Claire Peracchio

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.

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.

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.

AI will evolve the traditional PMM skill set, making soft skills, creativity, and critical thinking even more important, especially as AI automates repetitive work. Here are some skills I think that PMMs will need to develop or adapt: * AI fluency: Understanding AI trends and tools is crucial, and creating great prompts will become a foundational skill as language models gain even more applications. This will require not just abstract knowledge but also actively integrating AI into daily tasks such as data analysis, narrative development, and strategy building. The key is to view AI as a collaborator, combining its analytical and text-generation power with your expertise and strategic thinking. * Agility: As AI accelerates product development cycles and makes it easier to ship faster, marketers need to keep up. This means more frequent product updates and more agile and modular marketing strategies. PMMs need to think about how to maintain coherent narratives when underlying products constantly change. And cross-functional relationships become more important as moving faster requires seamless collaboration. * Mastering new pricing models: In an AI-driven world, PMMs will increasingly need to adjust pricing strategies to focus more on usage and outcomes rather than the classic subscription models. This means honing a deep understanding of how customers get value to develop flexible pricing that aligns to those outcomes. * Data literacy: As AI provides better insights, PMMs must become skilled at acting on data and using data to tell compelling stories. This involves translating insights into actionable strategies, such as using AI to identify key audience segments and predict customer behavior. It also means leveraging data to communicate the value of your products, with approaches like solution selling using concrete use cases and tools like ROI calculators. * Originality: We’ve all seen the explosion of AI-generated content in recent times. More companies are going to market with eerily similar messages and marketing. In this landscape, the marketing that stands out is original and authentic. Great PMMs will continue to develop outstanding marketing by grounding what they say in real customer needs and getting even more creative and innovative with the stories they tell.

Right now, a lot of Product teams are scrambling to identify the best places to apply AI, and businesses are eager to turn AI into their next wave of growth. The great news for PMMs is that you’re uniquely positioned to help guide Product and your broader business during this time of change. Here are a few areas to focus on: * Understand AI trends and technology: Follow AI news and trends, and learn more from leaders in the space. Become well-versed in key concepts to collaborate effectively with technical teams. This knowledge helps you anticipate market trends and translate product capabilities into understandable benefits. It also allows you to contribute meaningfully to product strategy discussions. * Understand the market and competitors: At Zendesk, we’ve doubled down on competitive monitoring to help inform Product and our company leadership. Track competitor announcements, and provide timely updates to your PMs. Use this intelligence to inform product strategy, identify gaps, and pinpoint opportunities for differentiation. * Understand your customers and GTM process: Leverage win-loss analysis and maintain feedback loops with Sales, Customer Success, and technical teams to figure out which feature gaps impact deals and where customers struggle and where they find the most value, quantifying ARR and TAM when possible. Share insights with Product to help them prioritize and build for the right customer profiles and segments. 

AI won’t replace product marketers, but PMMs that can leverage AI successfully will replace those who can’t. Product marketing requires strategic thinking, creativity, and a deep understanding of human behavior — all areas where humans still excel. I see AI freeing up PMMs from a lot of rote tasks like data analysis, copywriting, and audience segmentation to focus on more impactful work like developing strategy, building relationships with customers and stakeholders, and identifying new market opportunities. One slightly spicy take: AI will probably speed up the transition to leaner PMM teams, since each PMM will become more capable and productive.

It’s true that customers are both skeptical of AI outputs and excited about AI’s potential. At Zendesk, we’ve definitely seen this in the customer service space, since this is one of the most obvious areas that AI is poised to disrupt. Here are some tactics we’ve found useful: * Show value: Focus on demonstrating tangible value through real-world examples and clear use cases that customers can latch on to. Showcase specific customer success stories that highlight ROI and impact, making the benefits of AI adoption more relatable. * Streamline adoption: Simplify the adoption process by providing curated prompts, best practices, and guided implementation paths. This approach demystifies AI and helps customers quickly leverage it in their workflows. * Be transparent: Get specific about your AI's capabilities and limitations. Use plain language to explain how the technology works and how its outputs are designed to be clearly understandable by customers. * Mitigate fears: Position AI as a tool that augments human expertise rather than replacing it. Illustrate how human-AI collaboration leads to better outcomes, addressing fears about job loss. * Highlight responsible AI: Proactively address data privacy and ethical considerations. Clearly communicate your commitment to responsible AI development and usage, showing the measures in place to protect user data and ensure fair, unbiased results. * Share your unique perspective: Articulate a differentiated perspective on AI's role in your industry, which can come through in your marketing narrative and sales assets. Emphasize your commitment to guiding customers toward success, positioning your company as a trusted partner in their AI journey.

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.

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.

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.