What is an actionable insight?

Customer feedback is everywhere. And your qualitative data is some of the most insight rich data, where you can begin to truly understand what your customers need. They’re the source of your actionable insight — the thing that every CX professional needs to create meaningful change. 

Actionable insights don’t just describe what’s going on, they give relevant teams the fuel and evidence they need to improve products and services. From our work with housing associations and other regulated sectors, we’ve found that every truly impactful insight has three components:

1. An obvious next step
2. A measurable result
3. A human connection

Our Head of Insights and Innovation, Steve Erdal explains it beautifully…

So if an insight is missing one of those three things, it’s not ready for the boardroom or for your frontline staff to deal with. For example:

❌ “Repairs is the most common theme in our feedback this quarter.”
➤ Okay… but what should we do with that?

✅ “Complaints about delayed repairs have increased by 38% since January — especially in sheltered schemes in the north of the borough. If we can prioritise contractor capacity in those areas, we could reduce complaint volumes by around a third.”

Those two statements would both be received differently in the boardroom. In this post, we’ll walk you through the framework we use to help organisations surface insights like that, and drive meaningful change using Power BI. It’s all about:

1. Classification
2. Using a semantic model 
3. Showcasing your data in visualisations and dashboards

Step 1: Classification – Structuring your Qual Data

Before you can get to shiny dashboards or a semantic model, you need to wrangle the raw feedback — and that starts with classification. Classification is the process of grouping your customer feedback into meaningful, structured themes, and it’s where we see the biggest difference between organisations that are insight-led and those that are just reporting numbers.

Many teams start with manual tagging or inherited taxonomies that weren’t built with insight in mind — maybe they’re based on internal departments, process stages, or complaint categories.

The problem? These structures reflect how your organisation works — not how your customers think. Effectively classifying your data means investing in the right tools and tech that will allow you to classify your data and analyse your feedback effectively.

What good feedback classification looks like

• Thematic clarity: Each theme should represent one idea — avoid catch-alls and overlaps.‍
• Relevance to action: Every theme should be something a team can do something about.‍
• Consistency across data sources: Your model should work across surveys, complaints, social media, etc.
  

Once your classification process is in place — clear, structured, and actionable — you’re ready to build the logic that powers your dashboards and reporting. That’s where the semantic model comes in.

Step 2: The Semantic Model 

Understanding the Semantic Model

Power BI can't do much with a thousand angry comments about repairs. Text analytics tools like Wordnerds give it structure — themes, sentiment, topics. But the semantic model is what makes that structure work in Power BI: turning classifications into metrics, aggregations, and relationships your dashboards can actually use.

Steph explains how this works in our webinar:

The semantic model is the logic layer that turns your raw, classified feedback into metrics that business users can work with. It sits between your data and your visuals, and it handles things like:

• Aggregating sentiment by theme
• Connecting survey scores to topics
• Weighting different feedback types appropriately

Once your feedback is clearly classified and your semantic model is translating it into usable insights, you’ve got the engine running. But even the best engine needs a dashboard. This is where it all comes to life, when the insight actually lands in front of someone who can do something with it. It’s about making it obvious what matters, what’s changed, and what needs action. Too often, dashboards are treated as the end product. But in reality, they’re the bridge between analysis and action. Let’s explore how to build dashboards that not only look good, but drive change.

Step 3: Visualisation – Designing BI Dashboards for Action

Even the best semantic model won’t matter if your dashboards confuse people. We see a lot of dashboards that are beautiful but unusable — because they’re built for analysts, not humans.

The key is to design visualisations that are:

• Role-specific: Repairs managers need different views than Customer Experience leads.‍
• Frictionless: It should take < 30 seconds to get from insight to next step.‍
• Context-rich: A chart without quotes or examples won't drive empathy or action.

This isn't theoretical — we've seen it work. For one housing association, we built a dashboard where managers click on "damp and mould" and instantly see scores, sentiment, and actual resident quotes in one place. For a transport client, we set up alerts for when delay complaints spike on specific routes — so ops can act now, not three months later when the quarterly report lands.

Effectively Share Feedback Beyond the Insights Team

It’s not enough to have good insights — you need to make sure the right people see them and know what to do next. Here’s how:

• Role-specific dashboards: Build views for different teams (e.g., Contact Centre, Repairs, Exec Team).
• ‍Power BI training for non-analysts: Run short workshops to help staff filter, slice, and interpret dashboards confidently.
• ‍Insight digests: Create monthly one-pagers or email summaries for busy teams.
• Highlight wins: When feedback leads to change, celebrate it and show the loop being closed.
• The ultimate goal? Build an insights culture, where feedback isn’t just reviewed, it’s acted on, shared, and embedded into decision-making.

Making It Stick

Power BI is a powerful tool, but it's not magic. If your classification is messy, your semantic model isn't set up correctly, or your dashboards are hard to use, your insights won't land. 

But get those three steps right — Classification → Semantic Model → Visualisation — and you move from reports that describe problems to dashboards that drive real action. 

That's when feedback stops being a reporting exercise and starts being a strategic asset.

Want help getting qualitative feedback into Power BI?

We've helped teams across housing, transport, and travel build dashboards that actually drive action. Book a chat with the team — no sales pitch, just a conversation about what you're trying to achieve.