Customer Persona Research

My biggest priority at Junction is improving renewal conversations. Not by adding more touchpoints. By making every interaction count. Here are three tactics that actually moved retention: Tactic One: Segment Your Book Most CSMs treat all customers the same. Same cadence. Same agenda. Same deck. That's the fastest way to become background noise. Instead, segment your book by outcome they're driving: → Revenue growth customers → Cost savings customers → Efficiency/workflow customers When you group similar outcomes, you stop context switching between completely different value stories. You get in flow with relevant case studies, metrics that matter, and strategic conversations they actually care about. Tactic Two: Mine for Intelligence Not every customer call needs to drive immediate action. Sometimes you're gathering intelligence for the renewal conversation 90 days out. When you hear "gold nuggets" like: → Upcoming board priorities → Budget reallocation plans → New executive KPIs → Competitive pressure points You capture them. Then you use those insights to frame your value story around what their CFO actually cares about. Tactic Three: Outcomes, Not Features Your customer messages used to sound like this: "Checking in on adoption metrics and wanted to schedule our quarterly review..." Now they sound like this: "I noticed your team is focused on reducing time-to-market by 30% this quarter. Most ops leaders we work with are facing the same tension: pressure to move faster while maintaining quality and compliance." What's more likely: Your customer is thinking about the business outcome you impact? Or your customer is thinking about your product features? Message accordingly, and engagement increases. --- The shift isn't more customer touches. It's more intelligent customer touches. Stop optimizing for activity volume. Start optimizing for strategic relevance. How are you teaching your CS team to segment, mine intelligence, and lead with outcomes?

10 hours of customer discovery analysis → 20 minutes. I showed Aakash Gupta a workflow. But why it works is in the details 👇 Here's what most people still miss with AI analysis: When you're digging into spaces like retention or churn, the default questions are too vague. And unless you actually *really* know what you're looking for (and how to ask for it), often AI still doesn't tell you the questions you should be asking. Default: "What do you like?" "Why did you leave?" → AI will happily summarize surface-level answers into something that looks like insight. You need the right questions before you ask AI to do the heavy lifting. Some of what we covered in my live demo with Aakash: 1️⃣ The right questions still change everything. For retention, instead of "what do you like?" — ask "what specifically keeps you using this?" That's the value anchors framework. It pulls out retention drivers with actual evidence, not just sentiment or preferences. 2️⃣ A framework that makes interviews comparable. For interview work, I often use a 3-phase structure. For retention, that looks like: retention assessment → value anchors with timestamps → recommendations. It pushes AI to pull out more relevant detail, and it makes results comparable across interviews over time. That's how to get meaningful patterns to surface (not by winging this every time). 3️⃣ Analyze individually first, then synthesize. This is something I've taught in 5x cohorts of my AI Analysis course - it still makes a huge difference for results. Don't dump all transcripts with one prompt like "synthesize these". Analyze each interview on its own. Then look for patterns across them. The themes are sharper when you don't let AI blend everything together too early. 4️⃣ Agents for ongoing work. Manual for one-off. If your research runs weekly or monthly, build an agent. If you run the same kind of customer calls every quarter - and have a lot of data around the same things - build an agent. If it's a one-time project, don't over-engineer it. 5️⃣ Export everything. Don't leave findings in a chat window. Markdown files with executive summary, findings, quotes, recommendations. If it's not documented, it didn't happen. 6️⃣ Knowing how to reduce and optimize context window use cuts costs by 70%. - and improves results. You don't always need...the full interview transcript, every analysis step in one chat window, the same context always loading (what happens in Claude Projects)...there are tactics for using what you **need** and triggering less context rot. 7️⃣ Start manual before you automate. Do 10+ interviews by hand first. Nail a framework that works. Then build agents to scale it. Skip that step and you're scaling something you haven't proven. This is what *nearly everyone* I meet does - it's easily fixable. 8️⃣ Build in verification everywhere. Triple-checked, or they aren't insights. Full walkthrough on YouTube, Spotify, and Apple — links in comments 👇

When a customer walks into a jewellery store, nobody says: “The lighting temperature is off.” “The chair height is wrong.” “The staff energy feels tired.” They just leave. Over the last week at our new store, I wasn’t tracking sales. I was tracking micro-frictions. Here are small things most retailers miss: 1. AC Air Direction If cold air hits directly on the trial area, customers rush decisions. Comfort affects patience. 2. Chair Height vs Counter Height If the customer sits lower than the display tray, posture becomes awkward. Awkward posture reduces confidence. 3. Tray Weight Heavy trays subconsciously signal “burden.” Light trays feel easy and premium. 4. Tag Visibility If price tags are visible before storytelling begins, the brain anchors on cost, not value. 5. Staff Foot Positioning Standing too close invades space. Standing too far feels disinterested. There’s a 2–3 ft sweet spot. 6. Mirror Lighting vs Store Lighting If the mirror has a different tone of light than the display, the diamond looks different when she turns. 7. Music BPM Faster music increases decision speed but lowers ticket size. Slower music increases comfort and dwell time. 8. Glass Cleanliness at Eye Level Most stores clean the centre. Smudges usually exist at child-height or shoulder-height. 9. Billing Silence If the billing area goes silent, excitement drops. Light conversation maintains emotional continuity. 10. Staff Energy at 8:30 PM The last customer deserves the same enthusiasm as the first. Fatigue is visible. 11. Scent Consistency Inconsistent fragrance across days breaks subconscious brand memory. 12. Phone Usage Visibility Even one staff member checking WhatsApp signals low demand. None of these appear in daily MIS reports. But each one compounds. Retail isn’t won by marketing campaigns. It’s won by operational sharpness. The difference between a ₹70,000 bill and a ₹1,20,000 bill is often a 6- inch adjustment.

Every product loses users. Some people cancel subscriptions. Some stop opening the app. Some simply disappear. That’s called customer churn - when users leave your product. Most teams can see that users are leaving. But the real challenge is understanding why. Dashboards tell you who left. Good analysis tells you what went wrong. If you work in Data Analytics, Product, or Growth, finding the real reasons behind customer drop-off is one of the most valuable skills you can learn. Here’s a practical framework for Churn Analysis - 15 ways to find the real root causes 👇 1) Define churn clearly first Decide what “leaving” means for your product: canceled subscriptions, inactivity, no purchase in 60 days, or app uninstall. 2) Segment churn by customer type New users and loyal users leave for very different reasons. Always analyze them separately. 3) Check churn by acquisition channel Compare paid vs organic users to see if targeting or expectations are misaligned. 4) Analyze churn by cohort (signup week/month) Look for specific groups that dropped after a feature change, pricing update, or campaign. 5) Track churn by lifecycle stage Churn during onboarding is very different from churn after months of usage. 6) Find churn spikes over time Plot daily or weekly churn and match spikes to outages, bugs, or policy changes. 7) Measure usage drop before churn Most users slowly disengage before leaving. Track last active date and session trends. 8) Map feature adoption patterns Users who never use key features are much more likely to churn. 9) Build funnels to locate drop-offs Example: Signup → Setup → First Action → Repeat Usage → Subscription. 10) Compare high-churn vs low-churn segments Study what retained users do differently - then try to replicate that behavior. 11) Analyze churn by pricing plan or tier Sometimes users leave because the pricing doesn’t match their needs, not because the product is bad. 12) Study support tickets and complaint themes Group feedback around bugs, usability, slow response, onboarding confusion, or pricing. 13) Look at transaction failures and payment declines Some churn is accidental: card failures, renewal issues, or payment errors. 14) Run retention curves and survival analysis Identify exactly where retention drops sharply - that stage usually holds the root cause. 15) Validate with churn surveys or interviews Ask users why they left and use real feedback to confirm your assumptions. The key takeaway: Customer churn isn’t random. It leaves clues everywhere - in usage data, funnels, cohorts, pricing, support tickets, and payments. Great analysts don’t guess. They connect these signals into clear actions. Save this if you work with customer data. Share it with your product or growth team. This is how churn turns into insight.

Founders often say they don't do research. "I don't have time for that." "I know my users so I don't need it." But if you do any of these things, you're already participating in research:   1/ You read customer support tickets You're identifying patterns in pain points and understanding where your product is falling short   2/ You listen to Gong calls You're gathering insights on what resonates with your ICP and what objections keep coming up   3/ You check your product analytics You're tracking user behavior to understand what features drive engagement and where people drop off   4/ You A/B test your pricing page You're testing hypotheses about what messaging converts better with your audience   5/ You send out NPS surveys You're measuring customer satisfaction and identifying promoters versus detractors   6/ You run beta programs You're validating product concepts before you scale them to your full user base   7/ You ask "why did you choose us?" You're uncovering the jobs-to-be-done that your product fulfills for customers   8/ You track churn and interview people who leave You're understanding what drives customers away so you can fix it   9/ You read competitor reviews You're mapping where the market has gaps and where you can differentiate   10/ You prototype before you build You're validating demand and testing assumptions about what users actually need   11/ You check which email subject lines get opened You're experimenting with messaging that resonates with your audience   12/ You monitor what features get requested most You're prioritizing your roadmap based on patterns in customer demand   13/ You ask customers for testimonials You're discovering what value they're actually getting from your product in their own words   14/ You test landing page variations You're optimizing how you communicate value to drive conversions   15/ You have regular check-ins with your ICP You're building continuous feedback loops to stay connected to how their needs evolve   Even if you don't think of it as conducting research, you can consider it an exercise in building empathy. 

Churn doesn't always live in the loud customers..instead it is creepy quiet. These are tactics I use to find the quiet 🤫 before it results in churn--> 1)Track downgrades Pull a list of customers who've been at the same tier for 12+ months. Cross-reference with data you have on their company growth (headcount on LinkedIn, funding announcements, job postings). If they're growing but their usage with isn't, find out why. That gap is either a retention risk or an expansion opportunity you're missing. 2)Map workarounds Search customer Slack channels, community forums, or support tickets for phrases like "here's how I," "my process is," or "I built a script." Customers create a bandaid when a product doesn't do what they need. One Excel export template tells us more than 10 feature requests. 3) Run Healthy Customer Calls Pick 5 accounts with great health scores each month. Ask them to screen-share their workflow. Don't guide them. Just watch. You'll discover which features they ignore, which ones they use wrong, and which workflows you never designed for that they've made work anyway. 3) Measure feature abandonment, not just adoption Track how many people try a feature once and never come back. A feature with 5% adoption and zero return visits is screaming something. Either the first experience failed, the value wasn't clear, or the feature solves a problem people don't actually have. 4) Look for manual work adjacent to your product When customers export your data to manipulate it elsewhere, they're telling you what's missing. When they copy-paste between your tool and another, that's an integration gap. When they use spreadsheets "just to keep track of things," that's a workflow you don't support yet. 5) Ask about their job, not your product Instead of "How's the platform working?" try "What took up most of your time this week?" or "What are you trying to accomplish in the next quarter?" Their answers will include things your product should help with but doesn't. The gap between their goals and your capabilities is your roadmap. 6) Review who's NOT using new features When you ship something new, the non-adopters matter more than the adopters. Email a sample of them: "Noticed you haven't tried X yet—what's getting in the way?" The answers cluster into patterns: didn't know it existed, tried but couldn't figure it out, doesn't solve their problem, already solved it another way. 7) Calculate the "quiet satisfaction" risk** Some customers seem happy because they never complain. Pull their usage data. If engagement is flat or declining while their business is growing, they're not happy—they're indifferent. Indifferent customers leave the moment something better appears. What this looks like weekly-> Set aside time to review usage patterns. Who logged in & then stopped? Who started a workflow and abandoned it? Who's paying for features they've never touched? Pick 3 + act So much to learn from our quiet customers 😉! Rebels of SaaS