Utilizing Customer Data Analytics

Data is everything in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: a) What’s happening and b) Why it’s happening. Let’s break it down. What’s happening: - Business data tells us how the business is doing - Marketing/sales data tells us where our customers come from - Retention data tells us when and why customers are leaving us - Engagement data tells us how customers are using our product Why it’s happening: - User research gives us rich insight into why something is happening - Voice of the customer data shows us how customers talk about our product - Usability scores show us how people perceive our product or feature experience in a measurable way - Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments, either manually or via automated tools. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. If you don’t have the data structure for any of the above, speak to your product and data team to see if you can change that. If not, rely on the data that you can actually get. PS: The list of metrics is indicative: Actual metrics will differ greatly from one company to another and largely depend on the industry, niche, as well as your data infrastructure and setup. — If you found this useful, consider reposting ♻️ How are you collecting and using data in your design process? What else are you tracking?

At Amazon, we would often spend months working on a single paragraph of the PR/FAQ for a new product idea. This was the "problem paragraph". Done well, it could lead to a successful product. Done wrong, it will lead to failure. Here is how to write a successful problem paragraph: The “problem paragraph” defines the customer problem you’re solving. Without this, you will build a product that doesn’t address a customer pain point. It shows whether you truly understand your customer's needs, not just your company’s capabilities. To write this paragraph, start by precisely identifying the customer segment that will be served by your product. Great products are built for specific people with specific needs. For instance, designing a car for single urban professionals under 35 differs significantly from designing for suburban families with three kids and a dog. If you think your product is for everyone, you’re mistaken. A strong way to begin your paragraph is: “Today, [customer segment] has [problem], which they currently solve using [methods A, B, and C]…” Next, quantify the problem: → How large is the segment? (e.g., 17 million households) → What methods do they use? (e.g., 45% use A, 25% use B, 30% use C) → What are the tradeoffs? (e.g., speed, cost, quality) Here’s an example for a hypothetical robot vacuum product: “Today, 15 million busy urban and suburban professionals earning between $100,000 and $200,000 struggle to find the time and energy to keep their homes clean. Approximately 30% of these households use traditional vacuuming, which requires up to 2 hours per week. 55% hire a cleaner at a minimum of $50/week, and 15% use robot vacuums that cost $600 plus $100/year in maintenance, while leaving behind up to 30% of dust and dirt.” This problem paragraph quantifies the customer problem in terms of money, time, and other metrics where possible (in this case, the dust and dirt left behind). The problem should always be quantified; otherwise, how can you assess the potential value of a product that solves it? Well-defined customer problems are built on data-based insights. Insights are gleaned from swimming in data and metrics. This includes customer usage metrics, process or operations metrics, user interviews, demographic data, customer feedback, customer support data and anecdotes. The more data-based and specific your insight, the more accurate and helpful your problem paragraph will be. This is why the process can take months. However, distilling these quantified insights into a single paragraph gives you the best chance at building a truly useful product. At Amazon, this paragraph was always the most debated section in a PR/FAQ. This is because getting the problem wrong is the worst mistake you can make in building a product. Everywhere else, you can pivot. But if the problem is incorrectly diagnosed, nothing else matters. (cont. in comments)

Influencing people and attracting customers with technology is all about leveraging digital tools, platforms, and strategies to build engagement, trust, and value. What can you learn from this bird? - **Personalization and Data Analytics:** - Create Personalized Experiences: Utilize data analytics and AI to tailor customer experiences, such as personalized product recommendations and custom content, fostering a sense of relevance and connection. - Gain Customer Insights: Analyze customer behavior to understand preferences and needs, enabling businesses to refine their offerings and messaging effectively. - **Social Media and Digital Presence:** - Boost Social Media Engagement: Platforms like Instagram and LinkedIn facilitate direct customer engagement through interactive content creation and community building around your brand. - Collaborate with Influencers: Partnering with influencers extends your reach by leveraging their follower base and credibility. - **Automation and AI-Driven Marketing:** - Implement Chatbots and AI Support: AI-driven chatbots enhance customer support responsiveness and service quality, while automation tools streamline communication through email marketing and CRM systems. - Leverage Predictive Marketing: Use AI to anticipate customer needs, ensuring satisfaction and loyalty. - **Interactive Technology:** - Offer AR/VR Experiences: Immersive AR/VR experiences enable customers to virtually try products, enhancing the buying process and engagement. - Develop Interactive Websites and Apps: Intuitive platforms boost customer satisfaction, driving longer interaction times and increased conversion rates. - **Trust through Transparency and Security:** - Ensure Blockchain and Secure Transactions: Utilize blockchain and encrypted payment systems to foster trust and ensure secure transactions. - Showcase Reviews and Testimonials: Use technology to display user reviews, ratings, and case studies, building trust through social proof. - **Innovative Product Features:** - Integrate AI and IoT: Products incorporating AI or IoT attract customers seeking cutting-edge solutions. - Offer Mobile Apps and Tools: Complement your product with apps or digital tools like fitness trackers and others. #ai #technology #marketing via @ferarrigophoto

Many companies think they're set if they have product usage metrics and can track user engagement. But unfortunately, that's only part of the picture. The real value comes from connecting that usage data to actual business impact. The best product ops teams create the vision and ability to connect those data points. They help relate user behavior metrics to critical business outcomes like revenue, churn, and more. Imagine seeing a feature with rising usage month-over-month. Seems great, right? But what if you found that the usage spike was mainly from a customer segment you're looking to phase out... while adoption from your strategic focus segment had dropped 20%? Yikes. Having that analytical power to map product metrics to business metrics is the secret sauce. With product ops, you can scale those capabilities across the entire product org and executive team, guiding decision-making in the right direction. As Aniel Sud, CTO of Optimizely, puts it: "Product ops becomes data-driven over time, turning data into actual value." And according to Joe Peake of Featurespace, the goal is analyzing each product's revenue opportunity and ROI - not just relying on gut feelings about the market. True product insight means bringing all data together - from product usage to customer feedback to financial impacts. As Shira Bauman of Zapier notes, "Learning about the data that people care about, and partnering across data teams, is so important." With product ops connecting those dots, we get out of the "build trap" and can optimize for real outcomes. The path to successful products lies in combining engagement metrics with business performance. What's your experience been in tying product usage data to business metrics? Share your insights and lessons learned in the comments!

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

Segmentation is a powerful tool in data science—by grouping entities with similar characteristics, companies can tailor experiences, drive growth, and better meet the needs of distinct customer or supply groups. In a recent blog post, Airbnb’s data science team shared how they built a structured framework to segment their global supply into distinct “supply personas.” Rather than using traditional approaches like RFM (Recency, Frequency, Monetary) analysis, they grounded the segmentation in the platform’s unique business dynamics—especially calendar-based behaviors that reflect how listings are used throughout the year. The team began with exploratory analysis and identified four key behavioral features: availability rate, streakiness, the number of quarters with availability, and the maximum consecutive months of availability. These signals were then fed into an unsupervised clustering model (k-means) to group similar listings. To make the results interpretable and usable at scale, the clusters were used to train a supervised model (i.e., a decision tree), allowing for consistent and scalable persona assignments. This framework enables Airbnb to apply a shared language around supply—supporting decisions in personalization, experimentation, and beyond. It’s a nice example of how thoughtful segmentation can bridge human intuition, modeling techniques, and operational needs. #DataScience #MachineLearning #Analytics #Airbnb #Segmentation #MLInterpretability #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBu4gKpz

Smart CRM Basics Predictive Customer Behavior Modeling The Advantages of Predictive Behavior Modeling When Marketers can target specific customers with a specific marketing action – you are likely to have the most desirable campaign impact. Every marketing campaign and retention tactic will be more successful. The ROI of upsell, cross-sell, and retention campaigns will be more significant. For example, imagine being able to predict which customers will churn and the particular marketing actions that will cause them to remain long-term customers. Customers will feel the greater relevance of the company’s communications with them – resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals. Enhancing Customer Segmentation for Personalization Predictive analytics refines customer segmentation by identifying patterns within data. By understanding customer segments on a deeper level, businesses can personalize their interactions, marketing messages, and product recommendations. This tailored approach fosters a stronger connection with customers, leading to increased loyalty. Anticipating Customer Needs Through Lead Scoring Lead scoring becomes more accurate with the integration of predictive analytics. By evaluating customer data, such as interactions with emails, website visits, and social media engagement, businesses can prioritize leads based on their likelihood to convert. This ensures that sales teams focus their efforts on leads with the highest potential. Optimizing Sales Forecasting Accurate sales forecasting is crucial for effective resource allocation and business planning. Predictive analytics in CRM analyzes past sales data, market trends, and customer behaviors to generate more accurate sales forecasts. This empowers businesses to make informed decisions, allocate resources efficiently, and capitalize on emerging opportunities. Transforming CRM with Predictive Analytics Predictive analytics is revolutionizing CRM by providing invaluable insights into customer behaviors. From personalized marketing campaigns to proactive churn prevention, businesses can leverage these predictions to enhance customer relationships and drive growth. As technology continues to advance, integrating predictive analytics into CRM systems is not just a strategy for staying competitive; it's a key component in building lasting customer-centric businesses in the digital age. #PredictiveAnalytics #CRMInsights #CustomerBehavior #DataDrivenDecisions #BusinessIntelligence #CustomerRetention #SalesForecasting #MarketingStrategy #EthicalCRM #DynamicPricing

Last click measures are 𝗻𝗼𝘁 𝗲𝘃𝗲𝗻 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 𝗰𝗼𝗿𝗿𝗲𝗰𝘁. A large majority of brands know that last click (and/or MTA) measurement is wrong, but a majority continue to use it as the primary measure of marketing performance. There are typically two main reason why: • 𝗟𝗲𝗴𝗮𝗰𝘆 𝗼𝗳 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 𝗮𝗻𝗱 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 - This is a big challenge and tough to change quickly. I have shared a few methods we use to help with this which is linked in comments. • 𝗔𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗹𝗮𝘀𝘁 𝗰𝗹𝗶𝗰𝗸 𝗱𝗮𝘁𝗮 𝗶𝘀 "𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 𝗰𝗼𝗿𝗿𝗲𝗰𝘁" - Many brands assume that while the data is wrong, it is correct enough to optimise towards success. This is unfortunately not true, many of the strongest performance last click channels show the weakest incremental value. And vice versa. On the chart below we map campaign types on Last Click ROAS index (100 = best performing on last click ROAS) and MMM ROAS Index (100 = best performing on MMM ROAS). The first thing you should notice, is that the correlation is weak.  Virtually non existent. But there are some clusters of campaign types: 1. 𝗟𝗼𝘄 𝗜𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝗭𝗼𝗻𝗲 - Campaigns which look brilliant on last click ROAS but show poor incrementality. These look great on a marketing report, but drive little real value. 2. 𝗚𝗼𝗼𝗱 𝗼𝗻 𝗔𝗹𝗹 𝗠𝗲𝗮𝘀𝘂𝗿𝗲𝘀 𝗭𝗼𝗻𝗲 - These look good on Last Click ROAS and look good on MMM ROAS, campaigns which drive clear measurable performance and with strong incrementality. 3. 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁 𝘇𝗼𝗻𝗲 - These are bad on Last Click ROAS and bad on MMM ROAS. These campaigns just don't work, not every test succeeds. 4. 𝗡𝗲𝘃𝗲𝗿 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗼𝗻 𝗟𝗮𝘀𝘁 𝗖𝗹𝗶𝗰𝗸 𝗭𝗼𝗻𝗲 - These look terrible on Last Click ROAS, but actually drive strong modelled incremental performance. These campaigns drive really valuable indirect impact, but the last click measurement can't see their value. Normally on a quadrant chart, the bottom left is the troublesome corner. But here the real issues are in top left and bottom right. Campaigns in bottom left get turned off or changed, because they don't work on any measure. It is a failed test, we learn and move on. Campaigns in the top right get continued investment, and will continue to drive business value. The trouble lives in the top left and the bottom right.  Campaigns in top left get increased investment because the spreadsheet looks good, while they deliver little value. Campaigns in bottom right get turned off, then everyone wonders why overall performance got worse. While everyone's focus is on moving up on the chart, the 𝗿𝗲𝗮𝗹 𝗳𝗼𝗰𝘂𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗺𝗼𝘃𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗹𝗲𝗳𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗿𝗶𝗴𝗵𝘁. It will make your marketing reporting spreadsheet look worse, but make business performance better.

I set up 37 AI Agents for our $6M ARR outbound agency. These are the 6 AI Agents we deploy across our >$10M ARR clients. I used to spend HOURS trying to figure out what makes a cold email work. Which pain points hit hardest. What signals show someone is ready to buy. When to reach out. How to segment my lists without losing my mind. Now? I run everything through a squad of AI agents (built in n8n) that do the heavy lifting for me. Here’s the team: → 1. COMPANY_ANALYST Analyzes your website, case studies, and G2 reviews. Finds what problems you solve, what ROI you deliver, and what makes you different. Outputs: • Top pain points (ranked 1-10) • Customer impact metrics • Differentiation hooks • Real customer language → 2. PAIN_EXPERT Takes those insights and builds a Pain Point Matrix. Scores each pain by: • Frequency • Financial impact • Time savings • Risk reduction • Emotional relief • Urgency Then ranks them-so you know what matters MOST. → 3. SIGNAL_HUNTER Searches for digital breadcrumbs showing a company feels that pain. Looks at: • Tech stack • Website copy • Job posts • Social posts • Event attendance • Review activity Even gives you ready-to-use Boolean search strings for LinkedIn. (Yes, you get a literal playbook for signals.) → 4. SEGMENT_STRATEGIST Breaks your market into micro-segments (100-200 companies each). Maps the most intense pain for each. Defines how to spot them, what triggers that pain, and what NOT to target. Helps you focus on the best-fit group first. → 5. TRIGGER_SPECIALIST Watches for buying signals in that top segment: • Researching solutions? • Budget approved? • Leadership change? • Tech stack updates? Sets up real-time alerts and tells you exactly when/how to reach out. → 6. CAMPAIGN_BUILDER Takes all this and builds 3 outbound campaigns you can launch. For each: • Campaign name • Target audience • Trigger event • Messaging • Data sources • Personalization fields • Target KPIs • A/B test plan • Launch checklist If you want to see how these AI agents actually work in a real outbound workflow (step-by-step) - I'll be putting together an entire SOP over the weekend, let me know if you want it!

Some user groups have distinct usability needs, and to design experiences that truly meet those needs, we need to identify patterns in how different users interact with a product. Clustering helps group users based on shared behaviors rather than broad assumptions, allowing UX researchers to uncover deeper insights, optimize design decisions, and improve the overall experience. One of the most common clustering methods is k-means, which groups users around central points based on similarity. It is widely used for segmenting personas and analyzing behavioral trends but requires predefining the number of clusters, which can be a limitation. Hierarchical clustering offers an alternative by building a tree-like structure that reveals relationships between different user groups. This method is particularly useful for mapping engagement levels and understanding how different users interact with an interface. Density-based clustering, such as DBSCAN, identifies areas of high user activity while automatically separating outliers. This method works well for analyzing drop-offs, onboarding friction, and engagement patterns without assuming a fixed number of clusters. Gaussian Mixture Models take a probabilistic approach, allowing users to belong to multiple clusters at once. This is particularly useful for analyzing hybrid user behaviors, such as those who switch between casual and expert usage depending on the context. Fuzzy clustering is another approach that enables users to be part of multiple groups simultaneously. This is helpful when behavior is fluid and does not fit neatly into distinct categories. It is often used in personalization systems where engagement modes shift dynamically. Constraint-based clustering applies predefined business rules to the process, making it ideal for segmenting users based on factors like subscription tiers or access levels. Grid-based clustering, including the BIRCH algorithm, is particularly useful when working with large-scale datasets. Unlike other methods, BIRCH processes large amounts of data efficiently, making it a valuable tool for analyzing heatmaps, session recordings, and high-volume engagement metrics.