AMA: AssemblyAI Head Of Product Marketing, JD Prater on Product Marketing KPI's

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.

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.

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.

The key to effective PMM measurement is understanding that our work should directly ladder up to company OKRs, particularly through marketing and product team objectives. Here's how to approach this: First, identify which company OKRs your PMM work naturally influences. Product Marketing sits at the intersection of product, sales, and marketing, so our work typically feeds into multiple organizational goals. The trick is to be intentional about which OKRs you align with – don't try to attach to everything. Second, work backwards from those OKRs to define your PMM metrics. For example: * If supporting product team OKRs, focus on adoption and usage metrics * If supporting marketing OKRs, align with pipeline and awareness goals * If supporting sales OKRs, track enablement effectiveness and win rates Third, and this is crucial, document and communicate the direct line between PMM activities and these broader objectives. This helps stakeholders understand not just what you're measuring, but why it matters to the organization's success. Remember that OKRs evolve quarterly or annually, so your PMM metrics should be flexible enough to adapt while still maintaining consistency in how you demonstrate impact. The goal isn't to own the OKRs, but to show clear contribution to their success.

I view this as a 'both/and' rather than an 'either/or' situation. Product Marketing KPIs serve dual purposes, and their role often depends on the time horizon you're looking at. In the short term, KPIs absolutely serve as guiding metrics that help us prioritize and allocate resources effectively. They're like a compass, helping us understand if our programs and initiatives are moving in the right direction. For example, if we see low adoption rates for a new feature, that signals we might need to invest more in enablement, documentation, or awareness. However, over longer periods (quarters or years), these same metrics should absolutely factor into how we evaluate Product Marketing's performance and impact. The key is choosing the right metrics that: 1. We can meaningfully influence 2. Align with company objectives 3. Reflect true business impact rather than just activity The challenge comes when organizations focus too heavily on short-term KPI targets at the expense of longer-term strategic work. Good product marketing often involves initiatives that take time to show impact – like positioning work or market research. That's why it's important to have a balanced scorecard that includes both: * Leading indicators that guide day-to-day decisions * Lagging indicators that demonstrate long-term impact Ultimately, while KPIs should inform performance evaluation, they shouldn't be the only factor. Product Marketing's success should also consider qualitative impacts like market insights, strategic influence, and cross-functional collaboration effectiveness.

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.