Before joining Browse AI, I felt like I was always playing competitive intelligence catch-up.
"Hey, did you know [Competitor] dropped their enterprise pricing by 30%?"
"Wait, when did they add that starter plan?"
"We just lost three deals to their promotion. Did we know that was coming?"
The answer was always no. Despite spreadsheets, quarterly calendar reminders, and best intentions, I always felt like I was chasing competitor pricing moves (or considering expensive software to solve the problem).
Now, as Head of Marketing at Browse AI, I've built what I call the "Competitor Price Stalker" - an automated monitoring system that watches competitor pricing 24/7 and alerts me the moment anything changes.
But here's the thing: I didn't build this just because I work here. I built it because I needed it. And after seeing the results, I realized every marketer needs this.
The impact in the last quarter alone:
- Caught 3 major pricing changes within hours (not weeks).
- Saved revenue by countering promotions immediately.
- Discovered two competitors' expansion strategies through their pricing tests.
- Gave our sales team unbeatable competitive intelligence.
Here's exactly how I built it in 8 minutes, and how you can too.
Why every marketing leader needs automated price monitoring
Let's be honest – manually checking 5-10 competitor pricing pages daily isn't happening, or isn’t happening frequently enough. We're all too busy with campaigns, content, and strategy.
The problem is, your competitors are constantly testing and tweaking:
- Running promotions that steal your pipeline
- A/B testing prices to find the sweet spot
- Adding new tiers that undercut your positioning
- Shifting features between plans to change the value equation
- Testing regional pricing strategies
When you find out about these changes from lost deals, it's already too late.
What my price monitoring robot actually does
I built a Browse AI robot that:
- Monitors all competitor pricing changes 24/7 – I get alerted when things change.
- Catches A/B tests in real-time – see what they're testing before they roll it out.
- Tracks new plan additions – know about new tiers before the announcement.
- Spots feature shuffles – "API access" moved to a lower tier? I know immediately.
- Captures promotions – Black Friday starts at midnight? I have a counter-offer by 9 AM.
- Documents everything – full pricing history for trend analysis.
The best part? Once it's set up, I never have to think about it again. It just sends me alerts when something changes.
Scenarios you could catch
The sneak attack
Your biggest competitor quietly launched a "Growth" tier positioned perfectly between your Starter and Pro plans. Your robot caught it at 2 AM on a Tuesday. By your Wednesday sales standup, have have talk tracks ready.
The holiday ambush
During Black Friday, a competitor launched a 48-hour flash sale at midnight. You get the alert at 12:05 AM, and have a matching promotion live by morning.
The feature shuffle
Your competitor moved "Advanced Analytics" from their $299 plan to their $99 plan. This tells you two things: (1) they were getting price pressure on that feature, and (2) you could emphasize your advanced analytics as truly premium. Win rate on enterprise deals increased 15%.
The regional test
Caught them testing 30% off in the Southeast region only. This revealed they were struggling in that market. You increased our sales focus there and grabbed significant market share.
Why Browse AI is perfect for price monitoring
Working here, I've seen thousands of use cases, but price monitoring remains one of the highest-ROI applications for me because:
- No coding required – I built my first robot in literally 8 minutes.
- Handles complex pricing pages – dropdowns, tabs, dynamic content? No problem.
- AI-powered change detection – catches even subtle changes I'd miss.
- Integrates everywhere – sends data to Slack, Google Sheets, our CRM, anywhere.
- Scales effortlessly – monitoring 20 competitors costs the same effort as monitoring 2.
Plus, with 500k+ users trusting us for business-critical monitoring, I know the infrastructure is rock-solid.
My step-by-step build process
Here's exactly how I set up monitoring for each competitor:
Step 1: Quick prep (1 minute)
You’ll need:
- A Browse AI account (we have a free tier to start)
- The URL of your competitor’s pricing page
- An idea of what data points matter most
Step 2: Build the robot (5 minutes)
- Login to Browse AI
- Click " Build New Robot"
- Select “Extract Structured Data”
- Entered the pricing page URL and select “Start Training Robot”, and select “Use Robot Studio”
- Browse AI will then load the pricing page. From there click on each element you want to monitor, including :
- Capture text
- From a list - hover to capture the pricing details as a list.
- Just text - capture specific text on the page and label it.
- Screenshots - I like to capture a screenshot of the entire page, and a partial screenshot of data that’s difficult to structure.
- Capture text
- You can point and click to navigate through the page to continue to capture data. Perfect for ‘monthly’ vs. ‘annual’ toggles, tabs on the page, and more.
- When you’ve captured everything, click “Finished”.
- At that stage your robot will run and extract the data. Review and approve your robot by selecting “Looks good to me”.
- If you make a mistake, or the data doesn’t look accurate you can retrain your robot if needed.
Step 3: Configure monitoring (30 seconds)
- Once your robot is approved, select “Monitor”.
- Configure the monitor settings and alerts.
- Select “Save” and your monitor is live.
Step 4: Set up my analysis flow
- Connected to Google Sheets for automatic logging.
- Created a pricing history dashboard.
- Set up a Slack channel for competitive alerts.
- Built LLM templates for analyzing changes (more on this below).
Pro tips from 6 months of price monitoring
Handle complex pricing pages
- Dropdowns? Train your robot to click through them
- Regional pricing? Set different geolocations for different robots
- Login required? Use Browse AI's cookie saving feature
Always capture more than prices
I learned to monitor:
- Strike-through pricing (shows discount depth)
- "Most Popular" badges (reveals their focus)
- Annual vs. monthly gaps (indicates cash flow needs)
- Payment methods (enterprise readiness signals)
- Currency options (expansion market hints)
Intelligence goldmines from your data
Based on what you're capturing, also monitor:
- Usage allocation changes
- Feature movement between tiers
- New features appearing
- Enterprise readiness indicators
- Limit changes
- Support tier modifications
My integration stack
- Google Sheets: historical tracking and trends
- Slack: instant alerts for the team
- ChatGPT/Claude: deep analysis of changes
The power of screenshot monitoring
Why I always capture screenshots alongside data
Screenshots are your secret weapon for catching what structured data misses. Here's my approach:
1. Full page screenshots for design intelligence I capture the entire pricing page (yes, even if it's massive). Why? Design changes often signal strategy shifts before the numbers do:
- New "RECOMMENDED" badges appear → testing positioning
- Color emphasis changes → different tier focus
- Layout reorganization → buyer journey optimization
- New comparison tables → competitive positioning shifts
2. Partial screenshots for complex data Some pricing elements are nearly impossible to scrape cleanly:
- Interactive calculators
- Sliding scale pricing
- Complex comparison matrices
- Nested feature dropdowns
My hack? Capture a screenshot of just that section, then feed it to Claude or ChatGPT:
"Convert this pricing calculator screenshot into a structured table showing all pricing tiers and variables"
The AI extracts the data perfectly, even from complex visual elements.
How I analyze the data with AI
The real magic happens when you feed this data to AI. Here are my go-to prompts:
My favorite analysis prompt:
Compare our pricing vs this competitor update:
OUR PRICING: [paste our current pricing]
THEIR NEW PRICING: [paste the changes]
THEIR OLD PRICING: [paste previous version]
Tell me:
1. What strategy shift does this indicate?
2. How should we respond (if at all)?
3. What sales objections will this create?
4. Quick wins we can execute this week
For creating the ultimate comparison:
I need to create a competitive pricing intelligence report. Here's data from 5 competitors:
[PASTE ALL CURRENT COMPETITOR DATA]
Create:
1. EXECUTIVE SUMMARY TABLE
- Company | Lowest Tier | Highest Tier | Sweet Spot | Unique Value Prop
2. DETAILED FEATURE MATRIX
- Feature name | Competitor A | Competitor B | Competitor C | Us
- Mark where each feature becomes available
3. PRICING STRATEGY ANALYSIS
- Who's cheapest/most expensive and why
- Value per dollar calculations
- Positioning strategy for each
4. OPPORTUNITY IDENTIFICATION
- Features only we have
- Features everyone has but us
- Pricing gaps we can exploit
- Underserved segments
5. RECOMMENDED ACTIONS
- Immediate wins (this week)
- Strategic moves (this quarter)
- Long-term positioning (this year)
Format everything as tables I can share with my executive team.
Turning messy data into competitive intelligence
The problem with raw pricing data
After monitoring 5 competitors for 6 months, you'll have dozens of CSVs, screenshots, and alerts. The gold is there, but it's buried. Here's how I transform it into actionable intelligence.
My monthly competitive analysis workflow
Step 1: Consolidate the data I dump all my pricing CSVs and screenshots into an organized folder or project.
Step 2: Create the master comparison This is where AI shines. I use this prompt:
I'm sharing pricing data from multiple competitors captured over time. Create a comprehensive comparison table.
COMPETITOR A - CURRENT:
[Paste latest CSV/screenshot data]
COMPETITOR B - CURRENT:
[Paste latest CSV/screenshot data]
COMPETITOR C - CURRENT:
[Paste latest CSV/screenshot data]
Create a master table showing:
1. All pricing tiers across competitors (side by side)
2. Feature comparison matrix (what's included in each tier)
3. Key differentiators highlighted
4. Gaps and opportunities marked
5. Price-per-value calculations
Format as a table I can paste into Google Sheets.
The master intelligence dashboard
Every month, I maintain a single "Competitive Pricing Intelligence" sheet with tabs for:
- Current state: latest pricing across all competitors.
- Change log: every change detected with date and impact.
- Feature matrix: who offers what at which tier.
- Price per feature: value calculations.
- Trend analysis: patterns and predictions.
- Action items: how we should respond.
Common challenges (and how I solved them)
"But their pricing page requires login"
- Use Browse AI's cookie saving feature.
- Build the robot while logged in.
- It maintains the session for future runs.
"They have different regional pricing"
- Build separate robots for each region.
- Set the geolocation in Browse AI.
- Compare results to spot regional strategies.
"The page is super dynamic/complex"
- Try different combinations of extracting ‘Just text’ vs. “From a list” depending on how the page is structured.
- Capture a partial screenshot and convert that data into a table using Chat GPT, Claude or another LLM solution.
- Capture the entire page’s HTML, or just the text using these prebuilt robots.
Bonus: More LLM analysis prompts for pricing analysis
Here are some additional prompts you can use with the captured pricing data.
Prompt 1: Strategic Pricing Analysis
I'm sharing competitor pricing data collected over [time period].
Please analyze:
[PASTE YOUR PRICING DATA HERE]
Provide insights on:
1. Overall pricing strategy for each competitor
2. Target market based on price points
3. Value proposition implied by pricing tiers
4. Potential vulnerabilities in their pricing
5. Recommended competitive responses
Format as an executive brief with specific actions we should take.
Prompt 2: Price Change Pattern Detection
Here's historical pricing data from [competitor] over the last [X months]:
[PASTE HISTORICAL PRICING DATA]
Analyze:
- Frequency and timing of price changes
- Average discount depth during promotions
- Seasonal patterns or triggers
- A/B testing patterns (if detectable)
- Predicted next moves based on patterns
What does their pricing behavior tell us about their business health and strategy?
Prompt 3: Competitive Pricing War Room
Compare our pricing vs competitors:
OUR PRICING:
[PASTE YOUR PRICING]
COMPETITOR PRICING:
[PASTE ALL COMPETITOR DATA]
Create:
1. Feature-per-dollar comparison matrix
2. Market positioning map (value vs. price)
3. Competitive gaps and opportunities
4. Win/loss likely scenarios by customer segment
5. Suggested pricing adjustments with rationale
Include specific talk tracks for sales.
Prompt 4: Promotion Strategy Decoder
Recent promotional offers detected:
[PASTE PROMOTION DATA WITH DATES]
Analyze:
- Promotion patterns (timing, duration, depth)
- Target customer indicators
- Desperation vs. strategic discounting signals
- Market conditions that might trigger promotions
- How to counter without racing to the bottom
What's their promotion playbook and how do we beat it?
Prompt 5: Feature Monetization Tracker
Tracked feature changes across pricing tiers:
MONTH 1: [Features by tier]
MONTH 2: [Features by tier]
MONTH 3: [Features by tier]
Identify:
1. Features moving up/down tiers
2. New features and their initial placement
3. Feature unbundling/bundling trends
4. Value perception strategies
5. What this reveals about their roadmap
How should we adjust our packaging?
Prompt 6: Multi-Competitor Market Intelligence
Compile this competitor pricing data into market intelligence:
COMPETITOR A: [Pricing data]
COMPETITOR B: [Pricing data]
COMPETITOR C: [Pricing data]
US: [Our pricing]
Provide:
1. Market pricing bands by segment
2. Industry pricing trends
3. Our competitive position
4. Pricing pressure points
5. Blue ocean pricing opportunities
6. 90-day pricing strategy recommendation
Prompt 7: A/B Test Reverse Engineering
We've detected these pricing variations:
VERSION A: [Pricing/features]
VERSION B: [Pricing/features]
GEOGRAPHIC/TIME DIFFERENCES: [Details]
Determine:
- What hypothesis they're testing
- Target metric (conversion, revenue, ACV?)
- Which version is likely winning and why
- What this test reveals about their challenges
- How we can leverage this intelligence
Prompt 8: Sales Battlecard Generator
Based on this competitive pricing intelligence:
[PASTE ALL RECENT PRICING DATA]
Create sales battlecards addressing:
1. "They're cheaper" objection handlers
2. Value justification talk tracks
3. Feature comparison wins at each tier
4. ROI calculations vs each competitor
5. When to compete vs. walk away
Make it scannable for a rep in a live call.
Prompt 9: Pricing Psychology Analyzer
Analyze the psychological tactics in this pricing:
[PASTE PRICING PAGES CONTENT INCLUDING LABELS/BADGES]
Identify:
- Anchoring strategies
- Decoy effects
- Social proof elements
- Urgency/scarcity tactics
- Cognitive biases being exploited
How can we counter or adopt these tactics?
Prompt 10: Market Disruption Predictor
Given this 6-month pricing history across the market:
[PASTE COMPREHENSIVE MARKET PRICING DATA]
Predict:
1. Who's likely to disrupt pricing next
2. Potential new entrant strategies
3. Market consolidation indicators
4. Price war probability
5. 12-month market scenarios
What defensive moves should we make now?
Prompt 11: Meta-Prompt for Custom Analysis
I need to analyze competitor pricing data for [specific goal]. Here's my data:
[PASTE DATA]
My specific questions:
1. [Your question]
2. [Your question]
3. [Your question]
Provide actionable insights formatted for [audience: executives/sales/product].
Pro Tips for Using These Prompts
- Data Preparation:
- Include dates for all captures. If you’re using ChatGPT or Claude you can keep this data in a project.
- Note any special conditions (promotions, holidays).
- Include and capture a full feature list if you can, not just prices.
- Context Matters: providing more context about your business will get you better results
- Your industry/market
- Company sizes (startup to enterprise)
- Business model context (SaaS, marketplace, etc.)
- Iteration Strategy
- Start with broad analysis
- Deep dive into interesting findings
- Generate specific deliverables (battlecards, strategies)
- Combining Intelligence Feed outputs from pricing analysis into other prompts.
P.S. This is part 1 of my "Marketing Brain" series, where I'm sharing how I've automated competitive intelligence. Next week: How I mine competitor reviews for sales gold.
