Every company needs to make pricing decisions, but early-stage teams frequently spin their wheels trying to determine the best way to price their product. It’s far too easy for founders to find themselves overwhelmed, either ending up in analysis paralysis or barrelling forward while over-relying on their intuition. As founders and operators at M13, we’ve experienced this first hand, so we constructed a framework based on our collective experience across Growth, Product, Finance, and Data to guide founders.

This guide will introduce you to the framework that M13 uses to help founders launch, measure, and adjust their pricing models. We’ve created this framework as a step-by-step template that founders can use to identify the things that matter, analyze them properly, and proceed with confidence.

By the time you finish this module, you’ll know how to filter out the noise and break pricing decisions down into digestible components. This will consist of identifying key concepts, organizing market research, and using it to form a pricing model hypothesis that can be tested in-market, measured, and optimized.

A pricing model, simply put, is a set of rules that a business uses to determine who pays (or is paid), what they pay for, how much they pay, and when they’re paid. When you create your pricing model, you must ask questions from both the stakeholder view and the business view.

For stakeholders:

What problem is being solved?

What are the alternatives?

‍For the business:

What does it cost to serve customers?

What are the growth loops?

‍Both of these views must be considered before we can make the decisions that comprise the pricing model:

Who should I charge?

What should I charge for?

How much should I charge for it?

When should I charge for it?

In this guide, we’ll cover:

1. Understanding stakeholder and business views
2. The product journey map
3. Choosing a value metric
4. Defining your timing strategy and price scaling
5. Navigating the art of pricing and discounts
6. Measuring and optimizing
7. Takeaways & next steps

The principles in this guide can be applied to DTC, B2B, B2B2C, and marketplace businesses all within the same framework. Some businesses have two or three major distribution channels that each have a unique set of stakeholders. For example, Form Health sells its weight-loss telemed service DTC, but also leverages referring physicians as a distribution channel. If the physician’s referral is a critical part of the customer journey for a large subset of users, then both the physician and patient perspectives should be considered.This guide will be most helpful for those who aren’t already measuring and optimizing the impact that their pricing models have on growth. Founders who have already chosen a pricing model should reevaluate their pricing model if:

Its underlying assumptions haven’t been validated since launch (don’t assume your model is optimal if you haven’t tested it).

Meaningful new features have been introduced, internally or by competitors (e.g. if you recently delivered a new feature or an additional use-case that targets a different persona).

The costs to serve stakeholders have changed (e.g. packaging costs have changed so that bundles no longer result in expected cost savings).

The efficiency of growth loops has changed (e.g. your referral rate is changing over time, leading to changes in viral growth).

Every step in this guide will apply to the majority of startups, but some startups will benefit from additional frameworks that aren’t included in this generalized material. If you're an M13 portfolio founder or operator who wants help navigating this walkthrough and any other supplemental analysis, please reach out to us.

To get started, make copies of these templates:

M13 Template: Pricing Framework

And refer to the overview on slide 2 before continuing: How do I use this guide‍

M13 Template: How to Create a Pricing Model

The stakeholder view and the business view are both critical to understand before making monetization decisions. Your company’s stakeholders and prospects want to solve their problems as effectively and cheaply as possible. They’ll be evaluating the value of your products against all other viable alternatives. When addressing these considerations, you are taking a stakeholder view.

On the other hand, your business is seeking to maximize its profits by growing customers, revenue, and profit margins as quickly as possible– that is to say, businesses want to provide products at the highest price to the most customers as cheaply as they can. These concerns are all part of the business view.

Pricing models are at the intersection of the stakeholder and business view, so they must account for both perspectives.

The first step toward building a pricing model is gathering research and organizing it into a format that can be used to evaluate trade-offs and make decisions. We’ve developed a stakeholder journey map to get you started. This map summarizes the key interactions between your stakeholders and your product. As you make your way through this guide, reference the stakeholder journey map and think about how each of these concepts applies to your business.

What the stakeholder view tells us

Stakeholders include anyone who contributes to the value exchange that occurs when your product is utilized. This includes those who pay for it, but also those who don’t (free or promotional customers), and also those who are downstream beneficiaries of your product who also receive value. Note that the value exchange is just that – an exchange.

Stakeholders in your company receive value back from customers beyond just revenue. There is value to consider in the ways that your customers provide things like data, virality, and brand equity. Product journeys detail each step in the process.

The stakeholder view is the most important and the least pliable–in other words, your startup needs to solve customers’ problems at a price they’re willing to pay. A problem can take the form of a pain point or a missed opportunity that your product solves. Many product journeys will have more than one distinct outcome that a customer may want to pay for. Imagine, for example, that the CTO of an enterprise needs more software engineers. They may value “increased contractor hours” as an outcome or they may value “more full-time employees” as their preferred outcome.If your product can reasonably provide either outcome, you should estimate the CTO’s willingness-to-pay for each outcome. This will help you monetize your product to its fullest extent.

The key questions we need to answer are:

• Which pain points or missed opportunities do stakeholders encounter, and are they aware of them (how do they manifest/trigger)?
• Which outcomes do stakeholders place the most value on today, and are there other valuable outcomes they may be unaware of?
• How much are they willing to pay for these outcomes?
• When do they want to pay for these outcomes?

To answer these questions, we have to understand the product value relative to the baseline and the cost relative to competitors (the map will help you to visualize which steps in the journey competitors charge for and how much they charge).

We also need to know which stakeholders are extracting value from the product and which stakeholders are contributing value to it. For B2B examples, there are often several stakeholders within a single company. Each type of stakeholder should be kept separate for the purposes of the map because they’ll each have different points of friction and value creation.

For example, consider Thrive Global, selling B2B2C mental wellness software to an enterprise’s employees. The enterprise’s pain point is that they lose top talent to competitors that offer a more comprehensive benefits package. The value for the enterprise is created when they share benefits packages with prospective employees, or when they retain employees because of it. However, the value for the enterprise’s employees is only created when Thrive Global’s software helps them avoid burnout–and only for the subset of employees that actually use the service.

In a SaaS example, stakeholders belonging to the same entity (i.e. company) can be monetized collectively rather than separately. It’s also possible for the price burden to be shared across entities, such as in B2B2C business models where a company subsidizes a service for their employees.

Why the business view matters

In the same way that the stakeholder view accounts for the needs and behaviors of your stakeholders, the business view looks at the needs of your business. When it comes to monetization, the most important aspects of the business view are:

• Costs to serve
• Growth loops
  

How cost to serve affects the model

Some business models will incur significant variable costs at several different points in the stakeholder journey. Variable costs are costs that businesses incur with each new customer, and they must be managed to ensure that costs are growing in line with revenues. Businesses can dictate which step in the customer journey to monetize in order to manage costs. This decision is closely linked to the pricing model’s timing strategy (which we’ll talk more about in Lesson 4). Cost of service also has an obvious effect on pricing, since the entirety of the cost must eventually be accounted for in your business model. If some prospects are served at a loss, their service costs will eventually need to be covered by profitable customers.

Many companies have plausible plans to decrease variable service costs over time through scale efficiencies or operational improvements. When evaluating costs to serve, as a model input, make conservative assumptions with regards to future improvements. Ambitious targets are essential, but overly optimistic assumptions will lead to an inefficient pricing model that misalign costs with revenue.

By keeping revenues and costs tightly coupled, companies can reduce the risk of attracting unprofitable customer segments. For example, e-commerce businesses incur logistics costs when they ship goods and reverse logistics costs when they must process returns. They’d prefer to charge for returns to avoid attracting unprofitable customers, like Zara has recently begun to do. But keep in mind that anything that negatively impacts the prospect’s customer journey may affect growth loops negatively.

How growth loops affect the model

Growth loops are closed systems that generate more outputs (customers) with scalable inputs (sales and marketing strategies). In order to be self-perpetuating, a growth loop must have a scalable input that drives growth with positive unit economics, which can then be re-invested in the same input.

Warby Parker’s profitable paid customer acquisition is an example of a growth loop. In 2019 Warby Parker’s LTV:CAC (lifetime value to customer acquisition cost) was 3:1. They made $81 in gross profit per new customer—and it only cost them $27 to acquire each new customer. The growth loop goes like this: a new customer generates $81 of profit at Warby Parker; that profit is reinvested to acquire 3 new customers at $27 each; these customers spend $81 each, and the cycle repeats.

Paid growth loops can be dangerous because they aren’t self-limiting. All growth loops lose efficiency over time, but many paid marketing campaigns are scaled far beyond the point of profitable unit economics because marketing managers lose sight of the LTV and CAC and begin to make investment decisions based on historical budgets and growth targets, with less regard for profitable unit economics. In the upcoming Growth module we spend more time discussing the trade-off between growth and efficiency.

Growth loops can also be organic. If you look closely at any product that has a free tier you’re likely to find an organic growth loop. Most organic growth loops are network-based—like LinkedIn’s onboarding flow, which strongly encourages users to refer their contacts—but there are also other types of loops. For example, Spotify harvests data from users on their free plan to improve their Discover Weekly playlist—one of their flagship products—that drives more streams that are then translated into user acquisition, ad revenue, and paid conversions.

Some business models—such as marketplaces and social apps—rely more on network effects than others. Many of these companies are willing to price their services at a loss to capture the non-revenue value that each customer adds to the network.

Most businesses have multiple major and minor growth loops. Hubspot relies heavily on a sales-led growth loop, a product-led growth loop, and a content & SEO loop—all of which are affected by pricing decisions.

Novi's B2B adventure

Novi started a B2B company that sells a widget stamper that creates higher quality widgets in a safer approach compared to their competitors' widget presses. Darrell owns a DTC widget company, ACME, and he is one of Novi’s sales leads, currently using widget presses sold by Novi’s competitor to make widgets for consumers.

Darrell is ultimately making the purchase decision, but there are other stakeholders, including ACME’s:

• widget manufacturers, who will benefit from the safer machines.
• widget marketers, who will market the higher quality widgets.
• human resources, who will have fewer workplace injuries to manage.
• insurance company, which will have fewer injured worker claims.

In the next section, we’ll begin the follow-along portion of the module to create the product journey map.

The product journey is where the stakeholder view meets the business view. It involves all the necessary steps stakeholders must take to create and extract value from your product.

Important to note here is the distinction between the product journey and the customer journey. The product journey is a smaller part of the customer journey, which begins much earlier. We won’t cover the entire customer journey in this module, but it’s important to understand that many of the exposures and interactions that influence key parts of the product journey begin in the early stages of the customer journey (problem awareness, brand awareness, interest, etc.)

Many products have a complex journey with multiple stakeholders, so keeping track of all the perspectives that affect your monetization strategy can be a challenge. This is why we developed the product journey map.

The product journey map is a canvas on which we can organize all of the stakeholder and business research that we need to synthesize to develop a pricing model. As you progress through the guide and the Salesforce case study, you’ll see that the product journey map includes all the points where value can be exchanged between your product and your stakeholders. It will also help you visualize which touchpoints should be monetized based on the value delivered to—or friction experienced by—users at each step. This includes:

• Where and when the value is being created (both to and back from the customer)
• When the perceived value is highest
• Where friction can disrupt the journey
• Which steps the competitors charge for

We can’t formulate a truly well-informed pricing hypothesis until we can analyze all of the value exchange, friction, costs, and—crucially—the changes in perceived value and perceived cost that occur along the way. The product journey map is a forcing function for asking the questions that matter.

The product journey map isn’t a comprehensive checklist of every factor that should affect your pricing decisions—it’s a summary of the most important driving factors. Founders will have to determine which factors deserve the most weight based on their business model. For M13 founders, we can provide guidance along the way.

Follow along in the templates

M13 created the product journey map to synthesize the stakeholder view and business views into a template on this tab, including a pre-filled example using the Salesforce case study tab.

M13 Template: Pricing Framework

Refer to the slides in section #1 of the “Understand Your Stakeholders,” and follow along between slides 4 and 13. Read this lesson for additional context before continuing on to the exercises.

M13 Template: How to Create a Pricing Model

Moments to note in the product journey

Value metrics quantify behaviors or outcomes in stages of the product journey. They can generally be classified into three categories:

• Access: Access to a feature or set of features, regardless of usage or outcome
• Usage: Tied to the usage of your product, regardless of the value the stakeholder realizes
• Outcomes: Directly tied to the business or personal value for a stakeholder
  

Value vs. baseline is the value that your product creates for customers that are not using a close competitor’s product or a paid alternative solution. Note that value vs. baseline isn’t the same thing as willingness to pay. The inherent value a product provides a stakeholder is usually greater than their long-term willingness to pay, because customers need to overcome friction to pay for, implement, and use your product.

When a company sells a product to a customer that isn’t using a competitor’s solution or close alternative, the “baseline” is the absence of any closely related solution, and the value that your product delivers is the value vs. baseline.

The value vs. baseline should be estimated for the primary outcome-based value metrics, regardless of which metric is actually monetized. In the Salesforce example, the most important outcome is an increase in revenue driven by more precise audience targeting, but that can’t be plausibly measured. We should focus on the closest proxy we have: marketing audiences or impressions. In the Salesforce example, we estimated the value as $4K per audience segment, or $2 per 1,000 emails sent.

Stakeholder interviews can be a helpful input into the value vs. baseline. Founders in well defined markets may be able to start by reviewing GLG and Zintro for expert interviews related to incumbent products, but most founders will need to conduct interviews to understand how their prospects evaluate the cost/benefit of their solution compared to their alternatives. This market research can be collected in the normal course of the sales process, but should be done so intentionally and systematically to avoid bias.

Rather than asking customers how much they’re willing to pay, start by asking about alternatives, pain points, and value of their preferred solution, then understand the gaps between their preferred solution and your product to estimate the value that they place on your specific product. Try to estimate the value for their preferred solution (what your product could evolve into) as well as what your product currently delivers. When conducting market research, as a part of the sales process or otherwise, be wary of adverse selection. The people you base strategic decisions on should be representative of the market you’re targeting.

Competitor pricing is another important benchmark because value alone isn’t enough to estimate willingness to pay. We need to know who the closest competitors are and what they charge. Even if your product is differentiated among your competitors, think about the consumer’s point of view and how they would solve their problem if your company didn’t exist. Who would they have to pay, and what would it cost them? In most situations, the closest competitors will have a similar product journey, and they’ll be monetizing a step in that journey.

In our section on pricing and discounts, we’ll estimate willingness to pay for your product and its unique feature set based on the benchmarks that you collect in this section.

“Aha” moments are used to describe steps in the product journey where a stakeholder reaches an insight that increases their perceived value of the product. Growth efforts can—and should—drive increases in perceived value as well. Our upcoming growth module will provide frameworks and strategies for increasing perceived value prior to the beginning of the product journey, in a sales and marketing context.

Not all product journeys will have an “aha” moment. Familiar products and services in a mature market might not require one, but most innovative products will have a gap between their actual value and the customers’ perceived value. The actual value is the monetary value that you can place on solving a customer’s problem, but skepticism or a lack of awareness may cause the perceived value to be lower. “Aha” moments can either prove your product’s efficacy to skeptical users or enlighten users that don’t understand the value.

Prepared is a company that provides video streaming services for 911 callers and dispatchers. The perceived value of Prepared’s product is often much lower than its actual value because it’s an innovative product and prospects don’t intuitively understand all of the use cases. A 911 dispatcher recently had an “aha” moment when they realized they could use Prepared to help a bystander at the scene of an accident perform CPR correctly while the first responders were en route.

Use the product journey map to label the “aha” moments as high/medium/low according to their impact on perceived value.

Stakeholder friction describes any step that requires an investment of time, money, or other resources—especially any step prior to the “aha” moment(s). Companies selling enterprise applications, like Salesforce, must overcome several types of friction:

Internal justification and budget clearance: marketing, finance leadership

Contract review: procurement, legal

Time-intensive training: marketing, data

Costly implementation: product, engineering, data

Ongoing data engineering overhead: data

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Companies selling consumer products face fewer hurdles, but must still be mindful of potential friction points:

Activation: installing app, configuring a user profile, etc.

Browsing: product research & discovery

Delivery: shipping time/cost, stock issues, etc.

Payment: purchase decision, inputting payment information, user authentication, etc.

Gatekeeper approval: know your customer in fintech, medical evaluation by a professional in DTC healthcare

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Before selecting a value metric, we should estimate the friction at each step as low/medium/high. Designations should be based on the total friction incurred at the step, not the amount of friction per value metric. In the Salesforce example, the friction of pushing data to Salesforce is high because it’s a time-consuming task to complete in its entirety. The friction per individual contact record is much lower, but that’s irrelevant because the task must be completed in its entirety for the product journey to yield any value. Friction can vary significantly based on customer segments or the maturity of your market.

Novi's B2B market

When Novi sells their widget stampers to a customer with no existing capacity to create widgets, their customer receives the full value vs. baseline. The baseline is the customer’s inability to create and sell widgets.

Novi is mindful that their competitor sells widget presses at a lower price than their widget stampers. However, Novi has solid empirical data showing that their widget stampers are safer to operate and of higher quality than their competitors.

Novi must decide how to price their superior product based on the value, competitive pressure, and friction in their product journey.

In the next section, we’ll walk through how to use the product journey map to make hypotheses about your optimal pricing model.

A pricing model is just a collection of rules that determine who pays for what and when they pay. In our experience, the best way to build a model is by first examining all of the components parts to understand how each component will affect the customer journey. In the next three sections, we’ll synthesize all of the information in the product journey map and explain how the customer journey should influence your decisions about each component. These components are:

• Value metrics
• Timing strategy and price scaling function
• Pricing and discounts

We’ll start by determining the value metric your business should use, which will have major implications on the other components. We’ll make pricing and discount decisions last because they have the fewest implications on the other components.

What’s your pricing philosophy?

Before jumping into the specifics of the model, it’s worth considering your pricing philosophy more broadly. A pricing model has three major reference points that should be balanced:

Cost: pricing your product at a healthy margin above your costs

Competitor pricing: pricing at or near your closest competitors

Perceived value: pricing products in line with the perceived value to consumers and to capture as much of each customers’ willingness to pay as possible

The goal for most startups will be to differentiate themselves from the competition enough so that they can adopt a value-based pricing model as opposed to a competitor-based or cost-based model. Companies that can’t drive higher perceived value compared to their competitors usually end up adopting competitor-based pricing philosophy as a result.

Let’s look at AllVoices, a company that collects and analyzes employee feedback to solve HR issues for their clients. This is a great example of a company that has successfully differentiated itself. Legacy corporate whistleblower hotlines cost very little to provide, and their pricing reflected a typical cost-based pricing race to the bottom. AllVoices is gaining market penetration at a premium price point because its product has better design, functionality, and branding.Your pricing philosophy may change over time as you differentiate yourself on quality, branding, or other factors.

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Which value metric(s) should I monetize?

The first decision you should make is which value metric you’ll monetize. The most important factors from the consumer’s perspective are:

• The value metric type (access, usage, outcome)
• Product usage patterns
• Market norms

In cases where the product journey ends in a revenue-generating outcome, most consumers will have a strong preference to pay per outcome rather than paying for access or usage because we’re hardwired to prefer certainty, and we’re willing to pay more for sure things. However, not all meaningful outcomes are revenue-generating. Consumers place great value on time and peace of mind– things that can’t always be quantified easily. When a mortgage broker sells a home it often costs the seller more money than it saves them, but consumers value the certainty and time saved as important outcomes.

Companies that are executing a value-based pricing model will be able to capture the most willingness to pay by charging for outcomes. Why? Because the price that consumers pay scales (roughly) proportionally with the value that they get from the product.

Customers who get little value from the product can happily continue to use it sparingly—until they are nudged into higher usage—while customers who get a lot of value from the product pay much more than the average customer. When this option is available, it’s effective for minimizing churn from the low-usage segment and driving revenue expansion from the high-usage segment.

For example, DoorDash charges restaurants for completed orders—not for platform access or search impressions.When outcome-based metrics aren’t available, usage-based value metrics are typically an excellent proxy for value. Usage-based metrics have many of the same attractive features as outcome-based metrics, namely that customers perceive them to be lower risk because they have control over their usage. Many businesses charge for recurring services or subscriptions to “land and expand”—a strategy that generates increasing recurring revenue from each customer.In some rare cases, customers may prefer access-based metrics. Consumers may prefer access-based metrics when:

• Frequency or volume of usage is high and predictable
• Usage is not a good proxy for value
• When access-based pricing is the market norm
• If customers don’t clearly understand how their usage of the product translates into charges on their invoices, or if they have very little control over their usage, they may prefer flat (i.e. access-based) pricing.

Consider market norms, but don’t follow them blindly.

Differentiated products often have more pricing power than they’re comfortable exercising. Snowflake is an example of a company that understood pricing inefficiencies and developed a product that didn’t conform to market norms. Amazon Web Services’s (AWS) Redshift product set the market norm for cloud data warehouse pricing over a decade ago. Companies had to pay for data storage and data processing in fixed tiers. They couldn’t buy more storage without paying for more processing power, or vice versa. Snowflake, a newcomer, developed a pricing model that de-coupled data storage from processing power, which gave customers the ability to buy as much storage and processing as they wanted, independently of each other. Snowflake won many former AWS customers by ignoring market norms.

Most consumers prefer paying for outcomes or usage because it tethers their costs to the value that they derive from the product, and it aligns the business’s interests with the consumer’s interests. However, this doesn’t mean they’ll be willing to pay a flat rate. We’ll cover price scaling functions in the following section.

What are the important factors from the business perspective?

Founders should also consider cost to serve, revenue, and the product’s growth loops from the business perspective.

When evaluating the cost to serve, focus on your variable costs instead of your fixed costs. If marginal costs are low, companies can focus on pricing per outcome or per usage metric, but some businesses incur significant costs in the early steps of the product journey. This is most obvious for hardware businesses, because they have to manufacture and ship an item before it can be used… But it’s also true for software businesses with costly training and implementation requirements. Any company that charges an “implementation” or “onboarding” fee is charging for access. In some cases the fees can be circumvented by off-loading the training expense to a third party implementation consultant, but the price burden still falls on the customers.

Consider how “aha” moments and friction points affect the growth loops for your business. For example, video streaming services place the payment friction at the point of the product journey where perceived value is highest—at the end of a brief free trial—when customers are most excited about the novel selection. Companies that need prospects to experience the product and discover their “aha” moments can do so by moving the payment friction later in the customer journey (e.g. allowing a free trial or free tier). After paying for their membership (i.e. access), customers don’t pay for usage, so there’s no payment friction when customers are browsing content and developing their usage habits.

Usage metrics aren’t always a great proxy for value. Some streaming services provide diminishing marginal value to users after they’ve watched the most attractive titles. Over time, users begin to exhaust the catalog and perceived value will decrease. By that point, they’ve already adopted a habit and the friction is much lower.

When “aha” moments occur later in the product journey, the payment friction should be moved after that moment when the perceived value is highest.

Should you monetize multiple value metrics?

There are many cases where it’s optimal to choose two value metrics to capture the maximum willingness to pay. Fivetran is a company that builds data pipelines that enable data visualization and automation. They use pricing tiers to effectively price by both access-based and volume-based metrics. The features that a customer has access to determine the rate that they pay, but the total price is a function of (rate * usage). In other words, every customer pays a certain rate for monthly active rows (the usage-based metric), but customers can opt for higher rates in exchange for access to more features (the access-based metric).

If your product has auxiliary features or services that provide a lot of value but have a different cost profile and usage pattern than your product’s core features, it may be optimal to charge for those features à la carte.

Novi's product journey

Novi asserts that their widget stampers are safer than their competitor’s presses. The extent to which their stampers are safer can be considered the actual value of their stampers. For Novi’s sales prospects, these claims may not resonate because of skepticism or lack of understanding.

Novi realizes there may be a gap between the actual value and perceived value of their product. To make matters harder, their stampers require new training and procedures to use, so they’re expecting user friction from ACME’s manufacturers, who are more comfortable using their competitor’s presses.

Novi is confident that the manufacturers will value their product's safety if they can make a sale by lowering the perceived cost, placing the payment friction after the “aha” moment. Novi realizes that ACME’s human resources and marketing teams both stand to benefit from the stampers, so Novi appeals to them as well in their sales meetings.

The product journey doesn’t end when Novi makes a sale. Novi sells their stampers for a one-time fixed price, but they also sell maintenance services that their specialized technicians provide on-site. Employing this workforce of mechanics is expensive, and Novi will incur far more in maintenance costs from some customers than from others because maintenance is directly tied to usage.

Customers that use the machines frequently will make the most service calls. Customers that rarely use the machines won’t make many, so Novi decides that they’ll use two value metrics:

• Stampers sold: access-based per stamper
• Stampers serviced: volume-based on fixed widget capacity per service call

Your timing strategy describes what you charge for and when you charge for it. The value metric you choose is the most important part of your timing strategy because it determines which customer behaviors will incur a cost, but there are other important elements of your timing strategy.

Your timing strategy and price scaling functions are key parts of your pricing model that you can use to place the payment friction at the point of the customer journey where your prospects’ willingness to pay is highest. Your goal when choosing these timing and scaling components should be the same as the goal of choosing a value metric– to identify the elements that capture the value that the product creates, keep costs aligned with revenue, and keep growth loops moving. These are the variables you can change to strategically move your payment friction to the optimal points:

• Payment vs. usage timing
• The term of recurrence for value metrics
• Price scaling functions
• Contract terms
• Trial offers

If you choose to monetize a usage-based or outcome-based metric, you need to decide on payment vs. usage timing. Will customers prepay for a fixed number of credits per cycle that expire? Will they pay at the end of the term for what they used? Will they pay per transaction?

If the frequency of product usage is high and predictable, customers will be more likely to prefer a recurring prepayment subscription timing model that allows them to pay ahead for an allowable volume of usage. For example, LinkedIn Premium charges recruiters for a fixed number of InMail messages that expire at the end of each cycle. Prepayment models move friction earlier in the process and encourage customers to adopt a habit based on the psychology of sunk costs.

If the frequency of usage is lower and more variable (i.e. less predictable), customers will be less likely to accept a recurring prepayment plan. They’ll prefer a transactional payment plan or a recurring retroactive payment plan, where they pay at the end of the term for only the volume that they used.

The metric payment term is the cadence that you bill customers for a value metric. Typically all metrics will be charged to customers on the same payment term cadence, and this is the term that governs the payment vs. usage timing if customers are prepaying for a fixed amount of usage. The term may vary in some cases where a business sells distinct product lines (e.g. subscribe and save monthly recurring orders stacked on top of an annual prime membership).

The price scaling function is another powerful lever for capturing willingness to pay and shifting the friction. Linear scaling functions may be appropriate in cases where the variable costs to serve are high and the market norms support it (like in healthcare; there are no economies of scale in surgery). But in most cases, costs to serve will be low and diminishing marginal prices will be the market norm.

There are many mechanisms for decreasing marginal costs. Hurdle tiers (e.g. $10K per outcome up to a maximum of 3, $8K for 4-10, $6K for 11+) are common, but some companies use more complicated functions. Fivetran, a data movement SaaS provider, uses a logarithmic price scaling function to create a diminishing marginal price because the value vs. baseline scales much more slowly as their usage-based value metric grows. As a result, the willingness to pay per value metric is much lower for companies that use their product the most.

‍Trial offers can be an effective way to temporarily remove the friction preceding the “aha” moment. Trial offers can take many forms, but they’re generally most helpful for products or features with a low marginal cost to serve. Feature-limited offers (i.e. freemium) models are very effective for companies that capture network value from the users on their platform (e.g. social media, algorithmically optimized software). Time-limited offers are effective when the average customer lifetime duration is several months or longer. Volume-limited trial offers can also be used for products that have discrete usage value metrics. Trial offers are common in conversion flows, but can also be utilized for upselling customers to new features or tiers.

Novi’s timing & scaling strategies

Novi must decide when to charge for their two value metrics after choosing them. Novi is confident that ACME’s operators will adopt high usage if Novi can get them to start using their stampers. For this reason, Novi wants to reduce the perceived cost for new customers.

Timing: Novi decides to include a trial offer and a return window, during which Novi’ll provide a partial discount to mitigate restocking costs in case of a return. Novi’s thought process is:

• this will remove some of Darrell’s initial hesitancy and improve their chances of closing the deal
• when ACME's machine operators start using the stamper regularly, they’ll get more comfortable over time
• their perceived value will increase and their friction will decrease

Scaling: Service calls are labor-intensive and volume doesn’t offer many economies of scale. Novi plans to set a flat rate without volume discounts and charge customers retroactively as opposed to requiring pre-payment because:

• Novi wants to minimize payment friction before ACME’s machine operators adopt usage habits, and ACME may consider pre-payment too risky
• Novi expects the perceived value to increase quickly after adoption, leading to high usage and demand for service calls

Use your model hypothesis tab to outline your go-to-market model. Consider defining multiple options side by side, with variations.

In the next section, we’ll talk about the most obvious element of the pricing model hypothesis: price.

Pricing decisions can be difficult to make, even after collecting and synthesizing a vast amount of information, but it’s considerably easier once you’ve sketched out the other mechanics of your pricing model.

Using research to set prices

After completing the market research on alternatives and competitors, you should have determined two important benchmarks:

• value vs. baseline in the absence of a competitive solution
• competitive prices for your closest competitors

If you’re operating in a well-defined market where most of your target customers are already using a competitor’s solution, you might be more likely to adopt a competitor-based pricing strategy rather than a value-based one.In less mature markets, where most of your target customers aren’t already using a competitor’s product, you’re more likely to have success pricing the product higher and closer to the true value vs. baseline, because of the lack of competition. In the vast majority of cases, the value vs. baseline should be higher than the willingness to pay.

If competitors are charging for a different value metric than you are, then you’ll need to normalize across the value metrics by using a ratio between value metrics to estimate the total all-in price a customer would pay under your proposed pricing compared to your competitor’s pricing.

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Using discounts effectively

Almost all pricing models across industries will benefit from the right discounts. Discounts should be layered in to promote high-value actions. “High-value” actions may differ by circumstance but generally fall into a few categories:

• Acquisition
• Activation
• Retention
• Advocacy

‍Acquisition discounts (e.g. 15% off first order) should be used to temporarily remove payment friction from the early part of the customer journey. These discounts can be used to land a large client quickly, as a way of establishing a business relationship. For the most part, however, acquisition discounts are used to incentivize customers to reach an “aha” moment that will increase their future willingness to pay.

If there’s no “aha” moment following the acquisition discount, it’ll only help you acquire customers whose willingness to pay is lower than your product’s true price—which is a dangerous strategy that is likely to decrease your profit margins among customers with a higher willingness to pay. Many apparel retailers notoriously over-rely on steep discounts that are widely advertised. This is not a long-term winning strategy. Steep discounts should be selectively targeted at customers and prospects that aren’t willing to pay full price, then measured carefully.

In practice, any discount will cause some margin erosion among some customers, but effective discounts will drive enough incremental gross margin dollars from incremental customers to offset the erosion. The margin erosion effect of discounts can be measured through various means. For a consultation, you can reach out to M13’s data team on the M13 Community Slack channel.

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‍Activation discounts nudge existing customers to take actions that increase their likelihood of continuing or increasing usage. Grocery businesses and ride-hailing apps both frequently use large discounts spread over a certain number of orders (e.g. 15% of your next five rides) to create a customer habit that increases retention.

Think about how your activation discounts will encourage breadth vs. depth. For businesses with a large variety of SKUs or products, a percentage-based discount is effective for increasing product breadth (i.e. the number of distinct products the user has purchased) which is a strong leading indicator of retention. Activation discounts can also be used to tease auxiliary features that aren’t included in a customers’ current product tier, to motivate cross-selling and up-selling.

‍Retention discounts are targeted at users who have churned or have indicated they’re likely to churn. These discounts can be accretive to your bottom line, but they come with risk. If they’re targeted carelessly, they’ll decrease perceived value and willingness to pay among your healthy customers. Fortunately, it’s relatively easy to run retention experiments. Widely targeted retention discounts may cause margin cannibalization in subscription businesses or pull forward future demand in transactional businesses. ClassPass targets retention discounts only at customers who have signaled cancellation intent in order to mitigate margin cannibalization.

‍Advocacy discounts are commonly used by DTC companies to drive more referrals. If used effectively they will provide an extra nudge for happy customers to refer their friends. Advocacy discounts usually do not drive incremental revenue from the customer that receives the discount. Acquisition, activation, and retention discounts are all margin cannibalizing, but advocacy discounts require a different measurement framework. They should be considered a marketing expense of your referral program and be benchmarked against the customer acquisition cost (CAC) of your other acquisition channels.

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Using customer psychology to price your products

There are well-researched price effects that can cause perceived discounts.

Charm pricing is a tactic to decrease perceived cost by pricing items just below a round number to take advantage of left digit bias. Its efficacy has waned over the generations, but it’s still an easy win for consumer products that want to signal value. Insider uses charm pricing to decrease perceived costs at $99/yr, but there’s a drawback. Prices ending in “9” can signal lower quality in some cases. The Financial Times uses a more specific price of $375/yr to take advantage of the left digit bias without using a price ending in “9.”

The decoy effect is the placement of an obviously inferior option that increases the perceived value of another option. This tactic was used by The Economist and popularized by Dan Ariely. The Economist advertised a digital + paper subscription for the same price as a digital-only subscription, making the former option look much better by comparison. This works because customers don’t evaluate products in a vacuum. It’s too cognitively demanding. Customers will take subconscious shortcuts by looking for reference points to decide if something is a good value

The anchor effect exploits the same tendency. This when a company includes an outlier price in a context that makes the more common options seem like bargains by comparison. For example, some estimates assert that Uber’s premium offering, UberBlack, only makes up 6% of Uber fares. But every time we see the UberBlack price we’re subconsciously anchored to a more expensive product, making the more popular UberX appear to be better value.

Novi’s pricing & discounts

The last big decisions Novi needs to make are how much to actually charge and which discounts to offer to incentivize lifetime value.

The high variable cost of selling each machine creates inherent tension. Novi can’t charge as much as they’d like for a product that has high implementation friction.

They decide to price the stampers with a narrower profit margin than the maintenance calls. Novi expects to make most of their profit when their customers’ willingness to pay is higher.

Novi decides to add two discounts to capture even more value:

1. 20% discount for the first stamper, offered only to first-time customers. This prices the machine at cost but allows Novi to convert more prospects into happy, paying customers.
2. Prepayment option for 3 maintenance calls, billed upfront. Customers that choose this option will have an extra incentive to use the machines regularly so that they benefit from the sunk cost.

Finally, we’ll discuss best practices for data collection, measurement, and optimization.

The last step in bringing your pricing model to market is to define an optimization plan. In this section, we’ll cover:

• Target metrics
• Measurement frameworks
• Optimization strategies
  

What should you optimize?

Your pricing model is a powerful growth lever, and it should be measured the same as other growth levers: by its effect on profitable growth. Growth efficiency metrics include revenue collected from customers, the variable costs of serving those customers, and the cost of acquiring those customers.

Any significant changes to your pricing model are likely to affect multiple sides of your growth efficiency equation in opposing directions. Most price increases lead to lower conversion rates, but higher revenues from paying customers. Trial offers will increase conversion rates and revenue while increasing costs to serve. Make sure teams are aligned on a target metric that represents a profitable outcome for your business.

When your company’s leaders are all managing different KPIs, they may find it difficult to collaborate constructively. Imagine a company where:

• Sales leaders are compensated based only on annual contract value
• Customer success is evaluated based solely on retention rate
• The finance team is focused on improving contribution margin

These three functions can easily end up in conflict with each other over decisions that affect their metric negatively regardless of the effect on the total business.

What gets measured gets managed.

Peter Drucker

Ensure that your teams are optimizing the right metric and swimming in the same direction by creating metrics that accurately reflect value. If there are customer segments that bring significant non-revenue value, then estimate the non-revenue value in a KPI that you can make a goal out of.

In a famous example from Facebook’s early days, ad revenue was a poor leading indicator of any given customers’ future value to Facebook. Instead of measuring LTV or new users as a target KPI, Facebook analyzed the early customer habits that led to engagement and retention. They found a strong link between friend connections and retention, with diminishing returns for each additional friend. They arbitrarily set a cut-off point at 7 friends, and created a target metric for their growth team: “new users with 7+ friends”, which accounted for the non-revenue value that could be monetized at a future date.

How should you measure changes to the model?

There are a few common types of measurement plans:

A/B tests are split-tests that involve randomly splitting two or more groups and assigning each group a different treatment (i.e. pricing model). A/B tests are the gold-standard for measurement and inference, but they’re impractical in many scenarios because of the difficulty to execute them and the risks of cross-contamination (i.e. people in one group learning of the other pricing model) to the customer experience. A/B tests are viable when prices aren’t posted publicly and when the pricing variation being tested is an add-on—like a trial or discount—not a structural component of your model, like the value metric.

Side-by-side tests involve giving customers the option between two treatments (e.g. pricing models). They can be used to serve customers multiple options, and measure their preference between them. These tests can be used when founders aren’t trying to infer which single model is optimal, but are seeking to maximize conversion rate at the expense of other metrics (e.g. CAC payback, revenue retention).

Pre- and post-measurement is a strategy for estimating the effect of a change after it’s already occurred. This can be applied when a company has decided to switch from one obviously inferior pricing model to a better one. Pre- and post-tests are vulnerable to bias and measurement error. They should only be used when founders have high conviction that the new model will outperform the existing model.

In some cases, your pricing model hypothesis requires taking a “big swing” that can’t plausibly be A/B tested. In those cases, many teams rely on Pre- and post-measurement. Data from pre- and post-measurement is not as clean as data from A/B tests, but analysis of that data can be very useful in generating hypotheses for smaller changes and optimizations that can plausibly be tested.

Big changes lead to big effects that can typically be estimated without a formal A/B test. If you are considering a big change you may have hypotheses about which types of prospects or customers will be most affected. Use the Optimization Hypotheses section to list your assumptions and begin collecting the relevant data before you decide that you need it for analysis. In many cases it’s infeasible to retroactively collect data that you need for an analysis.

Once founders have changed the structural elements of the model (e.g. value metric, price scaling function), they may continue analyzing customer behavior in order to refine the smaller elements (e.g. pricing, discount amounts). When Oura tacked a $5 monthly membership payment to the one-time $299 price of their hardware, they took a big swing—and they mollified existing customers by grandfathering them into their previous free service.

Which data points do you need for your KPIs?

Before implementing your go-to-market strategy, you should establish a data collection process to ensure that you’re able to capitalize on future learning opportunities. Start by having each team formulate their hypotheses about optimizations that they want to make, then determine which data points you’ll need to collect to validate those hypotheses after taking your new pricing model to market.

Let’s take the Salesforce case study as an example, and suppose that they’re about to raise prices by 40%. Their head of sales might warn that the sales lead conversion rate will decrease because the higher prices will create more friction for potential customers, leading to a longer sales cycle and fewer new customers per sales rep.

In this example, if Salesforce uses Marketing LTV:CAC to measure their acquisition efficiency, the KPI would show that the new model was more efficient despite the lower conversion rate. However, that metric doesn’t account for the salary cost of the sales team, who will have to work more hours per customer to sell the product at the lower conversion rate.

If Salesforce properly accounts for sales hours in their target metric, they’ll see that the old model is more efficient when all sales and marketing costs are properly accounted for.

In this example, it’s not necessarily true that Salesforce should revert to their old pricing model. It’s possible that the new pricing model works quite well when Salesforce targets the right type of company. Perhaps they should target companies with 100+ employees, or Series B+ companies, or companies with a director-level retention marketer. These are all testable hypotheses, but they require a disciplined data collection effort. No matter what hypotheses your sales team has, it’s unlikely that they’ll be able to retroactively collect the data needed to validate them, so we recommend leading with the hypotheses and collecting the data proactively.

Before rolling out an A/B test, use the data you collected to find insights that will either support or refute your hypothesis. If you can’t find evidence that contradicts your hypothesis, it may be worth running a test. If Salesforce believes that companies with fewer than 100 employees are more price sensitive then they should compare Demo-to-Close conversion rates for companies with less than 100 employees to larger companies. If the close rates are much lower for companies with less than 100 employees they might decide to A/B test a 10% promotion for smaller companies.

Novi's A/B test

Novi mulls over their decision to give first time customers a 20% discount, which prices their machines at a loss. In order for this discount to pay off, their customers have to buy more stampers and pay for maintenance regularly. This is a risky bet with a high potential upside. So Novi decides to run an experiment.

For the experiment, Novi will offer a random subset of prospects the 20% discount and withhold it from others. Then, they’ll wait another three months after assigning discounts to measure the results.

After the experiment has concluded and the cohorts have matured, Novi will be able to compare the two groups to find out if the discount recipients have turned into more loyal customers. In that case, the small net loss on their first order would be justified.

An effective pricing strategy will lead to a strong go-to-market hypothesis, but it must also be responsive to insights that you learn from being in market. There are three steps to creating a pricing strategy:

1. Understand your stakeholders, including their problems and motivations and their options for addressing each
2. Define your model by using all of the parameters to create rules that scale prices in proportion to your customers’ willingness to pay
3. Measure and optimize your model by defining your target metric and implementing a measurement strategy

Optimizing your pricing model provides a significant competitive advantage by allowing startups to capture more of the value that they create as revenue so that it can be reinvested into growth—but founders have to consider a dizzying number of inputs to craft a good model. There are many important factors on the customer side (including value vs. baseline, competitor alternatives) and the business side (costs to serve, growth loops).This guide can help founders organize their research and the most pertinent factors. Each business has unique considerations—some of which can’t be accounted for in a streamlined template—but we’re always available to help you apply insights from other companies to your unique challenges.

Resources we love

Growth at All Costs is Perilous — This is How to Scale Sales Sustainably learn more about how acquisition efficiency is measured and used to improve acquisition efficiency for Sales-led acquisition models.

Outcome Based Value Metrics Reduce Churn, Increase Revenue review the data that Profitwell collected on the efficacy of the three types of value metrics.

Whether you need help selecting partners for your financial tech stack or polishing the messaging in a press release for your new pricing, you can reach out to your investment lead or the Propulsion team.