Most dashboards show you what happened, but they leave the digging to your team. Augmented analytics does that digging for you. It scans your data, flags what changed, and shows you why it happened.

For marketing and CRM teams, this saves time. You can spot a drop in repeat purchases early and respond before it grows.

In this guide, we explain what augmented analytics is and what to look for in a tool. We also compare seven tools to help you find the right fit for your team.

Tl;dr 

• Augmented analytics uses AI to find patterns in your data, explain changes, and predict what may happen next.
• For marketing and CRM teams, the best tools also help you act on what you learn.
• aiRA by Capillary is our top pick for teams that want to quickly go from customer insights to live campaigns.
• The other six tools suit different needs, from business users who want quick answers to analysts who build workflows.

What is augmented analytics?

Augmented analytics uses AI to handle part of the analysis work for your team. It looks for patterns in your data and explains why a metric went up or down. It can also predict what is likely to happen next. The key difference is that it does this without waiting for someone to ask. 

Traditional BI tools show charts and reports, and your team has to decide what to look for. With augmented analytics, the important changes come to you. To learn more about tools that answer questions in plain language, read our guide to conversational analytics tools.

What to look for in an augmented analytics tool

Marketing and CRM teams need more than a faster way to read data. The right tool should help you understand your customers and act on what you learn. Here are five things to check before you choose one:

• Automated insight discovery: A good tool spots changes in customer behavior on its own. You should not have to know the right question before it shows you something useful.
• Explanation of why: When redemptions drop, you need to know the reason. The tool should show which region, segment, or offer caused the change, in plain language.
• Predictive capabilities: Look for a tool that can score customers on how likely they are to lapse or respond. Your team should be able to use these scores without a data scientist.
• Understanding of customer and campaign data: Some tools treat customer data as just another set of tables. A better tool already knows what a lapsed member or a redemption means, so you get useful answers sooner.
• Ability to act on insights: Most tools stop once they explain what happened. The best ones also help you build the segment and launch the campaign from the same place.

How this list was tested and selected

An augmented analytics tool should do some of the thinking for your team. It should point out what changed, explain it clearly, and help you decide what to do next. It should also do this without months of setup.

We used these ideas to build our criteria. Here are the questions we asked about each tool:

Augmented analytics tools at a glance

The 7 best augmented analytics tools

1. aiRA by Capillary

Best for: Marketing and CRM teams that want to move from a customer question to a live campaign.

aiRA by Capillary is an AI agent for marketing and customer teams. Users ask questions about customers in plain English. aiRA by Capillary answers from the brand’s own customer data. Then it recommends offers, messages, and channels.

The tool suits teams that run frequent campaigns. Every customer-facing step can wait for human approval. A full deployment on brand data takes two to four weeks. Your team gets a dedicated success manager throughout.

Key features

• Users can ask about customer behavior and engagement trends without writing SQL.
• aiRA by Capillary builds segments from a plain description and scores each one.
• Teams can set up the audience and messaging from a single brief.
• Personal data is masked before it reaches the language model, and every AI interaction is logged.

See how aiRA by Capillary handles a real question from your own customer data. 

Request a demo.

2. Tellius

Best for: Business users who want automatic explanations of metric changes.

Tellius is an augmented analytics platform built around explanation. Users ask questions in natural language. The platform finds the factors behind a change in a metric. It also surfaces patterns nobody asked about.

Tellius suits business teams with clear metrics and a data team to connect sources. Teams that need heavily customized dashboards may need a second tool. They built the product for investigation first.

Key features

• Users can search business data in natural language.
• The platform finds unusual changes in metrics without a prompt.
• It shows which factors contributed to a change.

3. Qlik Cloud Analytics

Best for: Teams that want to explore data from many sources and get AI-generated insights.

Qlik Cloud Analytics is the cloud platform from Qlik for analytics and AI-driven insight. It builds on the associative engine in Qlik Sense. Users can click through data in any direction. The platform adds AI assistance to speed up discovery.

Qlik suits organizations with BI developers who build data models for the business. New users need time to learn load scripts. Business users get the most from the platform once that groundwork is ready. They offer both cloud and on-premises deployment.

Key features

• Clicking any value filters every chart at once.
• Insight Advisor generates insights and answers natural language questions.
• Users can combine data from databases, SaaS apps, and files.

4. Microsoft Power BI

Best for: Organizations that run on Microsoft 365 and want reporting inside that ecosystem.

Power BI is the business intelligence platform from Microsoft. It connects to Excel, SQL databases, and cloud sources. Quick insights surfaces patterns in a dataset. Copilot answers questions in plain language and helps build reports.

Power BI suits companies that already use Excel, SharePoint, and Teams. It works best with BI developers who maintain the data models. Basic reports come together quickly. Advanced analysis often needs DAX and modeling skills.

Key features

• Quick insights surfaces patterns with a few clicks.
• Copilot lets users ask questions in plain language.
• The key influencers visual shows which factors link to an outcome.
• Reports work inside Excel and Teams.

5. Tableau

Best for: Analyst teams that build visual dashboards for other people.

Tableau is a BI platform from Salesforce built around visual analysis. Tableau Pulse tracks key metrics and sends alerts when they change. Explain Data suggests possible causes for a value in a chart.

Tableau works best when analysts design and maintain dashboards. Business users can explore those dashboards. When their questions change, they usually wait for analysts to build new views.

Key features

• Users drag fields onto a canvas to build charts and dashboards.
• Users can follow key metrics and get digests when they change.
• Explain Data suggests reasons behind a value.
• Teams can connect to SQL databases and cloud warehouses.

6. ThoughtSpot

Best for: Teams that want search-led exploration of data and automated analysis.

ThoughtSpot is an analytics platform built around search. Users type questions about their data and get charts back. SpotIQ runs automated analysis to find patterns and outliers that users may have missed.

ThoughtSpot works best after a data team models the warehouse. Teams that need answers before that work is done may find setup slow. Much of the functionality depends on good data modeling.

Key features

• Users can ask questions in natural language and get visual answers.
• SpotIQ surfaces patterns and outliers automatically.
• Liveboards let teams share dashboards built from search results.
• The platform connects to modern cloud data warehouses.

7. Alteryx

Best for: Analysts who want to automate data prep and analytics in no-code workflows.

Alteryx is an analytics automation platform. Users build workflows by connecting tools on a canvas. The platform combines data preparation, analysis, and predictive modeling in one place.

Alteryx suits analyst teams that prepare data and run repeatable workflows. Marketing teams can use the outputs for segmentation and reporting. An analyst usually builds the workflow first. Business users who want plain-English answers may find the tool less direct.

Key features

• Users build data pipelines by dragging tools onto a canvas.
• The platform blends and cleans data from many sources.
• Analysts can run predictive models inside a workflow.
• Teams can schedule and share finished outputs.

Augmented analytics use cases for marketing and CRM teams

Augmented analytics is most useful when it helps marketing and CRM teams act on what they find. Here are four common use cases:

1. Spotting churn risk early

Augmented analytics watches engagement and purchase patterns across customers. It flags groups that start to slow down. Your team can act before those customers lapse. The best tools also explain which behaviors signal the risk.

2. Explaining a campaign dip

When a campaign falls short, the first question is what changed. Augmented analytics compares segments and offers to find the driver behind the drop. With that cause in hand, a team can respond within days rather than weeks.

3. Forecasting campaign outcomes

Predictive models can estimate how a campaign may perform before launch. Teams can compare options and fund the one most likely to work. Forecasts improve as the tool learns from each campaign.

4. Finding high-value segments

Some customers are far more likely to respond to an offer than others. Augmented analytics groups customers by how likely they are to respond and how much they are worth. That lets your team direct budget toward the customers most likely to buy.

Choosing the right augmented analytics tool for your team

Augmented analytics shows you why a metric changed. For marketing and CRM teams, that is only the first step. The next step is using that insight to plan and launch a campaign.

Each tool on this list works in a different way. Some are made for analysts who build dashboards for other teams. Others let business users look at data on their own. If you want to find an insight and launch a campaign in one place, choose a tool that does both.

aiRA by Capillary is built for that. Your team can ask questions about customers in plain English, build segments with projected value, and set up campaigns that are ready to launch. A person on your team approves every customer-facing step before it goes live.

Request a demo to see how aiRA by Capillary works on your own customer data.

FAQs

1. What is augmented analytics?

Augmented analytics uses AI to find patterns, explain changes, and forecast results. It does this with little prompting from the user.

2. What is the difference between augmented analytics and traditional BI?

Traditional BI presents reports that people must interpret. Augmented analytics adds automated discovery, explanation, and prediction.

3. What is the difference between augmented analytics and conversational analytics?

Conversational analytics lets users ask questions in plain language. Augmented analytics adds discovery and prediction. Many tools now offer both.

4. Will augmented analytics replace data analysts?

The tool handles routine questions and first-pass analysis. Analysts still build data models, review results, and handle complex work.

5. Which augmented analytics tool is best for marketing and CRM teams?

aiRA by Capillary is built for marketing and CRM teams. It answers customer questions and helps launch campaigns. People approve each customer-facing step.