Have you ever bought a new car and suddenly started seeing the exact same model everywhere?

Or learned a new word and then heard it three times that week?

That's the Baader-Meinhof phenomenon—once something enters your awareness, you start noticing it everywhere.

The same thing happens with business problems once you train yourself to spot them. Last week, I was having coffee with a fellow entrepreneur who mentioned—almost as an afterthought—how his team wastes nearly half a day every week manually compiling client usage reports. "It's just part of the job," he shrugged.

But is it? Within ten minutes, we'd sketched out an AI automation that could save his team 20+ hours monthly. Yes…I’m just that cool, I know I know!

What he had accepted as an inevitable business friction was actually a solvable problem hiding in plain sight.

Here's the thing: Once you become attuned to identifying business problems, you'll spot them everywhere—mentioned in passing during meetings, buried in customer feedback, or hiding in the "minor annoyances" people have simply accepted.

Let's get started

Beyond Personal Perspective

In Part 1, we explored how to dig into your personal experience for business problems. This is a fantastic starting point, but it has obvious limitations – you only experience a tiny fraction of the problems that exist in the market.

For better or worse you are just you! It’s a great starting point (and anchor to the industry at large) but to build a more comprehensive Problem Bank, we need to systematically explore beyond our own experience.

Sounds like I’m about to hand you some strange looking mushrooms. Don’t worry. Nothing that exciting!

I’m going to run through a bunch of techniques. And the name of the game is to go and collect as much as possible. Unstructured, messy data. That’s fine.

We’re going to take all of that noise and use a prompt to extract what we need. Let’s get to it.

1. Community Listening

Online communities are goldmines of problem statements. People gather specifically to discuss challenges, seek solutions, and vent frustrations.

Focus on

• Industry-specific subreddits or forums

• Facebook or LinkedIn groups for professionals in your target market

• Discord/Slack communities

• Specialized industry forums

For each community, search for terms like

• "frustrated with"

• "problem with"

• "alternative to"

• "help with"

• "how do I"

• "anyone know how to"

Reddit is my favourite for this personally. When I find a related discussion I will wholesale copy paste it all (length dependant obviously) and stick it in a Notion document. Or alternatively save the whole discussion to Raindrop.io.

Why get it all? Because I want to see the discussion and the language used. Not just what is being discussed but how.

2. Review Mining

Product and service reviews are essentially structured problem statements. When people complain about a product or service, they're highlighting an unmet need or inadequate solution.

Analyse reviews on

• G2, Capterra, Cluch, or TrustPilot (for B2B software and services)

• App stores (for software solutions)

• Professional service review sites (for consulting or agency services)

• Amazon (for books and products in your industry)

For example, looking at reviews for Xero accounting software might reveal frustrations with its reporting capabilities or integration limitations—each representing a potential opportunity for an AI solution.

Look specifically for

• Common complaints across multiple reviews

• Features users wish existed

• Workarounds people have developed

• Areas where even positive reviews mention limitations

Again though copy and paste it all. We’ll use AI to filter later.

3. Complaint Analysis

Beyond product reviews, people share complaints and frustrations across various platforms. This is a bit “heavier” - ie. BIG problems rather than minor quibbles.

These are direct windows into problems worth solving. Reviews tend to be more emotive and off the cuff. Complaints will generally be more structured. They are harder to find but highly valuable once in hand.

Check out

• Better Business Bureau complaints (or equivalent in your locale)

• Twitter/X searches for "[company/product/service name] + issue/problem/frustrated"

• Industry watchdog sites

• Consumer advocacy forums

Pay special attention to complaints that

• Appear repeatedly over time (indicating persistent issues)

• Generate significant engagement from others

• Describe workarounds people have developed

• Mention willingness to switch providers or pay for solutions

Again AI can do a lot of the filtering for you so focus on gathering up first and foremost.

4. Social Listening

Social media platforms can provide real-time insights into emerging problems and pain points. Social listening is a fancy social media marketing agency term for paying attention to what people are chatting about.

Monitor

• Industry hashtags

• Trending topics in your target market

• LinkedIn posts from thought leaders in your space

• Comments sections on relevant content

Tools like Hootsuite, Mention, or even just Twitter/X's advanced search can help you systematically monitor these conversations.

I've found that the comments sections on "how-to" content are particularly revealing. People often share their specific struggles and ask questions that highlight unmet needs.

5. Search Data

Google is the world’s largest Question and Answer engine. People have problems - they go to Google for the answer.

We can access all of Google's data via Adwords (free) or tools like SEMRush (paid).

As a good mid ground starting point that’s easy to use check out Answer the Public. It will specifically give you the questions that people have. Very very useful resource and you can use it a handful of times a day for free.

Using AI to Process Unstructured Feedback

One of the challenges with market observation is the sheer volume of unstructured data you'll collect. This is where AI can be incredibly powerful—helping you analyse large amounts of feedback to identify patterns and insights that would be difficult to spot manually.

Here's a comprehensive prompt to help you analyse customer feedback, reviews, forum posts, or any other unstructured text data you've collected:

You are an expert business analyst specialising in identifying business problems and opportunities from customer feedback. I'll provide you with a collection of unstructured feedback (reviews, forum posts, comments, etc.). Please analyse this data to help me identify potential business problems worth solving.

The feedback is related to [briefly describe the industry, product, or service].

Please provide a comprehensive analysis including:

1. PROBLEM IDENTIFICATION:
   - List the top 10 distinct problems or pain points mentioned
   - For each problem, provide 2-3 representative quotes from the feedback
   - Rate each problem's severity (1-5 scale) based on language intensity and frequency of mention
   - Estimate how widespread each problem appears to be (percentage of feedback mentioning it)

2. PATTERN ANALYSIS:
   - Identify any correlations between problems (e.g., problems that frequently appear together)
   - Note any patterns related to user types, contexts, or scenarios
   - Highlight any trends in how people are currently trying to solve these problems

3. VISUALIZATION:
   - Create a table ranking the problems by frequency and severity
   - Generate a visual representation of the problem landscape (e.g., concept map showing relationships between problems)
   - If possible, create a simple chart showing the distribution of problem mentions

4. OPPORTUNITY ASSESSMENT:
   - For each top problem, briefly describe how AI might help solve it
   - Rate each problem's suitability for AI solutions (1-5 scale)
   - Identify any gaps or unmet needs that aren't being addressed by current solutions

5. RECOMMENDATIONS:
   - Suggest the 3-5 most promising problems to focus on based on severity, frequency, and suitability for AI solutions
   - For each recommended problem, outline what additional information would be valuable to gather
   - Suggest specific follow-up questions to validate these problems further

Please be objective in your analysis and avoid confirmation bias. Focus on identifying genuine problems rather than validating preconceived ideas.

This prompt turns raw, unstructured feedback into a structured analysis that can directly inform your Problem Bank. It's especially powerful when you've collected dozens or hundreds of pieces of feedback that would be overwhelming to analyse manually.

For best results use a Reasoning model that can take longer to “think through” the data.

If you end up with too much (good job) then first “pre-process” the data by feeding individual methods in and asking for summaries. Then feeding the summaries into this prompt.

What's Next?

The prompt will spit out a handful of potential problems for you to focus on. Exciting!

But hold your horses - first we’ll do some more filtering, sorting and fine detail work to find the best problem. The more pre-production work you do now the better!

In Part 3, we'll tackle the crucial process of categorising and segmenting the problems you've discovered.

Keep Prompting,

Kyle