Customer Alert Systems

Most SaaS companies still rely on static health scores. The problem? By the time they fire an alert, the customer is already halfway out the door. Instead of static scores, you need a health system — a framework that tracks signals, triggers alerts, and connects to action playbooks, in real time. A score tells you what. A system tells you when and how to act. When alerts are tied to signals and playbooks, your team moves from reactive firefighting to proactive engagement. That’s the difference between waiting for churn… and staying one step ahead of it. So how do you actually build one? It comes down to 5 practical steps. 1️⃣ Map the customer journey -> Define the key checkpoints: onboarding, first value, adoption, renewal prep, expansion. -> Write down what “healthy” looks like at each stage. 2️⃣ Define the right signals -> Leading indicators (daily usage, exec engagement, QBR attendance) → trigger early. -> Lagging indicators (NPS, renewal outcome) → track for context, not action. 3️⃣ Set up two types of alerts -> ✅ Milestone alerts – pre-scheduled based on the journey (e.g. Month 6 QBR, Year 1 ROI review). They keep customers moving forward. -> ⚠️ Risk alerts – event-driven, triggered by negative signals (e.g. drop in adoption, sponsor silence, high support escalations). They help you act before churn. 4️⃣ Link every alert to a playbook -> An alert without a clear next step is just noise. -> Decide: who acts, what they do, and by when. 5️⃣ Close the loop -> Track which alerts triggered, which actions were taken, and what changed. -> Refine thresholds and signals over time — let data make the system smarter. What’s the most valuable alert you’ve built into your CS process? I’m building a library of best-practice alerts to share in a future post. Drop your most valuable one below 👇 #CustomerSuccess #CustomerHealth #SaaS #AIinCustomerSuccess #ProactiveCS

If you work in distribution, are you still guessing which customers need attention, which ones might churn, and how to prioritize your outreach? Guessing and corporate lore are no longer necessary when proactively managing B2B churn and driving up CLVs. Advanced analytics and predictive algorithms are democratized, and LLMs are here to help us build optimal predictive churn models tailored to our industry and business. Transactional, behavioral, and firmographic customer segmentation gives distributors a clear roadmap. By analyzing historical purchasing behavior, engagement patterns, and profitability metrics, you can identify which customers deserve proactive communication, tailored promotions, personalized discounts, or more generous credit terms. Moving beyond one-size-fits-all approaches lets you deploy your marketing budgets and sales efforts where they matter, driving sustainable customer lifetime value and organic growth. What if you could anticipate churn 90 days in advance and take action today? Modern machine learning techniques—now widely accessible—integrate seamlessly with your CRM. Or, if it works better for your sales teams, serve up the actions you need to take via daily/weekly emails, Excel tools, or Power BI / Tableau. Whatever fits better with your sales ops rhythm and commercial team analytics maturity. Sales teams receive daily or weekly alerts on their phones or tablets, pinpointing customers at the highest risk of leaving and explaining the reasons behind the risk. Armed with these insights, your sales team can proactively engage customers with relevant offers, from upselling new product lines to extending credit terms or introducing value-added services that strengthen loyalty. **** Consider a consumer durables distributor who recently deployed predictive churn capabilities. By layering advanced algorithms on top of their CRM, their sales reps saw a prioritized list of customers at risk, in descending order of revenue-at-risk. They leveraged targeted promotions and services—sometimes as simple as a timely check-in via email or in person—to re-engage customers before revenue evaporated. The result? Higher retention, increased cross-sell and upsell conversions, and a more efficient allocation of sales resources. **** This isn’t about adding complexity to your sales team’s day—it’s about giving them the tools and foresight to be proactive. When your reps know who’s likely to churn and why, they can deliver timely, personalized outreach that protects revenue and boosts lifetime value. These capabilities are no longer relegated to B2C or enterprise-grade B2B companies. Mid-market distributors of all sizes must build these capabilities to drive insights-based sales ops at scale. 

One thing I've noticed when working with clients and doing discovery calls is that a lot of companies are not using customer signals to be proactive instead of reactive. Being proactive rather than reactive is the key to ensuring customer satisfaction and retention. One effective strategy to stay ahead of potential issues is by documenting and understanding "customer signals" – subtle behaviors and indicators that can serve as red flags. Recognizing these signals across the organization allows businesses to engage with customers at the right moment, preventing issues from escalating and ultimately fostering a more positive customer experience. Teams should not just try to save the account once there is a request to cancel or an escalation. You need to pay attention to the signs before you hit this point. Ensuring the entire team knows what to look for means that everyone is empowered to care and improve the customer experience. Here's a list of customer behaviors that could be potential red flags, gradually increasing as they check out or consider leaving: 🔷 Reduced Engagement: Decreased interactions with your product or service. Limited participation in surveys, webinars, or other engagement opportunities. 🔷 Decreased Usage Patterns: A decline in frequency or duration of product usage. Reduced utilization of features or services. 🔷 Unresolved Support Tickets: Multiple open support tickets that remain unresolved. Frequent escalations or dissatisfaction with support responses. 🔷 Negative Feedback or Reviews: Public expression of dissatisfaction on review platforms or social media. Consistently low scores in customer feedback surveys. 🔷 Inactive Account Behavior: Extended periods of inactivity in their account. No logins or interactions over an extended timeframe. 🔷 Communication Breakdown: Ignoring or not responding to communication attempts. Lack of response to personalized outreach or engagement efforts. 🔷 Changes in Buying Patterns: Drastic reduction in purchase frequency or order size. Shifting to lower-tier plans or downgrading services. 🔷 Exploration of Alternatives: Visiting competitor websites or exploring alternative solutions. Engaging in product comparisons and evaluations. 🔷 Billing and Payment Issues: Frequent delays or issues with payments. Unusual changes in billing patterns.

Invoice disputes do not begin in SAP. They begin quietly inside customer emails that finance teams usually ignore. Below is a clean case card, written in simple, direct points. Case: Early dispute detection using NLP on emails Business problem: • Invoice disputes appear late in SAP, after cash is already delayed. • Finance teams only react once the dispute is officially logged. Hidden risk signal: • Customers express concern days earlier through email language. • These messages reach AR teams but are treated as routine communication. What NLP checked: ✓ Phrases indicating confusion or disagreement on charges. ✓ Mentions of incorrect pricing, missing credits, or contract mismatch. ✓ Negative or uncertain tone combined with billing keywords. How the system worked: • All inbound finance emails were scanned in real time. • Each email received a dispute-risk score based on language and intent. • High-risk emails triggered alerts before any SAP dispute was created. Action taken early: ✓ Finance clarified invoices proactively ✓ Sales and billing aligned before escalation ✓ Customers received responses before frustration built up Result: • Fewer formal disputes in SAP • Faster collections and improved cash flow • Reduced friction between customers, sales, and finance Core insight: Disputes start as language, not transactions. AI that listens early prevents problems that systems only see too late. Where else in your Q2C flow are early signals being missed?

Red text everywhere. I opened a medtech app during my audit. Every screen was screaming at me. The doctor using it didn't even notice anymore. That's when I knew they had a problem ↓ 𝐓𝐇𝐄 𝐀𝐋𝐄𝐑𝐓 𝐂𝐇𝐀𝐎𝐒: This app had 7 different error styles: • Red banners • Yellow tooltips • Orange pop-ups • Bold red text • Flashing notifications • Inline warnings • Modal alerts Everything looked urgent. So nothing felt urgent. I watched a doctor dismiss a critical alert without reading it. "I just click through them now. There are too many." A life-saving warning looked exactly like "username too short." 𝐓𝐇𝐄 𝐑𝐄𝐀𝐋 𝐃𝐀𝐍𝐆𝐄𝐑: When everything is urgent, nothing is. Users develop alert blindness. They stop reading. They auto-dismiss. They ignore what matters. One support ticket said: "I lost patient data because I didn't see the warning." The warning was there. Buried among 12 other "urgent" messages. 𝐓𝐇𝐄 𝐅𝐈𝐗: We built a 3-level alert system: 🔴 Critical (Red) System errors. Data loss. Stop everything. 🟡 Warning (Yellow)   Action needed soon. But not urgent. 🔵 Info (Blue) Nice to know. Totally optional. Simple rules: → One color = one meaning → Same position every time → Clear next steps always 𝟔 𝐖𝐄𝐄𝐊𝐒 𝐋𝐀𝐓𝐄𝐑: 📈 Critical alert response: Up 36% 📉 Support tickets: Down significantly   💙 User trust: Restored A doctor messaged: "I finally know what actually needs my attention. Thank you." How many alert styles does YOUR product have? #ProductDesign #HealthTech #UXDesign #MedTech #AlertDesign

7 early warning signals showing your customers are about to cancel their subscription: Your customers tell you they're leaving long before they hit "cancel." Here are the red flags I've spotted: 𝟭. 𝗧𝗵𝗲 𝗴𝗵𝗼𝘀𝘁 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 They stop: • Opening your emails • Using key features • Logging in regularly Silent customers = future cancellations 𝟮. 𝗧𝗵𝗲 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝘀𝘂𝗿𝗴𝗲 Sudden increase in: • Basic how-to questions • Feature complaints • Response time frustrations They're questioning their investment. 𝟯. 𝗧𝗵𝗲 𝘂𝘀𝗮𝗴𝗲 𝗰𝗹𝗶𝗳𝗳 Watch for: • Dramatic drop in logins • Fewer team members active • Core features ignored Low engagement = high risk 𝟰. 𝗧𝗵𝗲 𝘃𝗮𝗹𝘂𝗲 𝗯𝗹𝗶𝗻𝗱𝗻𝗲𝘀𝘀 They can't answer: • How much time they save • What ROI they're getting • Why they need you No clear value = easy goodbye 𝟱. 𝗧𝗵𝗲 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝘃𝗼𝗶𝗱 Red flags: • Ignore your surveys • Stop giving feedback • Don't join user calls Silence isn't golden. It's dangerous. 𝟲. 𝗧𝗵𝗲 𝗱𝗼𝘄𝗻𝗴𝗿𝗮𝗱𝗲 𝗱𝗮𝗻𝗰𝗲 Warning signs: • Ask about cheaper plans • Compare competitor pricing • Question feature value Price sensitivity spikes before churn. 𝟳. 𝗧𝗵𝗲 𝗼𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲 Look for: • Incomplete setup • Skipped tutorials • Missing key milestones They never really started = they'll never stay. 𝗛𝗼𝘄 𝘁𝗼 𝘀𝗽𝗼𝘁 𝘁𝗵𝗼𝘀𝗲 𝘀𝗶𝗴𝗻𝘀: ✅ Build a warning system ↳ Track these metrics weekly ✅ Set trigger points ↳ Define when to intervene ✅ Create rescue plays ↳ Have ready-to-go save strategies ✅ Measure intervention success ↳ Track what actually works The best churn strategy? Stop it before it starts.

Yesterday, Arsh Khandelwal and I talked at LinkedIn HQ about Orb's technical investment around Orb's alerting features at the scale of 1M+ events/sec. This is an incredibly important feature for Orb's customers to provide timely notifications to *their* customers on hitting a spend cap or usage limit → your customers don't like surprise overages, you don't want to swallow spillover infra costs for excess use. What makes implementing real-time alerting for billing hard? Why isn't this a solved problem a la Datadog? A preview of what's tricky: - Flexibility: Orb is the only billing system that lets you configure your billing metrics with SQL. This makes computing incremental query results significantly harder; traditional stream processing approaches don't work out of the box. Approximates aren't good enough... and remember that the number of groups explodes here quickly since each customer on each timezone has a different timeframe you're evaluating. - Business complexity: usually, your customers want to get alerted on accrued spend across all metrics they're subscribed to. You'll need to factor in a combination of credit burndown for some metrics, rollovers, minimums, tiered pricing, etc. This is a lot of domain data to load in a perf-critical path. Billing doesn't operate on a single p x q anymore. - Varying requirements: You might want to alert on a subset of self-serve, high risk customers with a much higher SLO than your trusted enterprise accounts. Being able to fast-lane some customers is critical.

Your alert hygiene killed the noise. It also killed the canaries! Every engineering team learns the same lesson early: Set thresholds. Filter noise. Only wake people up for "real" problems. However, at scale the reality is: - A 0.25% error rate means thousands of impacted customers - "Minor" latency spikes cascade across dependent services - Resource constraints appear and vanish before hitting thresholds - Pattern recognition depends entirely on who's on call The data is clear - every platform generates these early warning signals: - 5-10 sub-threshold anomalies daily - Each impacts hundreds or thousands of customers - Most correlate strongly with future incidents - Almost every major outage shows these patterns 48-72 hours early Three critical failures in our current approach: 1. Scale When you're processing millions of operations, by the time something hits your alert threshold, the damage is cascading. Yet we ignore smaller signals that could prevent this. 2. Knowledge Your experienced engineers spot these patterns instantly. But in today's world of global teams and 24/7 operations, relying on tribal knowledge isn't sustainable. 3. Process "We'll review it next sprint" is where pattern recognition goes to die. By then, the context is lost and new fires are burning. We already have all the data we need: - Every error is logged - Every latency spike is recorded - Every resource constraint is measured - Every pattern is there, waiting to be analyzed We just need to move from static threshold-based alerting to intelligent and proactive signal analysis.

One thing that we talk about a lot at incident.io is cohesion. Great products are cohesive. Each disparate part forms a united whole. You are able to start from an arbitrary part of the product, and use many other parts of the product, along your journey, to help you get something done: getting progressively more powerful as you go. Often, as you grow as a company, you may acquire other companies to add on parts of the product. There are some companies, like Datadog that have done a fantastic job of this—re-platforming acquired companies, rebuilding them inside the machine, and making them cohesive and united as a result. There's others where the products feel barely related to each other: Conway's Law in action. 3 years in, we have a mature and powerful platform, and we're starting to see the power of cohesion as more of our customers adopt all 3 of our products—Response, On-call and Status Pages. I'll give an example below. 📣 Our Status Page product allows you to communicate with your customers when something is up. 👀 We proactively monitor the number of visits to your status page for you, 24/7/365. 📈 If we detect a large spike in the amount of visitors to your status page, it's likely that something is wrong: your customers probably know before you do (this mechanism has saved many hours of downtime!) 🚨 We fire an alert to our Alerts product. Alerts integrates natively with On-call, notifying the right person to deal with the problem, and escalating further if required. 🧘🏼 Before the notification is even sent through On-call, Response has created a place for you to solve the problem, attaching all relevant context in the incident, and updating your internal status page so relevant stakeholders know. You don't need to write any code for this: it works natively in our platform. Click a few buttons and you're good to go. Cohesion is intentional: you have to design it in. p.s. whenever I think of cohesion, I always think of this Brentism.

You Can't Detect "Unusual" If You Never Defined "Usual" A business deposits $60,000 in cash monthly. Their onboarding form says "$0-$10,000." Analysts mark alerts as "consistent with profile." See the problem? Here's one of the most underrated truths in BSA/AML: Most institutions fail at detection not because their monitoring system is broken… but because they never set a baseline in the first place. Think about it: how can you call something "suspicious" if you never defined what "normal" looks like? What a baseline really is: When you onboard a customer, you're not just collecting documents. You're setting expectations: - How many wires per month? - Typical amounts? - Cash in or out? - Which geographies? - What products will they actually use? This isn't about perfection. It's about direction. Give me a range. Give me an anchor. Give me something to compare actual activity against. Without it? Your monitoring is blind. What goes wrong (I've seen this firsthand): Back to that business customer who checked "$0–$10,000" for expected monthly cash deposits but actually deposited over $60,000 every month. This wasn't just a paperwork error. It represented a 500% deviation that could indicate structuring, unreported income, or worse. Alerts fired, sure. But the narratives didn't compare actual vs. expected. So analysts dismissed them as "consistent with profile." Except… the profile was never tied to the baseline. The result? Examiners flagged the entire CDD → monitoring process as ineffective. How to fix it: ✔️ Capture expected activity at onboarding. Use ranges if exact numbers aren't practical. ✔️ Push it into monitoring. Your scenarios should reference those baselines (wires, cash, ACH). ✔️ Document baseline assumptions and their sources—customer statements, industry norms, comparable accounts. ✔️ Re-baseline when things change. New products, new volumes, new geographies = update the file. ✔️ Train analysts to reference it. Every disposition should start with: "Customer expected X. Actual activity was Y." 💡 Analyst tip you can try tomorrow: In your alert template, add a required field: "Baseline vs. Actual." Make it impossible to close the alert without writing that comparison. Watch how your narratives transform from "Large cash deposit noted" to "Deposit of $15K exceeds stated baseline of $2K, inconsistent with stated business model." Reality check for your program: Pull five recent alert narratives. Do they explicitly compare actual activity to the customer's baseline? If not, your monitoring isn't risk-based. It's just reactive. 👉 Here's my question: What's the biggest baseline vs. actual gap you've encountered? How did your team handle the re-baselining process? Because without "usual," you'll never know what's unusual. LFP Risk Solutions