Last week, someone on your team made a pricing decision based on competitor data.
The data showed competitors had dropped prices. They hadn't. The scraper captured a promo price that had already ended. By the time anyone noticed, you'd already reacted to a market reality that didn't exist.
Maybe it was a repricing algorithm that cut prices automatically. Maybe it was a recommendation to leadership that turned out to be wrong. Maybe it was a MAP notice sent to a retailer for a violation that wasn't real.
The pattern is the same: data looked right, decision was made, trust was broken.
This is the conversation we have with companies who come to us after using competitive intelligence tools for a year or more. They don't complain about missing data. They don't complain about the dashboard being hard to use.
They complain they can't act without checking first.
The data exists. It's in the dashboard. It updates regularly. But somewhere between "data collected" and "decision made," trust breaks down.
Untrusted Data Data you have to verify before you can use. The most expensive problem in competitive intelligence — because it's invisible on any vendor's feature list.
🏷️
Wrong Product
Matching errors cascade into every decision
💰
Wrong Price
Promo, variant, or offer-type confusion
⏱️
Wrong Time
Timestamps that don't tell the real story
📋
Weak Proof
Evidence that won't survive pushback
Quick Check: Is Your Data Untrusted?
Before the Monday meeting: Does someone pull up competitor pages to verify prices before presenting them?
Before presenting to leadership: Do you add caveats like "assuming this data is correct" — because you're not 100% sure?
Before sending MAP notices: Do you screenshot the violation yourself — because the tool's evidence isn't enough?
Before feeding the repricer: Is there a manual review step where someone eyeballs the data first?
After a bad decision: Has your team traced a mistake back to wrong data — and now everyone double-checks?
Who this hits hardest:
- Teams running automated repricing or alerts
- MAP enforcement programs with partner/legal consequences
- Anyone presenting competitive intelligence to executives
The Verification Tax
When teams don't trust their data, they build verification into their workflow. Someone checks the numbers before the pricing meeting. Someone spot-checks matches before sending MAP notices. Someone validates exports before feeding them to the repricing engine.
This is rational behavior. If acting on bad data creates risk, you verify first. But it's not free.
The Math
4-8 hours/week × $50-$75/hour = $10,000-$30,000/year per person
For teams with 2-3 people touching competitive intelligence, verification overhead quickly becomes a mid-five-figure annual cost. Money spent not on analysis, not on strategy — but on checking whether the data you're paying for is actually correct.
You pay for the tool. Then you pay again for the people who verify the tool's output.
Why Trust Breaks: Wrong Product, Wrong Price, Wrong Time, Weak Proof
Untrusted data follows patterns. Across many transitions from SaaS tools to managed feeds, we've identified four ways trust erodes.
If you sell mostly barcode-based products, matching is straightforward — and Wrong Price becomes your biggest trust killer. If you sell variants, bundles, or non-barcoded goods, matching is where trust dies first.
If the match is wrong, everything downstream is wrong — even if the price captured is "accurate."
Your dashboard shows a competitor undercutting you by $40. You react. Then you discover: they matched your premium leather version to the competitor's faux leather version. Not the same product. The "price gap" was a matching error.
Or it matches a 6-pack to a single unit and the repricer reacts to a fake undercut. Or it matches the US variant to the UK variant and creates phantom gaps in your regional analysis.
For MAP teams, wrong matches are even more dangerous. You send a violation notice. The retailer responds: "That's not even our product. Check your data." Now your credibility is damaged — and they'll question every future notice.
For categories without universal identifiers — fashion, home goods, anything with variants — matching failures are common. And they're hard to spot because the data looks right.
What this costs
- Repricing based on invalid comparisons
- MAP notices sent for wrong products
- Strategic recommendations based on phantom competitive gaps
- Hours spent manually verifying matches before acting
The scraper captures a price. But which price? Regular or sale? One-time or subscription? In-cart after coupon or displayed? 500ml variant or 250ml?
Most tools capture "a price." They don't capture the context that makes it meaningful.
What causes this:- Promotional timing. A flash sale ends. The scraper captured the promo price. Your team reacts to a price that no longer exists.
- Price type confusion. The tool captures the "Subscribe & Save" price but your comparison assumes regular pricing. The gap is real — but irrelevant to most customers.
- In-cart and coupon pricing. The displayed price is $49.99. Add to cart: $39.99. Apply the popup coupon: $34.99. Most scrapers capture the displayed price — missing the actual transaction price.
- Wrong seller / wrong offer conditions. On marketplaces: same ASIN, different seller, different shipping, different condition, different Buy Box state. The "price" is meaningless without offer context.
Your dashboard says "Last Updated: 9:00 AM." What does that mean?
It might mean every price was captured at 9:00 AM. More likely, it means the job finished at 9:00 AM — and the actual data was collected over several hours, with some prices from yesterday's failed run.
This is the timestamp problem. A global "Last Updated" tells you when the system finished, not when specific data points were captured.
For fast-moving categories — flash sales, limited inventory, competitive responses — the difference between "captured 3 hours ago" and "captured 12 hours ago" is the difference between actionable intelligence and historical trivia.
If you can't see captured_at per row, you can't tell what's still true.
For MAP enforcement, data quality isn't just about accuracy — it's about whether you can prove what you're claiming.
Screenshots alone can be challenged. Without supporting context — URL, timestamp, and a traceable record — your proof may not survive pushback from retailers or legal review.
Where tools typically fail:
In-cart pricing. The violation happens after "Add to Cart" — but the tool only captures the displayed price. You can see the $49.99 list price. You can't prove the $39.99 cart price that violates MAP.
Coupon stacking. A retailer offers a popup coupon that drops the effective price below MAP. Without capturing the full session, you can't document what customers actually saw.
Session-specific offers. Some sites show different prices based on cookies, location, or browsing history. A single screenshot doesn't prove what the typical customer experienced.
What weak evidence costs: If your evidence doesn't hold up, you can't enforce. Violations continue. Retail partners lose trust in your MAP program. And if you've sent notices based on bad evidence before, they'll question every future notice.
What Happens When Trust Breaks
Automation Gets Turned Off
Teams buy repricing automation, hate the data quality, and go back to doing it manually. The automation still exists. Nobody uses it.
Verification Becomes the Job
Pricing analysts hired to develop strategy spend their days checking if the data is right. CI directors add caveats to every presentation.
Evidence Goes Unused
You see violations in the dashboard. But you can't send cease-and-desist notices based on evidence that won't hold up.
Why they disconnect:- Wrong match feeds bad decisions — you destroy margin on comparisons that were never valid.
- Stale data triggers false responses — you're chasing prices that already changed back.
- Missing context creates blind spots — the algorithm defaults to uncompetitive static pricing.
What Users Actually Say
This isn't theoretical. Public reviews on G2 and Capterra show consistent patterns:
- Reviews repeatedly mention incorrect prices after site changes
- Many users say they still have to manually check competitor pages before acting
- Common complaint: monitoring becomes unreliable when competitor sites update frequently
"I often have to check the prices myself and raise the issues." — Capterra review
The pattern is consistent: users describe having to verify manually. The core promise of automated monitoring fails, and humans become the QA layer.
Your Trust Audit
Track verification time — For two weeks, log every minute spent checking or correcting data before using it. Multiply by 26 for your annual verification tax.
Audit automation status — List every automated workflow you planned to use. Check which ones are actually running. If most are disabled or bypassed, trust has broken.
Test evidence quality — Pull a random MAP violation from your dashboard. Could you send a notice based solely on this evidence? If you'd need to manually verify or capture additional screenshots — your evidence isn't enforcement-ready.
Review your last executive presentation — Did you include any caveats about data accuracy? If you're hedging on your own data, trust has broken.
Ask your team directly — "Do you trust the data enough to act on it without checking?" The answers will tell you everything.
When This Is Worth Solving
Untrusted data is tolerable if:- CI is background reference, not decision input
- Team has bandwidth for manual verification
- You're not doing MAP enforcement
- Pricing decisions are infrequent
Untrusted data becomes expensive when:- Pricing decisions need to happen fast
- Automation is part of your strategy
- MAP enforcement has legal consequences
- You present CI to leadership
- Bad data has real margin impact
What Trusted Data Actually Requires
Trust isn't a feature you buy. It's the outcome of the right processes.
1. Validation at multiple layers. Automated checks catch format errors. Business rule checks catch values that don't make sense. Human review catches edge cases algorithms miss.
2. Matching you can rely on. Generic matching algorithms fail on complex catalogs. Trusted data requires matching logic built around how your products actually work — not one-size-fits-all automation.
3. Transparency about freshness. Not just "data updated," but captured_at per row so you know what's still true.
4. Audit trails. When something looks wrong, you can trace back: what was the source URL? When exactly was it captured?
5. Human accountability. Someone who investigates when things look off. A direct channel — not a ticket queue where issues disappear.
What We Do Differently
We run a managed scraping service. We don't sell a tool and leave you to figure out if the data is right.
4-Layer QA Process
1
Automated Validation
Format checks, type validation, duplicate removal
2
Business Rules
Price ranges, MAP thresholds, unusual change alerts
3
Human QA
Spot-checks, flagged items, edge case handling
4
Audit Trail
URL retention, timestamps, change tracking
Matching Built for Your Catalog
We don’t rely on generic matching algorithms that give you a score with no explanation.
Signals We Use- GTIN / UPC / EAN
- Brand, model, variant
- Size, color, capacity
- Image comparison (optional)
Category Rules- Electronics: exact model
- Apparel: color/size OK
- CPG: pack size must match
- Custom rules for you
Human Review- Borderline cases flagged
- Reviewed before delivery
- Request re-review anytime
- Audit trail for decisions
What This Looks Like in Your Data
| source_url | https://competitor.com/product/12345 |
| captured_at | 2026-01-15 08:42:31 UTC |
| price_type | sale_price |
| match_status | Same |
| match_confidence | 94 |
| match_reason | GTIN match + brand/model confirmed |
| qa_status | clean |
| match_status | Similar |
| match_confidence | 78 |
| match_reason | Brand/model match, pack size differs (6-pack vs 8-pack) |
| qa_status | flagged |
| notes | Review before using for price comparison |
No guessing. No "is this right?" Just data you can use — or clear flags when you shouldn't.
Evidence Packages for MAP Enforcement For MAP monitoring, we provide evidence packages on request: screenshot with timestamp, source URL, capture time, price type identified, and audit trail. Cart/coupon pricing when capturable (varies by site).
Evidence packages designed for partner scrutiny — capture trails you can stand behind when challenged.
Accountability When Something's Wrong You get a direct channel and clear ownership when data looks off. We investigate and fix it. You're not on your own.
What Trust Looks Like in Practice
BEFORE
Previous solution didn’t provide evidence quality needed to take action.
AFTER
Evidence-ready data. Sent proof to retailers. Stopped supplying violators.
BEFORE
Previous tool couldn't access key competitor. Data gaps broke algorithm trust.
AFTER
5 years later, still a customer. Data feeds directly to pricing system.
BEFORE
“30–40% of data missing always. Can’t see pricing trends.”
AFTER
“Making decisions for dynamic pricing on regular basis.”
See What Trusted Data Looks Like
If your team spends hours verifying data before acting on it — or if you've disabled automation because you can't rely on the inputs — that's worth a conversation.
Here's what we can do:- 1Send us 3 competitor URLs and your required columns
- 2We'll return a sample feed with timestamps, price-type labels, match confidence with reasons, and QA flags
- 3See what "ready to act on" actually looks like
Request a Sample DeliveryNo commitment.