‘Percentage of Annual Revenues’ is my preferred reference for pricing.
My overarching goal for every pricing project is to determine what a given product is worth to a given customer, and make recommendations to move that number closer to my clients’ business goals.
Lately my preferred answer to “What’s it Worth?” is a % of the customer’s annual revenues (%AR, pronounced ‘PAR’) rather than dollars. The percentage gives us more context while normalizing for varying customer sizes.
%AR suggests, every customer is ‘budgeting’ (whether deliberately or not) a similar percentage of annual revenues to fix a similiar problem. For some of them, this percentage translates to a smaller dollar amount and others a larger amount – but despite the specific price point differences, the problem is prioritized similarly for all of them. Therefore it’s worth the same.
For example, on a recent B2B SaaS pricing project, the median %AR for one product was a fairly consistent 0.1%. Translated to dollars – and purposely using some very simple numbers – this means a business generating $10M in annual revenues was budgeting $10K/yr to solve this problem and a $5M business budgeting $5K/year, and conceivably a $1M business would pay $1K (this particular analysis didn’t get that small).
Other products weren’t so consistent, and some varied quite widely. The percentage spread on one spanned 0.14-0.54% with a median of 0.23%. Using the same hypothetical businesses above:
$10M business
0.14% = $14K
0.23% = $23K
0.54% = $54K
$5M business
0.14% = $7K
0.23% = $11.5K
0.54% = $27K
$1M business
0.14% = $1.4K
0.23% = $2.3K
0.54% = $5.4K
Now that we know the %AR spread, the question becomes what the accounts for the differences in value.
This always sparks a lot of great conversation with clients about what the differences. It’s often rationalized with a number of internal factors; vintage of customer, specific sales person, our pricing model shifted and they didn’t, their business shifted and pricing didn’t. I’m less concerned about the internal reasoning – and more interested in what it says about customer value.
As each of these sales represents the customer declaring this an acceptable ongoing price and yet every customer could potentially be paying $54K/yr
But they’re not.
We have a ~4x spread across the %AR and a $30K spread in dollars.
That’s a lot of money leave on the table, so we need to start digging a little deeper to identify the differences between what the high, low, and median customers are buying.
In an ideal world, the spread is sufficiently addressed by different packaging; more premium features, more premium service, enterprise IT requirements like SSO and SOC-2. My client’s Chief Product Officer or Chief Customer Officer would pull me another report confirming it’s all neat and tidy packaging differences. Case closed.
But we don’t live in an ideal world.
Over time whether due to CRM migrations or SKU migrations or something else all together, what a given customer purchased, may not be easy to access. Especially across all the customers. Especially if it includes an infrequently accessed or frequently changing feature like a premium service package as the triggers to confirm or deny these benefits often aren’t.
To make things even more interesting, based on some sales data I’ve analyzed this year, it’s highly likely the premium packaging is sprinkled throughout every price point of the spread.
So, we’re left with customers of similar size and budget paying a wildly different amount for every variation of the product ever.
If this were evaporating inventory like single airline flight or a single car rental, it wouldn’t really matter. But, in B2B SaaS, we’re talking ARR and ongoing support commitments. Without some clarity everyone – product, finance, customer success – is going to be frustrated year over year about what a specific customer can and can’t do. This is the confusion is what we’re preventing by cleaning up pricing & packaging.
At this stage, I’d run a round of qualitative customer interviews across the entire spread to get clear on what’s valued and what isn’t. Then use those findings to re-work the packaging, re-work the segmentation, shrink the spread, and develop a renewal strategy for each segment.
Of course, the goal is to continually grow %AR, moving the median from 0.23% to 0.3% to 0.5%, resulting in millions in additional ARR. That growth doesn’t necessarily require net new features, it does require getting crisp on segmentation and what each segment values.
All too often the most meaningful distinctions between segments isn’t obvious. But that’s for another day.