Hey everyone! 👋
I was getting so tired of our sales and procurement teams having to scramble every time a vendor renewal came up. We were all guessing if a quote was "fair" or not, and it felt like we were leaving money on the table or, worse, straining relationships over price hikes.
So, I spent the last few weeks building an internal pricing benchmark dashboard! It pulls data from:
* Our anonymized contract database (stripped of client names, of course)
* Approved quotes from the last 24 months
* Public pricing pages we've scraped (where allowed)
* Some anonymized data shared in forums like this one (thank you!)
It's not super fancy, but it gives us a quick view of things like:
- Average seat price for marketing automation platforms
- Common discount ranges for annual vs. monthly commitments
- Add-on costs for modules like advanced analytics or extra send limits
The immediate win was spotting that our CRM add-on pricing was about 15% above what we now see as the benchmark range. That gave us the ammo we needed to negotiate before our auto-renewal kicked in! 🎯
I'm curious—what metrics or data points do you all find most valuable when trying to benchmark a tool's price? Do you track things like support tier costs or implementation fees?
Cheers
Keep it simple.
That's a fantastic win, spotting that 15% discrepancy on the CRM add-ons! Those automatic renewals are sneaky.
When I set up something similar, I found the most useful metric was tracking the *variance* in per-seat pricing based on total contract value. It's one thing to see the average seat price, but seeing how much the price drops (or doesn't!) when you commit to 500 seats vs. 50 gave us way better negotiation footing. We built a simple scatter plot for that.
Do you also factor in support tier costs? That's an area where we've seen huge swings, and it often gets buried in the final quote.
Automate everything.
Great point about the public pricing pages! The scraping is a clever idea. We tried that but got blocked by so many bot detection services, it became a full-time job just to keep the data fresh.
How do you handle the legal side of scraping? I'm always a bit nervous about ToS violations, even for "public" data.
We ended up using a third-party data vendor for that piece, which added cost but let us sleep at night. Curious if your legal team had any input.
Automate everything.
That's a solid foundation, pulling from contract data and quotes. I'd argue the most valuable metric isn't any single data point, but establishing a *unit cost* that can be compared across vendors and contract structures.
> average seat price for marketing automation platforms
This is a start, but "a seat" is often not a comparable unit. One vendor's basic seat might include workflow automation, while another's requires an add-on. You need to model the cost of a standardized capability bundle, like the cost per 10,000 automated emails sent per month, which normalizes for those feature discrepancies. This lets you benchmark truly dissimilar tools on the same functional output.
The discount range data is useful for setting expectations, but be careful not to let it anchor you poorly. A 40% discount off a wildly inflated list price is a worse deal than a 15% discount off a fair market price. The benchmark should help you identify that fair market price first; the discount negotiation comes after.
Data doesn't lie, but folks sometimes do.
Absolutely spot on about defining a functional unit cost. We hit this exact wall comparing API gateway vendors - one charges per "request", another per "million events", and a third has a flat fee for the first 10,000 connections. The "per request" metric was useless until we built a workload translator that normalized everything to the cost of processing 1 million of our specific API calls, which have a known mix of simple and complex operations.
Your point about the discount anchor is crucial, too. We started tagging our benchmark data points with a "list price credibility" flag based on how often that list price actually appears in real quotes. It helped filter out the vendors who play the high-list, deep-discount game.
Have you found a good way to handle non-linear pricing, like steep volume tiers or committed-use discounts? That's where our unit cost model starts to bend.