JD Prater

Now this is a fun challenge. Assuming you did your homework during the interview process, you should have a good idea of what you're getting into. That doesn't mean you won't find some skeletons lurking behind close doors. Rather you should understand how the team views product marketing, what kind of executive support you can expect, and their expectations of you. With that mind, here are a few key things I would want to accomplish after 90 days. * Everyone knows what product marketing does and what we're responsible for. That means internal evangelism and roadshows. You will need to educate internal teams on product marketing and get everyone on the same page. Don't assume they have the same definition. You define and evangelism it with executive support. * Get product marketing added to the cross-functional agenda specifically product and sales teams. It's crucial that you're seen as a leader within these teams. * Find and knock out any quick wins. This will make you look like good and earn respect among other teams. People want to work with A-players and people that can count on to get shit done. * At the end of 90 days, you should be prepared to present a long-term strategy of how why we're gonna win. Depending on the company, long-term could be a year, 6 months, or a quarter. Whatever that timeframe is I would want to see a presentation outlining our go-to-market strategy. Lastly, be sure to check out my Sharebird podcast called Thrills & Chills where I interview first product marketers and those who have established product marketing in company.

I suggest combining pieces from my answers to these questions. 1. What's your framework to prioritizing needs/deliverables when you're the first Product Marketer at a company establishing the function? 2. How do you think about your first 30/60/90 day goals when coming in as the Head of Product Marketing in a startup that didn't have product marketing before? 

That's an interesting question. I see the PMM role as the GTM strategy which includes a success launch. And I see PMMs as the owner of product messaging. Not sure I can help here. Now if you're looking to move beyond those tasks and elevate your role then that's different question with a different answer.

This can definitely be a challenge whether you're the first or tenth PMM at a company. I'm a fan of working backwards from the customer, rather than starting with an idea for the product team or from the sales team. From there, I like to ladder needs/deliverables up to team goals and business goals (impact). Then I'll stackrank them based on perceived effort of the deliverable. Essentially, I'm creating an 2X2 grid based on business impact and perceived effort to complete the task.

I like to start with the purpose of creating a 30/60/90 plan. I view the plan as a set goals to help me strategize my first three months in a new job. I use it to help maximize my work output and stay focused. That said, I have rarely completed any 30/60/90 plan perfectly. Working in startups means lots of change and course correcting. It also means you're going to learn new information and have to adapt to it. Therefore, I advise PMMs to create a lose outline rather than a follow a template line-by-line. Here's what I'm currently doing in my new role at AWS (reminder it's a big company). 1. 30 days 1. Meet as many people as possible. I met over 40 people in my first days. Some meetings where very fruitful others where one time meetings. But this allowed me to introduce myself, what I'm going to be working on, undestand their role, and how we will work together moving forward. 2. Complete HR & IT stuff (payroll, setup computer, etc) 3. Get access to tools that I'll use everyday 4. Set up 1:1 meetings with my manager and monthly skip-levels with appropiate stakeholders 5. Understand team goals and how I contribute to them 6. Ask lots of questions with fresh eyes 2. 60 days 1. Start to internalize how the teams operate and cooperate 2. Understand how the business generates revenue and the levers that can be pulled to influence it 3. Understand the different team's goals (how are teams measured and how is success defined) 4. Complete necessary internal trainings 5. Formulate and document gaps in current process 6. Ask to be added to standing meetings to get additional context 7. Keep meeting more team members 3. 90 days 1. Start working on a cross-functional project and/or take the lead a new one 2. Complete outstanding onboarding tasks 3. Keep meeting more team members

The first PMM must provide a ton of value for the company. Generally speaking, it's value measured by impact on revenue. They also need to get along with other stakeholders (sales, product, CS, marketing). Lastly, they need to have execuitve sponsorship. That's the trifecta all PMMs should strive for.

At AssemblyAI, we track PMM effectiveness through program-specific metrics that align with our dual PLG and sales-assist motion. Let me break this down by our key programs: For Competitive Intelligence: * Win/loss rates against specific competitors * Competitive battle card usage rates by sales * Feature comparison coverage (% of key features we've documented vs competitors) * Competitive mention rate in deals and how it changes over time For our Self-Serve Motion: * Developer documentation engagement metrics * Time to first API call & upgrade after signup * Conversion rates at different usage tiers * Feature adoption rates For Sales Enablement: * Sales content usage and effectiveness scores * Ramp time for new sales team members * Win rates for deals where PMM materials were used * Deal velocity changes after enablement sessions For Product Launches: * Developer signups within first 30 days * Feature/usage adoption rates * Coverage and sentiment in developer communities * Pipeline influenced by new features For Win/Loss Analysis: * Reasons for wins/losses categorized by themes * Price sensitivity patterns * Technical requirements gap analysis * Competitor displacement rates The key is that we tie these metrics back to two core business outcomes: developer adoption rates for our PLG motion and pipeline influence for our sales-assist motion. This helps us stay focused on impact rather than just activity metrics. What I've found particularly effective is measuring the delta – how these metrics change after specific PMM interventions. For example, if we release new competitive battle cards, we track the before and after win rates against that specific competitor.

While PMM metrics can vary greatly depending on your go-to-market motion and audience, I believe the most critical metrics should track your core customer journey. At AssemblyAI, where we provide speech-to-text APIs for developers, we focus intensely on developer journey metrics across the lifecycle of our capabilities. Our key metrics framework centers on three critical areas of the developer journey: 1. Initial Activation * Time to first API call (measuring how quickly developers can get started) * Signup-to-implementation conversion rate * Drop-off points in the initial setup process 2. Feature Adoption * Usage rates of new capabilities within first 30 days of release * Time to adopt new features after release * Cross-feature adoption 3. Usage Expansion * API call volume growth patterns * Progression through usage tiers * Expansion into new use cases or features 4. Win Rates by Market Segment * Win rates in key verticals (e.g., conversational intelligence, media & entertainment, creator tools, etc) * Win/loss patterns by company size * Competitive win rates in specific use cases The reason we focus so heavily on these journey metrics is that they directly reflect the effectiveness of our product marketing efforts. For example ,if we see rapid adoption of new features, it validates our launch messaging and technical content. What's particularly powerful about these metrics is that they serve as leading indicators for business growth. Strong developer journey metrics typically translate to higher retention and expansion rates down the line.

At AssemblyAI, where we're launching new AI model capabilities like automatic language detection (ALD), we focus on measuring launch success through clear adoption signals and usage patterns. Our launch measurement framework has three key phases: Immediate Launch Success (First 30 Days): * Number of customers using ALD * Volume/usage of API calls using the new language detection parameter * Distribution of languages being detected (helps us understand market reach) * Initial use case patterns (are developers using it as expected?) * Support ticket volumes related to the feature Sustained Adoption (60-90 Days): * Weekly active users of ALD * Growth in minutes processed with language detection * Language detection patterns by region and customer segment * Usage patterns (standalone vs. combined with other features like speaker diarization) * Conversion of existing customers to using the new capability Long-term Impact: * Revenue impact from increased API usage * Most commonly detected languages and their growth trends * Competitive win rates where language detection was a key requirement * New market segments unlocked by specific language support * Impact on overall platform usage (did ALD drive increased use of other features?) We use this data to make GTM adjustments. For instance, if we see high usage for certain languages, we might prioritize optimizations for those specific models. Or if we notice regional patterns in language detection, we could adjust our go-to-market strategy to better serve those markets. The key is having clear benchmarks based on previous model launches while recognizing that each capability is unique. We maintain a launch retrospective document comparing metrics across our various model releases, helping us better predict and optimize future launches.

Let me share how we approach this at AssemblyAI because measuring positioning effectiveness is one of the trickier challenges in product marketing. At first glance, it seems almost impossible to quantify something as intangible as perception, right? But here's what we've learned: while you can't directly measure perception, you can measure its effects. Think of it like measuring wind - you can't see it, but you can see what it moves and how it influences behavior. In practice, we look at this through three lenses. First, there's market response. We track win/loss patterns and particularly pay attention to competitive displacement. When we sharpen our positioning, do we start winning more against specific competitors? Does our sales cycle accelerate because customers 'get it' faster? The second lens is fascinating - it's about developer understanding. We pay close attention to how developers describe our product in their own words during technical discussions. Are they naturally echoing our positioning? Even more telling is how they implement our API - their usage patterns tell us if they truly understand our unique value. The third lens is all about validation through usage patterns. For example, when we position ourselves as the most accurate speech-to-text API, we look for corresponding behaviors: Are developers choosing our enhanced accuracy models? Are we retaining customers with high accuracy requirements? Are we winning more deals where accuracy is crucial? Here's the crucial part though - positioning measurement is a long game. You need to view it over quarters, not months, because perceptual changes take time to manifest in behavioral data. Just like wind patterns, you might feel small gusts day-to-day, but it's the prevailing winds over time that truly tell you which way the market is moving. Once you see those sustained patterns - in wins, in developer behavior, in usage - you know your positioning has caught the right wind and is moving the market in your direction. What makes this approach powerful is that it combines both quantitative and qualitative signals into a complete picture of positioning effectiveness. When these three lenses align, that's when you know your positioning is truly resonating in the market.