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It’s important to recognize that pricing isn’t just about placing a price point on a product. It’s a strategic exercise that touches every part of your business. We’ve designed this 5-step framework that when sequentially applied across these steps will help take you through the process of a pricing strategy exercise. (Note that diagnosing a pricing issue, is then conversely looking at all these steps from the bottom-up.)
Step 1: Goals & Segments - Setting the Foundation
The first step in any pricing transformation is to clarify your goals and identify your customer segments. Without a clear understanding of what your company aims to achieve—whether it’s boosting margins, capturing market share, or reducing costs—your pricing efforts may fall short. Additionally, there needs to be complete clarity of “who” your customers are, i.e. what segments are you selling into and what are their Ideal Customer Profiles (ICP).
In many cases, pricing problems stem from a lack of alignment within the executive team. Different leaders may be advocating for different pricing strategies, each with their own assumptive goal and understanding of customers segments.
This happens because there’s often no clear consensus on the company’s go-to-market strategy or an updated ICP. Misalignment at this stage can lead to significant downstream issues, where there is often a lot of confusion, arguing and friction. All the while packaging and pricing that is created as a result of misalignment will likely not resonate with your target customers. There are many epic cases of failure with this being the root cause. In fact, most pricing problems are truly problems of misalignment at this stage.
Here’s the bottom line: alignment is key. When everyone is on the same page about your strategy and segments, everything else flows more smoothly.
In fact, later in the book we dedicate a small chapter to a case study on organizational alignment at Citrix and usage of the ADKAR model by Prosci for change management.
Step 2: Positioning & Packaging - Designing Offers That Resonate
With your goals and segments clear, the next step is to focus on packaging (and the corresponding positioning). Packaging isn’t just about bundling features; it’s about creating offers that make sense for your specific customer segments.
For example, it’s common to see companies start by creating a “good-better-best” pricing structure and slotting features into three plans. But when done without a deep understanding of what each segment truly values, this approach can backfire.
Imagine throwing all your enterprise features into a high-end package without really understanding what your enterprise customers need. You might end up with customers choosing a package that doesn’t fit them or, worse, discounting heavily just to make a sale. This can lead to what’s known as “shelfware,” where customers pay for features they don’t use, which ultimately harms your long-term financial results.
Or when your packaging is too granular it slows down sales cycles and can actually work well for bespoke enterprise type deals, but this will completely not work for a mid-market customer segment as these move quickly and the adage of “time kills all deals" applies.
Step 3: Price Metric - Aligning Pricing with Value
The next decision around pricing metric selection is to be made independent of package design. It is most often a step considered after package design.
The price metric is how you capture value from your customers. This is where you decide whether to charge based on usage, subscription, or another model. The right price metric aligns with your customer’s perception of value and your business’s revenue model.
For instance, in the early stages of a company, a usage-based pricing model might be appropriate as it encourages adoption by reducing upfront costs. But as this company grows, it may seek more predictable revenue, and might shift to a user-based pricing metric.
Or consider a case of launching a new AI-based SaaS product, you may encounter many considerations around pricing this product. The existing product category may still be entrenched with a user-based pricing model, however your product will drastically reduce user count as it automates a lot of work humans were doing. Additionally, with an AI-based product the ongoing cost implications are non-trivial, which increases the need to select a usage-based pricing metric. Even here the specific metric within the realm of usage-based pricing (execution, resolution, by unit tokens, or by output tokens, etc.) needs to be a carefully considered decision that helps both you and your customers win.
The point needs to be made here that many companies will combine Step 2 & 3 and introduce pricing metric as the primary way by which packages are differentiated – this is not a smart decision because you signal to your customers that the quantity of the product is how you will tier the offers and not by differing customers needs. We recommend treating it as a separate decision where packaging is first about the capabilities offered in the product and not the quantity of usage.
Step 4: Rate-setting - Finding the Right Price Points
Once the two most important decisions/levers have been made around Packaging and Pricing Metric, it’s now finally time to focus on rate-setting—choosing the actual price points. This is where the rubber meets the road in terms of pricing.
Rate-setting should reflect your overall strategy, market conditions, and the Customers’ willingness to pay . It’s a complex step that involves understanding pricing psychology, market competition, and customer expectations. For example, while a conjoint analysis might suggest a specific price point, you’ll need to consider how this aligns with your customer’s perceived value and your competitive landscape.
Also, as much as qualitative and quantitative research may provide detailed answers, it is always important to test them before launching broadly as the main offering or line-up.
Step 5: Operationalization
The final step is about the structure that helps your pricing strategy actually succeed on a day-to-day basis.
Operationalizing pricing is crucial to ensuring that a company’s pricing strategy is effectively implemented and drives the desired business outcomes. This phase involves integrating the new pricing model into every aspect of the business, requiring the alignment of systems, processes, and teams to ensure smooth adoption.
Essential tasks include instrumenting the pricing metric, implementing either a basic calculator or CPQ system and integrating with an ERP and/or Billing system. Depending on complexity these implementations can take months or even years and can make or break the pricing strategy.
Additionally, it requires effective sales enablement, deal management (Deal Desk), discounting policy setup and enforcement.
In this chapter we are assuming that the 1st level alignment for a pricing strategy project around goals for the pricing project has already happened.
Segmentation is all about dividing up your potential customer base into smaller, more focused groups that share specific characteristics and needs from your product category. Segmenting well starts with a clear understanding of the dynamics of groupings of customers – what are their needs? what are their common attributes? how do they value products like yours? etc.
Let’s break it down by looking at two common segments in horizontal B2B SaaS companies: mid-market companies and enterprise giants. Both might need technology solutions, but they have very different expectations and requirements.
Mid-market companies are generally the scrappy up-and-comers. They’ve grown beyond the startup phase and are now looking for software that can scale with them without breaking the bank. They need tools that are easy to implement, offer flexibility, and don’t require a ton of internal resources to manage. Price sensitivity is key here, so they’re often looking for a solution that gives them the most bang for their buck. They value simplicity, speed, and ROI, and they’re not looking to spend months or years deploying a new system.
On the flip side, enterprise technology segments are the big players—the tech giants with complex needs. These companies have established processes, huge teams, and a laundry list of requirements. They’re not just looking for software; they need robust, scalable solutions that can integrate with the multiple systems they’re already using. Security, compliance, customization, and top-notch support are non-negotiable for them. They’re willing to invest significant time and money into a solution, but they expect it to be tailored to their unique needs and to deliver high reliability and performance at scale.
Generally, in order to truly get in the shoes of a company in either segment, you will want to create an Ideal Customer Profile. An ICP is a detailed description of a hypothetical company or customer that would get the most value from your product or service and, in turn, provide the most value to your business. It's essentially a blueprint for the type of customer you want to attract and retain.
Here is a simplified example of ICPs for both segments mentioned above:
The problem that very often occurs in a company is ICP Drift and a gradual confusion around “who” the product is being sold to. ICP Drift refers to the gradual misalignment or deviation of a company's Ideal Customer Profile (ICP) from its original definition. This can happen over time as the company evolves and the sales teams begin to focus on different types of customers than in the original plan docs.
Now in this new state of play a pricing problem cannot be resolved without getting a fresh understanding and alignment in ICP definition. Often differing assumptions about ICP will lead to different pricing strategy recommendations inside a company. Many times these hidden assumptions lead to months or even years of wasted efforts (amongst the rotating hire/fire cycle of employees) without agreement on the “who” we are even selling to.
Positioning is how you carve a place of influence in your buyer’s mind.
l “Positioning is not what you do to the product; it’s what you do to the mind of the prospect. It’s how you differentiate your brand in the mind. Positioning compensates for our over-communicated society by using an oversimplified message to cut through the clutter and get into the mind. Positioning focuses on the perceptions of the prospect not on the reality of the brand.“
- Al Ries, Author of ‘Positioning: The Battle for Your Mind. Warner Books. 1981
There is a scene in the TV series Man Men that has one of the most striking examples of positioning that I’ve ever seen depicted on screen. If you haven’t seen Mad Men, I suggest watching the first episode of the first season, or simply search for “it’s toasted mad men” on Youtube so you can follow along.
As the Creative Head of an advertising firm, the show's protagonist, Don Draper is charged with repositioning cigarettes for a major client, Lucky Strike during a time when Big Tobacco companies were under considerable fire. There was growing medical evidence that cigarettes were injurious and fatal, in many cases. To comply with FDA regulations, tobacco companies had to work on messaging that did not indicate that cigarettes had any health benefits. While Lucky Strike did not have any differentiating element when compared to any of their competitors, what Don Draper did for them was that he focused on Lucky Strike’s buyer. He honed in on one part of their manufacturing process, which was the toasting of tobacco leaves to dry and cure them. He then came up with a simple tagline that said, “It’s Toasted.”
Here’s why this was a stroke of genius by Don Draper:
Once this position was firmly established in the cigarette buyer’s mind. Could Lucky Strike not have charged a premium for their products? No other brand could elicit a feeling of freedom like Lucky Strike could.
What I’m trying to point out is that pricing is intimately connected to positioning. Pricing cannot be set without the positioning being clearly thought through.
Now let’s take an example that’s closer to home.
Let's assume for a moment that you are responsible for positioning a new AI-based Sales Productivity software targeting a VP of Sales as your buyer persona.
How do we get inside the head of this VP of Sales? How do we ‘position’ ourselves?
We leverage heuristic thinking.
In his seminal work, Thinking Fast and Slow, Nobel Laureate Daniel Kahneman highlights two systems - System 1 which is heuristic, and System 2 which is analytical. A heuristic mindset is like muscle memory for the brain. It enables us to develop micro-second preferences and biases. It’s what tells us that an Apple iPhone is a premium product, while an Android phone isn’t. It’s what makes us hold a BMW in higher esteem in comparison to a Honda, which isn’t viewed as a premium product. As the buyer in these cases, we are pegging these products and brands a certain way before taking into consideration the horsepower in a car or the storage capacity of an iPhone.
Coming back to our example, if you approached the VP with a pitch for your product, she is going to run a few heuristics to get a handle on how to think about your product.
There could be three possible heuristics:
While Sales and Marketing Tech will indicate that there are thousands of companies in this space, these three companies are seen as the most prominent ones for AI. The comparison for your offering will be against these popular companies and their offerings. The VP is going to compare your company to Gong, People.ai, and Chorus (see Figure 3).
Apart from the prominent competitors, the VP would also consider what the counterparts in her space, and the people in her network with similar needs, are looking at.
She would also look to the opinions of thought leaders in this space, and insights from popular podcast platforms such as Sales Hacker or Inside Sales to help guide her decision.
Once we understand the buyer and their mind space, we can then think about how to find our own niche in that mind space.
There are many questions that arise.
Is your product a ‘tool/widget’ or is it a ‘platform’?
Is it a vitamin or a pain-killer?
How is it uniquely different compared to available alternatives?
What you want is for your buyer to recognize you as a product that solves a particular problem in a differentiated way.
Maybe you already understand positioning. Why am I bringing it up?
The thing is, most pricing problems aren’t pricing problems. In fact, they are rarely pricing problems.
They are just the causal impact of poorly understood and/or communicated positioning of a product leading to a whole host of down-stream issues.
The three essential questions one must always answer, to be clear about positioning are:
One of the important things to keep in mind is that we are discussing Positioning in the context of a Pricing exercise. If this were a pure Positioning exercise you would have to do a lot more in order to operationalize the positioning and that can include things like influencing analysts, writing research reports, or organizing key domain-relevant events.
When it comes to a Pricing exercise, the goal is to understand and confirm the existing positioning of the company and/or a specific product in question (assuming of course that the company has had a working GTM and this isn’t the first time it will be selling an offering).
The confirmation of the positioning could be as simple as making sure that your understanding of the key buyer personas and the positioning statement roughly matches that of your executive team, sales leadership as well as marketing leadership across every main market-segment that you are targeting.
Real-world example & implications
The criticality of positioning lies in the fact that without clear positioning, no pricing model can work. The year is 2018, and I was working for a startup that offered a product for chat based customer service. At the time, there was a debate on what exactly should be the positioning of our customer support software that primarily worked through a chat widget and via advanced AI-based chatbots.
After examining the market landscape, my boss and I wanted to call our offering a Conversational-AI based Customer Service Platform to highlight the unique differentiator of the product within a large segment of Customer Service software. Our hypothesis was that ‘Conversational-AI’ would limit our field of competition to one we would do well against.
However, when we brought up this topic with our CEO, who was earlier at a much bigger established company, she disagreed with our approach. Her direction was that we were supposed to be competing with Salesforce and that our eventual destiny was to become a CRM solution. For us (my boss and I), this was totally discordant with the fact that the product was just not a CRM at that point and this direction did not address our target buyer or our unique differentiation for that buyer.
This dilemma persisted with the company for quite a while and not only impacted the pricing effort but also slowed down the entire sales engine as the sales team was hearing completely mixed messages (changing every quarter) on who they were to be pitching and what the positioning was in the market.
The point of the story: Before you embark on a pricing exercise, make sure that there is alignment on the positioning within your own company, and if any disagreement exists (especially at the executive level) that it be resolved before you actually work to finalize the pricing.
Once you’ve determined positioning, the next step is to create ‘offers’ that will work for your targeted market segments. These offers are the right combination of feature-sets, services and price for a market segment that will ensure that you can derive the maximum possible value from within that segment.
The set of capabilities in an offer, except the price itself, constitutes a package. In most cases, you will want to create a set of packages that map 1-1 to the number of clearly defined market segments.
Let’s use the following chart to understand how packages help.
If we were to draw out a curve that represented how prospects valued a solution when varying the feature-set, we would obtain a curve that looked similar to the one in Figure 4. Intuitively, prospects who need more features (or more advanced features), tend to pay more than prospects who need fewer features. If no pricing model exists, then a time-averaged curve of product ACV (Annual Contract Value) across different customers who had a varied usage in features would also look similar.
The goal of establishing a pricing model is to standardize ‘packages/offerings’ that allow your firm to run a sales engine that helps you achieve a curve like the one above. The curve is maximized for its return from prospects and one that helps you achieve efficiency with your sales engine (via a well defined pricing model). Unless you sell to a very homogenous set of prospects (e.g. Fortune 100 - Pharma companies), you will want to come up with a packaging structure that lets you capture a progressive amount of value across your customer segments.
What does packaging look like in everyday life?
Let’s lead with a very simple example of Popcorn (Figure 5). The most basic feature that is being charged for is the popcorn itself, and pricing varies based on the quantity offered. This in essence is packaging.
The differentiating feature in the packaging is the quantity - Small, Medium, and Large. Customers will pay for the feature they want.
Let us look at another example (Figure 6), that of the Ford Mustang.
A customer may be looking to buy a Ford Mustang. All Mustangs are built on a common base but each model is priced based on the variations in features offered - horsepower, engine specs, shell, interiors, etc.
The sports car aficionado, who is heuristically inclined to associate a Mustang as a status symbol, will be willing to spend more and opt for the Mustang Shelby GT500. The base model of the Mustang EcoBoost Fastback would not have the level of specs that the GT500 would offer. There would also be discerning customers who want more features than the base model offers, but aren't willing to splurge on the most high-end model. The manufacturer assesses this segment as well and offers the Mustang BULLITT for them - priced between the base and high-end models. This is a good example of how the manufacturer is looking to maximize the area beneath the curve by offering multiple features of a relatively horizontal product through packaging variations.
This logic applies similarly to software packaging.
So how do you create packages for software?
In my experience there are roughly two ways to go about this exercise:
The following figure illustrates roughly how I visualize the decision of selecting a specific approach. A Good-Better-Best model is better suited for when there is a large ‘n’, i.e. number of customers/accounts available in the market to be sold that are somewhat homogenous in their needs. For example, direct to consumer, digital-first companies in the ecommerce vertical. On the other hand the modular approach would be better if the market was more heterogeneous. For example when the same product is sold to insurance, telco, retail and/or hospitality where all these industries have varying needs. The total market ‘n’ is small and the market is also heterogeneous, in that case depending on the circumstance one can make an argument of doing away with packaging as such. For example, a selection of 20 top financial issuers with varying use cases for a data science platform. At the end of the day there are no rules to this, but hopefully this helps make your decision process clearer. Once you understand these techniques, you can always mix and match them for best effect.
In order to understand good-better-best packaging let’s start with the example of Salesforce’s Service Cloud pricing (Figure 7):
Looking at Salesforce’s Service Cloud packages, you can see that each package variant provides a range of sub-features that are common and the higher priced packages offer more features. In ‘Essentials’ they are only going to offer case management, in ‘Professional’ they will further offer CRM and case management. CRM is where you can store all your account information and with just the ‘Essentials’ package, you may not be able to.
In the ‘Unlimited’ edition, they offer 24x7 customer support and may offer AI features or some more enterprise features and charge you higher for those impressive or lucrative features that are on offer. This is rudimentary software packaging and pricing.
The packages are graded based on features that map to companies of different sizes and needs (segments). One point of Salesforce’s packaging grid is telling. They’ve clearly outlined on their website what the prices for the different packages are. We can infer from this that Salesforce is trying to achieve a high degree of price standardization and transparency, which tends to be useful when an organization has a large sales engine and a target market that it seeks to attract.
The drawback of such an approach is that prospects can easily comparison-shop to choose the right capability no-matter which industry vertical they are in, making the sales more transactional and missing out on pricing elasticity variances between industries that generally tend to be able to pay more, e.g. Finance vs industries that tend to be very price conscious, say, Retail.
Another example is that of a company called Amplitude, which offers product analytics software. Here we can see that Amplitude has three plans, one of which is a completely free plan and the other two seem to be catered to mid-sized growing companies and larger established enterprises respectively (Figure 8).
It’s interesting to note that they do not expose the pricing for the ‘Growth’ and ‘Enterprise’ plans. This indicates that they are going after a broad market across verticals where comparably sized clients might have differing price elasticity. Not publishing a price allows them to capture greater value from clients that are able to pay more. In the subsequent chapter, we will see how this is accomplished by creative discounting buffers and approval rules for your sales team.
The ‘Free’ tier offered by Amplitude is another indicator that they intend to reach a wide market, the reason being that ‘free’ plans have a poor conversion rate in general to paid plans and the offer would only make sense if a) They were going after a large TAM and/or b) They want to undercut a competitor.
Now, this is not how the pricing page always looked for this company. I always find it interesting to do some research using the Wayback Machine and seeing how a company’s positioning and pricing have evolved. Here are two historical snapshots of their pricing in 2016 (Figure 9).
Can we infer anything from the evolution of their pricing page?
I would suggest that what likely happened is as the company found new customers, they realized many of them could pay much more than the company initially charged. Now, this is all well and good, since the company in 2016 probably was a new entrant to the market with fewer feature differentiators and less of a brand cache. In 2020, as they innovated with their product and became a more trusted brand, they probably charged more and hence removed the specific pricing from their product page as they didn’t want to peg their prices low.
OK, so now that we understand how this type of packaging looks like in real life, how do we create this for our own product?
Well for one we know the output of our effort needs to fall into roughly three (or four) packages or tiers. The goal is to bucket features together such that there is:
Start by thinking of broad segments of your customers based on your sales motion. One way to do this is by company size and simply use a standard grouping like SMB, Mid-Market, Enterprise. Now create a set of feature groupings such that there is a 1-1 mapping between the feature groupings and the prior defined customer segments.
Here is a fictional example of this exercise for a marketing automation product (Figure 10). We have grouped features to map to our assumed needs for each market segment. The groupings are graded and more complex at the top end.
At this point, we have our initial hypothesis of packaging, but this needs to be tested against our selected customer segments to see whether there truly is package <> segment fit. In the next chapter, we will cover research methods to answer this very question.
But before that let’s now take a look at modular packaging.
This discussion brings up an important point on pricing transparency. According to a recent report by Chartmogul roughly 75% of SaaS companies publish pricing on their websites and 25% do not. In my opinion, this is interesting data but by no means a reason to follow the herd per se. I’d like to propose a simple 2x2 matrix (Figure 11) to decide when it would make sense to publish the prices along with the packages on your website.
When you have a large market with a high degree of homogeneity, then it is feasible to emulate other SaaS companies and publish the complete pricing structure online (replete with packages and prices) as it can help you scale your sales engine and maximum value from the market. On the other hand, if your market size is limited (say Fortune 100 Retailers), or heterogeneous (say, across Retail, Pharma, Airlines, etc.) then the call is more subjective. I’ve worked in enterprise SaaS companies that have opted to not publish any pricing publicly so as to give their Sales teams more ability to sell a targeted offer to their prospects. In those cases, the packages were defined internally but there was lower price transparency which dissuaded package comparisons and enabled them to extract requisite value from prospects that had varying amounts of willingness to pay or price elasticity. In this specific context, sales reps appreciated their ability to offer their prospects the right package without necessarily getting into the ‘shop-a-package’ discussion.
Now let’s take a look at a more flexible approach to packaging that can enable us to tailor a bespoke offering to prospects.
This type of packaging can help us capture greater revenue than standardized packages that either offer more capabilities for some prospects than they need (leading to shelfware) or don’t offer all capabilities needed for some prospects (leading to essentially not delivering all the value that is possible and thereby leaving money on the table).
In this case, we are not forced to come up with a universe of just three packages. In principle, this approach can lead to a high degree of permutations and combinations of features and capabilities.
Here is how to approach it:
To illustrate the concept let’s look at Figure 12. In this example, we’ve taken a fictional CRM product that has features A through T along with a core platform infrastructure base. These features are then mapped to use cases such as ‘Lead and Opportunity Management’, ‘Team Collaboration’, ‘Marketing database’, and so on. Finally, we’ve estimated the value derived to different customer segments using a t-shirt sizing (M, L, XL) approach. What does this reveal to us? Both the SMB and Enterprise segment will be well suited to have a base package bundle that includes ‘Team Collaboration’ and ‘Lead and Opportunity Management’ related features. Further, it seems like ‘Sales Forecasting’, ‘Sales Rep Performance Management’ and ‘Account Health Reporting’ related features can be specific add-ons for Enterprise customers, and need not be offered as options to the SMB segment. This leaves the ‘Marketing Database’ feature set which can be an add-on offered to SMB customers. Now, technically we don’t need to include this for Enterprise customers. But the concept of gradation still applies and we will include it in the base package for Enterprise customers.
The final output will look something like in Figure 13. Do note that the use case names need not be final module names. Now that we have our packaging hypothesis, we can now test this against our customer segments and see whether there is package <> segment fit and how to actually arrive at the pricing for the packages and the add-ons. That is what we cover in our next chapter.
Price justification by packaging hidden differentiators in a monolithic enterprise software product
I’ve included this case study to illustrate a facet of packaging that can be useful for companies selling an enterprise product either facing a competitive challenge or a need to justify higher product ASPs. In many cases, products such as these have hidden capabilities that can creatively be brought to the fore to create a more favorable customer perception.
This story dates back to 2013 and 2014. Medallia had recently gone through a series of funding rounds and had been gaining considerable traction in the market. Medallia had always been the leader in the Customer Experience Management SaaS category and had been charging a premium price at the same time we had onboarded a brand new sales team that was chartered with rapidly scaling Medallia’s growth, which included goals around achieving higher product ASPs (Average Selling Prices). At the same time, our value proposition and benefit statements started to look surprisingly similar to that of the competition, largely because the competition had started to adapt a lot of their value proposition to match ours. This left us with a conundrum. Prospects would ask us why we were priced so high when other vendors were making similar promises to them. While we could definitely show them our superior client roster and success stories, we were missing a crucial ‘why’ behind our superior product. The reason largely lay not just in the visible features of our product, but a superior product architecture that allowed us (and uniquely us) to serve the needs of giant Fortune 500 organizations in a way competitors could not.
At this point, we commenced an exercise to create a marketecture (a marketing-architecture). This exercise results in a hierarchical, market-oriented representation of the software which breaks a monolithic product into specific modules. These modules can be given names that allude to their differentiation and value, something that wasn’t apparent beforehand. Once every module within a marketecture is defined, a new sales deck is created around this functionality capturing why it is important, what it does, how it works, and associated customer success stories.
At Medallia one of the modules that we packaged and named distinctly was ‘OrgSync’.
OrgSync was named to denote Medallia’s capability in mapping highly-complex and fluid organizational hierarchies into its software which could then aid user management and analytics. At some organizations, we could support as many as 70,000 users. Something that the competition could not. The act of crisply defining this module, along with differentiators and customer success stories, provided a shot in the arm of our sales team. They were better able to handle price related objections and even put the competition on the defensive.
This example illustrates that packaging is not just an exercise of outlining which features to charge money for, but about clearly communicating the value of the product such that its value proposition becomes clear and the price is perceived to be fairly justified.
When deciding whether to introduce a new feature as an add-on or incorporate it into the main package, several factors should be considered. From a revenue maximization standpoint, features with broad appeal that are likely to attract a wide range of prospects should be integrated into the main lineup, as this can drive overall package value and increase conversion rates. On the other hand, specialized features that cater to specific needs or niche segments are better suited as add-ons, as they allow for targeted monetization without diluting the core product offering. Adding them to the packages in the line-up may deter buyers, as the package may appear too specialized, therefore not the right fit.
In terms of ease of launch and time-to-market, adding a feature as an add-on is typically faster and less disruptive, as it avoids the need for redesigning the existing lineup or migrating the customer base to new SKUs. However, it's important to balance this approach, as too many add-ons can lead to customer confusion, lower conversion rates, and the perception of "nickel and diming." Broadly incorporating a new feature into all packages can drive widespread adoption and benefit long-term strategy, but it may not always maximize short-term revenue. Ultimately, the decision should weigh the feature’s popularity, and the complexity it introduces to the customer experience.
Selecting your key pricing variable is perhaps the most consequential decision you will make in your entire pricing and packaging exercise. If you get it right, almost every other piece of your pricing model and operations can be fine tuned. If you get it wrong, you may not only waste the time taken in conducting this project but can significantly slow down sales velocity and introduce completely unneeded friction in the sales engine.
At the end of the day the goal for pricing is to find a way to charge your customer based on a heuristic that allows a) You to obtain revenue for your product that maximizes the return from the market (i.e. larger companies are able to pay more) b) Your client to pay a price that is proportional to the value perceived to be derived by them.
There are many ways to think about this variable, including pricing on usage, license, seat, cost, and so on. As mentioned by Zuora’s CEO Tien Tzuo in his book Subscribed, I’ve found it very clarifying to look at two main approaches at the highest level as Capability and Consumption based pricing (see Figure 14). (The simplifying caveat here is we assume subscription based pricing models.)
Given this information, how do you decide on your main pricing metric/variable?
Here is a proposed checklist:
3.1. Tie to client value: Is the metric proportional to client value? And to what extent?
3.2. Fits for clients: Will clients perceive it to fit with the value they derive?
3.3. Measurable: Can you instrument your product so that the metric is easily measurable?
3.4. Predictable: Can clients estimate how much they will spend with this metric? If the metric isn’t predictable then clients could be hit by unexpected bills that could take them by surprise.
3.5. Diminishing or Scaled Costs: As your metric increases, do your costs level out, or do they scale with usage? Sometimes there may not be much choice in the matter but this is important to note as the subsequent decisions on pricing structure, price point, and discounting will be critical in case costs increase steadily with consumption.
3.6. Deal Economics: Does this model help you create a workable sales engine? There can always be two different metrics that check all the above boxes but differ considerably in the resulting revenue and margin you accrue for each deal. Everything else being equal, you’d want to maximize revenue capture.
Now let me illustrate how this decision can have a meaningful impact with a few real-life examples:
