Who should own pricing? 💸
Deciding whether pricing strategy stays with product/GTM, the costs of pursuing foundational models, and positioning towards JTBDs not personas.
Welcome to the 91st edition of The SaaS Baton.
A fortnightly newsletter that brings you hand-curated pieces of advice drawn from the thoughtful founder-to-founder exchanges and interviews taking place on Relay (curated with 💛 at Chargebee for Startups) and the interwebz. So, stay tuned!
In this edition, you’ll find the following instructive and inspiring pickings:
#1: Sendbird’s co-founder and CEO, John S. Kim, tells us where the pricing discipline must ideally be housed — “I don’t recommend putting it in finance orgs” — across the various growth phases of a startup.
#2: Tara AI’s co-founder and CTO, Syed Ahmed, breaks down what it takes — “can cost anywhere from $10M to a $50M” — to train one’s own Large Language Model.
#3: Paperform’s co-founder, Dean McPherson, writes about — “one of the biggest strengths and frustrations we’ve had” — decisively not picking a niche in the most horizontal of SaaS markets.
Finding these discerning founder takes valuable? Please consider sharing this edition with an ever-curious teammate or a much-cherished SaaS friend. 🙂 New readers can sign up here.
🗞 Recently on Relay:
➡️ Heuristics and Hunches (February 9th) — “Sharing Our Experiences, Wins, and Lessons is Also Humbling. It Keeps Us Honest,” and Other Notes on Building a Customer-First, Bootstrapped Business with Canny’s Co-Founder, Sarah Hum
— Documenting Canny’s path in public
— Why they didn’t have to revisit PMF
— Building in a tough market
— Being self-funded
— Making bets as a lean team
— Canny’s freemium experiment
— What they’ve gotten right from the start
— How they look at competition and integrations
#1: Who should own pricing? 💸
(From: Sendbird’s John S. Kim) (Source: Code to Cash)
If you’re early on, it’s probably the CEO/CTO. Somebody does it, right? But as you go beyond $10m/$50m in ARR, then pricing becomes such a huge factor. Because if you just have a 5%/10% optimization through pricing, that’s a material difference (could be millions of dollars worth for your company.)
Somewhere along the way, let’s say, series A to series B…Based on competitive dynamics, if, generally your product is in a differentiated market, you have a unique value proposition, then I actually recommend having as a product manager or a head of product or the CEO herself/himself own the pricing as long as they possibly can.
But if you’re in a market that’s highly competitive, very easily commoditised, then have it with, say, the marketing function. Because the pricing is more reflective of what’s out there in the market. The differentiation then will come from brand and positioning, rather than your whole product’s unique differentiation.
So it really depends on what’s your core strength and what are the competitive dynamics of the market. That’s kind of the big pillar.
After that, once you’ve reached a certain scale… it does make sense to consider other options. Having a pricing specialist. Having revenue operations and other departments form a joint task force to understand pricing and for you to iterate and review gross margins and other pricing factors.
Also the continuous process of getting feedback from the market, win/loss analysis, getting all this feedback back into your pricing organization.
I still don’t recommend pricing put into organisations like finance. It has to be more directly connected to either the product/market/customers. Having some form of oversight or decision-making power ultimately on the go-to-market function or product function by having a specialized pricing organization would make sense if you’ve reached that scale.
Also consider bringing on other experts like Simon-Kucher or other pricing experts. Who cost anywhere from six figures to seven figures, but let’s say you spend half a million dollars on the pricing exercise that’ll get you a few million dollars in net new ARR. It’s worth it, right?
Thankfully, a lot companies that went through this pricing exercise actually share on their blogs. I think Fivetran shared their model, going from a subscriptions based to a consumption based model. Learn from other best practices too. Especially thankfully in tech community, a lot of people do pay it forward. They shared their mistakes and learnings.
#2: Should you pursue your own LLMs? ✨
(From: Tara AI’s Syed Ahmed) (Source: LinkedIn)
I get asked this question a lot.....As a startup should we pursue training our own Large Language Model (LLM)?
My answer to this question is usually along these lines.
How diverse and sizable is your dataset?
Foundational models depend on both a diverse and large dataset so that they can provide utility to end users. Similar to a human, your dataset will need to be a mix of knowledge sources such as Math, Code, English grammar, History, etc. in order to create a foundation that meets the needs of your end user persona.
Do you have the resources to structure and tokenize data?
This is the actual bulk of both time and cost when going out to train your foundational models. It can potentially make or break your model. Having run this process many times in my career. The data pipeline, statistical and heuristic work on identifying what data is useful for my model vs what is not is a tedious process. This can take anywhere between months to years to get right even if you're using acquired data for tokenization.
Does my team have the expertise to parametrize the model configuration?
Once your data is in the right shape to be processed, the next step is to articulate how the model will be configured for training and evaluation. This is also an iterative process, rarely is this figured out in one shot.
Do I have the compute resources to train, retrain and maintain this foundational model?
The compute resources needed to process your data, train, evaluate and publish the model are all dependent on variable compute costs. For example it cost Meta approximately $4M-5M in compute to train LLaMa on 2048 Nvidia A100 GPUs.
If you're willing to go through all of the above, then training your foundational model in totality can cost anywhere from $10M to a $50M, dependent on data, team, compute and expertise.
#3: Not niching down 🔑
(From: Paperform’s Dean McPherson) (Source: Relay)
Not niching down is both one of the biggest strengths and biggest frustrations we’ve had. From the very beginning, we always thought of Paperform as more than just another form builder.
We wanted it to give people the ability to make their own solutions to their own problems. Over time, the phrase, “no code” started being thrown about, and it was like remembering a dream — we finally had the words to explain what we were trying to do.
There’s certainly wisdom in the early startup advice of niching down.
But I believe that it’s just a common solve for the problem of differentiation. We didn’t want to differentiate by being ‘the form builder for South African dental practice patient registrations.’ We wanted to empower people to create for themselves.
Over time, our ‘niche’ has actually been to be a broadly horizontal tool.
Paperform is a digital swiss-army knife.
Today, customers use us for data capture, surveys, quizzes, landing pages, selling products, scheduling appointments, lead generation, client onboarding, subscriptions, consent management, and hundreds of other things.
As a self-serve product, this means we spend a lot of time thinking about what people are aiming to achieve when they sign up for a trial.
Unlike a heavily verticalized product, we don’t get much value out of thinking of our customers in personas. Instead we’ve found it much easier to both articulate and focus by thinking about “Jobs to be done”.
The frustration that not niching down brings, though, concerns both how you target and communicate with customers. We’ve gotten much better at this over the years, but it’s something that we will always need to contend with.
In some ways, this means we’re actually aiming for two AHA moments in our customer journey. The first AHA moment is, ‘I can do the thing I came here for.’
For example if you’re running an event and you’re looking to create an event registration form, then the first AHA moment might be that you’ve copied a beautiful event template and can tell that it’s going to suit your needs.
The second, and more powerful AHA moment though is, ‘look at all the other problems I can solve with Paperform!’.
We rely a lot on customers self reporting what their needs are for the first AHA moment (we literally ask what kind of form you’re trying to create in our onboarding flow). But for the second AHA moment, we try to gently expose more and more of the capability and breadth of problems that the product can address, over time.
This means relying a lot on automated messaging, be they onboarding emails, or in-app tips, help documentation, or high quality video guides.
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Thanks for reading! 🌻
Team Relay (Chargebee for Startups)