Exploratory analysis scenarios

Exploratory Analysis is a lightweight, real-time data exploration tool for business users. This topic walks through four progressive scenarios that demonstrate how to move from high-level trends to actionable insights.

Background

Business Pain Points

For business users, report metrics and dimensions are fixed and cannot support flexible, in-depth analysis. Fine-grained analysis often requires manually downloading data from multiple reports and merging tables in Excel—a tedious and inefficient process.
For data analysts, constantly changing business requests create a cycle of ad-hoc data extraction with long response times, delaying decisions and preventing focus on higher-value analysis.

Solution

Exploratory Analysis breaks free from the fixed perspectives of traditional dashboards. Business users can quickly start exploring data with low barriers, driving enterprise-wide data democratization.

Features

Exploratory Analysis provides an instant, flexible analysis experience—no technical background required.

Flexible Multidimensional Exploration: Freely combine dimensions and measures through drag-and-drop to meet evolving analytical needs.
Intelligent Chart Recommendations: Automatically recommends or lets you switch among tables, bar charts, line charts, pie charts, funnel charts, and combo charts to find the best visualization.
Powerful Quick Calculations: Built-in aggregation methods—such as sum, average, and distinct count—let you perform period-over-period, percentage, and cumulative calculations with one click. No complex formulas needed.
Deep Analytical Capabilities: Compare groups across campaign periods or user segments, and use custom drill-downs to move from high-level data to granular details.
Easy Sharing and Collaboration: Export and share results with one click to DingTalk, WeCom, or Lark to accelerate team decision-making.

Scenario Examples

The following four scenarios progress from a macro business view down to actionable insights, demonstrating how exploratory analysis drives concrete business decisions.

Scenario 1: From Overall Trends to Anomaly Detection—Identifying Divergence Between GMV and Gross Profit

Business Question

The business team needs a quick overview of year-over-year trends in GMV and gross profit to assess overall business health.

Analysis Path

Procedure	Example
Step 1: Gain macro insight and detect anomalies	Click My Dashboards > Exploratory Analysis and select a dataset. In this example, choose the “Order Sales” dataset. Select Line Chart as the chart type. Drag “Order Date (month)” to the x-axis. Drag “Order Amount” and “Profit Amount” to the y-axis and set the aggregation method to “Sum.” Click Generate Analysis to view results. The chart shows that during the March promotion, order amount rose while profit declined. What is eroding gross profit behind GMV growth? The next steps drill down to identify the root cause.
Step 2: Segment by channel to narrow scope	Click Show More Settings. Add the “Shipping Method” field to the color legend area and click Generate Analysis. The chart suggests that air freight’s gross profit declined in March, dragging down overall performance.
Step 3: Drill down layer by layer to identify problematic categories	Switch the chart type to “Table” to view detailed data. Drag “Product Type” to the rows area. Drag “Profit Amount” to the metrics area and set aggregation to “Sum.” Drag “Profit Amount” again to the metrics area, set aggregation to “Sum,” and configure advanced calculation as “Month-over-Month” to quickly see monthly profit change rates. Apply filters: Order Date = “March,” Shipping Channel = “Air Freight.” Drag “Product Subcategory” below “Product Type” to create a drill-down hierarchy. Click Generate Analysis to enable drill-down. Through layered drill-down and month-over-month calculations, the root cause is identified: the decline in air freight profit in March was driven by significant losses in the “Copiers & Fax Machines” subcategory under “Technology Products.” Next, trace specific Order IDs to review loss-making orders and collaborate with product and operations teams to resolve the issue.

Scenario 2: Post-Campaign Review—Compare Key Metrics Before, During, and After a Promotion

Business Question

The sales team just concluded a major promotion in Week 11 of March (March 16–22) and wants a rapid review of regional campaign performance to inform strategy for the upcoming 618 sales event.

The classic approach is time-based group comparison: compare key metrics—GMV, gross profit, and order volume—across “Pre-Promotion,” “During Promotion,” and “Post-Promotion” phases to quantify incremental gains and assess lasting effects.

Analysis Path

Procedure

Example

Step 1: View business metrics by region

Select Table as the chart type.
Drag “Region” to the rows area. Drag “Order Amount,” “Profit Amount,” and “Order Count” to the metrics area and set aggregation to “Sum.”
Click Generate Analysis to view results.

Step 2: Define comparison periods

Click the Group Comparison button.
In the Group Comparison settings, add three groups named “Pre-Promotion,” “During Promotion,” and “Post-Promotion,” with date ranges “Mar 9–15,” “Mar 16–22,” and “Mar 23–29,” respectively.
Click OK. The data appears as follows:

Group comparison reveals that GMV and gross profit across all regions peaked during the promotion, confirming strong traffic and conversion. However, metrics dropped to pre-promotion levels within one week, indicating the traffic boost did not translate into lasting retention. For the 618 promotion, design strategies to lock in customers and encourage repeat purchases.

Scenario 3: A/B Test Analysis—Quantify the Impact of Different Strategies

Business Question

The R&D team launched a new “Recommended for You” algorithm (Group A) and wants to compare it against the old algorithm (Group B) to determine whether the new version increases user transaction value.

Analysis Path

Procedure	Example
Step 1: Create A/B groups	Select Line Chart as the chart type. Click the Group Comparison button and select the corresponding user groups in the filter conditions.
Step 2: Compare average revenue per user (ARPU) trends	Drag “Order Date (month)” to the x-axis and the “Group Comparison” dimension to the color legend. Drag “Order Amount” to the y-axis and set aggregation to “Average Value.” Click Generate Analysis to view ARPU trends.
Step 3: Drill down to identify causes	Hypothesis 1: Is the new algorithm more effective for high-value customers? Switch to a bar chart. Move the group comparison dimension to the x-axis and drag “Customer Tier” to the color legend to compare performance across user segments. Hypothesis 2: Does the new algorithm recommend certain categories more accurately? Keep the same chart structure but replace “Customer Tier” in the color legend with “Product Type” to see which category shows the clearest ARPU improvement under the new algorithm. Group A shows significantly higher ARPU than Group B. Drill-down reveals this gain comes mainly from VIP Customers who increased spending on Technology Products—indicating the new algorithm excels at recommending high-ticket items to high-value users.

Scenario 4: Customer Value Analysis—Custom Segments and Metrics to Understand Profit Contribution

Business Question

In Scenario 3, high-ticket customers positively impacted revenue during the promotion. Now dig deeper: Does profit come from many low-value orders or a few high-value ones? How do users with different spending power differ in purchasing behavior and profit contribution?

Analysis Path

Procedure	Example
Step 1: Define a metric (gross margin ratio)	Click Create Calculated Field in the data panel. Define the gross margin ratio formula as: `SUM([Profit Amount]) / SUM([Order Amount])`. Set Number Format to “Percentage.”
Step 2: Define core analysis dimension (spending power tiers)	Click Create Group Dimension in the data panel. Name the dimension “Spending Power Tier” and define the spending groups.
Step 3: Configure chart and generate analysis	Drag “Spending Power Tier” to rows. Drag “Order Count,” “Order Amount,” and “Gross Margin Ratio” to the metrics area. Click Generate Analysis to view results. Click the column header for “Gross Margin Ratio” and select “Descending” sort. You can now clearly see how different spending tiers contribute to profitability. The chart shows that while Regular Members generate the most orders, VVIP users—despite far fewer orders—contribute over half of GMV, reflecting a high-value-user-driven model. The Regular Members tier has a negative gross margin, suggesting heavy subsidies were used to attract traffic orders. Next steps: optimize subsidy strategies by designing precise incentives for VIP users and high-ticket products to strengthen the core business while improving profitability.