Questions for Sam Lee - PricingSaaS AMA on April 28

#5-events AMA
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PricingSaaS AMA :fire:

Featured Expert: Sam Lee

Sam’s led pricing at ServiceNow, Snowflake, and now HubSpot- some of the biggest names in SaaS.

In this AMA, he’ll cover qs on:

  • How to build pricing models that scale
  • What a high-performing pricing org looks like
  • Tying pricing to business goals

Please leave your questions for Sam below by repying to this topic

He’ll be answering them live during the session on 28 April at 1pm EDT / 6pm BST / 10am PDT.

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My question for Sam:
Have you encountered a friction where companies only want to touch pricing once every few years, or keep things squarely away from product and under CRO, and if so, how did you manage to pitch having a more long term focused pricing team?

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Looking forward to this discussion! I have a few burning questions that focus on the intersection of a multi-product offering and your internal systems.

  1. When becoming a multi-product offering that targeted distinct personas, how did HubSpot manage the challenges of subscription management and billing? What was the secret to effective scaling here?
  2. Over the course of the years, I’ve noticed HubSpot has pulled back on enabling self-service purchasing for plans targeted at businesses and enterprise - what has been the benefit of this change?
  3. Self-servicing trials don’t seem to be a part of the current strategy - were they ever? Why aren’t they today?

Thanks!

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Question from @Chris_Aliani:

Thanks Sam, In my experience pricing roles or organizations never start off as greenfield. People are already entrenched in “this is how we’ve always done it”. What are some practical tips for wrangling pricing away from i.e. Product Line Manager inorder to realign the pricing process.

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Hey Sam! Thanks for joining.

Credit models are having a moment. Curious if you have any advice for implementing a credit model given your experience at Snowflake?

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Question for Sam:

Recently, Anthropic said in an interview, “AI employees are a year away.”

How do you predict “AI employees” will be priced? Will it be comparable to paying someone a salary, or more outcome-based?

source: https://www.axios.com/2025/04/22/ai-anthropic-virtual-employees-security

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Is there a link to join? Sorry if I’m missing something super obvious.

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Hey @Serge - no worries! You’re in it. The AMA is Reddit-style, so everything happens in this thread. You are right where you need to be :grinning_face:

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Hi everyone! I’m on!

@Rob_Litterst Great question! I like to start with the question of understanding what you’re solving for with credits, and focus on the value metric and understand how value accrues to customers, and how that ultimately can/should be measured, and charged.

The framework I’ve used and are using at HubSpot is: 1) Feature drives customer usage, customer usage creates customer value → what is the value metrics that best represents the value metric? Then 2) Decide what usage metrics is the best proxy and representative of the value metric - ideally the usage metric (i.e. what you charge) should scale with value and scale with COGs. Finally, 3) layer on the credit model and the pricing mechanics that’s associated with the credit model as a pricing / abstraction and GTM enabler to help drives certain outcome or solve for the sales motion.

@Serge to your questions. I’ve always structured pricing strategy team under a “tripod” model:

  1. Product Monetization - This is the “bread and butter” of pricing, and the team that works with the product managers to set price, bundle, packaging, etc. I sometimes call this “inbound pricing” because this team is really focusing on the product value and value capture of products and services
  2. Commercial Pricing Strategy - This would encompass pricing programs (ex. ELA, parter pricing, etc.), discounting, and other commercial terms that impacts how customer interacts with the “price” - I sometimes call this “outbound pricing” as its focus is on how price lands in the different market segments, with partners, sellers, etc.
  3. Governance, Analytics, Operations - This is like the back office function - which includes the governance and policies management of how the company makes pricing decisions. tracking, forecasting and readout of relevant KPIs and business performance as related to pricing. And finally, the way the company operationalize a pricing decisions - how it flows into different teams, systems, etc.

In terms of prioritization, Monetization (1) is usually the top priority. Follow by the Governance / Ops side of things. Early on the pricing leader will probably do most of the work directly on governance / ops etc, but one amazing analyst that can span across different areas can give you tremendous leverage.

Commercial strategy is typically the last to be layered in and not a priority until a company reaches sufficient scale and have a more complex business portfolio. Also often the deals desk, sales ops and legal would pick up the slack until they can’t anymore.

Pricing team can report to any functions. In my career I’ve reported into Product, Finance, Sales, Marketing, and Strategy/Operations. It really depends on how a company define the scope of different functions. Where a pricing team sits are less important than who the top leaders are and if c-suite have sufficient understanding of pricing and what their vision of this function is.

There are pros and cons to having pricing report into every function. Generally speaking I feel like the best place to have pricing is either 1) Product, 2) Strategy/Operations, 3) Finance (if there is a strategic finance function).

Pricing sits at the intersection of Product, Finance, and Sales. So there’s no “perfect” place for it. Everyone needs to understand what the team’s goal is and what they’re charged to solve for. Sponsorship and air cover from the very top is key to maintaining autonomy and objectivity.

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Q for Sam:

When building a pricing function inside a scaling SaaS company, how do you recommend structuring the pricing team within the broader org?

  • Who are the key roles you’d include early on?
  • Who should the pricing team report to?
  • And how much autonomy should the team have versus being embedded within product, revenue or finance?
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This is a great question! I’ve found a couple of “ins” in my past - 1) is around solving for pricing complexity - typically when individual product line manager or GM “owns” pricing. They will optimize for their business but sub-optimize for the company. This often leads to “pricing sprawl” and creates a patchwork of pricing models that becomes increasingly hard to manage.

Often this is when a company decides they need a centralized pricing function. However, with out true executive leadership buy in from the very top (like… c-suite). A pricing leader brought in to “untangle” the pricing complexity will likely fail.

Another “in” I’ve found is around governance and control - basically, as a company grow and scale, top leaders begin to lose line of sight into all the pricing and business model decisions. A startup gearing up to go public is usually a key inflection moment because pricing is a key business control that needs to be managed formally. Typically a pricing leader is also in charge of chairing a “Pricing Committee” and to help facilitate formal decision making and approvals on pricing decisions.

@Sarah_Burgess

Have you encountered a friction where companies only want to touch pricing once every few years, or keep things squarely away from product and under CRO, and if so, how did you manage to pitch having a more long term focused pricing team?

I’ve actually mostly seen the opposite - where product or pricing team are locked away in their “ivory tower” and make pricing decisions and pushes out changes without adequate consideration on sales impact. This was notorious when I was at Microsoft when the BU would roll out these new licensing models that are just about impossible to explain or understand, and sales just wouldn’t even try to sell it.

I’ve also seen an interesting dynamics too… best described as sales essentially declaring “You price however you want, I’ll sell however I want” - essentially ignoring pricing guidance and simply use discounting and other field enablement to get the deal done.

In either case, best practice is to ensure your pricing function pays attention and considers the need of both products and sales. Sales leaders should be regularly consulted and certainly informed early on potential pricing changes and ideally, bring them along the decision making journey.

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Hey @jkotowski ! This is a really hot topic right now and I fear I won’t have enough time to discuss all of these. Here are some talking (thinking) points on where my head is at… (in no particular order)…

  • The key to value capture in “AI employee” is to capture labor or HC budget. HC budgets are orders of magnitude more than software at every company so we need to make it ease for customer to understand that tradeoff and also for sellers to articulate that value
  • Treating AI employees like an FTE is not possible in a traditional sense - there is no time or even speed constraint on AI employee - an AI customer agent can work 24/7 and (in theory) only constraint the speed in which the end user can interact with it. So if we’re talking about pricing an AI employee, we’re not talking 1:1 to human employee, but some kind of proxy for “FTE equivalent” - which I think will at the end of the day be made up of different jobs and tasks.
  • The question of how it will be priced is therefore to me almost like a packaging decision - how do you “bundle” a bunch of jobs and tasks capacity into something that resembles, or can be discussed with customers, that lend the conversation to go after the larger HC budget?
  • I’m not entirely sure where it goes. I can see (and I think some companies are experimenting now) with some kind of big flat subscription fee (hire fee) for an AI employee that is based on capacity to do certain amount of work. On the usage front you can easily tag on “overtime” as a concept for usage beyond the subscribed capacity.

But yeah, I think performance, outcome, output, and capability will all play a role in pricing AI employees… just like today you would pay an entry level analyst differently than a Staff analyst, similar type of price discrimination will be at play with AI employees.

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@Sam_Lee might be a bit late for the AMA but can you share insights on pricing career trajectories? I know there are more strategic paths, there are more operational paths, and more analytical paths, but I’m trying to design a few ladders for my team and curious what frameworks there are for thinking about this

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