Most SaaS companies know they're leaving money on the table with pricing. The hard part is figuring out how to change pricing without confusing customers, damaging trust, or triggering unnecessary churn.

In this playbook, you'll learn how Superhuman redesigned pricing and packaging for its email app. Rather than spending hundreds of thousands of dollars on a pricing consultancy, the team built the strategy largely in-house, combining stakeholder interviews, customer research, prospect research, and conjoint analysis to design a new packaging model.

But the biggest lesson wasn't about pricing research. It was about execution.

You'll learn how Superhuman narrowed packaging options before quant, why they treated prospect willingness-to-pay data differently than customer data, how they wrote separate requirements for each customer type, and how they rolled out pricing changes while giving customers meaningful choice.

The outcome: Superhuman found a stronger monetization path — and today, most self-serve customers choose the new, higher-priced plan.

If you're designing SaaS pricing, this playbook gives you a practical blueprint for researching, launching, and optimizing a pricing change without breaking customer trust.

This playbook is for SaaS PMMs, growth leaders, and pricing owners who are staring at an existing product and asking: “Is our pricing and packaging still right?”

It is especially useful if your product has expanded, your current packaging no longer reflects the product’s value or roadmap, or leadership believes pricing could help the business capture and deliver more value — but the team needs to make the change without confusing customers or damaging trust.

Atlassian's first State of Product report influenced millions of dollars in pipeline, brought 90,000 visitors during launch week, and earned hundreds of thousands of organic impressions via partnership with influencers. The bigger win: a branded point of view that newsletters, analysts, and operators could cite as Atlassian's State of Product report.

Pulling it off meant treating original research as a product marketing campaign, not a research project. The market was already crowded with AI-heavy "state of" reports. Atlassian had developer credibility but was still building its authority with product managers.

So we audited the field, decided to own the report instead of white-labeling someone else's, used a research firm only for survey rigor and respondent recruitment, pressure-tested every question with product, mined the data for a narrative that was both true and commercially useful, and launched the report like a product.

This playbook captures what we learned building it — including the debates over whether a single adjective overstated the data, the question that produced the report's biggest finding, and the launch decisions that turned the asset into one of the company's top campaign drivers.

This playbook is for PMMs and content leaders who need to turn original research into a credible, differentiated market asset, especially in a category where the company has a right to play but does not yet own the conversation.

It will help teams that need the report to do two jobs at once: earn practitioner trust and create commercial momentum through pipeline, MQLs, sign-ups, sales outreach, executive social, and campaign distribution.

Most PMMs want to support every important deal. The problem is that the more useful PMM becomes, the easier it is to become the bottleneck.

In this playbook, you’ll learn how Iterable’s VP of Product Marketing, Gray Hardell, built an internal AI sales coach to scale product marketing judgment across the field. The goal was not to create another content library. It was to give reps deal-ready access to the messaging, positioning, product context, proof points, and customer nuance they usually needed Gray to provide live.

But the biggest lesson was not about AI. It was about codifying PMM judgment.

You’ll learn how Gray mined repeated sales questions for product requirements, turned product and business context into modular context packs, tested the tool against real deal scenarios, drove adoption through selective access, and connected usage back to sales outcomes.

The early signal: when the tool was used, Iterable saw 42% faster sales cycles, a six-point higher win rate, and more than 65% of reps report higher confidence across AI POV, roadmap, and messaging.

If you’re trying to scale PMM support without joining every sales call, this playbook gives you a practical blueprint for turning your field knowledge into an AI coach reps can actually trust and use.

The project started because I was spending too much time on individual deals.

That was a good problem. Reps trusted me for product context, proof points, messaging, roadmap nuance, and help applying those details to specific customer situations.

But the more useful I became to Sales, the more I became a bottleneck. The business needed my focus on broader GTM initiatives, not just one deal at a time.

I needed to build a way for the field to get that product marketing context without waiting on me.

The project touched more teams than I expected. I needed input, validation, adoption, or operational support from:

That cross-functional spread mattered because the agent had to work for the real field motion. If it only worked for one type of rep, one segment, or one use case, it would not scale.

The goal was to increase sales efficiency. I wanted reps to get faster answers, move faster within deal stages, use better assets, and apply consistent messaging and positioning without waiting for me to join a call or build something custom.

I also wanted the work to be measurable. "Reps like it" was not enough. I needed to know whether the agent was helping with sales cycle time, win rate, confidence, closed-won and closed-lost patterns, and ACV.