Tools For Analyzing Customer Needs And Preferences

I’ve been using Cursor to communicate product thinking visually - a quick prototype can speak louder than ten PRDs. But the true game changer I've found is using AI to scale customer understanding. Back at Notion, our team used Enterpret across every stage of building product: 1. Strategy & Roadmapping We brought together feedback from Zendesk, Slack, app store review, social media, Gong, and more. Enterpret automatically categorized themes—top requests, bugs, positive signals—and surfaced them in clean, usable dashboards. Before that, synthesizing feedback was a manual, messy process. PMs spent hours hopping across tools and teams just to find signal. 2. Project Scoping & Validation Once we aligned on priorities, we used Enterpret to dig deeper: What exactly were users asking for? What did they mean? It surfaced quotes, summarized needs, and even helped us identify users for UXR or early testing. The Wisdom feature let us ask questions like: - “What are the top security asks from IT admins?” - “Which integrations do paid customers request most often?” …and get real answers, fast. 3. Post-Launch Sentiment & Closing the Loop After GA, we’d track how sentiment shifted. Did we actually solve the right problems? Who originally asked for the feature—and did we follow up with them personally? Enterpret made that easy, especially for teams without a dedicated UXR or Product Ops teammates. It helped us act faster and more confidently—anchored in real customer signal. If you’re working on similar problems or want to chat about how we approached this at Notion (and now at Enterpret), always happy to connect.

Are you generating enough value for users net of the value to your company? Business value can only be created when you create so much value for users, that you can “tax” that value and take some for yourself as a business. If you don’t create any value for your users, then you can’t create value for your business. Ed Biden explains how to solve this in this week's guest post: Whilst there are many ways to understand what your users will value, two techniques in particular are incredibly valuable, especially if you’re working on a tight timeframe: 1. Jobs To Be Done 2. Customer Journey Mapping 𝟭. 𝗝𝗼𝗯𝘀 𝗧𝗼 𝗕𝗲 𝗗𝗼𝗻𝗲 (𝗝𝗧𝗕𝗗) “People don’t simply buy products or services, they ‘hire’ them to make progress in specific circumstances.”  – Clayton Christensen The core JTBD concept is that rather than buying a product for its features, customers “hire” a product to get a job done for them … and will ”fire” it for a better solution just as quickly. In practice, JTBD provides a series of lenses for understanding what your customers want, what progress looks like, and what they’ll pay for. This is a powerful way of understanding your users, because their needs are stable and it forces you to think from a user-centric point of view. This allows you to think about more radical solutions, and really focus on where you’re creating value. To use Jobs To Be Done to understand your customers, think through five key steps: 1. Use case – what is the outcome that people want? 2. Alternatives – what solutions are people using now? 3. Progress – where are people blocked? What does a better solution look like? 4. Value Proposition – why would they use your product over the alternatives? 5. Price – what would a customer pay for progress against this problem? 𝟮. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 Customer journey mapping is an effective way to visualize your customer’s experience as they try to reach one of their goals. In basic terms, a customer journey map breaks the user journey down into steps, and then for each step describes what touchpoints the customer has with your product, and how this makes them feel. The touch points are any interaction that the customer has with your company as they go through this flow: • Website and app screens • Notifications and emails • Customer service calls • Account management / sales touch points • Physically interacting with goods (e.g. Amazon), services (e.g. Airbnb) or hardware (e.g. Lime) Users’ feelings can be visualized by noting down: • What they like or feel good about at this step • What they dislike, find frustrating or confusing at this step • How they feel overall By mapping the customer’s subjective experience to the nuts and bolts of what’s going on, and then laying this out in a visual way, you can easily see where you can have the most impact, and align stakeholders on the critical problems to solve.

My Favorite Analyses: Conjoint Analysis Let’s talk about conjoint analysis, a valuable tool for businesses seeking to understand consumer decision-making. Rather than asking consumers what they want, this approach goes further by revealing the trade-offs people are willing to make and at what cost. What is conjoint analysis? Conjoint analysis examines how customers make decisions when faced with options. It presents various product combinations and asks for choices, helping businesses understand what their target audience values and how much they are willing to pay for it. For example, a conjoint analysis for smartphones may present customers with the choice of two different options to choose from: Smartphone A: 24-hour battery, 24 MP camera, $700 price, Apple brand Smartphone B: 18-hour battery, 48 MP camera, $900 price, Samsung brand Conjoint analysis imitates real-life decision-making situations and can be applied to a range of industries and products. Data is commonly collected through surveys, with respondents selecting between 8-12 sets of product profiles. The more choices they make, the better we understand their priorities for each attribute. How Is The Analysis Conducted? The first step in analyzing survey results is determining what is called “partworths” and evaluating the relative importance of each attribute. Partworths quantify how each element of a product is valued (as the name implies, it expresses how much each PART of a product is WORTH to a consumer). Statistical models are used to calculate partworths, and we will typically rely on a good R package to do the heavy statistical lifting once the data has been collected and cleaned. Next, we must assess the significance of each attribute by examining their partworth ranges. A larger range indicates greater impact on consumer choice, while a smaller range suggests that the element is more of an expectation for consumers than a differentiating product feature. What Does Conjoint Analysis Tell Us? Conjoint analysis provides valuable insights into customer preferences, including the relative importance of product attributes and how much customers are willing to pay for specific features. It helps businesses tailor their products and pricing to maximize customer satisfaction and revenue. Marriott used conjoint analysis to design its Courtyard brand for business travelers, while the Portland Trail Blazers used it to create ticket packages that appealed to fans and increased revenue. Whether developing a new product or making decisions about an existing one, conjoint analysis ensures choices are based on real customer preferences, not assumptions. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics