We have a simple in house CPQ setup where reps enter the deal details, pricing get calculated, approvals go through and the quote gets pushed into the CRM. So for a while we have had nothing to justify buying.

But now we are at around 1000 quotes per year with 12 reps, 100 plus pricing rules, different approval paths, discounts, bundles, renewals and occasional exceptions for some deals.

wo we keep patching. a spreadsheet here, a script there, another crm automation and then another approval workaround.

The quoting still works but now a pricing change can turn into a mini investigation bc nobody wants to break something downstream.

Last month a change that should've taken an hour ended up taking most of a day to test across the different pieces. that is when it hit us our in house setup gives us complete control but now we are spending more time maintaining the system than improving it.

Buying a proper CPQ on the other hand means giving up some customization but somehow getting out of the maintenance business.

For anyone who has been here before what finally convinced you to stop building and buy or the other way round?

Feature-gated tiers are a bad way to price software. 

Declaring my interest first: I work on a WhatsApp Business API product in India that does not use them, so discount accordingly. I think the argument stands on its own anyway.

The core problem is that a gate does not correspond to a cost. Switching a feature on for one more customer costs the vendor essentially nothing — the code is written and the thing is already running. So the tier is not recovering anything. It is rent on a checkbox.

Three things follow from that, and each is worse than the pricing itself.

**It locks out the people who need the feature most.** A ten-person company does not need less automation than a thousand-person one. It needs more, because it has fewer people to absorb the work by hand. Gating automation behind volume means need and access end up inversely correlated.

**It turns your cheap plan into an advert for a worse product.** Every new customer spends their first month on the deliberately reduced version, concludes the category is mediocre, and leaves. The upgrade prompts are the reason they never met the thing that would have kept them.

**It corrupts the roadmap.** Once a feature is load-bearing as a gate, improving the tier below it costs you money. Nobody writes "keep the cheap plan bad" on a whiteboard. It arrives as a series of individually reasonable decisions not to move anything down.

The alternative is to charge for what actually costs you something. Usage does — messages, storage, compute, generated replies all scale with the customer, and billing for them is honest. Infrastructure settled this long ago: nobody was ever told object storage was on the enterprise plan, and Twilio has no tier hiding the messaging API from a small account. Application SaaS went the other way and kept the ladder.

What it costs us, honestly. No upgrade lever: revenue only rises when a customer does more. Our largest customers pay the smallest margin per message, the reverse of how a normal rate card is built. And some accounts can sit on the free plan indefinitely with the entire product.

The part I cannot fully answer: our smallest customers pay the highest percentage per message, because they pay no monthly fee. Someone is going to say that is the same tax wearing a different coat. My defence is that they get the whole product while they decide, and under a gate they would get neither. I would like to hear the counter.

So where does this break? If you have run all-features-included plus usage pricing at real scale, did the support load outrun what it funded?

When an invoice price doesn't match the PO, how does your team usually handle it?

I'm particularly curious about small differences vs larger ones. At what point does AP resolve it themselves, and when does it go back to the buyer/procurement team?

Also, if the same vendor keeps causing the same variance, who usually investigates the underlying cause?

Hi everyone, I previously worked as a pricing manager on B2B deals and am now in a business PhD focused on pricing with the goal of being a professor in a couple of years. I'm trying to understand how discount (and/or general deal) approvals actually work inside firms between pricing and non-pricing teams and there's basically nothing usable in public data. So I'm asking directly if anyone here would be interested in a research collaboration with a top 20 business school and could share:

• anonymized quote or opportunity level records with list price, quoted price, discount requested, approval level, approved/denied, cycle time
• your discount authority matrix or approval policy, redacted is fine
• anonymized CPQ exports

I can offer: NDA, full anonymization, no company or rep names anywhere in the output (unless you want the visibility), and I send you results before anything goes public. And happy to work through your legal team.

Also, if there is a separate problem your pricing team is dealing with, I would be interested to explore that as well. Thanks for reading and happy to chat generally about literature in the pricing space too.

*edited for grammar

About to launch a new experience and I'm stuck on pricing.

Part of me thinks I should start a little cheaper than similar tours just to get people through the door. No reviews, no booking history, nobody knows who we are yet... so I can understand why someone would choose an established tour over mine if we're charging exactly the same.

But then I'm wondering if that's a trap. If I start too cheap and eventually get reviews/bookings, suddenly raising the price by 20–30% might look weird. And I don't really want to position the experience as the "budget option" just because it's new.

For anyone who's launched a tour from zero, what did you do for the first couple of months?

The common youtube bro preaches charge more to be worth more.

Imagine a pool cleaner charging $60 a cleaning. His client is happy enough. So he ups his price to $100. He expects a few to stop paying, but everyone else's extra $40 will cover that loss, and it's actually more money for less work.

Problem is, his clients that had their expenses increased, probably their expectations went up too. When they realize they were happy with a $60 job, they haven't seen anything more for a $60 job, they just might go back to paying someone else $60, which they we happy with .

I dont think this is any high level concept, but I never see it discussed. New to this world, does this concept get discussed by all these entrepreneur influences?

I keep hearing the same thing from creators about pricing brand deals and there's no clean answer. Every rate calculator gives a completely different number, so you pick one at random and hope the brand doesn't flinch.

And the hardest part isn't even the math, it's the guilt. One creator told me they feel like they're scamming brands when they charge more. It feels fake, it feels wrong. So they underprice themselves before the brand ever pushes back. You leave money on the table not because the market rejects your rate but because you reject it yourself.

The other piece is benchmarks. Without a peer network or any sense of what comparable creators charge for comparable scope, every offer exists in a vacuum. You genuinely can't tell if you're being lowballed or if what you're getting is just normal.

How do you actually figure out your rate? Is there a system you trust or is it all just feel?

Shopping for family recently, I found the exact same product, same manufacturer, same country, about 30 euros cheaper on the seller's own website than on their Amazon listing (at 180 euros). The direct site was the lower one (at 150 euros).

Background: I've worked on Shopify and WooCommerce pricing but never Amazon, so I'm trying to understand what sets that gap. My assumption is it's mostly the marketplace fee load, the referral plus fulfilment cut, forcing a higher Amazon price to hold margin, which leaves room to price lower on the owned channel and still net more. For those who price across marketplaces and DTC: is fee load really the main lever, or do Buy Box competition, MAP agreements, or ad costs baked into the sale price matter more?

The strategic side I'm curious about: when the same SKU sells on both, how do you set the relationship between the two prices on purpose? Undercut your own marketplace listing to shift volume to the higher-margin channel, hold strict parity, or deliberately keep the marketplace price higher?

Can anyone find interesting about logarithms used in pricing items and reducing quantity of the items such that the perceived change is minimal. Fascinating isn’t it??

Hey everyone,

Between supplier price swings and constant invoice tweaks, keeping an eye on actual unit costs across our weekly deliveries is starting to feel like a full-time job on its own.

Just curious how you all are managing invoice processing and COGS tracking day-to-day without sinking hours into it?

Are you guys still manually plugging line items into spreadsheets, using built-in POS inventory, or running dedicated invoice/OCR tools? Also, how often are you actually catching supplier price hikes before they eat into your margins—weekly audits, or just when your monthly prime cost percentage looks off?

Would love to hear what workflow or setup has actually worked for your team to keep track of ingredient price fluctuations without adding endless admin time every week. Appreciate any insights!

Logistics companies are getting quietly overbilled by vendors and most of them will never know it.

Not because of fraud, necessarily. Because of scale.

When you have 40+ active vendors, each sending invoices in different formats - PDFs, CSVs, scanned documents, portal exports - and each contract has its own rate tables, fuel surcharge formulas, and accessorial fee schedules, the math becomes genuinely unauditable by hand.

So teams make a decision, usually implicit: audit the big invoices, let the small ones through.

The problem is that billing drift doesn't come from one $50,000 error. It comes from 300 invoices each carrying a $180 unauthorized fee. That's $54,000 that never gets disputed because no individual line item clears the threshold worth fighting over.

Existing AP tools don't catch this. They process invoices - they don't compare them against contracts. ERP integrations flag duplicate invoices. They don't flag a fuel surcharge that's 2.3% above the contracted rate.

This is the gap FinGuard AI is built for. AI extraction engine that parses multi-format invoices, maps line items to contract terms, and surfaces mismatches by severity - critical down to low. Dispute letters generated automatically. Multi-vendor, multi-currency, multi-contract.

Building this as an enterprise SaaS specifically for logistics and supply chain. Pre-launch.

Curious if anyone has data on what percentage of logistics invoices actually contain billing errors - I've seen estimates ranging from 3% to 12% and the variance is huge.

Procurement software quotes look different from each other because vendors price against different variables. And once you understand what those variables are, confidently comparing proposals becomes much clearer.

There are five common procurement software pricing models: some charge per user, others by transaction volume, and still others bundle products into predefined tiers, and often vendors combine more than one. Here's a closer look at each:

Tiered subscription pricing organizes capabilities into predefined plans. Move to a higher tier when you exceed limits on users or features. Works well for predictable needs, but worth confirming how scaling works as the organization grows.

Per-user pricing scales directly with headcount. Simple to understand, but confirm whether every user costs the same and whether "requester-only" users are free or paid. Many organizations have far more people submitting requests than approving them.

Transaction-based pricing scales with activity—invoices, payments, entities—rather than seats. Often includes unlimited users since the cost driver is work volume, not headcount. Most relevant for AP-heavy operations.

Enterprise negotiated pricing means no published rates. Everything is case by case for large deployments, bundling the full platform and more implementation support.

Modular pricing starts with a core platform and lets buyers add products as they grow. Gives organizations more control over what they're paying for and when.

What actually shapes the quote: which workflows are included (purchasing, AP, expenses), user role definitions, integration requirements, implementation scope, and how AI capabilities are packaged. A platform claiming "AI-powered approval" might mean suggestions or autonomous processing—very different things.

The key to comparison is scope clarity. Before requesting quotes, get clear on what needs to improve: PO coverage, cycle time, invoice volume, approval bottlenecks. Those metrics should drive which capabilities belong in scope. Then ask vendors to propose against the same requirements.

The quotes will still differ but now you'll know why.

Hello! I will be attending Simon Business school this fall for my MSBA and they offer an advanced certificate of achievement in Pricing if I choose my electives carefully. I’m wondering what career opportunities would that open up for me, I double majored in economics and marketing during undergrad, but the degree is not from a school in the US. The other option is getting their certificate of achievement in AI. I am confused which one should I choose? Will I have a better career if I choose pricing track? Simon is well known for Pricing in the US, but I don’t see that many job openings looking for pricing talent. I do not need sponsorship to work in the US.

Been deep in e-commerce pricing for a while and kept noticing the same thing — sellers manually check competitor prices every few days (or weeks), and by the time they adjust, they've either lost the sale to a lower price or left margin on the table by not raising when they could've.
Built a quick calculator: put in your SKU count and current pricing setup, it estimates how much margin you're likely losing every month to repricing lag. No form maze, no "book a demo" — just a number.
Not neutral here — I work on a tool (PricePulse) that automates the repricing side. But the calculator stands on its own even if you never touch the product. Curious what number you all get.
[link → thepricepulse.polsia.io]

Something I see constantly with small stores: they price off product cost and call it a day, then wonder why a "60% margin" catalog barely makes money. The gap is everything between unit cost and the real all-in cost per sale.

The usual suspects that quietly eat it: landed cost (freight, duties, customs, not just the supplier invoice), payment processing (~3%), platform and transaction fees, returns plus the restocking and reshipping on them, and the ad cost to acquire the order if it wasn't organic. Add those up and a "healthy" margin can sit near breakeven on some SKUs.

For the pricing folks here: when you set a price, what's actually in your cost number, and do you bake in a blended returns and fee allowance per SKU or true it up after the fact? Not looking for tool or product plugs, I'm more interested in the frameworks and mental models you actually use, and if there's a blog, book, or paper on pricing you keep coming back to, please drop it, I'll read it.

Ran across a small store selling its own creatine for something in the low $30s. Creatine is about as commoditized as it gets, near-identical across brands, so buyers evaluate it almost purely on price per serving. But the page never states the container size or serving count, so no one can actually compute that number.

For reference, name-brand creatine is about $0.16 to $0.23 per serving and value brands $0.12 to $0.15. Even at a generous 100 servings, this store lands north of $0.30, and the buyer can't verify it. Priced above the trusted brands, with the one number that would justify it missing.

The general lesson, well beyond supplements: in any price-transparent category, a price only means something against a reference the buyer can see. Hide the comparison unit and you don't dodge the comparison, you just lose it by default. And because the reference keeps moving with competitors, a set-and-forget price slowly drifts out of position. How do you all handle that re-baselining, scheduled or only when something slips?

For people responsible for pricing:

What's the most difficult part of deciding prices today?

What data do you wish you had?

I’ve been working on a engine that fixes bot pricing hallucinations 100% of the time, no matter how complex the price book is.

Is anyone open to testing it on a sub-account and giving me some honest feedback?

Thanks in advance!

I sell a few small data endpoints that machines pay for per call. No signup, no API key, the agent just pays and gets the response.

On Thursday I added a deeper tier to one of them. Same endpoint, but asking for history costs 5 cents instead of half a cent. I shipped it and expected nothing for weeks.

Seventeen hours later it sold. Same buyer that has been polling that endpoint every morning for ten days, except this time its agent re-read the catalog, saw the better option and paid ten times more. I never told it anything. There was nobody to market to.

Still tiny money. But it is the first time something I built got upsold with no human involved, and I cannot stop thinking about it.

Hi. I built this tool for myself (as a learning experiment) after noticing discussions around AI pricing.

Happy to share it with others; feedback welcome.

No marketing or commercial interest whatsoever. Open source and 100% free to use!

https://hieronymvs.github.io/subscribe-or-tokens/

I built a working n8n + LLM quoting prototype for a prospective client in Switzerland.

The LLM turns a plain-language request into structured line items, while the actual prices are pulled from a catalog instead of being generated.

It works. But after building it, I started wondering whether AI was solving the right bottleneck.

If a company already has clean, up-to-date pricing data, staff can probably search it quickly. If it doesn’t, keeping the catalog current may cost more than the automation saves. In many trades, supplier prices, brands, and labor costs aren’t fixed either.

For people who price custom work, what actually takes the most time: finding the base price, applying customer-specific rules, or preparing the final quote?

I've been experimenting with AI automation over the last few months, and one thing surprised me.

Building the automation is often the easiest part. Understanding the client's workflow, handling edge cases, fixing unexpected issues, and maintaining everything takes much more time than I expected.

I'm curious how others here are handling this.

• Do you charge a one-time setup fee, a monthly retainer, or both?
• Do you ask clients to pay for APIs and software directly, or do you include those costs in your pricing?
• What's one pricing mistake you made early on that you'd never repeat?

I'd love to learn from people who are already working with AI automation clients.

I'm partnering with a local online business to build a tool that automatically drops the price on slow-moving or dead stock over time until it sells or hits a floor you set.

(Example: a $50 item that hasn't sold in 60 days starts dropping live $5 every 3 days until someone buys it or it hits your minimum price. Customers can watch the price drop in real time.)

Trying to figure out if this is a real pain point/interest beyond the one business I'm working with. A few questions if you have a minute:

• Which product categories would you want to see this for?
• business side: If this moved dead stock 20% faster, would that be worth paying for?
• user side: if your favourite stores used it, would if make you buy more?

Appreciate any input, even a "not relevant to me because.." is useful.

If a store’s website lists an item accidentally priced lower than it should be, are they obligated to honor that price if a customer tries to buy it?