“Are we under-pricing?”, “How much should we charge for our product?, “Should we price higher than our competitors?” - Do these questions sound familiar?
A 2019 pricing study done by McKinsey & Company observed that a 1 percent price increase would yield 22 percent increase in EBITDA margins. Striking the right balance, and optimizing your pricing can be the gamechanger you need and deserve. So how exactly do you figure that out? Joshua Bloom, Managing Partner (North America) for the global strategy consulting firm – Simon-Kucher & Partners, puts these queries to rest. Here’s what he has to say, as originally featured in author Ajit Ghuman’s book, Price to Scale.
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Pricing structures can be on a spectrum — on one side, you can have a continuous consumption-based pricing structure where companies pay for what they use.
On the other end of the spectrum, it can almost be a fixed price structure sized, similar to using a t-shirt sizing approach M, L, XL etc. The sales person just has to get it generally right with a generous buffer. Here the usage may be based more on the honor system.
In the middle you can have more of the cell-phone plan models, where you purchase blocks of ‘usage’, with incentives to move to higher tiers.
In order to select which pricing structure to use, here are 3 ways to help you decide:
1. Predictability: The more predictable and measurable the metric is, the more granular you can make your units and tiers (pricing variable). On the flipside, purchasing groups will always want predictability. So if the measurement isn’t as easy or the usage less predictable, in this case larger buckets with buffers might be helpful as they will deliver more cost/expense predictability to the customer.
2. Usage Growth Rates: Predictability is one thing, but the natural growth of the metric is another. Imagine a scenario with a rapidly growing metric — for instance with big data software, where data volume is growing exponentially for the applications they are covering. In a cloud infrastructure model, to some degree it means that you have to get pretty granular, because your own cost structure could be under significant pressure by say month nine of a contract - if you didn’t have a very granular way to charge, then you face cost pressures quite fast.
3. Correlation To Compute/Data Costs: The third element to think about is how close the structure is to the infrastructure layer, versus sitting one or two steps removed — essentially, how much cost risk are you bearing? If you’re just talking about something at the application layer, that is very divorced from the underlying cost structure, you can get away with big pricing buckets. The closer you get to compute and storage resources being meaningful, the granular you have to make things.
A 2019 pricing study done by McKinsey & Company observed that a 1 percent price increase would yield 22 percent increase in EBITDA margins. Striking the right balance, and optimizing your pricing can be the gamechanger you need and deserve. So how exactly do you figure that out? Joshua Bloom, Managing Partner (North America) for the global strategy consulting firm – Simon-Kucher & Partners, puts these queries to rest. Here’s what he has to say, as originally featured in author Ajit Ghuman’s book, Price to Scale.
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Pricing structures can be on a spectrum — on one side, you can have a continuous consumption-based pricing structure where companies pay for what they use.
On the other end of the spectrum, it can almost be a fixed price structure sized, similar to using a t-shirt sizing approach M, L, XL etc. The sales person just has to get it generally right with a generous buffer. Here the usage may be based more on the honor system.
In the middle you can have more of the cell-phone plan models, where you purchase blocks of ‘usage’, with incentives to move to higher tiers.
In order to select which pricing structure to use, here are 3 ways to help you decide:
1. Predictability: The more predictable and measurable the metric is, the more granular you can make your units and tiers (pricing variable). On the flipside, purchasing groups will always want predictability. So if the measurement isn’t as easy or the usage less predictable, in this case larger buckets with buffers might be helpful as they will deliver more cost/expense predictability to the customer.
2. Usage Growth Rates: Predictability is one thing, but the natural growth of the metric is another. Imagine a scenario with a rapidly growing metric — for instance with big data software, where data volume is growing exponentially for the applications they are covering. In a cloud infrastructure model, to some degree it means that you have to get pretty granular, because your own cost structure could be under significant pressure by say month nine of a contract - if you didn’t have a very granular way to charge, then you face cost pressures quite fast.
3. Correlation To Compute/Data Costs: The third element to think about is how close the structure is to the infrastructure layer, versus sitting one or two steps removed — essentially, how much cost risk are you bearing? If you’re just talking about something at the application layer, that is very divorced from the underlying cost structure, you can get away with big pricing buckets. The closer you get to compute and storage resources being meaningful, the granular you have to make things.
