Mapping Customer Experience Ecosystems

If I joined a new company as a Customer Success Manager, here are three ChatGPT prompts I'd use to quickly get up to speed. 1) Determine key stakeholders within my customer's business 2) Figure out what they do day-to-day and common points 3) Create messaging that resonates immediately Here they are: Prompt One (Identify Key Stakeholders): "I'm a Customer Success Manager for [product/platform] that helps companies with [primary use case]. Our solution delivers these outcomes: [Outcome one] [Outcome two] [Outcome three] [Outcome four] Help me build a stakeholder map. Include anyone who might be influenced by our product, who might have a project that touches our product, or anyone who might be involved in the buying committee. Which stakeholders and departments should I prioritize for relationship building to ensure successful adoption and expansion?" Prompt Two (Understand Stakeholder Motivations): "For each of those stakeholders, please summarize the following points in tabular format, with each point as a column header and each stakeholder as its row: --> What business outcomes and metrics matter most to this stakeholder --> How this stakeholder typically measures success in their role --> Common challenges this stakeholder faces when trying to achieve their goals --> How our solution specifically addresses these challenges and supports their success metrics --> What risks might cause this stakeholder to disengage or question our value" Prompt Three (Create Tailored Communication): "Now, let's create four different communication templates: - An introduction email for a new stakeholder explaining our partnership vision - A QBR summary highlighting value delivered to their specific department - An at-risk account message addressing potential adoption challenges - An expansion opportunity message tied to their business objectives For each template: 1) Open with a specific pain point or opportunity relevant to their role 2) Acknowledge their current approach and its limitations 3) Explain how our solution delivers unique value for their situation 4) Include a clear next step or action item Keep it under 150 words and easily scannable Use straightforward, jargon-free language." With these three prompts, I can hit the ground running in any new CS role - understanding who matters, what they care about, and how to communicate our value effectively from day one. This is just a small example of how ChatGPT could impact your day-to-day role as a CSM. It should really become a companion to your work. What other AI prompts have you found helpful in customer success roles?

Have you ever wondered when people are most irritated when calling customer service? I've been diving into Replicant's sentiment data to uncover when customers are most likely to express anger toward our AI agents (and our customers). The results reveal fascinating patterns that connect human behavior, seasonal shifts, and time-of-day preferences. 🌡️ Seasonal Impact: Autumn shows consistently higher anger rates in customer interactions (up to 25% higher than summer). Do people's moods change as winter approaches? ⏰ Time-of-Day Patterns: Early morning interactions (6-8 am) show notably higher frustration levels, suggesting that no one is really a morning person." 📈 Escalation Trajectory: The steady increase in negative sentiment from mid-morning to evening reveals how customer patience deteriorates throughout the day. 📱 Behavioral Shifts: Summer callers call earlier while winter callers cluster later in the day - a perfect example of how environmental factors directly impact customer interaction patterns. These insights aren't just interesting data points - they're actionable intelligence for designing more responsive AI systems that adapt to human behavioral patterns. By implementing time-sensitive response protocols, we can potentially reduce negative interactions by 15-20%. What patterns are you seeing in your customer interaction data? The answers might transform your approach to AI implementation.

Data alone can often feel impersonal and hard to relate to but professionals have found an interesting way around it - at least in the consulting world. I found it interesting that Bain & Company tackles this by using "customer journey mapping" - an approach that transforms data into vivid narratives about relatable customer personas. The process starts by creating detailed personas that represent key customer groups. For example, when working on the UK rail network, Bain created the persona of "Sarah" - a suburban working mom whose struggles with delays making her miss her daughter's events felt all too real. With personas established as protagonists, Bain meticulously maps their end-to-end journeys, breaking it down into a narrative arc highlighting every interaction and pain point. Using techniques like visual storyboards and real customer anecdotes elevates this beyond just experience mapping into visceral storytelling. The impact is clear - one study found a 35% boost in stakeholder buy-in when Bain packaged its conclusions as customer journey stories versus dry analysis. By making customers the heroes and positioning themselves as guides resolving their conflicts, Bain taps into the power of storytelling to inspire change. Whether mapping personal experiences or bringing data to life, leading firms realize stories engage people and shape beliefs far more than just reciting facts and figures. Narratives make even complex ideas resonate at a human level in ways numbers alone cannot.

Gain a data-driven understanding of your customer through Importance-Performance Maps. In today's competitive business world, differentiating your brand by understanding and delivering what truly matters to your customers is crucial. That’s where Importance-Performance Maps (I-P Maps) come in, providing a powerful visual tool to drive strategic decisions. What exactly is an I-P Map? It's a two-by-two grid that allows you to evaluate how well your brand performs in the areas that are important (as well as *not* important) to consumers. The vertical axis represents the importance of various attributes in consumers' eyes, while the horizontal axis shows your brand's performance in those areas. You can include other brands in your market, too, in order to see how your brand stacks up against the competition along those. When done correctly, every critical attribute of your offering -- whether it's product quality, customer service, or pricing -- is plotted on the I-P Map based on these two dimensions. Why does it matter? I-P Maps reveal your brand's strengths and areas where improvement is needed. Here's a breakdown of the quadrants: - Keep It Up (High Importance, High Performance): These are your strengths—attributes that are both highly important to customers and where your brand performs well. Maintain focus here to keep your competitive edge. - Concentrate Here (High Importance, Low Performance): These are critical areas where your brand is underperforming, despite their high importance to customers. Improving performance here can significantly boost customer satisfaction. - Low Priority (Low Importance, Low Performance): Attributes that are less important and where performance is lower. These areas may not require immediate attention but should be monitored for any shifts in customer priorities. - Possible Overkill (Low Importance, High Performance): Here, your brand may be over-delivering in areas that are not as important to customers. Resources invested here might be better allocated to areas of higher impact. How do I use I-P Maps? Use I-P Maps to make informed decisions backed by data that align with customer expectations. Fix those areas of underperformance that are important to consumers. Stop investing in attributes of your product or service that consumers just don't care about. Prioritize investment in product offerings, elevate aspects of customer service, or reallocate resources to close competitive gaps or strengthen your advantages. Use I-P Maps to make informed choices that improve your business performance in impactful and efficient ways. 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 #DataStorytelling

How do you and your teams synthesise and select which customer needs or pains to progress in your #product, #design, or #innovation projects? Imagine you've just completed some great customer discovery research, including observing, interviewing and being the customer. You've built some good empathy for who your customers are, what is important to them, what pains them, and what delights them. Then you unpack your findings into some form of empathy map, and you've got 100s of sticky notes everywhere. You've then started to narrow them down to the most promising and interesting observations, but this still leaves you with a sizeable collection and you want to add some rigour to your intuition on which ones to take forward first. Well, here are 3 different methods that I’ve used and iterated over the years: Number One – The Opportunity Scale This first one is the simplest and is inspired by how Alexander Osterwalder et al rank jobs, pains and gains in their book Value Proposition Design, 2014. As a team, you take your short list of observations from your empathy map and rank them from how insignificant/moderate to how important/extreme the need/pain is for the customer with the most important/extreme being prioritised to explore further first. Number two – The Opportunity Matrix A The opportunity matrix increases the rigour and confidence of your prioritizing by adding ‘strength of evidence’ as another dimension. Strength of evidence at this stage of journey can be determined by the number and type of data points. For example, if you heard from several customers that a pain point was extremely painful then you could be more confident this was worth solving than one highlighted by only one customer. Likewise, observing customers do something provides stronger evidence than customers saying they do something. Here you prioritise the most important needs with the strongest evidence first. Something to watch out for is when your team selects an observation that has strong evidence but isn’t that important of a need or pain to customers. Teams can be blinkered by numbers and end up over-investing in time wasting-opportunities. Number three – The Opportunity Matrix B The third method swaps out evidence for fulfilment of the need - how satisfied are customers with their ability to fulfil the need/solve the pain with the solutions they use today? By matching this with the importance of the need/pain we can select those observations that we understand to be the most important and unmet for our customers. You can then overlay the strength of evidence across this ranking to make your final selection even more robust. And to take it to a whole new level and really de-risk your selection you can test your prioritised observations, written as need statements, in quantitative research with customers. This is something that Antony Ulwick shares in his book Jobs To Be Done, 2016. I hope you find these methods useful. #designthinking #humancentreddesign

Thirty years ago, Target Account Selling taught sellers to think about accounts, not leads. To map influence. To run disciplined internal deal reviews. TAS is still a strong internal framework. Where it breaks down is in customer conversations. Not because TAS teaches bad questions. But because many sellers operationalize it with internal “why”-heavy questions that doesn’t work with buyers. Why buy? Why buy us? Why buy us now? In high-stakes deals, “why” sounds like judgment. Buyers are managing risk, optics, and internal politics at the same time. So they defend instead of reveal. That’s why answers sound logical and deals still stall. If you want real motivation, change the language. Replace why with what, when, and who. What • What happens if this stays the same for the next 6–12 months? • What outcome are you personally accountable for? • What does success need to look like for leadership? When • When does this become painful enough to force action? • When do missed targets become visible upstairs? • When would delay create real exposure? Who • Who feels the downside if nothing changes? • Who signs when this becomes real? • Who owns the consequences if this slips again? Same rigor as TAS. Very different effect. You’re no longer interrogating. You’re helping buyers articulate consequences, ownership, and urgency in their own words. This is particularly important for founder-led sales, which often sound productive and still don’t convert. The logic is there. The motivation isn’t. Keep TAS for planning. Use What, When, and Who to get deals funded.

If you're a UX researcher working with open-ended surveys, interviews, or usability session notes, you probably know the challenge: qualitative data is rich - but messy. Traditional coding is time-consuming, sentiment tools feel shallow, and it's easy to miss the deeper patterns hiding in user feedback. These days, we're seeing new ways to scale thematic analysis without losing nuance. These aren’t just tweaks to old methods - they offer genuinely better ways to understand what users are saying and feeling. Emotion-based sentiment analysis moves past generic “positive” or “negative” tags. It surfaces real emotional signals (like frustration, confusion, delight, or relief) that help explain user behaviors such as feature abandonment or repeated errors. Theme co-occurrence heatmaps go beyond listing top issues and show how problems cluster together, helping you trace root causes and map out entire UX pain chains. Topic modeling, especially using LDA, automatically identifies recurring themes without needing predefined categories - perfect for processing hundreds of open-ended survey responses fast. And MDS (multidimensional scaling) lets you visualize how similar or different users are in how they think or speak, making it easy to spot shared mindsets, outliers, or cohort patterns. These methods are a game-changer. They don’t replace deep research, they make it faster, clearer, and more actionable. I’ve been building these into my own workflow using R, and they’ve made a big difference in how I approach qualitative data. If you're working in UX research or service design and want to level up your analysis, these are worth trying.

As founders, we're bombarded with advice: "Know your customer!" "Listen to your audience!" But amidst the buzzwords, a crucial question lingers: how do we truly understand what matters to our customers, beyond the surface-level preferences and fleeting opinions? My journey as a founder has been a constant dance between chasing "customer feedback" and uncovering the deeper desires fueling that feedback. I've learned that listening isn't enough; we need to actively decode and prioritize what truly resonates with our users. Enter the Customer Value Compass: Step 1: Chart the Terrain: 1. Gather diverse data: Collect feedback through surveys, interviews, user observations, social media sentiment analysis, and support tickets. 2. Identify recurring themes: Analyze the data for common threads, challenges, and desires expressed by your customers. Don't get bogged down in individual details; look for patterns. 3. Categorize by impact: Segment your identified themes into two categories: "surface-level preferences" and "core value drivers." Surface-level preferences: These are fleeting opinions, often influenced by trends or personal experiences. They can provide valuable insights for specific features or campaigns, but shouldn't define your core offering. Core value drivers: These are deeply held needs, desires, and motivations that underpin customer behavior. These are the true north stars you need to align with. Step 2: Calibrate the Compass: 1. Dig deeper into core value drivers: Conduct in-depth interviews, focus groups, or user testing to truly understand the "why" behind these themes. 2. Prioritize based on impact: Not all core value drivers hold equal weight. Assess their prevalence, intensity, and alignment with your business goals to determine which ones deserve the most attention. 3. Validate with data: Look for quantitative evidence to support your qualitative findings. Analyze usage data, conversion rates, and customer satisfaction metrics to ensure your understanding aligns with actual behavior. Step 3: Navigate with Confidence: 1. Align your product and strategy: Use your Customer Value Compass to inform product development, marketing messages, and customer support initiatives. 2. Communicate with clarity: When making changes or introducing new features, explain how they address the core value drivers you've identified. 3. Continuously iterate: The Customer Value Compass is a living document. Gather new data, conduct regular reviews, and be prepared to adjust your understanding as your customer base and market evolve. Remember, the Customer Value Compass is not a destination, but a journey. By prioritizing what truly matters to your users, you build a foundation for sustainable growth, loyalty, and success. So, silence the buzzwords, listen deeply, and let your customers guide your voyage. #FoundersJourney #CustomerInsights #DecodingValue #ValueCompass #CustomerCentricity #BuildingForUsers

Most leaders do not fail because of strategy. They fail because they misread the temperature in the room… In high-stakes environments, government, public sector, crisis operations, cross-functional corporate systems: Stakeholder Heat Map is not a tool. It is a survival skill. Here is what the strongest leaders understand: 1. Influence is not evenly distributed. Some stakeholders hold positional power. Others hold informal power, culture keepers, gatekeepers, silent resistors, quiet champions. Ignore them, and your plan dies privately long before it fails publicly. 2. Resistance is rarely personal, but always predictable. A heat map reveals: • Who is cold (indifferent) • Who is warm (curious) • Who is hot (activated positively or negatively) When leaders do not take time to understand these dynamics, they mistake friction for failure instead of preparation. 3. Pressure travels through systems, not org charts. A VP can block you. But so can a supervisor, a union rep, a policy analyst, an overwhelmed SME, or a frontline team with zero psychological safety. Heat maps show leadership where the real constraints live. 4. Alignment is not consensus, it is clarity. Stakeholder heat maps force leaders to answer: • Who needs to understand? • Who needs to decide? • Who needs to execute? • Who needs to feel safe? Without clarity, alignment is accidental. With it, momentum becomes intentional. 5. Change succeeds when people feel seen. Every transformation breaks when leaders forget one truth: • People do not resist change. • People resist being changed without context, dignity, or voice. A heat map helps leaders anticipate emotions, not just deliverables. The leaders who thrive in 2026 are not the loudest or the most authoritative. They are the ones who can read the room, see the system, and move people forward without burning them out. That is not a spreadsheet skill. That is a leadership skill. Where in your work would a stakeholder heat map prevent friction or accelerate change? 🎥: @jordangoldenstateyt Taipei live ♻️ Repost to help a leader in your network navigate complexity with more clarity. 🔖 Follow Izabela for insights on leadership, strategy, and high-stakes decision-making.

Stakeholder analysis isn’t just about who’s in the room. It’s about who designs the room.. Owns the building… or locked the door before you got there. Likewise, the loudest voices aren’t always the most powerful. And the quietest ones often hold the keys to trust, access, or legitimacy. If your stakeholder list only includes the “usual suspects,” you’re missing the edges... The overlooked actors, the informal brokers, the invisible resistors. Inclusive stakeholder analysis asks deeper questions: Who sets the rules? Who benefits from the status quo? Who’s too often labeled “not important” or “too hard to reach”? Don’t just map influence. Map exclusion. Here's how. 🔹Step 1: Scan the Edges Don’t just list the obvious players. Ask: Who’s affected but rarely included? Who’s doing invisible labour such as the caregivers, informal leaders, grassroots workers? 🔹Step 2: Look for Layers Go beyond names and titles. Ask: How do these stakeholders relate to each other? Who’s a gatekeeper? Who’s a connector? Who’s resisting? 🔹Step 3: Name the Power Not all influence is formal. Ask: Who can stop this work or scale it? Who benefits the least from what we’re doing? Who holds credibility, trust, or legitimacy, even without a title? 🔹Step 4: Cluster for Strategy Now group stakeholders by how you’ll engage them: Collaborate with co-creators Consult trusted informants Watch potential blockers or swing voices Shift those who need winning over 🔹Step 5: Interrogate Your Biases Pause and reflect: Who did I list first and why? Who felt “difficult” to engage, was it them, or my assumptions? Am I valuing lived experience, or just institutional clout? #StakeholderAnalysis 🔥 Follow me for similar content and let me hear your thoughts in the Comments section below.