We take a look at our database, which comprises pricing multiple data on over 1,000 products across 300-plus vendors, to see if our data bears out the efficacy of the 1x, 2.2x, 5x rule
May 2022
Author: Bryan Belanger
What is the ‘1x, 2.2x, 5x’ rule?
We were recently exchanging emails with a SaaS founder and he mentioned that a cornerstone of his firm’s current pricing strategy is to anchor prices to the “1x, 2.2x, 5x” rule. We got into a bit of a back and forth about whether this model was right for his business, and how to think about pricing relative to the packaging tiers he was planning to offer for his SaaS.
You might be asking yourself at this point, “What is the 1x, 2.2x, 5x rule?” We’ll dig into whether it should actually be deemed a “rule” later. But first, a little history and some definitions.
The earliest mention we can find of this “rule” is from a session at MicroConf 2014 led by Ryan Delk, who at the time was head of business development and growth at Gumroad and is now CEO of Primer. The key takeaway is summarized well in this blog post. The rule resurfaces regularly on startup forums; for example, it was recently mentioned by a commenter on this Indie Hackers post about pricing strategy.
The 1x, 2.2x, 5x rule is intended to break through complexity by defining a standards-based framework that can be used to set pricing for your offering. If you’ve determined a price for any of your offerings, you can use the rule to determine pricing for your other tiers. For example, if your entry-level offering is priced at $10 per user, per month, then your middle tier should be priced at $22 ($10 x 2.2), and your most expensive tier should be $50 ($10 x 5).
This rule is a tactical model for executing a Goldilocks pricing strategy, which itself is based on the broader Goldilocks principles of cognitive and behavioral psychology, named for the children’s story Goldilocks and the Three Bears. The idea is that by providing three choices, the customer is naturally guided to select the middle choice, which is the “ideal” or “recommended” offering.
Is the 1x, 2.2x, 5x rule actually accurate?
Short answer: yes and no.
One key data field that we track in XaaS Pricing is the pricing multiple of a given product relative to its closest preceding edition. The calculation for this data field is simple — if a product is priced at $10 per user, per month for the entry-level tier and the next successive tier is priced at $20 per user, the pricing multiple is calculated as $20 divided by $10, or 2.0.
We took a look at our database, which as of this writing comprises pricing multiple data on over 1,000 products across 300-plus vendors, to see if our data bears out the efficacy of the 1x, 2.2x, 5x rule.
There’s one variable right off the bat that doesn’t align. The 1x, 2.2x, 5x rule relies on the concept that your product should have three editions. While having three editions is certainly a common strategy, there is variability from this norm across categories and vendors. We generally see between three and five tiers as the normal approach. Vendors should choose a packaging strategy based on their target customer profiles and the problem that their product(s) solve for those customers. If segmenting the product by more than three tiers makes the most sense, then the 1x, 2.2x, 5x rule may not directly apply for setting the pricing of that offering.
Across our entire dataset the average pricing multiple for any given edition over its previous edition is 2.3. The average of the minimum uplift across all companies, products and editions is 2.0, and the average of the maximum uplift across all companies, products and editions is 2.6.
Aligning this data directly to the 1x, 2.2x, 5x rule is difficult, as that comparison comes down to the individual vendor, product(s) and product edition(s). But we can infer a comparison based on that overall average multiplier of 2.3.
Let’s assume a company wanted to launch a product with three editions. If that company were to use our average multiplier to set up those editions, the second edition should be priced 2.3x the lowest-priced edition, and the third and most expensive edition should be priced 5.3x the lowest-priced edition (2.3 x 2.3 = 5.3). Our data says the rule should be 1x, 2.3x, 5.3x, which is remarkably close to the 1x, 2.2x, 5x rule.
This certainly validates the quantitative validity of the 1x, 2.2x, 5x rule. But does that mean you should use it to price your SaaS product?
Should I use the 1x, 2.2x, 5x rule to price my SaaS product?
There are benefits and drawbacks to using this rule as the basis for pricing your SaaS or XaaS product. The applicability of this type of approach also relates directly to the stage of your company; startups and early-stage companies with less user and/or competitive market data are more likely to lean on this rule than established companies with broad access to customer data.
Some of the main pros of the 1x, 2.2, 5x rule:
- Easy to implement
- Market recognition as a viable pricing methodology (including validation from the XaaS Pricing dataset)
- Can be used as a simple tactic to “design packaging around pricing”
- An easy way to anchor customers around a “recommended” package
- Inherent flexibility; you can change the core package price and choose to update the other packages, or adjust those packages independently over time, as you collect more data
Some of the cons of the 1x, 2.2, 5x rule:
- One-size-fits-all approaches rarely fit all
- May not fit your firm’s pricing goals
- May not align to your chosen packaging strategy
- You may be leaving money on the table for higher-end plans
- You may be overpricing your plans
What’s interesting about the 1x, 2.2x, 5x rule is that it’s often discussed as the go-to rule for setting up SaaS pricing, but there are actually many other similar “rules” that might apply to your business. This article and this one do a good job of breaking down those other options.
A first consideration to evaluate is how the 1x, 2.2x, 5x rule fits with your product’s high-level pricing strategy and desired value proposition. Using this form of Goldilocks pricing has to align to your broader ambitions regarding target customers and relative market price positioning (economy, skimming, penetration, premium).
You also need to ensure that you are broadly anchored to the right acceptable price range for your customers, your value proposition and your product. If you set low- and high-end tiers based on a middle-tier price that is $500 when customers in your target market are only willing to pay $100 for what you’re offering, building tiers around the 1x, 2.2x, 5x rule isn’t going to do you much good. You still need to do the heavy lifting to ensure you are in the right market-aligned customer-validated pricing ballpark before deploying this rule.
Lastly, you need to approach using this rule with an openness to adaptation: It’s OK to rough out initial pricing using the rule to see where things stand; but a better approach is to use market and/or customer data to understand if there are opportunities for optimization. For example, perhaps your market, category and/or product differentiation dictates that your high-end tier commands a 10x multiple instead of a 5x multiple. This may be an opportunity to solidify your premium positioning for larger customers and capture additional revenue. Customer research, as well as market and competitor platforms like XaaS Pricing, can provide the right sources of data to support this analysis.
Netting this all out, the 1x, 2.2x, 5x rule is valid, according to our data. Much of this, however, can be attributed to the compounding properties of “follow-the-leader” approaches — if all the biggest and brightest in SaaS are setting up pricing around this paradigm, then others are likely to follow suit. As more companies deploy strategies based on this model, this adds additional confirmatory data points validating the model; said more simply, it becomes a self-fulfilling prophecy.
That doesn’t mean this rule isn’t a viable starting place. What the market does is what customers come to expect. Our advice is to use the rule but use it sparingly, and identify potential differentiation opportunities that may arise by understanding where the deviations from the rule exist and learning how to exploit them in packaging and pricing.
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