Is good-better-best the right SaaS packaging strategy for my company?

“Good-better-best” refers to a tiered packaging model for a product or service, in which each successive tier provides additional access to features and/or usage beyond the preceding tier

June 2022
Author: Bryan Belanger

What is good-better-best?

“Good-better-best” refers to a tiered packaging model for a product or service, in which each successive tier provides additional access to features and/or usage beyond the preceding tier. This strategy is also known as the t-shirt sizing model (small-medium-large-extra large) and the medallion model (bronze-silver-gold).

Good-better-best is a common SaaS packaging strategy, but the model extends well beyond SaaS to the other categories we cover at XaaS Pricing, including IaaS, PaaS, HWaaS, Telecom as a Service, even professional services.

Professional services, in fact, have been standardized on this model for some time. Hardware and software support services as well as managed services packages are very commonly sold using the good-better-best construct and have been using this model for decades. Even outside of the high-tech sector, good-better-best strategies are commonly used. Harvard Business School breaks this down in detail in this article on the topic.

At XaaS Pricing, we look at good-better-best as part of a broader view on “as a Service” packaging models. The first element that we analyze is whether a given company uses a tiered packaging strategy in any form. For companies that do use tiering, we measure the number of tiers that are used for each product. In this construct, a good-better-best strategy would suggest a tiered packaging model with three (3) product tiers. We also measure the design and content of each product tier by analyzing how tiers are defined (usage, features or a hybrid) and how many factors define usage.

Good-better-best: The most common model, but is it the right model?

Our SaaS database comprises over 25,000 companies overall. Over 300 of those are focus companies on which we currently provide data in our beta products, including our beta SaaS platform and a monthly dataset that is delivered to our early access clients.

For this cohort, the data we have on good-better-best is outlined in the chart below. Over 90% of the products we track have between two and four tiers, and the good-better-best model is the most common approach, used for ~34% of the products we track. This data supports the prevailing qualitative sentiment that good-better-best is the most commonly used approach to packaging SaaS products.

XP Percentage of Products by Number of Product Editions

But does most common mean it’s the right model for your product? Not necessarily

In a recent Twitter thread ProfitWell founder and current Paddle Chief Strategy Officer, as well as all-around SaaS pricing expert, Patrick Campbell shared some thoughts on tiering. To quote:

“Add upsell tiers to your pricing. You have features or services you can push to more premium tiers. I guarantee it. A huge pricing mistake people make is mashing everything into a couple of packages. You know the average number of tiers the top 1% of growing apps have? 16.2 tiers. Yeah a lot more than your good-better-best. A customer never sees more than 3 tiers in their lifetime, but if you don’t have something to sell an existing customer, they can’t buy.”

I was fascinated by this data, particularly that data point of having 16.2 product tiers, so I responded to Patrick to mine for more details:

“Amazing thread! Thanks for sharing. Would love to see a breakout on the 16.2 tiers, and how you’re defining that — whether it includes volume tiers, etc. Our data tells us that between 3 and 5 (good-better-best or free-good-better-best-enterprise) is most common.”

While I didn’t get any more details on the breakout, Patrick did respond to remind me “most common isn’t highest growth”.

I have to concede that this is totally, completely true, and is generally good advice about how to use peer and market data to inform pricing strategy. Without belaboring the point, Patrick is basically saying that you should consider benchmarking data in context. Don’t just go with the crowd or take overall averages and try to apply them to what you’re doing.

Things like your size, growth, average contract values, category, products and/or services you sell, region, goals, and most importantly, your customers will inform which strategy makes the most sense for your business, whether it’s choosing the right packaging model or anything else.

Two important takeaways from my mini conversation with Patrick Campbell

First, definitions are really important when it comes to building pricing strategy. For example, in our data, we index and benchmark the current listed products, editions of those products, and any available add-ons based on what is currently listed on vendor pricing pages. We also measure the volume tiers that companies list for a given product and edition, though track this as a separate field. This provides a current point-in-time view of what the saleable packages are for a given product.

We don’t have the exact definitions for the data Patrick references in his Twitter example, but we could see where some definitional differences might skew the conversation around packaging. Lumping together editions and volume tiers, counting both current and legacy tiers of a given product, and/or defining A/B tested tiers as different tiers, for example, would change the benchmarking metric.

Patrick mentions “a customer never sees more than three tiers in their lifetime.” It’s not completely clear whether this is commentary on how plans are presented to the customer, or how customers are upgraded through their lifetime, so this is another definitional factor that would then change the view.

Second, there’s probably a middle ground that works best for most companies. Somewhere between common, or said differently, “defined as the norm by our category,” and differentiating is likely a comfortable place to be.

Using pricing strategy to define and capture your company’s unique value proposition is essential. But trying to reinvent aspects of pricing strategy that are “settled” in a given category can be risky. This is particularly true for things viewed as functional or fundamental, such as general packaging constructs. For example, if you are entering an established category like CRM and selling to mid and larger enterprises, introducing 16.2 tiers and a new usage-based pricing model may be confusing to buyers that are well aware of other approaches.

How to decide whether good-better-best is best for you

As with most things, in determining whether good-better-best is right for you the best place to start is with customers and prospects. (We also recommend reading our ultimate guide to conducting SaaS pricing research.)

Qualitative and quantitative customer research can help you to define and segment your customers. That research will help you understand product preferences and measure willingness to pay for different types of customers, culminating in the creation of ideal customer profiles. Ideally, you’d then design a package for each of those target customer segments. If you have three customer segments, then you’ve engineered a good-better-best packaging strategy. If you have 16.2 customer segments, then you’d want 16.2 packages to align to each of those segments.

As part of this process, we’d also recommend analyzing the prevailing packaging strategies in your category overall as well as specific to your current and/or aspirational peer cohort. Based on your company life cycle, that cohort could be filtered based on growth or other performance metrics, size, types of customers, region, funding and/or many other characteristics.

This analysis will help ensure that you have anchoring in the typical best practices your aspirational peers are using with customers in the market. This does not mean you need to deploy these practices in any shape or form but understanding how others are approaching the category is valuable data to inform how you choose a strategy. Even if you deploy a different approach than peers, having this data can help you best articulate why your approach is different.

If you’re looking to build packaging strategy comparisons to relevant cohorts or have any questions on the content you’ve read here, contact me at [email protected].