Your competitors have blind spots. They’re spending money in the wrong places, ignoring customer complaints, and repeating the same positioning as everyone else in your market. They just don’t know it yet.
AI can find those blind spots in minutes.
Ai can do something marketing teams never have time to do well: systematically gathering, organizing, and analyzing everything your competitors are saying publicly, then identifying the gaps between what they promise and what customers actually experience.
Here’s how to do it.
The Old Way vs. The AI Way
Traditional competitive research meant 20 browser tabs, a half-organized spreadsheet, and the nagging feeling you were missing something obvious. It took days, and depended entirely on whoever had time to do it.
AI-assisted research changes what’s possible. You can now pull live data on what competitors are doing this week, analyze the structure and language of their messaging, and surface strategic gaps – all in a single session.
The key is knowing what each AI tool does best, and using the right one for the right task.
As the time of this writing, here’s our preferred workflow for competitor research:
- Perplexity: What’s happening right now. It pulls live web data with cited sources. Use it to track recent product launches, messaging changes, and customer reviews. A good prompt: “What are recent G2 and Capterra reviews saying about [Competitor]? Focus on complaints and friction points.”
- Claude: What does it all mean. It handles longer inputs and produces structured analysis. Feed it competitor homepages and product pages, and ask it to identify positioning patterns, value proposition hierarchies, and what pain points they’re addressing — or ignoring.
- ChatGPT: Where’s the opening. Once you have structured analysis, use ChatGPT to find the white space. Ask: “Based on this competitor analysis, what positioning gap exists that neither competitor is addressing?”
You don’t have to use all three every time. But understanding what each does well means you stop treating them as interchangeable — and start getting better answers.
Three Tools, Three Jobs
Step 1: Perplexity for live reconnaissance
Perplexity functions as a field scout. It pulls live web data, recent articles, and current signals with cited sources, and it answers a question no other tool in this workflow can answer as well: What are competitors actually doing right now?
The table below shows where Perplexity’s strengths are clearest in gathering raw material.
Suggested prompts
- “What are [Competitor]’s latest product launches in the past six months?”
- “Summarize [Competitor]’s current positioning and messaging with sources.”
- “What are recent reviews or complaints about [Competitor product]?”
What to collect
Look for messaging patterns, pricing signals, recent campaign themes, product updates, and any customer language worth noting. These become your raw inputs for Step 2.
Free-tier note
Perplexity’s free tier includes live search, which makes it genuinely useful here. Daily query limits mean each search should target a specific research objective rather than broad exploration.
A focused Perplexity query might reveal, for example, that a competitor launched a “one-click onboarding” feature in January and reinforced that message across multiple product updates, with customer reviews specifically calling out faster setup time.
That specificity creates raw material. Vague queries create noise.
Step 2: Claude for structural analysis
Claude functions as an analyst. It handles longer inputs than most tools and produces organized, structured breakdowns.
This step converts the scattered data from Step 1 into a coherent picture of how a competitor thinks about their market and their customers.
Feed Claude competitor homepages, product pages, and messaging samples, then ask it to identify patterns and structure rather than summarize.
Suggested prompts
- “Analyze this competitor homepage and break down their positioning, value propositions, and implied target audience”
- “Compare these two competitor landing pages and identify their key strategic differences”
- “Extract the core messaging themes and hierarchy from this content”
What to look for
Positioning frameworks, the order in which claims appear (headline through call to action), repeated language patterns, and signals about the target audience’s pain points all deserve attention at this stage.
Free-tier note
Claude’s free tier supports moderate-length inputs, though conversation limits can become a constraint when working with large volumes of content. Breaking inputs into smaller sections, or prioritizing key pages, keeps the analysis manageable.
Step 3: ChatGPT for synthesis and strategy
ChatGPT functions as a strategist. It takes structured inputs and produces insight, narrative, and direction. By this step, the gathering and structuring work is done. The focus shifts entirely to turning analysis into decisions.
Suggested prompts
- “Based on this competitor analysis, what positioning gaps can we exploit?”
- “Turn these insights into a differentiated messaging strategy”
- “What strategic risks do these competitor trends create for us?”
What to look for
Specific differentiation opportunities, a clear strategic framing, actionable recommendations with reasoning, and a narrative you could actually present to a team.
Free-tier note
ChatGPT’s free tier does not include web browsing, which aligns perfectly with its role in this workflow. The data gathering is already done. ChatGPT needs only what you give it.
Example: Finding Competitor Gaps
BrightDesk is a fictional B2B SaaS company competing in the crowded customer support space. Their marketing team suspected their two main competitors had a weakness; they just couldn’t put their finger on it. So they ran three targeted queries, one tool at a time.
They ran the three-step workflow outlined above, using targeted prompts at each stage to move from raw data to a clear strategic recommendation.
Step 1: Perplexity surfaces the landscape
They started with what customers were actually saying. Two prompts:
“What messaging changes has Zendesk made to its homepage and product pages in the past six months? Include sources.”
“What do recent G2 and Capterra reviews say about Zendesk and Freshdesk? Focus on complaints and repeated friction points.”
Perplexity returned cited results showing both competitors had shifted their messaging toward “AI-powered ticket resolution.” Customer reviews told a different story: speed was praised, but onboarding kept coming up as a frustration — multiple reviewers, unprompted, mentioned struggling to get their teams operational.
That’s the raw material. On to Step 2.
Step 2: Claude structures the patterns
The team fed both competitors’ homepages and product pages into Claude with this prompt:
“Here are the homepages and product pages for two B2B customer support platforms. Analyze both and identify: their primary value proposition, the order in which they present their key claims, what pain points they address, and what they don’t address. Note any patterns that appear across both.”
Claude confirmed what the reviews hinted at: both companies led with automation and buried onboarding support deep in secondary navigation. Neither treated ease of setup as a selling point — even though customers were flagging it as their biggest frustration.
Now the question was what to do about it.
Step 3: ChatGPT finds the opening
The team fed ChatGPT the Claude analysis and the Perplexity review data, then asked:
“Based on this competitor analysis, what positioning gap exists that neither competitor is addressing? Recommend a primary value proposition for a competing product that could own the space they’re leaving open.”
The answer was sharp: both competitors were racing toward the same “do more, faster” message. Neither was speaking to the buyer who lies awake wondering how long it’s going to take to get their team up and running.
The recommendation: lead with “Your team is fully onboarded in a week.” This was a claim no competitor was making, aimed directly at the frustration their own customers kept raising.
Three tools. Three focused prompts. One clear direction.
Where AI-Assisted Research Breaks Down
AI sounds like it knows what it’s talking about, even when the underlying analysis is thin. A readable report is not the same as rigorous reasoning, and the failure points are predictable — which means they’re preventable.
Garbage in = garbage out. “What are my competitors doing?” tells you almost nothing. “What pain points do Zendesk’s customers mention most in recent reviews?” gives you something to act on. The quality of your question determines the quality of your answer.
Citations still need verification. Perplexity cites its sources, but even live search results can lag on fast-moving topics. Our rule of thumb: AI gets you 80% of the way there; humans need to add the final 20%.
AI can’t see what isn’t public. Your sales team hears real-time competitor objections the moment they happen. Support tickets carry emotional context no dataset captures. When the market shifts fast, one conversation with a sales rep can tell you more than a week of AI research. Build those internal signals into your competitor research.
Marketer Takeaways
Competitive research gets stronger when the workflow matches the tools to what they actually do well.
- Sequence improves output quality. Assign each tool a clear role rather than using them interchangeably. The order matters as much as the tools themselves.
- Start with current data, end with strategy. Gathering, structuring, and synthesizing each require different capabilities. Collapsing them into a single step weakens all three.
- Prompt with specificity. Vague inputs produce vague outputs. The more precisely you frame the question, the more useful the answer.
- Work within free-tier constraints deliberately. Query limits and input restrictions can push you toward more focused, better-designed research rather than broad exploration.
- Combine AI with internal intelligence. Sales, support, and direct customer knowledge strengthen every conclusion the workflow produces. Build that combination into your process, not as an afterthought.
Media Shower’s AI marketing platform helps you turn competitor insights into competitive content. Click here for a free trial.
