return on investment.

5. Q: How can session analysis be better understood?

A: Imagine a website or app as a shopping mall, and user behaviors as shoppers browsing different stores. Just as a shopping trip consists of a series of consecutive actions, a session represents a series of user interactions on a website or app.

Session Analysis connects individual user actions into a holistic view, helping businesses understand how users interact with specific events during a single visit. Key session metrics include:

● Number of sessions per user

● Number of pages visited per session

● Average session duration

● Average time spent on a specific page

● Bounce rate per session

For example, in an online education platform, session analysis can answer questions such as:

● How often do users visit the platform?

● How many pages do they browse per visit?

● What is the average session duration?

● How long do users spend on key educational content pages?

6. Q: Which model can be used to evaluate a product’s value to users?

A: Retention Analysis. Retention analysis helps assess user engagement and stickiness, measuring how many users return and perform key actions after their initial interaction. It is a crucial method for evaluating how valuable a product is to users.

Retention analysis can answer questions like:

● How many users from a particular day’s new cohort complete key conversions over the following days?

● After implementing a new onboarding flow for new users, does retention improve compared to previous versions?

● If a new social feature (e.g., inviting friends) is introduced, does it increase user engagement and retention?

7. Q: What is heatmap analysis?

A: Heatmap Analysis visually represents user interaction intensity on a webpage or a set of similar pages (e.g., product detail pages, company blogs). By highlighting areas with different levels of engagement using colors or data labels, heatmaps illustrate how users interact with a webpage.

Heatmap analysis helps businesses analyze:

● Click distribution (which elements users click the most)

● Scroll depth (how far users scroll down a page)

● Engagement density (which areas attract the most user attention)

This model provides an intuitive way to understand user behavior and optimize page layout and UI/UX design.

8. Q: What are the common attribution analysis models?

A: Here are four commonly used attribution models:

1.  First-Touch Attribution – Assigns 100% credit to the first interaction. This model is useful for businesses focusing on brand awareness and lead generation, as it attributes conversions to the initial touchpoint.

2.  Last-Touch Attribution – Assigns 100% credit to the final touchpoint before conversion. This is the most widely used attribution model and is commonly applied in e-commerce for internal attribution calculations.

3.  Linear Attribution – Distributes credit equally across all touchpoints in a user’s journey. This model is suitable for businesses where each touchpoint contributes evenly to conversions.

4.  Position-Based Attribution (U-Shaped Attribution) – Assigns 40% weight to both the first and last interactions, with the remaining 20% distributed evenly among intermediate touchpoints. This model balances initial lead generation and final decision-making.

9. Q: What is the user behavior path analysis model?

A: User Behavior Path Analysis tracks and analyzes how users navigate through an app or website, uncovering patterns in user interactions. It helps businesses optimize critical user flows, such as:

● Increasing conversion rates for core modules

● Enhancing app navigation and UI design

● Analyzing user drop-offs and their next actions

For example, in e-commerce, businesses expect users to move through a structured journey:

Login → Homepage Browsing → Product Search → Add to Cart → Checkout → Payment Completion.

However, in reality, users may:

● Return to homepage after adding a product to the cart

● Abandon checkout and browse other products

● Cancel their order after finalizing payment details

Since real user behavior paths are often non-linear, this model should be combined with other analysis techniques to identify drop-off reasons and guide users toward optimal conversion paths.

10. Q: How does distribution analysis provide business value?

A: Distribution Analysis presents structured segmentations of user behavior based on frequency, transaction value, and other key metrics. It allows businesses to:

● Identify key user groups and their behavior distribution

● Understand data concentration levels

● Determine which segments have the highest business impact

By analyzing event distributions across different dimensions, businesses can evaluate:

● Cumulative counts and frequency of key events

● Business performance trends

● Health metrics and structural insights

For instance, an e-commerce company can analyze the distribution of purchase amounts among different customer groups to determine pricing strategies, promotional targeting, and retention priorities.

Conclusion

Each data analysis model serves a specific purpose in understanding user behavior, optimizing conversion rates, and improving decision-making. Businesses should choose the appropriate model based on their goals and use a combination of analysis techniques to gain deeper insights and drive growth.

Note: The data used in this article is for illustrative purposes only.