Threading the AI pricing needle 🪡
Alexa Grabell from Pocus shares how they are evolving with AI, lessons on finding the right pricing model, and why 90% AI tools are built wrong for enterprise.
Some people think AI will replace sellers. Others think it’s making them dumber. Alexa Grabell doesn’t buy either. She believes AI should set reps up for deeper critical thinking.
When Alexa Grabell set out to build Pocus in 2021, she ended up building a whole new category. PLS, or product-led sales had been around but it was far from ubiquitous.
Alexa knew she was signing up for a tall order. So they decided to bet on a unique approach: building a community, a category, and a product at the same time.
“There would be no way we could build this robust of a product in this short of a timeframe if we didn’t do the category and community part.”
It worked and Pocus signed on customers like Canva, Linear, Loom, and Superhuman.
Now, Alexa and the team are setting out for another tall order. Building an evolved, AI-enabled Pocus and taking sales tools “from signal aggregation to intelligent guidance”.
Not a sci-fi shake-up of sales tooling or an all-sentient AI SDR, but a grounded sales solution that combines the best of Pocus 1.0 with the new-found super capabilities that AI can offer.
We sat down with Alexa (again) to borrow from her hard-won lessons from preparing for this evolution. Hopefully this will be useful for everyone navigating the pre-AI to with-AI journey. ↓ A TL;DR of some of the key insights that surfaced:
Why 90% of AI tools are built wrong for enterprise,
How Pocus is confidently differentiating from the incumbents,
Why jumping to “using AI” is counterproductive as a builder, and
Navigating AI pricing for a buyer persona that is too used to the predictability of the seat-based model.
🚫 Why 90% of AI tools are built wrong for enterprise
While a lot of AI startups are either focused on building for individuals or building point solutions for a single workflow, Pocus is taking a different approach. They are building for the entire sales teams at enterprises.
The reason lies subtly hidden in what made B2B explode in 2010, as Alexa shared in a LinkedIn post:
“There’s a reason B2B SaaS exploded in the 2010s. Companies realized that individual productivity tools (think: personal Excel sheets, local databases) were creating chaos at scale. The winning B2B products?
They built for teams first:
- Salesforce replaced individual contact lists
- Slack replaced person-to-person emails
- Figma replaced solo design files
- GitHub replaced individual code repositories
So why are AI companies ignoring this lesson?”
Pocus doesn’t want to make a champion out of one rep, they want to deliver success at scale. Alexa believes this org-wide consistency is a true unlock for any enterprise adoption of an AI tool.
When all reps are able to learn from all sources and all teammates, you get a true picture of what works, what doesn’t work, and iterate to collective success over time instead of getting siloed successes with outliers.
Another important reason behind this org-wide emphasis is Alexa’s understanding of AI’s fundamental tenet: it learns and gets smarter over time as you use it more.
“If you have just one rep using an AI point solution, it’s getting smarter only for that one person and their context. When the entire organization is able to use it and operate off of the same platform, it learns from all the reps and their context, and you get to realise compounded benefits.”
Building for enterprises is drastically different from building for individuals, though. Alexa shares two unique characteristics of selling AI to enterprises:
» First is the nature of evaluation. Enterprises want to know how they can use AI to roll out capabilities to their entire user base and hit revenue goals. You need to have a clear answer to how you enable this.
» Second is the role of the “unsexy features” like compliance — echoing RunLLM’s Vikram Sreekanti. To this effect, Pocus had to add a few tactical features such as:
Retrieval intelligence on top of data (think CRM notes, call recordings, product data, and enablement materials),
tools for admins to roll out features,
reporting to help them monitor results, and
dashboards for frontline managers.
🏰 How Pocus is confidently differentiating from the incumbents
Sales intelligence isn’t a space devoid of incumbents.
Alexa thinks they have 3.5 main differentiators against them.
The 0.5 first. Pocus is more intuitive and user-friendly. It isn’t enough to win, but it does make your case stronger when reps love you.
The other differentiators are more technical.
» First, Pocus has built a trademarked “Relevance Agent” that constantly learns from internal and external data to help reps understand their accounts deeply and suggest hyper-relevant next steps.
It sits on a sophisticated AI-oriented backend which if an incumbent tried to replicate would cost them a rebuild of their backend, Alexa shares.
» The second is the prescriptive nature of their approach.
Alexa, during her time in sales at a startup, saw how traditional sales leaders work. They would give junior sales reps a “phone book” and expect them to know what to do. It never worked.
Pocus is determined on being way more prescriptive, to the extent of surfacing signals like “here’s the account you need to focus on”, “these are the people you need to target within the account”, “here’s the messaging”, and so on. Incumbents don’t do this as well.
» The third is the way they tackle data overload.
On any day, reps using traditional systems have to go to Salesforce, Google Sheets, internal admin, Tableau, ZoomInfo, sales nav, company website, and a million other tabs to figure out who to go after and how.
Pocus consolidates this information and gives the “so what” of this data, including why it matters, and how someone can use it well.
“All of these become our right to differentiate and win.” says Alexa.
👀 Why jumping to “using AI” is counterproductive and how Pocus’ product development has embraced AI
“I think a lot of people today start building with a sentiment that they want to “use AI”. They start with the solution and then try to find a problem. We never try to do that.”
Alexa and team have not thrown their hat into the frenzy that is bolting-on AI for the sake of it. Instead AI has found its place as a potent, relevant consideration in their solution set.
They still start with the problem, and work towards finding the solution, the only difference is that now it’s second nature to see if the solution can be built better, faster, or cheaper with AI.
Alexa exemplified a recent build of their AI strategy feature to elaborate on this. This feature, a AI-heavy capability, does the job of creating an account plan for reps by digesting internal and external data.
Reflecting on how it came about, Alexa shared that it didn’t come from a place of “we need AI account plans”, instead it started with the problem like always.
Alexa and the team had seen reps spending too much time in bringing data together and doing the first level of research. Leaders, much alike, were also spending more time than necessary in trying to enable their reps with this step.
Their product development began with the problem, then mockups, followed by customer conversations, iteration, and then seeing where AI could make the solution faster, better, cheaper.
That said, there are some parts of their product development process where AI has changed things significantly and for the better.
Alexa shares that it has become much faster to prototype features and get to a working alpha or beta with AI. The wow moments, when customers see an AI feature, are also bigger. “When you take an otherwise manual workflow and automate it in seconds with AI, users find it fascinating.”
Alexa pauses to add one important caveat, as advice to other founders: AI has an effect.
“It is almost a given that you’ll have a wow moment the first time you demo an AI capability, but you also have to make sure that the wow moments happen on the third, fourth, fifth, sixth time as well, not just the first time.”
🏄♂️ Navigating AI pricing for a buyer persona that is too used to the predictability of the seat-based model
Historically, Pocus has been primarily seat-based. They would sell a plan based on the number of reps with a limit on first-party data access.
They are still using the same approach, with one change: usage credits. Instead of applying a limit on the CRM or warehouse data ingested, they have introduced credits for the additional data they are providing now akin to a hybrid model.
We asked Alexa if they considered any other pricing model such as usage-based or outcome-based. She brought in a personal anecdote about CROs:
“We sell to CROs and a lot of them only think in seat-based models. So if you’re trying to sell usage-based or AI-based or work-based to a CRO, they’re not going to like it because it’s hard for them to predict.”
What about the compute costs, one would wonder.
Alexa is aware that they are “threading a precarious needle” and that their pricing model will have to become usage-based in order to drive meaningful business while also covering costs.
For now, the way they are managing this is by selling credits in bundles. Bundles allow companies to buy credits in bulk instead of a pay-as-you-go model. This helps teams feel that costs are less uncertain and unpredictable.
Alexa can see a future moving away from seat-based but doesn’t think it will happen this year. CROs aren’t ready to buy in a new way and as a startup, Pocus doesn’t want friction. It would be interesting to see how this evolves.
In a different conversation, Sudowrite’s Amit Gupta had shared how difficult yet critical it is to find levers that correctly factor in your compute costs. They had a thorough evolution of their own, going from flat-fee to charging for outputs to model-specific usage-based credits in an effort to strike a balance between creating value for customers and covering their costs.
Pocus, with their usage-limited pricing have zeroed-in on four main levers here.
» First: first party data — how much data you are ingesting from your CRM/warehouse.
» Second: website visitors — the number of people who land on your website/month.
» Third: email lookup — used when you want to see contact information for a person.
» Fourth: Watched Accounts — number of prospects being scraped on using AI.
The rationale behind these was simple: it costs them money to perform these actions, it costs them more money to perform more of these actions, and it gives the end users more value the more they use them. It’s a win-win.
Currently, they offer a set limit for all four with the base plan and users have the option to buy more credits if they need more.
P.S. This interview was originally published on Relay here. It has been editorialized for clarity and flow. Thanks to Alexa and Sandy from Pocus for taking the time.
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