Dear Pricing Fellows,
This newsletter was inspired by a few pricing projects I'm personally leading. To be precise, while keeping confidentiality in mind:
- an AI workflow and process documentation platform
- a huge construction ERP business
- an agentic restaurant marketing management system
- the property management system for hotel chains
Projects from the US, DACH, Poland, and the Nordics, so I would say it’s a small but good sample of a general SaaS business.
All of these projects, given the scope of the engagement and the proposal we created with the clients, include a competitive scan analysis. In other words: we were asked to check the prices, models, packaging, and commercial strategies of a few peers.
Most of the drafts I have received from my clients, along with the pre-work they have done and the competitive analysis, are good. They are very strong, nicely sourced materials, especially when the data is publicly available.
Of course, most of them were AI-generated, which is completely normal right now; however, this leads me to a more important question: Is scanning your competition still valuable in the AI world, and was it even valuable in the past?
This newsletter is based on the separate article I have written on my AI Monetization podcast, so if you want the whole thing, here it is:
A fast pace kills your insights.
First, price changes are faster and more dramatic, and top players iterate on their commercial strategy every 6 weeks, given faster product life cycles. If you ship faster, you change pricing faster, simple as that. Let’s say you’re competing on an AI product, and you have about 6-12 peers; half of them are your direct competitors, the other half are benchmarks from other markets - that’s how companies usually approach it.
Given the pace, there is a non-zero chance that if scanning them takes a week (even with an LLM), and you discuss the insights with the team and iterate on them, someone already changed something. It doesn’t mean the whole exercise goes to trash, but the data's actual value drops dramatically.
Secondly, and it always has been like this: it's one thing to know and another to mimic. You probably wouldn't outsource your product strategy or sales department to your competition, yet companies tend to outsource their pricing strategy. They are “pricee” rather than being a “pricer”. Let me remind you that pricing and monetization are the single strongest source of margin creation, much stronger than increasing your sales or reducing your costs.
The usual motion is: “competitor X changed something, we need to do the same,” and it kills your entire roadmap or brings additional anxiety (like you don’t have enough of it).
To sum up, in some verticals, it's almost impossible to run an analysis that will hold for 3 weeks, if not longer. Relying on these insights means you don't necessarily copy the right model just because you decided it was a good idea. Then the competitor drops it because they have their own internal data. It's like mirroring something when you don't know whether it's good.
A common denominator problem
This is also purely about capability and pricing knowledge. AI can run a solid analysis of your competitors and gather all the data, which is super useful and saves a ton of time. That said, it still leaves us with the free AI problem: essentially putting all the numbers and data into a common denominator.
In my experience, clients are great at sourcing raw materials and gathering pricing pages, but not necessarily synthesizing them into a useful analysis. A simple example is comparing two players with different value metrics. We may learn that one company charges per user and the other charges by active projects, but what does that mean for a potential customer, and how should we respond?
One method we're using is a standard, old-school total cost of ownership (TCO) analysis that lets you pick one use case and compare prices across it.
Here's the catch: you probably have 6 or 7 different use cases. This means that, to run this analysis, you need to multiply it by the same number of use cases for your peer group. That matrix creates an enormous exercise, and it gets harder with every step you take.
Also, your product creates a completely different differentiating value than your peers, because at the end of the day, you wouldn't create a product at all if you were the same. This leaves you focusing on something that isn't really comparable.
So what do you do with it? The answer is: ruthless prioritization.
The way out of the competitive scan problem
There are no silver bullets, but you need to adopt a value mindset: think about what the potential customer wants to do with the product (aka jobs to be done), then think about which competitors are actually answering the same jobs as you do.
Before you start scanning pages, comparing packages, and everything, try to figure out the commodity problem: how many of the products on the market are actually homogeneous and comparable to each other. If there's even a small chance we are answering slightly different jobs, I would either drop them from the analysis or caveat them properly.
Then, think about things that allow you to be actionable in terms of your own pricing strategy. Usually, there are three things you can look at and act on.
- Use TCO as a potential exercise to compare prices for solving the same job to be done. This gives you a more or less accurate estimate of how well you're priced to solve this job versus competitors. You then need to answer whether you are actually better at performing this job or worse. This compares the value you create with the value you captured in your overall economic model.
Simple rule of thumb: if we roughly give the same value as the competitor but are underpriced, we can consider increasing; and vice versa. If our value is lower, it may be a good way to incorporate that into our pricing strategy. The relationship between value and price should always be your first thought when changing anything related to prices and the monetization model.
- Focus on value-metric comparisons to identify which model is more scalable and better aligned with the actual value created. We don't necessarily want to compare which model is more cost-efficient for a customer, but rather whether we have signs that someone is using a model. That better reflects the value the platform creates. A good example is when we work with legacy per-user model companies and try to figure out whether we can even change the per-user model to something more license-based or credit-based.
Look for someone’s approach that is more connected with what the product is actually doing in terms of jobs to be done. If you see those signs, you probably have a good indication that you should think further about whether such a model could work for you, especially if it's coming from disruptors rather than incumbents.
- Look for early signs of additional monetization. A peer charges startups, or someone is very good at pushing companies to pay a base fee plus license metrics, securing the floor price the right way. Add-ons, which I personally love, are a great way to push 10% to 15% of your additional revenue from customers. Be careful not to copy this directly; use it as inspiration. The competitive analysis helps with that.
A way forward
Don't try to understand the whole market because this is not how customers think. They never try to understand the whole economic model, and they don't run parallel economic value analysis.
Don't overthink. Remember that most companies, even those with procurement, don't necessarily build their own assessment models. Most of these comparison exercises, even if they do, are more heuristic-based and still rely on gut feel: how it works, who answers first, and the general look and feel of doing business with you.
Also, apply a cherry-picking approach. Choose things that are actionable now, something that is an easy fix, but don’t rely on your whole pricing strategy on that.
Your product is different; that’s why you built it in the first place. You have all the information you need inside: your clients, data, internal analyses, and intuition about what will and won’t work.
Maybe your whole value proposition is about simplicity and being easy to buy, so don’t overstress the model with too-complex pricing - remember that commercial strategy never works in a vacuum.
At the end of the day, it all comes down to the value, how your product works, and which jobs it solves. Simple as that. Relying on competitors won't get you anywhere close. |
