Customer Feedback in Sales

Your feedback process should act as a funnel, catching data from all the various sources and bringing it into a centralized location. As you get feedback from various sources, it’s helpful to be consistent in what you collect. Capturing data in a handful of key areas is particularly useful, including: >Touchpoint. What was the touchpoint, or where was the customer in their journey? For example, this could be after a repair, or an interaction with customer service. >Objective. What was the customer’s objective? For example, they wanted to get their cable working again. >Experience. What was the actual experience? The cable got repaired but it happened outside the promised window of time. >Emotional impact. What was the emotional impact of this experience? The range you establish could be very satisfied to very unsatisfied, on a scale. I’ve seen alternatives such as very happy to very frustrated. What words best capture emotion in your setting? These factors give you a solid foundation for comparing both structured and unstructured feedback. UL, a global company that provides product testing and certification, made a push to more completely capture the on-the-fly feedback their employees were hearing. They created a simple feedback form inside their CRM system. The link can be accessed quickly by any employee, anytime. For example, they can easily pull up the form from their phone and enter the customer’s feedback. Nate Brown, who spearheaded the effort, said at the time, “This is a complete game-changer in how UL understands customers.” Find more examples here: https://lnkd.in/e-t5Zs2b #customerfeedback #customerexperience #customerservice

Generative AI surveys: where your feedback is interactive, valued, and promptly discarded. But hey, at least it’s efficient! Sorry, I know it’s a bit early to be snarky. Seriously though, closing the loop with your customers on their feedback - solicited or unsolicited - is a game changer. Start by integrating customer signals/data into a real-time analytics platform that not only surfaces key themes, but also flags specific issues requiring follow-up. This is no longer advanced tech. From there, create a workflow that assigns ownership for addressing the feedback, tracks resolution progress, and measures outcomes over time. With most tech having APIs for your CRM, also not a huge lift to set up. By linking feedback directly to improvement efforts, which still requires a human in the loop, and closing the loop by notifying customers when changes are made, you transform a simple data collection tool into a continuous improvement engine. Most companies are not taking these critical few steps though. Does it take time, effort, and money? Yes it does. Can it help you drive down costs and drive up revenue? Also, a hard yes. The beauty of actually closing the loop is that the outcomes can be quantified. How have you seen closing the loop - outer, inner, or both - impact your business? #cx #surveys #ceo

If you’re an operations manager at a tech company, your “beta testing process” is probably just… vibes. Does this sound familiar?👇 → You want to try a new feature or service → You pick a few customers from memory → You send some emails → You hope they say yes → You hope they remember to give feedback You hope you can find that feedback later A lot of hope. Not a lot of system. Here’s the part nobody says out loud: Beta testing isn’t just for products. It’s ANY new way of doing business. • New pricing. • New onboarding. • New way you invoice. • New way you communicate with customers. All of that should be tested. But not manually. Do this instead: 1) Pick the right customers (on purpose, not by memory) Use your CRM (Salesforce, ideally) to: • Find customers who are a strong fit for the change • Or flag the next 5–10 new customers to go through the new experience Stop scrolling through lists. Let the system tell you who to invite. 2) Automate invites and reminders One email is not a process. Set up a simple flow that: • Sends a clear beta invite • Tracks who accepts • Sends 1-5 spaced out reminders to people who never opened or replied No manual follow-up. No spamming good customers. 3) Collect feedback in a structured way Feedback in random email threads = lost. Use a short form or survey that: • Is easy to answer • Feeds straight into Salesforce • Attaches to the Contact or Account So when you pull up a customer, you can see: “What did they think about this beta?” Without digging. 4) Use data to decide what happens next When the beta is done, you should be able to answer: • Did customers like this? • Did it make things easier or harder? • Should we roll it out to everyone, tweak it, or kill it? No more “I think it went fine” based on two loud opinions. If you’re an operations manager at a tech company and you want beta testing to feel like a system, not a guess, I broke down the full flow (step-by-step, inside Salesforce) in a short YouTube video. 👉 Watch it here: https://lnkd.in/gHK2q4nf

𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝙘𝙤𝙡𝙡𝙚𝙘𝙩 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸. 𝗕𝘂𝘁 𝘃𝗲𝗿𝘆 𝗳𝗲𝘄 𝙡𝙚𝙖𝙧𝙣 𝗳𝗿𝗼𝗺 𝗶𝘁. Why? Because turning messy feedback into meaningful insights, at scale, is messy, manual, and slow. That’s where 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 shine. 👉 𝗗𝗮𝘆 𝟱: 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗳𝗼𝗿 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗟𝗼𝗼𝗽𝘀 What if intelligent agents could do the 𝗵𝗲𝗮𝘃𝘆 𝗹𝗶𝗳𝘁𝗶𝗻𝗴? From clustering support tickets to summarizing reviews. From detecting trends to surfacing top customer pain. 𝗔𝗴𝗲𝗻𝘁𝘀 𝗰𝗮𝗻 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗲𝗻𝗴𝗶𝗻𝗲 that drives real product decisions. Here’s a high-level agentic workflow to start with: 𝟏. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗜𝗻𝗽𝘂𝘁 A PM initiates the loop through a simple frontend interface setting feedback goals or product areas of interest. 𝟐. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 Coordinates specialized agents and connects them to tools, APIs, and databases. 𝟑. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗼𝗿 𝗔𝗴𝗲𝗻𝘁 Collects raw data from CRMs, support tickets, app store reviews, surveys, and social automated and multi-channel. 𝟒. 𝗣𝗿𝗲-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 Cleans, filters, and normalizes input data for structured downstream analysis. 𝟓. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 & 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗔𝗴𝗲𝗻𝘁 Groups similar feedback using embeddings and vector database queries to surface themes. 𝟔.𝗧𝗿𝗲𝗻𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁 Identifies rising issues across timeframes, geographies, or product segments. 𝟕. 𝗨𝗿𝗴𝗲𝗻𝗰𝘆 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗦𝗰𝗼𝗿𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 Scores feedback based on sentiment, frequency, and business impact. Escalates critical signals. 𝟖. 𝗣𝘂𝗯𝗹𝗶𝘀𝗵 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗔𝗴𝗲𝗻𝘁 Creates shareable summaries for product, marketing, and engineering teams. 𝟗. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 Runs compliance, bias, and quality checks before sharing insights. 𝟏𝟎. 𝗠𝗲𝘀𝘀𝗮𝗴𝗶𝗻𝗴 & 𝗣𝘂𝗯𝗹𝗶𝘀𝗵𝗶𝗻𝗴 Insights are delivered to dashboards, Slack, or email where your teams work. 🚀 𝗧𝗵𝗲 𝗥𝗲𝘀𝘂𝗹𝘁? • 📉 Faster bug resolution • 🎯 Higher product-market fit • 📈 Better roadmap decisions • 💬 Customer signals, not just noise It’s 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗼𝗻 𝗮𝘂𝘁𝗼𝗽𝗶𝗹𝗼𝘁. N͟o͟t͟e͟:͟ This is a conceptual workflow an idea to start with. Production workflows will differ based on specific requirements. Please repost ♻️ if you found this useful. Follow me for more insights on Agentic AI. Join our community: https://agentbuild.ai to learn and build together. #AIAgents #AgentArchitecture #WorkflowDesign #AgentBuildAI

Every day, two AI sales manager agents review every external call we take. How it works: At 5pm two agents fire in sequence. One for prospect calls (AEs), and one for customer calls (CSM) Each agent: - IDs the calls, pulls the transcripts from our recorder via a custom built MCP (I built it) - Filters to the right call type using participant-email rules - Loads pre-built ICP docs + current account data and insights, including customer risk scores from our CRM… This is an evolving/living set of docs that guides the agents - Loads prior engagement history from a markdown log so today's analysis is customer and deal-aware, not starting from scratch - Not only writes a leadership brief but also a coaching brief per call and updates the logs, deduping by account and preserving full history - Shares output with the right people in real time via Slack for any immediate actions needed The output and why it’s valuable - Rep coaching: 2–3 specific, direct items per call, calibrated against prior calls. Consistent coaching without leaders sitting through 30 calls a week. Cross-call patterns surface automatically. - Customer risk grade changes: A/B/C/D vs. the current SFDC grade, with trajectory (improving / stable / declining) and repeat-complaint escalation. Churn detection moves from quarterly to real time. Grade changes are evidence-backed, not political. - Structured product feedback: feature requests, complaints, and positive reactions bucketed automatically, with cross-account pattern detection. Why it matters: Product roadmap input that used to die in Slack DMs and CS inboxes now has a capture layer. - Deal intelligence: next-step quality (committed ✓ / vague ✗), ICP tier verdict, pull-forward signal, key risk. Forecasting stops being vibes. Weak next steps get flagged the same day they happen, not in a weekly pipeline review. - Persistent institutional memory: every account has an engagement log with prior objections, next-step discipline history, and outcomes. Deal-aware analysis. When a rep rolls off an account, the full history is already there; no tribal knowledge lost. - Self-updating playbook: Agents flag when any ICP doc, positioning, or commercial model needs updating based on what's actually happening on calls. The strategy docs stop drifting from reality. Call signal closes the loop back into the playbook. The stack: Claude + Chorus MCP + SFDC + scheduled tasks + well maintained context docs + reusable "skills". The unlock isn't the AI. It's that 4 disconnected human loops collapsed into one automated pipe with a memory layer. So now us humans are focused on…  Selling. The most modern sales leadership teams are doing every thing they can to have humans focused on engaging with other humans (prospects, customers, partners) - we’re tracking 15,000 of these orgs here: https://lnkd.in/ehAQqyy2 ✌️

Inline and offline evaluation, graph structures, and Reinforcement Learning (RL) collectively form an integrated, multi-layered system designed to move beyond simple prompt engineering and establish a rigorous discipline for creating agents that are not just functional, but also adaptive, self-correcting, and capable of systematic improvement over time. 💥 The shift in evaluation methods creates a two-tiered feedback system. Offline evaluation serves as a strategic development lab, allowing a human developer to diagnose systemic flaws after a task is complete, guide targeted improvements, and rigorously validate changes between different agent versions. In contrast, inline evaluation functions as a tactical real-time reflex for the agent itself, empowering it to detect and recover from errors mid-execution, thereby ensuring the final result is based on high-quality intermediate steps. This dual approach makes both the developer and the agent smarter. Using a graph as a workflow engine provides a logical, stateful skeleton that can control the flexible LLM, enabling complex, non-linear behaviors like re-planning that are impossible in a simple script. This structure provides reliability. Complementing this, using a knowledge graph to organize data transforms disconnected information into an interconnected web of facts. This grounds the agent's reasoning, enables sophisticated multi-hop queries, and mitigates the risk of hallucination, thus providing dependability and deeper intelligence. Furthermore, by making normal patterns and relationships explicit, the graph makes the agent exceptionally good at suggesting anomalies, significant deviations from established patterns. For example, it can spot when a high-value deal has been stalled in one stage for an unusually long time or when a sales representative's win rate suddenly drops. This ability to detect anomalies is what transforms the agent from a reactive question-answerer into a proactive intelligence tool. Instead of just fetching data, it can actively monitor the relationships within the data to flag risks or opportunities, such as a critical mismatch between the positive status of a deal in a CRM and the negative sentiment discovered in recent meeting notes. Reinforcement Learning represents the culmination of this process by closing the improvement loop. It elevates the system from one that is merely evaluated to one that actively learns from that evaluation. By treating the comprehensive suite of metrics as a reward signal, the agent’s internal decision-making model can be automatically fine-tuned. This allows the system to learn an optimal strategy for navigating complex trade-offs, moving beyond manual, reactive prompt adjustments to a proactive, data-driven process of self-optimization.

"We'll never make more revenue just because we're improving how we collect client feedback." Some companies are drowning in feedback, others barely receive any—or worse, they don't realize they're already receiving valuable signals. But for most, the result is the same: feedback isn't driving growth. Scenario 1: If you're not getting enough of it—or don't realize you're already receiving some—the problem isn't the lack of feedback. It's tools, culture, and process. User Feedback is everywhere, and it matters, but it’s scattered, unrecognized and unreadable, Here are 3 ways to uncover it and make it count: 1. Meet client feedback where it is—and centralize it. ▶️ Your feedback system should work like a CRM, syncing input from everywhere: Product feedback emails, Feature requests from support tickets, CSM notes from calls and meetings, Public comments or reviews. ▶️ Centralize this information into a single system where you can track, prioritize, and act. (Google Sheet, Notion, ProdCamp). 2. Engage users intentionally. ▶️ Skip one-off surveys and focus on tools users ALREADY engage with on their schedule. ▶️ Share a public roadmap, backlog, or feedback widget that shows users you value their input and lets them share ideas when it’s convenient for them. ▶️ Make feedback a part of your culture. Let users and Teams know it’s central to how you make decisions. 3. Look beyond direct feedback. ▶️ Recruit your customer-facing teams they are your best proxies for user needs: Build a feedback culture, and reroute relevant customer requests and tickets as feedback pieces. ▶️ Sometimes users don’t tell you what they need—they show you: Analyze specific behaviors at different stages of your product funnel. Scenario 2: You've got feedback pouring in from every direction—surveys, support tickets, NPS scores. It could be a good problem to have but instead of clarity, it's chaos. Without structure, feedback becomes noise. Everyone thinks nobody cares or listens. What's missing? A revenue-centric feedback loop. A system to prioritize feedback that aligns with business outcomes and act on it. Here's how: 🧭 Identify high-value signals. Not all feedback is equal so you need to tie it with data. Focus on input from your ICPs, wedge the chunk of revenue impacted. 🧭 Close the loop. Notify users when their feedback drives a choice. It builds trust and improves revenue metrics. 🧭 Turn feedback into action. Use it to signal product opportunities to derisk decisions and even re-engage lost prospects. The best part? It gets really easy with ProdCamp 😇 ____________________ Hey👋🏻 I'm Matei, CRO of ProdCamp (The revenue-centric user feedback platform) and I provide consulting services as a Revenue Operating Partner for B2B SaaS. #B2BSaaS

The worst product ops folks are the ones moving customer feedback from one information silo to another "better one" I've seen this pattern too many times (even in product orgs you look up too) Every lead comes to us with the same problem: "We have scattered feedback sources, we're sleeping on customer insights and our product team has no centralized place to search for customer context" Then they conclude they need an "Insights repository". The thing is, most "insights repositories" out there are static. Sure you'll build a beautiful database of insights and get a shiny dashboard to spot trends across your data. But after 6-12 months, the value ("insightfulness") of that repository will start declining - Outdated feedback - Feature that got shipped still in there - Research projects that never made it to a PRD You'll be tempted to "start fresh", get a clean sheet and you'll find yourself back at square one. You don't need a "repository" You need a system or in other words, a repository that cleans itself with workflows How do you get this? 1/ Pick a tool that makes customer insights actionable by linking them to roadmap items 2/ Check if the integration with Jira/Linear/Github/Clickup syncs statuses too as this will allow the system to actually "live". (Many tools claim they integrate with Jira/Linear but they simply do an API call that creates the issue, they don't keep statuses synced) 3/ Close feedback loops to get data "out of the system" and keep the system healthy Hope this helps! 😃 Drop your questions below, happy to help you out 👇

What if you never missed another critical moment to collect customer feedback? Most organizations manually trigger surveys after key events - project completion, case closure, opportunity conversion. But manual processes mean missed opportunities and inconsistent data collection. Record Lifecycle Maps in SurveyVista automate feedback collection at precise stages of your Salesforce records, ensuring you capture insights when they matter most. For Salesforce users managing complex customer journeys, this automation transforms how you optimize workflows and close feedback loops. Instead of remembering to send surveys, your system intelligently triggers them based on record status changes - from campaign engagement to case resolution to opportunity outcomes. Four key benefits of automated lifecycle feedback: ✅ Consistent Data Capture: Never miss feedback opportunities during critical customer moments ✅ Workflow Optimization: Eliminate manual survey sending and reduce admin overhead ✅ Precise Targeting: Custom triggers and filters ensure surveys reach the right people at the right time ✅ Survey Fatigue Prevention: Built-in throttling and timing controls protect customer experience When customer insights flow automatically into your CRM based on business processes, you transform reactive reporting into proactive business intelligence. Ready to automate your feedback collection? Check out SurveyVista's free knowledge base for survey templates and implementation guides. https://lnkd.in/d4N3TXir

1 in 2 interested leads in home improvement don't go ahead with the installation because of one reason.. Pricing These are people who want your product from your brand, but the price point doesn't work for them at the moment. The usual sales approach is to make it work; if not, move on to the next lead. These leads then sit in your CRM systems for years. We solved this for one of our clients by capturing and standardizing rejection reasons in lead disposition (feedback) data. Every lost sale gets a tagged rejection reason: pricing, timing, competitor, etc. Then, automated nurturing flows are triggered based on the rejection reason. In this case of rejection due to pricing, when a new offer launches, price changes, or a limited-time promotion goes live, outreach can be triggered automatically through email, text, or even AI agent support to these people and try to win them over. The real problem is that most companies never capture why a sale didn't close in any usable way. They measure how many leads convert into sales. Almost nobody is measuring why the others didn't. This is a goldmine, and if used well, you can create an always-on retargeting engine that improves conversion and maximizes lifetime value.