How We Used AI to Transform Data Into Revenue-Driving Insights

Identifying Actionable Insights From Market Research Data

Your customers are filling out your feedback forms, leaving reviews, sending emails, and chatting with your sales team. You've got spreadsheets full of survey responses and an inbox bursting with customer comments. You know there's valuable information in there—insights that could help you make better decisions about your products, marketing, and business strategy. But who has the time or resources to make sense of it all?

This is the reality for most small and medium-sized businesses today: swimming in feedback but struggling to extract actionable insights. In conversations with business leaders, we keep hearing the same thing: "We're collecting more data than ever, but we're not sure what to do with it." One business owner recently told us, "I've got three years of customer surveys sitting in a folder, and I know they contain answers to our biggest business challenges—I just can't find them." If this sounds familiar, you're not alone. 

The Evolution of Market Research

Remember those traditional market research surveys? The ones where respondents dutifully tick boxes and select numbers from 1-10? There was a good reason for this approach: when technology was limited, quantitative research techniques were the only practical way to analyse large amounts of feedback. Multiple choice questions were king because they were easy to count.

But let's be honest—how many times have you wanted to explain your answer but were forced to choose between options that didn't quite fit? How often have you thought, "It's more complicated than that"?

For several years the main method of surveying customers at scale has sacrificed quality of response in favour of simplicity of analysis. However, with the advent of AI, there’s a blurring of the lines between quantitative and qualitative research methods. We can now analyse open-ended responses at scale, drawing precise, measurable values from descriptive written feedback. Unlocking the ability to uncover nuanced insights that traditional methods might miss.

Cracking the Customer Acquisition Code

We recently worked with a company facing a common challenge: despite their efforts to track installs with attribution links, they couldn't identify the source of 80% of their new customers.

Understanding what led to these customers installing was a key growth opportunity, and one that we unlocked with AI. 

We augmented the data gathered from our mobile measurement partner (MMP) with a simple open-ended survey question that we included within the onboarding flow: "How did you learn about us?". 

Traditionally surveys would have restricted customer choice, to aid the process of analysis, but we wanted to allow customers the freedom to share their feedback without any limitations. We prioritised the collection of descriptive, unbiased data that would draw attention to blind spots in our marketing strategy, knowing that this would put more strain on post-collection data analysis. 

For a company with 1 million weekly active users this could have quickly overwhelmed, and many years ago it likely would have, but in this scenario we leveraged an AI tool called Enterpret to analyse thousands of open-ended responses to identify themes in the feedback and generate quantifiable values that we could use to identify trends and unlock new channels. 

What emerged was fascinating, two channels that are notoriously difficult to measure were driving their growth - Existing User Referral and Podcasts. At the time the company had neither a referral program, or an active Podcast marketing campaign, so the likelihood of these channels being included in a list of predefined responses to a traditional survey question was very low. 

This method of augmenting attribution links with survey data also helped build a greater understanding of the full customer acquisition journey. When looking at attributed installs alone, their data suggested most customers came directly from Apple Search Ads, the reality was more complex. Many customers first heard about the product from friends, podcasts or press mentions. The Apple Search Ads were merely the final step in a much longer journey.

This insight led to a reallocation of their marketing budget, shifting focus from last-click channels that were simply intercepting customers on their way to installing, and focusing on generating awareness with key audiences through media and content partnerships that drove referral. 

Extracting Value From Small Data

Another powerful example can be found in small data, where AI makes it easier to identify clear patterns and make immediate tactical decisions

Traditional competitive analysis might involve manually reading through competitor reviews or relying on star ratings. But what if you could analyse thousands of reviews instantly, identifying patterns and opportunities that the human eye might miss?

We recently helped a hospitality client do exactly that, analysing over 10,000 reviews of both their property and their competitors to unearth opportunities and identify unmet customer needs. The AI didn't just count positive and negative mentions—it identified specific themes and emotional triggers that influenced guest satisfaction.

The insights gathered from this process were incredibly valuable, and informed decisions ranging from marketing messages, listing optimisation and operational changes. 

Making AI-Powered Research Work for Your Business

You might be thinking, "This sounds great for big companies, but what about smaller businesses?" Here's the good news: AI tools have democratised advanced market research capabilities. Here's some ideas to get started:

Start with existing data

• Customer service emails
• Social media comments
• Sales call notes
• Customer reviews
• Discussions taking place about your company on third party sites like Reddit. 

Adapt your data collection process 

• Replace closed questions with open-ended ones
• Focus on understanding the "why" behind customer behaviours

The gap between data collection and insight generation is costing businesses growth opportunities every day. Whether you're sitting on years of unanalyzed customer feedback or starting fresh with market research, AI tools can help you uncover the insights you need to drive revenue growth.

You can start this process with a general purpose LLM and scale up to more sophisticated tools, such as Enterpret, as needed. 

The Future of Market Research

As we look ahead, the integration of AI into market research isn't just an option—it's becoming a competitive necessity. But here's the key: AI isn't replacing traditional research methods; it's enhancing them. The goal isn't to remove human insight from the equation but to give researchers and business leaders better tools to understand their markets and serve their customers.

Consider this: What if every customer interaction could contribute to your understanding of market needs? What if you could identify emerging trends months before your competitors? With AI-powered research tools, these aren't just possibilities—they're becoming reality.

Ready to transform your market research data into revenue-driving insights? Let's have a coffee and explore how AI-powered research could work for your business.

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