Claire Peracchio

When it comes to a launch, market research allows you to understand your market opportunity and validate your go-to-market strategy. It helps you answer the most important questions for product marketers: * What problem are we solving? (Customer needs) * Who are we solving it for? (Target audience) * How do we win? (Competitive positioning) For example, when we were launching a new AI product at Zendesk, our market research combined: * Direct customer feedback (e.g. interviews from our beta program) * Competitive analysis (understanding competitors’ positioning, features, and pricing approaches to ensure differentiation) * Market data (industry trends and analyst insights) * Sales input (qualitative feedback and win/loss analysis) Good market research doesn't just gather data — it drives decisions. Every launch KPI you set should tie back to insights from your research. The formula could go something like this: Initial AI use case adoption predicts long-term success → Dig in to understand first 30-day usage patterns → Define success milestones for adoption. This approach helps measure what’s happening and figure out why, and allows you to continue to refine your strategy post-launch.

I’ve found it helpful to orient cross-functional teams, including product marketing, around clear metrics linked to revenue. Here's a good way to think about some of the key metrics that PMM can influence: 1. Direct Revenue Impact * Bookings and pipeline touched by PMM initiatives like sales plays * Win rates (by segment, product, competitor) * Average deal size 2. Sales Effectiveness * Pipeline conversion rates by stage * Sales content usage and engagement * Deal velocity and sales cycle length * New rep ramp time 3. Customer & Market Validation * Product adoption rates * Feature usage trends * Customer satisfaction scores * Win/loss analysis insights The secret sauce is to combine quantitative data with qualitative feedback through: * Regular conversations with Sales and your GTM teams (what's working/not working) * Customer interviews (what their pain points are and why they bought) * Deal desk reviews and interviews (patterns in successful vs. unsuccessful deals) That way, you can understand the story behind the numbers and suss out reasons for any trends or anomalies. And from there, you can put together an action plan to iterate and improve.

AI is going to change market research in some big ways. It will enhance data analysis, allowing PMMs to process vast amounts of information from diverse sources and uncover deeper insights. AI-powered tools will offer continuous, real-time market intelligence and more automated reporting and analytics, so teams can make quicker and more informed decisions. However, one thing I’ve run into in my own market research focused on AI products is that customers still don’t fully understand what AI can do. Here are some ways to address that: * Ask about current challenges: Dig into time-consuming tasks and inefficiencies, for example: "What are the most tedious parts of your job?" * Focus on outcomes: Frame discussions around business goals and pain points rather than the specifics of the AI technology. * Use analogies: Compare AI capabilities to familiar concepts to make them more relatable. For example, AI that can automatically parse through customer service requests is like having an extra agent on hand to do triage. * Let customers get hands-on: Allow customers to experiment with AI tools firsthand to grasp their potential, maybe via a targeted pilot. The key is to support customers as they’re trying these tools out and to provide regular check-ins to understand their experience.

As PMMs, it’s very easy to fall into a pit of despair here! You spend weeks crafting the perfect pitch deck or ROI calculator, only to discover months later that Sales isn't using it. Or worse, you find out they've gone rogue and created their own version because they don’t like yours. The biggest trap is getting caught up in measuring activity, like the number of assets created, rather than actual impact. This is why regular, honest feedback loops are crucial. At Zendesk, we made it a habit to check in with Sales teams to understand what's working and what isn't — and we bake their feedback into the process of creating every asset. We track engagement with our collateral and work to understand which assets show up in winning deals and try to ruthlessly deprecate those that don’t. The key is to measure what helps your team win, not what helps you look (or feel) busy.

This is a great question that I’m sure a lot of companies are grappling with. I think the path to success here lies in differentiating based on how you safeguard customer data, taking a proactive approach to compliance so your customers don’t face disruptions down the line, and even teaming up with regulatory bodies and industry leaders to innovate on standards for responsible AI. I also think that doing these things will become table stakes for companies selling AI products, particularly those selling into regulated industries.

Longer or more unpredictable sales cycles make it challenging to know if your GTM approach is working. While it's tempting to focus solely on closed deals as a measure of success, waiting months for validation isn't ideal. You need to identify and measure the key moments that show momentum along the way. This became clear when launching our AI product at Zendesk. Customers were excited about the potential, but some were uncertain on how to prove value. One big cross-functional effort that’s helped us address this has been creating a standardized "value realization framework" that maps directly to customer outcomes. This keeps everyone focused on business impact rather than feature checklists, and prevents deals from spiraling into endless evaluation cycles. And by aligning our sales process with clear product adoption milestones, we created a consistent path to success that extends beyond purchase. So the same metrics we track in trials and pilots become customers' early adoption goals — proving that when you nail the path to value during the sales process, you can set the foundation for successful activation and long-term growth.

Isolating campaign impact is tricky, and there's rarely a perfect 1-to-1 correlation between what you did and what moved the needle. Here are some ways to navigate this. Start by building a clear baseline before your campaign or launch by assessing: * Normal conversion rates * Typical sales cycles * Standard deal sizes and win rates Then look for specific patterns or changes in behavior once you’ve launched. For example, when we created a new value selling framework, we didn't just track overall sales cycles. We compared deals that used the framework versus those that didn't to gauge impact. If your win rate or deal velocity jumps right after launching a new sales approach or a sales play focused on a competitor, and Sales is specifically citing those initiatives in their feedback, you've probably found a real connection. Perfect attribution is tough, but consistent patterns help confirm that you’re on the right track.

Of course, AI is extremely useful for text-heavy tasks, especially tackling the dreaded blank page when you’re just starting out the writing process! Here are some other great ways AI can help: * Acting as a detective: Find and synthesize information faster. You can use AI as a turbo-charged search engine for quicker answers and more thorough research. For example, use it for conducting competitive analysis and market research and developing ideal customer profiles (ICPs). * Acting as a coach: You can lean on chatbots to act as a trusted and informed advisor, giving you the insights you need to set strategy. This is a great fit for tasks like segmenting your customer base, positioning a new product, or building a GTM plan. * Acting as a copilot: This is one we’ll see more and more of, as AI gets embedded in the software we use to help us make better decisions. AI can proactively guide you through complex tasks, flag issues, recommend data-driven actions, and even anticipate trends. Imagine AI helping you optimize a campaign in real time or alerting you that a key data pipeline is broken, skewing your analysis.

Some of the key launch metrics worth tracking: * Pipeline and bookings generated * Win rates * Adoption rates * Customer activation milestones * Sales team confidence The important part is not just collecting metrics, but actually using them to understand performance and adjust your strategy as you go. For example, are you noticing deals getting stalled in the early stages? Are customers getting stuck during onboarding? Does it seem like your messaging is targeting the wrong segments? Then you can make targeted adjustments: updating your sales process, simplifying onboarding flows, or refining your ICP. You want to have analytics in place and respond quickly to what the data tells you, rather than waiting for a bigger, one-off report or a quarterly review to change course. I’ve also found that having fewer, more actionable metrics is better than drowning in data you can’t act on.

With complex products, buyers need different content at each stage to build confidence and prove value. You can't just share a list of features and expect that to get deals over the finish line. Here are some ways to think about content that keeps the momentum going: Early stage: Focus on education and awareness * Customer stories showing measurable outcomes * Industry trend insights, ideally tailored to your customer’s segment or vertical * Third-party ROI studies or analyst reports to add credibility * Proof points from similar companies Middle stage: Enable technical validation * Clear evaluation guides with success criteria * ROI calculators focused on business impact * Trial or POC playbooks aligned to specific use cases * Security and compliance documentation Late stage: Gain consensus and close * Executive summaries for different stakeholders * Deal one-pagers that crystallize your value prop * Competitive differentiation slides * Implementation frameworks Focus your content on helping customers see what value looks like for them, step by step and at every stage. When selling AI solutions, for instance, we found that it made sense to focus on easy-to-understand use cases first and establish the trust needed to tackle larger transformations.