How to Build a Marketing Analytics Dashboard with Alibaba Cloud Quick BI

Most marketing teams lack dedicated analysts, making data-driven decisions harder. This guide shows how to use Alibaba Cloud Quick BI to connect campa.

Most marketing teams have data. What they are missing is visibility into that data at the right time and in the right format.

Email open rates live in the campaign tool. Website traffic lives in a web analytics platform. Conversion data lives in the CRM.

Ad spend lives in the campaign manager. A weekly report exists somewhere as a spreadsheet that someone updates manually on Friday afternoon.

The problem is not a shortage of data. The problem is that the data is fragmented, delayed, and formatted for whoever created the report rather than whoever needs to act on it.

A well-built marketing dashboard solves all three of these problems at once.

It pulls data from multiple sources, refreshes automatically on a schedule you control, and presents it in a format that answers the questions your team actually asks.

Alibaba Cloud Quick BI is the tool that makes this practical. Its drag-and-drop interface delivers visualizations from KPI scorecards to geo-maps without requiring any coding, and real-time drill, filter, and cross-component linking accelerate enterprise-wide adoption.

For marketing teams specifically, this means you can build and iterate on dashboards yourself without waiting for an engineer to do it.

This guide walks through the full process, from connecting your first data source to publishing a polished campaign performance dashboard that your team actually wants to open.

What Quick BI Is and Where It Fits

Before jumping into setup, it is worth clarifying where Quick BI sits within the Alibaba Cloud ecosystem, so you know what you are working with.

Quick BI is a cloud-native business intelligence platform.

It supports multiple data sources, including ApsaraDB for RDS, AnalyticDB, MaxCompute, MySQL, SQL Server, and local files, and provides drag-and-drop operations, powerful data modeling, and data visualization to help you build data portals quickly.

For marketing analytics, the most common data source combinations are:

ApsaraDB RDS for MySQL is the right starting point if your campaign data already lives in a relational database. Many marketing automation platforms and CRMs export data to MySQL, making this the simplest path to Quick BI for most teams.

MaxCompute is the better choice if you are working with large volumes of historical campaign data, running complex aggregations, or pulling data that has already been processed through a DataWorks pipeline. If you followed the DataWorks and Direct Mail automation guide earlier in this series, your campaign data is already in MaxCompute.

Local files (CSV/Excel) give you a quick way to get started with existing export data from any marketing tool, without setting up a live database connection first. This is a practical option for teams who want to see the workflow before committing to a permanent data source.

Quick BI's core workflow follows three steps every time: connect a data source, create a dataset from that source, and then build charts and dashboards from the dataset. Once you understand that pattern, building any dashboard becomes a repeatable process.

Prerequisites

You will need:

An Alibaba Cloud account
Quick BI activated (a free trial is available from the Quick BI product page)
A data source ready to connect, which can be one of:
- An ApsaraDB RDS MySQL instance containing campaign data
- A MaxCompute project with campaign tables
- A CSV file exported from your email platform, ad manager, or CRM
Workspace admin or developer permissions in Quick BI (required for adding data sources)

If you are using RDS, make sure you have added the Quick BI IP addresses to your RDS whitelist before starting.

Quick BI accesses your database over the public network, and a whitelist configuration is required to allow the connection.

Step 1: Connect Your Data Source

Log in to the Quick BI console and navigate to Data Sources in the left navigation panel. Click Create Data Source.

Quick BI organises its supported connections into tabs. The tab labelled Alibaba Cloud Databases contains the native cloud sources: RDS, MaxCompute, AnalyticDB, PolarDB, Hologres, and others. The Self-built Databases tab covers MySQL, PostgreSQL, SQL Server, and other databases hosted outside Alibaba Cloud.

Connecting ApsaraDB RDS for MySQL

On the Alibaba Cloud Databases tab, select RDS for MySQL. The connection form asks for the following:

Field	What to Enter
Display Name	A label you will recognise, e.g. "Campaign DB Production"
Database Address	The public endpoint of your RDS instance
Port	3306 (default for MySQL)
Database	The specific database name containing your campaign tables
Username	Your database user (use a read-only user for dashboards)
Password	The corresponding password

Click Test Connection. Quick BI will verify connectivity before saving. If the test fails, the most common cause is a missing whitelist entry. Check that your RDS whitelist includes the Quick BI IP address for your region.

Once the test passes, click OK. Your data source now appears in the list.

Connecting MaxCompute

On the Alibaba Cloud Databases tab, select MaxCompute. You will need your MaxCompute project name, the AccessKey ID and Secret of a RAM user with read permissions on the project, and the endpoint for your region.

After you add a MaxCompute data source to connect MaxCompute to Quick BI, you can analyze and view data in the Quick BI console. Quick BI will load the table list from your MaxCompute project, which you will see when creating a dataset in the next step.

Using a Local CSV File

If you are starting with a CSV export, click Upload File on the data source page. Quick BI accepts CSV and Excel files and automatically detects the schema from the header row. This is the fastest path from "I have campaign data somewhere" to "I can see it in a chart."

The uploaded file becomes a static dataset that you can refresh by uploading a new version at any time. For a dashboard that needs to update automatically, migrate to a live database connection once you are happy with the layout.

Step 2: Create a Marketing Dataset

With your data source connected, navigate to Datasets and click Create Dataset. Select the data source you just added.

Quick BI displays the tables available in your connected source. For a marketing analytics dashboard, you are typically working with one or more of these table types:

A campaign summary table with one row per campaign showing spend, impressions, clicks, and conversions
A daily metrics table with one row per campaign per day, useful for trend charts
A channel table that breaks performance down by traffic source or channel type

Drag the table you want onto the dataset canvas. If you need to join multiple tables, drag them all onto the canvas and connect them using the join configuration. Quick BI supports inner, left, and right joins, configured visually without writing SQL.

Adding Calculated Fields

Once your table is on the canvas, Quick BI shows you the detected columns split into two categories: Dimensions (text fields like campaign name or channel) and Measures (numeric fields like spend and clicks). Quick BI makes this classification automatically, but you can override it.

The most useful step at this stage is adding calculated fields for the metrics your team actually tracks rather than raw numbers. Click the Add Calculated Field button on the measures panel and create the following:

Click-Through Rate (CTR):

clicks / impressions * 100

Conversion Rate:

conversions / clicks * 100

Cost Per Acquisition (CPA):

spend / conversions

Return on Ad Spend (ROAS):

revenue / spend

These calculated fields become available as measures in every chart you build from this dataset, so you only need to define them once.

Click Save and give your dataset a clear name like "Campaign Performance 2026." You are now ready to start building charts.

Step 3: Create Your First Dashboard

From the dataset page, click Create Dashboard in the top navigation bar. Quick BI opens a blank dashboard canvas where you will build your charts.

The dashboard editor works by adding chart components to the canvas, then binding each component to your dataset fields.

Every chart follows the same pattern: choose a chart type, choose which fields go on each axis or in each role, and configure display options.

For a marketing analytics dashboard, build the following components in order. Starting with the summary metrics at the top and working down to the detail views keeps the layout logical and easy to scan.

Chart 1: KPI Summary Cards

KPI cards sit at the top of the dashboard and give viewers an instant read on the four numbers that matter most. Quick BI calls these Indicator charts.

Click Add Chart and select Indicator from the chart library. Drag it to the top of the canvas.

In the chart configuration panel:

Set the Metric field to "Total Impressions"
Enable Compare to previous period if your dataset includes a date dimension, which shows the percentage change automatically
Set the card title to "Total Impressions"

Resize the card to roughly one-quarter of the canvas width. Then create three more KPI cards for CTR, Conversion Rate, and Total Spend by duplicating the first card and changing the metric. Line them up horizontally across the top of the dashboard.

Your dashboard now has a summary row that any marketing manager can read in three seconds.

Chart 2: Daily Performance Trend (Line Chart)

Below the KPI cards, add a Line Chart that shows how your key metrics have moved over time. This chart answers the question your team asks most often: "Is this week better or worse than last week?"

In the chart configuration:

Set the X axis to your date dimension (e.g., report_date)
Set the Y axis (left) to Clicks or Impressions
Add CTR as a second Y axis measure on the right axis (since it is a percentage and has a different scale)
Set the time grain to Daily in the date field settings

Quick BI will render a dual-axis line chart. If your dataset has multiple campaigns, add the campaign name as a Color dimension to break each campaign into a separate line with a distinct colour.

Chart 3: Channel Comparison (Bar Chart)

Add a Bar Chart to the left half of the middle row. This chart shows which channels are delivering the best results, comparing spend, conversions, and ROAS across channels at a glance.

Configure it as:

X axis: channel name
Y axis: Total Spend (as bars)
Second measure: ROAS (as a line overlaid on the bars)

This combination tells the story of both how much you spent on each channel and how efficiently that spend performed, in a single chart.

Chart 4: Campaign-Level Table

Add a Pivot Table to the right half of the middle row. Tables are underused in marketing dashboards, but they serve a specific purpose: letting viewers sort and compare specific campaigns quickly without hunting through a chart.

Set up the table with:

Rows: Campaign Name
Columns: Spend, Impressions, Clicks, CTR, Conversions, Conversion Rate, CPA
Enable conditional formatting on the CPA column so cells turn green below your target CPA and red above it

Quick BI's conditional formatting is configured in the chart settings panel under the Style tab. Set your target CPA as the threshold value.

This turns the table into a visual alert system: anyone looking at it immediately sees which campaigns are in good shape and which ones need attention.

Chart 5: Geographic Distribution (Optional)

If your campaign data includes location information such as country or city, add a Filled Map or Bubble Map chart to the bottom of the dashboard.

Quick BI includes built-in geographic chart types that recognise standard country names and city names automatically.

Drag your location field into the Geographic Dimension slot and your conversion metric into the Colour Measure slot. The map renders with colour intensity showing where your conversions are concentrated.

This chart type is particularly useful for teams running regional campaigns or evaluating whether paid spend in different markets is performing proportionally.

Step 4: Add Filters and Interactive Controls

A static dashboard answers fixed questions. An interactive dashboard lets your team ask their own questions by filtering the data in real time.

Click Add Component and select Filter Bar from the component list. Quick BI's filter bar sits at the top of the dashboard and applies filters to all charts simultaneously when a viewer changes a value.

Add the following filter controls to your marketing dashboard:

Date Range Picker: Bind this to your date dimension. Set the default range to "Last 30 days." This is the single most useful filter on a marketing dashboard, letting viewers instantly switch between weekly, monthly, and quarterly views.

Campaign Dropdown: Bind this to your campaign name dimension. Set it to multi-select so viewers can compare two or three specific campaigns side by side.

Channel Filter: Bind this to your channel dimension. A checkbox-style filter works well here, letting viewers toggle individual channels on and off.

Once the filter bar is configured, click a dropdown value in preview mode. You will see all five charts on the dashboard update simultaneously to reflect the filtered view. This is what Quick BI calls cross-component linking and it works automatically because all charts share the same dataset.

Step 5: Configure Data Refresh

Your dashboard is only as useful as the freshness of the data behind it.

By default, Quick BI queries your data source live each time someone opens the dashboard. For most marketing use cases, this works fine, but for large datasets, it can cause slow load times.

Quick BI addresses this with dataset-level caching. In the dataset settings, enable caching and set a refresh interval that matches how often your underlying data updates:

If your campaign data updates hourly, set cache refresh to every hour
If it updates nightly via a DataWorks job, set cache refresh to run at 7:00 AM daily
For CSV uploads, the cache refreshes automatically when you upload a new file

For teams using MaxCompute with a DataWorks pipeline feeding fresh data every morning, the nightly refresh schedule means the dashboard is always showing yesterday's final numbers by the time the marketing team starts their day.

Step 6: Share the Dashboard with Your Team

A dashboard nobody can see is not useful. Quick BI gives you several ways to share your finished dashboard, depending on how your team works.

Workspace sharing: From the dashboard's three-dot menu, click Share. You can share with specific Alibaba Cloud RAM users in your workspace, or with everyone in the workspace. This is the right option for internal teams who all have Alibaba Cloud accounts.

Public URL (with access control): Quick BI can generate a shareable link that does not require the viewer to log in. This is useful for sharing with stakeholders who are not Alibaba Cloud users. You can set an expiry date on the link and optionally protect it with a password.

Scheduled email reports: In the dashboard menu, select Subscribe. Quick BI can automatically export the dashboard as a PDF or image and email it to a list of recipients on a schedule you define. A weekly Monday morning email with last week's campaign performance summary requires no manual work from anyone once configured.

Embedding into internal tools: You can embed the Quick BI dashboard into your own third-party systems and use it to view the reports and charts directly. This is relevant for teams who have an internal portal or wiki and want campaign metrics to live there rather than requiring people to visit the Quick BI console separately.

Step 7: Control Who Sees What

If your marketing team covers multiple regions, brands, or product lines, you probably do not want everyone to see everyone else's campaign data. Quick BI handles this through row-level permissions.

You can set the access permissions for individual rows so that the same report is displayed differently for each user role.

In practice, this means you create a single dashboard and a single dataset, then configure rules that filter the rows each user sees based on their role or the tags associated with their account.

To set this up, go to your dataset settings and enable Row-Level Permissions. Create a permission rule that maps a user field (such as region or team) to the dimension in your data that controls what they should see.

A marketing manager in Southeast Asia sees only Southeast Asia campaigns. The regional head sees all of them. The dashboard looks identical to both; the data underneath is filtered automatically.

This saves you from maintaining separate dashboards for each team and ensures that sensitive campaign data does not reach people who should not have it.

Step 8: Use SmartQ for Quick Ad-Hoc Analysis

Quick BI includes a feature called SmartQ that lets you ask questions about your data in plain language and get a chart back instantly. Quick BI's SmartQ AI automates dashboard construction and delivers data insights through natural language.

For marketing teams this is genuinely useful for ad-hoc questions that do not justify building a permanent chart.

Type a question like "show me conversion rate by campaign for last month" and SmartQ returns a chart based on your dataset. If the chart type or grouping is not quite right, you can adjust it and save it to the dashboard.

SmartQ is available in the Quick BI console from the dataset page. Connect your dataset to SmartQ by clicking the SmartQ icon and selecting the fields you want it to be able to query.

A Practical Note on Dashboard Design

Building a dashboard is the easy part. Building one that people actually use takes a little more thought. A few principles that hold up well for marketing dashboards:

One question per chart. Each chart should answer a specific question. "How did CTR trend this month?" is a good question for a chart. "Everything about our campaigns" is not. When a chart tries to show too much, it ends up showing nothing clearly.

Top to bottom: summary then detail. Put KPI cards at the top. Put trend charts in the middle. Put campaign-level tables at the bottom. People scan top to bottom and most of them will stop at the KPI cards if those numbers look fine.

Default to the most common time window. Set your date filter default to the period your team reviews most often. If the team does weekly reviews, default to "last 7 days." Nobody wants to change filters every time they open the dashboard.

Name things the way your team talks. Rename "ctr" to "Click-Through Rate (%)" and "cpa" to "Cost Per Acquisition (USD)." Labels that match how your team speaks mean less time explaining what a chart means.

What to Build Next

Once your campaign performance dashboard is live and your team is using it, a few natural extensions are worth considering.

If your subscriber and audience data is already in MaxCompute through a DataWorks pipeline, connect that dataset to Quick BI to add audience-level breakdowns to your dashboard.

You can visualise audience segment size, engagement rates by segment, and how segment composition changes over time.

For teams running automated campaigns through Alibaba Cloud Direct Mail, Direct Mail provides delivery metrics through its console API.

You can pipe those metrics into a MaxCompute table via a scheduled script and surface them in the same Quick BI dashboard alongside your ad and web data, giving you a single view of every marketing channel in one place.

If you want to go further with data preparation and transformation before it reaches Quick BI, Alibaba Cloud DataWorks gives you a visual pipeline builder for cleaning, joining, and reshaping data from multiple sources before the dashboard ever queries it.

Summary

In this guide, you built a complete marketing analytics dashboard in Quick BI, covering:

Connecting ApsaraDB RDS, MaxCompute, or a CSV file as a data source
Creating a dataset with calculated marketing KPIs
Building five chart types: KPI cards, a trend line chart, a channel comparison bar chart, a campaign table with conditional formatting, and a geographic map
Adding interactive filters so your team can explore the data themselves
Configuring automated data refresh and scheduled email reports
Controlling data access with row-level permissions

The dashboard you built today is a starting point. As your campaigns grow, you add new data sources and new charts to the same workspace. Quick BI scales with the data, so the architecture you set up here does not need to change as your marketing operation grows.

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