Nothing is more critical to a software-as-a-service (SaaS) business than pricing strategy. Pricing is the moment of truth for a new product … and doubly so when it is a company’s first product. But far more often than not, I’ve observed new startups leaving “money on the table” when it comes to pricing enterprise products. I’ve seen founders say their product saves hundreds of thousands of dollars — yet their product is priced as if only saving thousands of dollars.
image: Andreessen Horowitz
One reason for this is assuming the need to price and program similarly to competitive products. With a potentially disruptive product, however, falling into the trap of pricing like a legacy competitor not only leaves money on the table — but it could fail to surface your differentiation.
Said another way, your product is your price and how you price your product reflects value from the buyer perspective as well as what your company believes is valuable. SaaS products also have the advantage that they are priced not just for the service they offer, but for the potential of saving massive capex/opex spent directly by the customer.
From your business perspective, SaaS products have a level of stickiness that would be the envy of the packaged-and on-premise software generation.
Since the uncertainty and social science aspects of pricing can be uncomfortable, especially for technical founders, here is a framework — from the perspective of a product manager — to consider when pricing new SaaS products. The product manager role is critical in SaaS because the ability to fine-tune the monetization of the product is closely tied to its features and implementation. The product needs to be designed with such flexibility in mind when it comes to making features available, prioritizing features, or even just choosing where to spend engineering time.
Just remember that “business isn’t physics”, as Bill Gurley notes in his excellent post on some of the metrics here. Andreessen Horowitz also has a detailed primer on understanding SaaS valuations as great background for pricing discussions. Because pricing is math, there’s a tendency to create the spreadsheet model and assume it will all work. But there’s also a ton of psychology that comes into play: beyond math, pricing involves judgment, vision, and flexibility.
How do you solve an unsolvable problem? Bound it.
In a new business, it’s easy to spend money, but the combination of a new product and the unknown cost of acquiring customers leads to an “unsolvable” problem. One approach is to take lean/iterative methods and apply them to finding the right pricing fit. This post is about a framework to arrive at such early prices, which will change. (This is very different than what happens in an existing company with existing customers, where you really only get one shot at pricing something right).
The business side of SaaS involves a complex array of variables such as customer lifetime value (LTV), customer/subscriber acquisition cost (CAC), average revenue per user (ARPU), cost of goods sold (COGS), and churn; as well as pricing models such as freemium, tiered, and time based. Then, depending on whether you’re targeting consumers or enterprises, there are very different sales models that influence your pricing approach (for example, business products invite complexity, especially when dealing with purchasing managers). Similarly, the product side of SaaS is a complex set of equations related to usage patterns, scenarios, and variable costs of a large number of resources.
The most critical costs are related to customer acquisition and sales/marketing expense — which can appear to erase any potential for profit by traditional accounting measures — so the key to early-stage SaaS businesses is to focus on understanding customer acquisition costs relative to the estimated long term value of a customer. Since we don’t know how much it will cost to acquire a customer yet, we will just have to move forward assuming some budget (along with some allocation for margin in the ultimate price relative to this long term value). This post focuses on the pricing models relative to product and features and assumes a higher level view of customer acquisition costs and long term value.
One way to approach this is to establish upper and lower bounds on pricing:
A lower bound for your pricing
The lower bound represents your costs to serve a customer your product. One common example is the basic costs of spinning up the IaaS/PaaS elements of what you do — creating accounts, allocating minimal resources, other infrastructure, and then subsequent usage.
It might be convenient to think of this lower bound as what you could offer for “free” to some customers. You might make some assumptions about the use of variable resources such as compute, egress, storage, etc. in order to arrive at this lower bound. (Note, since this is a product-centric view these bounds are absent the allocation for fixed and variable costs outside the product/technology, so does not include opex, S&M, etc.)
It is important to understand this bound across the full breadth of your product. While you might initially view some features as “premium”, you also want to assume that over time capabilities will migrate from advanced to essential and you will fill in new features at the top. I think it is a good exercise to consider the full product as a base case initially.
An upper bound for your pricing
The upper bound represents your costs to serve a “depth” user: in this case, the customer using the parts of your product that drive ongoing costs to scale (for example, this customer is using increasingly more bandwidth, storage, or compute).
Now this is where you can look at what you offer relative to your competition, and want to understand if you have scale attributes that are better/worse/same. By knowing this you can begin to separate out variables for your model. Presumably in developing your product, you created a unique architectural approach relative to existing competitors. Do you scale better for more tenants, use storage more effectively, or maybe your mobile app is more efficient at bandwidth?
The importance of knowing your own strengths and weaknesses will inform what variables to use in your pricing. You can also think of your upper bound as a competitive foil — the stronger you are on some attribute, the more you should use this attribute to differentiate your offering. This might allow you to charge more for capabilities that are just too expensive for your competition.
These are the core attributes for pricing
When you’re pricing a new offering, it is worth understanding where your product is today relative to a core set of potential pricing attributes.
Whether it is Bronze/Silver/Gold, Free/Select/Premium, Trial/Select/Premium, or Individual/Business/Enterprise, the norm for SaaS is to offer a “3xN” matrix of 3 pricing plans and N attributes — as in these examples. The more mature a SaaS product, the more rows and columns its matrix has. (One SaaS product I researched had five top-level features organized into an array of 27 price points based on combinations of the three to five of the features and number of users.)
A broad range of SaaS products can be considered across the following core service attributes:
Features
If your product lends itself to dividing the features themselves — such as import/export, visualization, view/edit, or connectivity to other products — into good-better-best then differentiating price points here might work. In a freemium model, dividing must-have features among free v. paid users can be a customer-hostile way to differentiate or optimize pricing, so beware.
The reason to hesitate on this dimension is because customers understand that you’re basically just inhibiting access to code that is already there and hence being draconian. Another reason to be cautious with this model is that as usage of the product deepens over time, paid features will tend to get pulled into lower-priced tiers — which means you need to fill in new features/prices with every release or update. As easy as it is to communicate general-use features in pricing tiers, there’s a level of distaste with this approach for many customers.
Administration/IT
One of the most common approaches to differentiating a SaaS product designed for business is to separate out the IT-focused features as a pricing attribute. These could be features for security, audit, identity integration, domain names, sharing, control, management, etc. Businesses understand what it is like to both value and pay for these features.
Commonly this approach is used to rectify a product that has become viral within an enterprise, so be careful about how you approach an enterprise with pricing here. Otherwise you might come across as an arsonist-firefighter who is offering to contain the very situation you knowingly created.
Scale in consumption
Another broadly used SaaS pricing attribute is storage consumption (even for products for which storage is not a primary attribute): It’s easy to measure, easy to articulate, and is relatively expensive. The benefit of using storage is that people “get it” to some degree. It also gets cheaper faster than people can consume it (and in most scenarios customers need to be doing something fairly extreme to consume vast amounts of storage). At the same time, the platform companies have been steadily increasing free storage or ultra-low priced storage as a base, no-frills service so simply using storage as a one-dimensional offering might not work. With a new SaaS product, be sure to consider ways to avoid basing costs on storage given challenges.
One novel approach seen recently is using third-party storage and letting the customer establish a paying relationship such that storage is not part of the pricing of your product, since that way you do not serve as a pure pass-through for a visibly priced third-party element of your product. There are many novel attributes in modern software that can be used as consumption variables; one relatively new one is to use depth consumption of APIs/calls as a price tiering structure. (Box, where I’m an advisor, recently announced pricing for Box APIs as an example.) Developer-oriented products work especially well for consumption pricing because developers understand the product architecture and what can drive costs, even if those costs are variable with usage.
Scale in consumers
SaaS products used by small teams, cross-organizations, or that just scale with more members collaborating/sharing/using are almost always priced by number of unique users (and subsequent integration with organization-based directories). Pricing this variable is straightforward and over time you will see distribution of engagement and resource usage that will further let you refine the discrete price points.
Because most products priced this way also want to encourage more users/usage, carefully consider where you put the first step or two. But large-scale customers like this approach because it allows for predictable pricing on a metric they understand: number of employees/users. In general, you can think of this as per-seat pricing but can also apply to device end-points, servers/CPUs, VMs, etc.
Segmenting your customers
Every product is used by different customer segments — whether measured by size of organization, industry segment, geography, or type of individual within an organization. Common pricing tier labels here include “government”, “non-profit”, “academic”, “healthcare”, “small business”, and so on.
As a product matures, you will almost certainly either label or expand your pricing tiers to account for this. Before you jump into this level of differentiation, however, you want to gain more data on usage — are you seeing customers across some set of segments, and are they using the product differently? More importantly, do you see a path to develop differentiation that allows you to target and sustain these segments (or are you just optimizing revenue along these lines)? Some products are designed only for specific segments like education, which allows you to further refine within them: e.g., public, private, post-secondary, etc.
But one customer segment that is almost always special is the engaged technical user. These folks can push a product through an organization when required, or develop custom solutions on your platform that either deliver or enhance the value of your work.
Developers are key in this regard. For any platform-oriented product, it is worth considering how you offer developers the ability to experiment with and use the full product in a development environment at a very low price. One way to accomplish this is to separate out usage-as-development versus usage-as-production, and price accordingly.
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As a new offering with any established competitors, pricing will be the easiest point of attack. And if you are a disruptive product, you want to have the deepest possible understanding of the value you are bringing to the table so you can maximize the initial pricing model. So the most important suggestion for pricing I have here is to wait until the last possible moment to price and announce.
Even for enterprise products, things like round-numbers, 9′s, and discounts all matter. Do keep in mind that discounting will be substantial in enterprise products with direct sales and 50% or more off “list” price is not uncommon (and often required). That’s not an excuse to bloat the price, but it is important for purchasing managers and for empowering your salesforce that you enable a level of customization — and know what variables you are using to do so.
Some say that you can never change or raise your prices once you’re out of the gate. Always keep in mind that once you have customers, price changes or product composition relative to price are never viewed as positive changes, even if you think for some customers you are lowering the price. And when you do change your prices, always offer existing customers time to adapt and grandfather them in (at least). Finally, remember to engineer a product framework that can support pricing flexibility.
Create the model, use the model, but don’t let the model do your thinking. Price carefully!
Steven Sinofsky is a board partner at Andreessen Horowitz, an adviser at Box Inc., and an executive-in-residence at Harvard Business School. Follow him @stevesi and on his blog, Learning by Shipping.
This post originally appeared on TechCrunch.