Making Pricing Work for You

<Updated with inputs from my wonderful Google colleagues: @Dirk Nachbar, @Ulrich Keller, @Evan Otero & @Nick Krasney>

I have been talking to different startups about pricing/monetization strategy and it looks like it is a common challenge; hence consolidating my thoughts here.

When to start thinking?

As early as possible

What should a good pricing strategy take into account?

• Realistic, feasible Total Addressable Market (TAM)?that is right-sized to product offerings (not everyone has a 0.01% chance of using the products)?
• A clear, objective, data-driven* plan for revenue (calibrated demand based on proposed pricing)
• Benefits of this product proposition over alternatives for the right audience - right product-market fit at the right price
• Lifetime Value (LTV) vs. short-term conversions (how to optimize to attract high-LTV users?)
• Understand key stakeholders' needs & decision-making process to address concerns (e.g. end users of products might not be the budget approvers)?

*data-driven is not equal to set-in-stone, it means updating with the most updated & accurate numbers

?Who to involve??

Cross-functional effort, not just Finance

• Definition of strategy likely require multiple iterations and cross team calibrations (not one-way street)
• Cross-function inputs (e.g. Sales, User Feedback) are especially important if product is ‘disrupting’ existing market because this is not just a financial decision, but also impacts business model (e.g. software SaaS vs high-touch consulting) & overall strategy: OKR (users, revenue, # bookings, ARPU...), GTM, target clients & TAM, product features prioritization, hiring...

How granular or accurate should this be??

Depends on the stage where strategy is applied

• Early - realistic TAM & plans to address their needs; accuracy of forecast not as important; be open & flexible to adapt based on feedback/tests
• Late - traction-adjusted forecast

Now we are talking: How can we do this?

• What is the right pricing unit (per API call/ user/ data usage or fixed monthly fee)?

1.??Check how cost, profit, margins scale with client's size

2.??Define the most granular building block & set price at this level

3.??If very different from existing models, need to demonstrate clear product value to users/clients

• ?How to price? (suggest to do the following sequentially, but can be done in parallel if the team has a good understanding of value-add & MVP is ready)

1.?Value-add to users: quantify improvement (e.g. A/B tests, pilot clients...) & ensure price <= noticeable/perceived value. Types of value-add:

A.?Efficiency: do existing products work faster/with less resources

-?Breakdown value chain/user journey into respective components

-?Map out pain points/barriers + resources needed to do the job?

-?Quantify value of each step/block

-?Identify perceived risks for switching to new processes & quantify costs

? B.?Effectiveness: improve on existing job through better output (e.g. new dashboards, BI tools)

-?Expand potential use cases, e.g. making decision vs monitoring performance; substituting vs. supplementing existing sources

-?Expand scope of pilots after initial use cases have been validated

? C.?Do New Job - new approach required, which is not feasible with existing ways of working

-?The most value-add, but no one-size-fits-all approach, needs clients to work closely to quantify value

-?Requires good understanding of key stakeholders involved & how existing solutions fit into overall business process to expand scope (e. g. algorithm developed for one function can be adapted for non-adjacent functions who traditionally do not have the right talents)

? 2.?Price-demand curve/price sensitivity: Optimal point for right business objectives (margins, profit, revenue, users...). Requires at least a MVP.

A.?B2C: Run different campaigns with different propositions on platforms such as Product Hunt, Kickstarter... to collect data; If live campaign is not feasible yet, consider running surveys (Common techniques include Gabor-Granger, van Westendorp and Conjoint Analysis)

B.?B2B: Different pilot clients across sizes; leverage contract renewal & negotiation to understand price sensitivity; notice willingness to pay before & after the project & understand what changed along the way to refine value equation & price-demand curve

C.?Platform: Quantify value to both parties on the marketplace to understand who gains the most from using the platform

-?define price sensitivity for both sides of platform & identify optimal point(s) to maximize OKR (users, revenue, profit...)

-?user acquisition & pricing to prioritize the party who gain less so that the other party will join automatically

-?price users according to the value gained; might be asymmetrical between the 2 parties

-?users can be both buyers & sellers in different transactions, hence the value equation has to be very clear & transparent

-?Evaluate impact of pricing on Frequency & Stickiness of platform usage

?3.??Competition: Right positioning vis-a-vis existing models

A.?<For all> Identify opportunities to provide standardized way for comparing price differences (usually opaque, as different companies offer different features at different prices)

B. <If creating a nascent category> important to define noticeable & material impact, industry norms, standards & pricing models

C.?<If attempting to disrupt incumbents> clearly communicate how the new pricing model supports the new value provided; less emphasis on comparison vs. incumbents; more on value-add

?4.?Best practices tying them all together:

A.?Identify decision-makers & decision-making process/framework + key concerns/information needed to make decisions

B.?Illustrate how startups can value-add end-to-end through key competencies/enablers

C. Standardize pricing process & aggregate by the most granular pricing block

D.?<For B2C> standardize into 3-4 price tiers for the most common use cases

E. <For B2B> System to automatically generate pricing based on # of pricing blocks required for each client

F.?<For B2B> BD/Sales team can layer upon bundle discount - as cost-to-serve does not change much by # building blocks, margins can be deprioritized for more strategic reasons (e.g. permission to build testimonial use cases, willing to be pilot partner for A/B tests...), but need to define clear principles of discounting & capture into JBP/contract

G. <For Platform/B2C> User habits are difficult to change, hence do not assume that pricing can induce users to behave differently in a sustained manner (i.e. any short-term platform usage changes in frequency/stickiness due to pricing are not likely to persist).

For example, if an average buyer buys 1 product monthly, even if transaction fees go to 0, the frequency might increase to at most 4 (but they are actually front-loading their purchase, so their purchase in subsequent months when prices go back to normal will be 0) Failure to account for this will result in a wonky price-demand curve.

H.?Most likely requires multiple iterations before an acceptable range is identified (important to align internally on OKRs & key considerations to narrow down # of potential variations to test

However the above is not a one-size fits all approach

In practice (from my experience in working with start-ups) some may require different pricing models/units (non-exhaustive examples below). The decision lies in the trade-off between willingness-to-pay, value-add, cost-to-serve & business OKRs

• SMEs vs. Large corporations

1.?SMEs - Value-based, as they're more price sensitive, hence need to clearly quantify incremental value provided vis-a-vis existing solutions/alternatives

2. Corporations - Cost-based, as their key requirement is likely standardized, scalable insights & reports across all business needs,?

• B2C vs. B2B

1.?B2C - API calls, as solutions are standardized?

2.?B2B - SaaS/Consultant model, through white-labeling or integrating your product into clients' consumer-facing offerings

• Tiered pricing by transaction volume or stage of growth

1.?High volume/gradually-growing companies - fixed monthly fees & predictability of spending

2.?Low volume/high-growth - per usage & pay for performance

Additional Resources (good starting points to other resources):

• SaaS Pricing - ProfitWell's ebook
• Finding the right Product-Pricing Fit - Monetizing Innovation
• The Psychology of Upfront Pricing
• Disruptive Unbundling and Bundling
• The Startup Guide to Cash Flow

Please feel free to reach out if you would like to chat more about this.

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