What's the best way to collect and analyze product marketing data?

Collecting and analyzing product marketing data is crucial for understanding customer behavior, refining marketing strategies, and optimizing product performance. Here's a structured approach to collect and analyze product marketing data effectively: ? Define Objectives and Key Metrics ? Utilize Analytics Tools ? Customer Surveys and Feedback ? Competitor Analysis ? Social Media Monitoring ? Email Marketing Analytics ? A/B Testing ? Data Visualization and Reporting ? Iterative Analysis and Optimization ? Data Privacy and Compliance

Not having data is like driving a race car in the rain without rain tires. At first, everything looks ok, then you get overtaken by competitors and finally you end up in the crash barrier.... or, if you're likely you get the rain tires fitted, but you're now 2 laps behind. You must be clear on your audience, value proposition, messaging and how you differentiate from the competitors as a minimum. This comes from data. Data will help internal conversations to strongly position your product to get resources and help you engage customers more deeply during go to market.

Analytics help us understand: 1. User Behaviour - How users are navigating through the product. Strong and weak touch points. 2. Channels - What channels are leading to high conversion? What channels are the TG concentrated in? 3. Campaign Analysis - Which campaigns are leading to what kind of impact - clicks, time spent, conversions, discovery as per the campaign objective? 4. UI/UX - How the product design is impacting certain metrics or vice versa

Quantifies performance to identify successes and shortcomings objectively. Measures impact of marketing efforts. Reveals customer segment behaviors, needs, and responses. Guides more relevant targeting. Provides benchmarking to optimize strategies against competitors and industry standards. Informs data-driven decisions on positioning, messaging, channels, and campaigns. Reduces guesses.

Let's acknowledge the complexity that PMMs face. We're not just dealing with one-dimensional metrics here, we're working across multiple funnel stages, teams and stakeholders. PMMs need to connect the dots between various data sources to understand the evolving impact of, for example, sales assets updates, the performance of acquisition channels, feature retention rates and many more. Moreover, data serves as the common language that bridges the gap between marketing, product, sales, and other teams. It's not merely about collecting data for the sake of it, it's about leveraging data as a powerful instrument to answer multidimensional questions, align strategies and drive meaningful outcomes.

Marketing data is enormous. At any given point, there are more than 30+ metrics that can be looked at. Step 1: Understand the goals. "What are the actionable outcomes that I want to make by looking at this data?" Ex: I want to increase retention Step 2: Think of the Actions that users would take to achieve these goals Step 3: Associate metrics with these actions to measure the performance. Step 4: Evaluate how this particular metric is performing over time, concerning industry standards etc Tying the data to goals and objects would give a clear direction of what to look for in the data.

Aah, there is much to cover, but I'll zero down on three standard metrics for most products. User Demographics: To understand your audience (Company size, industry, geographic location) You can use these to tailor your message. - What features to highlight? - What could be the pricing strategy? User Behaviour Metrics: To track how users interact with your product (Feature usage, session duration) - What are the popular features? - What are the user pain points? Conversion Funnel Data: To monitor the entire user journey (Sign-up to activation to subscription) - What are the activation rates? - What are the churn and upgrade rates?

Website analytics on traffic, conversions, referrals, and engagement. Measures digital marketing impact. Sales and revenue data tied to marketing campaigns and segments. Quantifies ROI. Customer demographic, behavioral, and sentiment data from surveys and profiles. Informs targeting. Competitive analysis tracking awareness, market share, and messaging. Helps differentiate. Campaign response rates by channel, creative, offer, etc. Identifies most effective levers.

Product marketing data to collect span across the funnel. The most critical ones include: ??Web visitors, soft leads (eg. whitepaper downloads, webinar attendees) ??Handraisers (eg. demos), product signups ??Active users (eg. casual, core, power users segments) and churned users ??Paying vs. free users ??NPS score ??Pipeline (eg. value of each conversation) You should then be able to break down the above metrics by various segments: ??Firmographics (eg. company size, vertical) ??Buyer personas (eg. roles, seniority, etc.) ??Marketing source (eg. content, paid search, etc.) ??Web page (eg. pricing page, product pages, etc.) ??Activity (eg. paying user, active, churned, etc.) This will give you much more insights.

Understanding what to track is 90% battle won. This is a big problem as you can collect literally anything as data especially in marketing. For example, our sales pipeline to conversion rate and sales velocity were beating industry benchmarks but we were not getting enough sales conversations. On deeper analysis, we found that traffic -> PQLs was a problem. By revamping the product messaging and laying out the information better, we managed to 3X the time spent on page and reduce the bounce rate by 40%, ultimately the site wide conversion rate increased by more than 3X. Also, understanding how to use excel is invaluable. This saves a lot of time without you having to depend on any data analyst.

Instrument websites and apps to track visits, downloads, sign-ups, purchases, and other conversions. Integrate analytics with ad platforms and CRM to connect marketing efforts to business outcomes. Survey customers directly via email campaigns, on-site forms, pop-ups, and mailers. Ask structured questions. Use A/B testing and experiments to gather quantitative data on marketing creative, offers, and placements. Monitor social media and review sites for organic consumer conversations. Sentiment analysis.

I normally use these tools to collect product marketing data: ??GA/Plausible: website visitors ??Hotjar/Full Story: website engagement/heatmaps ??Typeform: surveys ??tldv/Grain: user interviews notes ??June.so: product analytics and engagement ??Common Room: intent signals ??Attio: CRM ??RevenueCat/Paddle: monetization data ??Braze/customer.io: lifecycle emails/newsletters

Identify trends and outliers in performance data across segments, campaigns, channels, and time periods. Look for patterns. Conduct statistical analyses on results to determine significance and correlations. Quantify impact. Build dashboards and visualizations to spot high-level themes and insights from the data. Synthesize multi-channel data to map complete customer journeys from awareness to conversion. Compare campaign results to goals and KPIs. Evaluate effectiveness.

One thing I've found helpful in analyzing product marketing data is to start with a clear understanding of your goals and questions. Before diving into the data, take the time to define what you want to achieve and what specific insights you're looking for. This will help you stay focused and prioritize which data and metrics are most relevant to your analysis. Visualization tools like Tableau or Google Data Studio can help you uncover patterns, trends, and correlations that may not be immediately obvious from raw data alone. Create charts, graphs, and dashboards to make your findings more digestible and actionable for yourself and your stakeholders.

Here are some ways to analyze product marketing data: 1. Dive deep, but strategically: Treat each data point like a valuable clue, but choose relevant ones based on your specific questions. 2. Visualize your story: Transform complex data into clear and compelling charts and graphs. Think of these visuals as chapters in your data-driven narrative. 3. Challenge yourself and collaborate: Partner with colleagues to uncover hidden connections and interpretations. Friendly competition and diverse perspectives can spark deeper understanding. 4. Ask critical questions: Don't just accept the data at face value. Challenge assumptions, test hypotheses, and see where the data leads you. Remember, good analysis is about asking the right questions.

Partner with your social team! Leverage the power of social listening to decode the nuanced language and unfiltered experiences shared by customers. It's more than just using their words; it's about understanding, embracing, and integrating their unfiltered expressions into the heart of your product marketing strategy.

Know the mind of the customer: who buys, why the buy, how they buy. For a given segment, you need an importance-weighted model of decision criteria across functional, commercial, and strategic concerns. You need to be realistic about your strengths and weaknesses vs. alternative solutions and focus on those segments where your company (not product) delivers the best buyer choice. Win/loss analysis will provide the outside-in perspective needed for this analysis and complements input from the field, customer requests, secondary research (analyst reports), etc.

In my experience, direct customer feedback is invaluable. Understanding their pain points and objections creates a bed of data. Taking time to talk to them and consult so you clarify comments and go beyond top line or high level feedback. Always look to use this technique along side your other sources of data to inform your opinion and have a more robust set of overall data.

Tactics to improve your PMM data skills: ??Do at least 1 customer (sales) call per week ??Do monthly research surveys ??Participate to design crits and product brainstorms ??Listen to customer call recordings ? Build a strong relationship with the sales team ??Use AI to extract insights from customer call transcripts

Take courses in web analytics, data visualization, and marketing stats to level up your quantitative skills. Stay on top of latest measurement platforms and tools. Continuously expand your toolkit. Practice techniques like segmenting audiences and A/B testing campaigns on side projects. Build proficiency. Follow thought leaders publishing studies and insights from analytics. Absorb their best practices. Partner with data-savvy teammates to learn from their approaches and technical expertise.

In my experience, more-so for products already in market (even if they're MVPs), is how do I get data that is most meaningful and will result in the most tangible improvements on the product, how it is positioned and marketed. In one example, this meant working with customer service teams to create improvements in data collection and analysis on user experiences. In this instance it was possible to use existing consumer touch points to better solicit, collate and ultimately analyze data, turning it into a program of on-going actionable insights. Sometimes its about getting more out of what you already have.