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The hidden cost of competitive intelligence isn't time. It's whose time.
A luxury fashion marketplace had 20 account salespeople. Each spent 6-8 hours per week manually collecting competitor data from retailer websites.
That's 120+ hours every week. Not selling. Not negotiating. Copying numbers from websites into spreadsheets.
The data they collected was 10% complete. Outdated by the time anyone used it. And the negotiations they weren't having? Those were costing the company millions in missed assortment opportunities.
This is Wasted Expertise: when your most expensive talent does your cheapest work.
You've probably calculated the hours your team spends on competitive intelligence. The tool maintenance. The data cleanup. The export formatting.
But hours don't tell the full story.
The question isn't "how much time does this take?" It's "whose time is this taking?"
The same pattern shows up in competitive intelligence:
Each of these people could hire someone at half their salary to do the operational work. But they don’t. So they do it themselves.
Let's make this concrete.
| Role | Typical Comp | Hourly Rate |
|---|---|---|
| Category Manager | $135,000 | ~$65/hr |
| Competitive Intelligence Analyst | $115,000 | ~$55/hr |
| E-commerce Manager | $108,000 | ~$52/hr |
| Data Entry Specialist | $38,000 | ~$18/hr |
When a $135K category manager spends 8 hours a week on data cleanup, you're paying $520/week for $144 worth of work. (Use your own fully-loaded numbers — the ratio is what matters.)
Weekly waste: $376 × 52 weeks = $19,552/year per person
That's one person. Most teams have 3-5 people touching competitive data.
A 5-person category management team with everyone spending 20% of their time on data ops:
The luxury fashion marketplace I mentioned? Here's what their account sales team was doing:
The situation: 150+ third-party sellers, 200+ brands, 10+ categories. Each seller also had their own website with products that weren't listed on the marketplace.
The problem: To negotiate with sellers about adding products, the account team needed to know exactly what was missing. They had no data.
What they did: Manual checking. 6-8 hours per salesperson, per week. 20 salespeople.
The result: After all that work, they had about 10% of the data they needed. The rest was outdated or incomplete. Negotiations failed because they couldn't give sellers exact numbers.
The work wasn't unnecessary. The data mattered. But the people doing it were the wrong people.
Account salespeople should be selling. That's what they're good at. That's what they're paid for. Copying prices from websites is a $18/hour task being done by people who should be closing deals worth thousands.
A furniture retailer in the Gulf had a retail analyst building and maintaining web scrapers.
The situation: Competing against IKEA, Danube, and other major players. Needed competitor pricing data to stay competitive.
What they did: The retail analyst—someone hired for analysis—was writing scrapers. Managing them daily. Fixing them when sites changed. Building PowerBI reports on top.
Time spent: 6+ hours per week on scraper maintenance alone.
Data quality: 30-40% missing. The PowerBI dashboards showed incomplete trends. Dynamic pricing decisions were based on partial information.
This analyst had the skills to build scrapers. That's not the point.
The point is: every hour spent on scraper maintenance is an hour not spent on the analysis the company actually hired them to do.
Nobody plans for this. It just... accumulates.
Week 1: "I'll just write a quick script to pull competitor prices."
Week 10: "The script broke again. Let me fix it."
Week 30: "I spend more time maintaining this thing than using the data it produces."
Week 50: "I'm the only one who understands how it works. I can't hand it off."
By the time anyone notices, it's embedded. The analyst is now the scraper maintainer. The category manager is now the data cleaner. The e-commerce lead is now the export formatter.
The luxury fashion marketplace switched to a managed data service. Here's what shifted:
The 20 salespeople got their 6-8 hours back. Every week.
But the bigger change: they finally had data they could use. When they sat down with a seller, they had exact numbers. "You have 270 products on your site. Only 250 are on our marketplace. Here's the list of what's missing."
That's a different conversation than "we think you might have some products that aren't listed."
The furniture retailer saw something similar:
The analyst still used their skills. Just on analysis, not on scraper babysitting.
Ask yourself:
If the answer to that last question is "more than 10%", you're probably paying strategic salaries for operational work.
We run competitive data collection as a managed service. That means:
Your team gets clean files. Weekly. Without touching a single line of code or fixing a single broken scraper.
The 120 hours that luxury fashion marketplace got back? That's not time savings. That's 120 hours of strategic work that can now actually happen.
If your team spends more than 15 minutes per week on operational handling of data we deliver, we'll credit your account.
That covers feed issues, format problems, missing fields, delivery failures — anything that pulls your people back into data ops. It doesn't include your internal analysis time (that's the point — we want you doing analysis, not ops).
Assumes agreed delivery format and stable destination. If you change systems mid-month, we'll adjust together.