User Insights Overview

Features

The User Insights module provides the following features to help you with consumer operations:

• Data Source Integration: You can connect to multiple data sources, such as AnalyticDB for MySQL 2.0, AnalyticDB for MySQL 3.0, and AnalyticDB for PostgreSQL, to access your consumer datasets. You can also build consumer operation models, such as AIPL and RFM models, using built-in rules. This process quickly segments consumers into structured layers and provides data for insight analysis.

• User Analysis: Quick Audience has a powerful built-in analysis engine. You can use the insight analysis feature to detect significant consumer features, observe the distribution of profile tags, and analyze custom consumer operation models, such as AIPL and RFM models, as needed. This helps you quickly understand your current user base and provides decision support for future operations.

• Audience Management: You can quickly select target audiences of a specific size that meet specific filter conditions during insight analysis. The feature supports tag filtering based on user tag datasets, behavior filtering based on behavioral datasets, model filtering based on RFM or AIPL models, and cross filtering. This provides a foundation for future audience outreach applications.

• Asset Management: You can view statistics on tag popularity to guide tag creation and application. You can also create custom tags during analysis. This allows business personnel to create tags based on specific scenario requirements.

• Brand Databank, Damengpan, and Kafka pushes: You can push your first-party tag data, AIPL models, and audience groups to Brand Databank. You can also push audience groups to Damengpan, Alibaba Cloud Kafka, or open source Kafka. This enables integrated online and offline consumer analysis and online re-engagement, improving the efficiency and effectiveness of your brand's omni-channel consumer operations.

• Repeat Purchase Prediction: This feature predicts the likelihood that a user will make a repeat purchase within a specific period. It trains an algorithm model by analyzing past purchase records. The feature identifies large groups of potential customers who have a high probability of making a repeat purchase but are not part of your core audience. This lets you expand your outreach to new opportunity groups. You can then focus your marketing efforts on these high-probability groups to increase your brand's repeat purchase rate.

• Product Recommendation: This feature trains an algorithm model by analyzing past purchase records. It intelligently determines the relationships between users and products, and between different products. This improves operational efficiency and increases your brand's conversion and repeat purchase rates.

• Analytics Dashboard: You can embed reports generated by Quick BI Professional Edition into Quick Audience to visually analyze relevant data and gain insights from your marketing data.

Workflow

1. Add a data source for analytical data. An administrator can add a workspace data source. For more information, see Data Source (Analytical Data Source). An organization administrator can add an organization data source and grant permissions to the workspace. For more information, see Organization Data Source Management.

2. An administrator creates a dataset from the data source. For more information, see Dataset Documentation.

3. An administrator grants permissions to non-administrator users to use the dataset. For more information, see Dataset Permission Settings.

4. Users with the required permissions can perform various operations. These operations include user analysis, audience filtering and analysis, tag management, creating custom tags, pushing audiences and datasets, repeat purchase prediction, product recommendation, and using analytics dashboards. For more information, see the documentation for each feature.