Generate campaign insights using AI: A practical guide for 2025

If you’re on a marketing team, you probably know the feeling: you’re drowning in data but starving for actual insights. A campaign goes live, and suddenly you’re hit with a tidal wave of performance reports, dashboards, and spreadsheets. But the story behind the numbers, the why, often gets lost in all that noise. Just figuring out what worked and what didn't can feel like a full-time job.

The old way of doing campaign analysis is slow, manual, and all over the place. You’re trying to connect hard numbers like click-through rates with the kind of real feedback that explains why people did what they did. Why did that one ad do so well? Was the landing page confusing? Did the discount code even work? Getting answers to these questions can feel like you're a detective on a case with no clues.

This is where AI can really help. It can sift through all that scattered data for you and pull out useful campaign insights in minutes, not weeks. This guide will walk you through what it actually means to generate campaign insights using AI, the different kinds of data you can look at, a few practical ways to get it done, and how to pick the right tools for your team.

What does it mean to generate campaign insights using AI?

Generating campaign insights using AI is all about going deeper than surface-level metrics like clicks and conversions. It’s about using tech like machine learning and natural language processing to automatically figure out how a campaign landed with your audience, what they thought of it, and how they felt about it, all at a scale no human could ever manage by hand.

The old way looks a lot like exporting a bunch of CSVs from six different dashboards, trying to mash them together in a spreadsheet, and then staring at them for hours hoping a pattern will magically appear. The new way uses AI to do that heavy lifting, giving you back easy-to-read summaries, pointing out hidden trends, and analyzing customer feedback for you.

Here are the key bits of tech that make this possible:

• Natural Language Processing (NLP): This is how AI reads and understands human language. It’s the key to making sense of customer reviews, social media comments, and support tickets.

• Machine Learning (ML): This is how AI learns from data. It’s great at spotting trends and making educated guesses about what might happen next.

• Generative AI: This is the tech behind tools like ChatGPT. It’s really good at taking a pile of raw data and turning it into a short, easy-to-understand summary or report.

The types of data needed

The best insights don't come from just one place. They come from mixing different types of data to get the full story. AI is especially good at connecting the dots between what happened during a campaign and why it happened.

Quantitative performance data

This is the "what" of your campaign. We're talking about the hard numbers: impressions, click-through rates (CTR), conversion rates, and cost per acquisition (CPA). These tell you whether you hit your goals.

AI can help here by automatically flagging when something looks off (like a sudden drop in CTR on a key ad), spotting trends, and even building performance reports for you. Tools like Google Analytics and Microsoft Clarity are great for tracking these numbers, but they often leave you guessing about the reasons behind them. A dashboard can tell you a landing page has a high bounce rate, but it can’t tell you why people are bouncing.

A screenshot of the Google Analytics dashboard, a tool used to generate campaign insights using AI.

Qualitative customer feedback

This is the "why" behind the numbers. It’s all the stuff hidden in social media comments, product reviews, surveys, and, the real goldmine, your customer support tickets. This feedback is full of honest, unfiltered opinions about your campaign.

AI uses sentiment analysis and topic clustering to sort through all of this feedback, figuring out if a campaign is making people confused, excited, or frustrated. This is where a lot of traditional analytics tools just can't keep up, because they have no way of tapping into the conversations happening in your help desk. For example, a platform like eesel AI can analyze thousands of past support tickets to show you what customers really thought about that new feature you just launched. It draws a direct line from your campaign's impact to your customer's voice.

A screenshot of the eesel AI platform analyzing past support tickets to generate campaign insights.

On-site behavioral data

This is all about "how" people interact with your campaign's landing page once they click. Did they scroll? Did they click the big shiny button?

AI can look at thousands of user sessions to find friction points, like "rage clicks" (when someone furiously clicks on something that isn't working) or "dead clicks" (clicking on something that isn't a link). This helps you see exactly where your landing page isn't working as it should. It’s useful stuff, but it still only shows you what users did, not what they were thinking or feeling.

A screenshot of Microsoft Clarity, which helps generate campaign insights using AI by tracking on-site behavior.

Practical ways to generate campaign insights using AI

Alright, let's get down to brass tacks. Here are three common and effective ways teams are using AI to get campaign insights right now.

Automated performance summaries

This is one of the most common uses for generative AI. You can feed it performance reports, slide decks, and dashboards, and it will give you back a quick summary of the wins, weak spots, and ideas for what to do next. Tools like Dropbox Dash or HubSpot's Breeze Assistant can connect to your documents and create these summaries for you.

This video demonstrates how AI can generate innovative and compelling tactics for your next campaign, backed by strategic context and web research.

The main drawback, though, is that these tools are usually limited to the documents you feed them. They often don’t have real-time data and can miss the important context from live customer conversations happening over in your help desk. A summary of a report is nice, but it's not the whole story.

A screenshot of Dropbox Dash, a tool that can generate campaign insights using AI from your documents.

Sentiment analysis and VoC

This is where AI starts to get really interesting. Using NLP, you can scan huge amounts of text to get a read on public opinion and pull out the main themes related to your campaign. The AI can be taught to spot positive, negative, and neutral language, then automatically sort the feedback for you.

This is a huge strength of modern support tools. For instance, a tool like eesel AI can be set up to automatically tag all incoming support tickets related to a "Summer Sale" campaign. It can then analyze the sentiment of those tickets as they come in. This can tell you right away if customers are confused by the discount code, annoyed with shipping times, or excited about the new products. You're no longer just guessing; you're getting direct, usable feedback in real time.

A screenshot showing how you can customize and set up automated actions in eesel AI to generate campaign insights.

Predictive analytics and forecasting

This involves using old campaign data to predict what might happen in the future. For example, an AI model could predict which types of customers will respond best to a certain message or forecast demand for a product you’re promoting. This helps you spend your ad budget smarter and personalize content for different audiences. Some companies, like Persado, have built their whole business around this idea, using AI to write marketing copy that's designed to convert.

The big hurdle here is that this kind of thing often needs a massive amount of data and a pretty complex, enterprise-level setup. It's incredibly powerful, but it can be out of reach for a lot of teams that don’t have their own data science department on call.

Choosing the right tools (and their limitations)

The market for AI marketing tools is blowing up, but they generally fall into three buckets, each with its own good and bad points.

All-in-one marketing platforms

Think of tools like HubSpot or Salesforce Einstein. Their biggest selling point is that they're already part of your marketing and sales workflow. They can pull from a ton of CRM data to find insights.

The downside is that they can be a bit of a "walled garden." They mostly generate insights from the data that lives inside their own system, which means you might be missing important information from your other tools. They also tend to be expensive and pretty much require you to go all-in on their software to get real value.

A screenshot of the HubSpot platform, which can be used to generate campaign insights using AI.

Standalone analytics and creative tools

This category includes specialized tools like Improvado, which pulls all your ad data together, or AdCreative.ai, which analyzes the performance of your ad creative. They're very good at the one specific thing they do.

The problem is, they often just create another data silo. Now you have one more tool to check, and it probably doesn't play nicely with other key systems like your help desk or your internal wiki. You get a really deep look at one piece of the puzzle, but you still can't see the whole board.

A screenshot of Improvado, a standalone analytics tool that helps generate campaign insights using AI.

Modern knowledge and automation platforms

A new type of platform is showing up that’s designed to break down these silos. They work by connecting all your different sources of information, your docs, wikis, help desks, and chat tools, to power all sorts of AI applications. This approach gives you a single source of truth and a lot more flexibility.

eesel AI is a great example of this new way of thinking. It can connect to your marketing briefs in Google Docs, your internal chats in Slack, and your customer conversations in Zendesk. This allows it to generate campaign insights that cover all your bases in a way no single-purpose tool can. Instead of just analyzing ad performance, it can show you how that ad campaign led to specific questions and feedback from your actual customers. Best of all, it's designed to be self-serve with transparent pricing, so you can get started in minutes without ever having to talk to a salesperson.

An infographic showing how eesel AI integrates with various knowledge sources to generate campaign insights using AI.

Pricing models for AI tools

Pricing for AI tools can be confusing and, let's be honest, unpredictable. It’s good to know the different models so you don't get a surprise bill at the end of the month.

A screenshot of the eesel AI pricing page, which shows a clear, tiered pricing model to help you generate campaign insights using AI without unpredictable costs.

From data overload to actionable insights

Using AI to generate campaign insights isn't about replacing marketers; it’s about making them better at their jobs. It allows teams to finally connect the "what" from their performance data with the "why" from customer feedback, closing the loop between a marketing campaign and the actual customer experience.

The best insights come from having a clear view of both your numbers and your conversations. While plenty of tools can summarize reports for you, real understanding comes from analyzing what your customers are telling you directly.

The most honest feedback on your campaigns is probably already sitting in your customer support inbox. If you’re ready to unlock those insights and use them to build better campaigns, check out how eesel AI can help. You can connect your help desk and start seeing what it can do with your past tickets in just a few minutes, completely risk-free.

A screenshot of the eesel AI homepage, where you can learn how to generate campaign insights using AI.