Sales Feedback Loops

Following user feedback is a Product Management virtue. Is there an actual way to implement it, between all the noise, bugs, and stakeholder requests? Well… Most teams claim they are customer-driven. Yet the moment you open Zendesk, App Store reviews, survey results, and Slack threads, you instantly remember why everyone quietly avoids this work. Feedback is everywhere, contradictory, emotional, duplicated, and nearly impossible to turn into decisions.  It is chaos disguised as “insights.” This is why the new Amplitude AI Feedback release caught my attention and made it all the easier to decide to partner with them on this update. It successfully connects what users say with what they actually do, in one workflow. No extra tools.  No extra tabs. You see their words, frustrations, and praise. You see their behavior. And AI transforms it into ranked themes, rising trends, top requests, and complaints. Noise turns into clarity. Opinions turn into patterns. Patterns turn into action. And because it is native inside Amplitude, it kills the biggest problem in feedback work: Fragmentation. Everything flows into analytics, session replay, and cohorts, creating a full loop from insight to fix. You can trace why an issue matters, how many users care, how it impacts behavior, and which actions you should take. Finally, a single source of truth for PMs, UX, CX, and marketing. I’m also genuinely impressed with the supported sources of feedback: App Store, Google Play, Zendesk, Intercom, Freshdesk, Salesforce Service, Gong, Trustpilot, G2, Reddit, Discord, and X. Slack arrives in Q1, and there will be more! If you ever felt overwhelmed by feedback, this is one of the first attempts I have seen that genuinely solves the operational pain, not just the reporting part. It launches… Today! Take a look: https://lnkd.in/dAJKeTez What was the most successful update you know that came from the product’s users? Let me know in the comments. #productmanagement #productmanager #userfeedback

4 loops beat 2, and here's why: Inner and outer loops were fine for 2005. They fix incidents, they close tickets, and they make dashboards look super busy. They also cap your upside and make you measure the wrong thing (e.g., problem solved vs. email delivered). I have seen “closed the loop” everywhere while revenue still leaked and costs kept rising. It's also a dated philosophy that too many push and isn't helping you create long-term customer value. First, some definitions: The inner loop is direct recovery with one customer after a bad moment. The outer loop is fixing the root cause. Useful, but mostly reactive. We cannot solve tomorrow’s problems with yesterday’s control loops. Now, let's modernize the stack a bit, shall we? 1. Recovery loop is 1-to-1 service recovery from any signal, not just surveys. 2. Removal loop is a two-week sprint eliminating the defect and verifying it's gone. 3. Orchestration loop is turning customer signals into the next-best-action for growth and efficiency across flows and channels. 4. Learning loop is the write-back of outcomes so models, rules, and playbooks get smarter, and corporate debt like tech debt gets cut. Closing the loop is a receipt. Compounding the loop is a result. This only works when leaders run it together: CX develops the priority and the value lens from the customer's perspective. Product and Engineering own removal with a real backlog and delivery dates. Sales and Marketing run orchestration so the right accounts get the right nudge or education at the right time. Service and Customer Success lead recovery with clear SLAs and authority to make it right. Data brings the signals together with field level controls. Finance verifies lift and keeps us honest. Legal and Risk set boundaries that protect customers and the brand. You hold a bi-weekly value standup to review prioritization for value at risk and value unlocked. Put it on one page with the owners named. Additionally, have a monthly review with Finance & Executives to greenlight bigger system changes only when the value story is clear. You want to focus on throughput here. Here's a concrete example. A commercial payments portal sees Friday 3 p.m. file upload failures spike. Recovery loop fixes impacted clients within an hour and credits fees where needed. The Removal loop delivers a batch size fix and a clearer progress widget within two sprints. The Orchestration loop sends a short in-app guide on Thursdays to high-risk users and alerts bankers for top accounts. The Learning loop shows failures down 62 percent, Friday contacts down 35 percent, and three at-risk clients adopting premium file services within a month. That is compounding value. Comment 1, 2, 3, or 4 with the loop your team is missing and the single constraint blocking it. Type "Fix the Loop" below, and I will share a Google Doc checklist you can steal for your team. #customerexperience #productmanagement #sales #engineering

Like a medical diagnosis, criticism in the workplace serves to pinpoint problems, inefficiencies, or shortcomings. It highlights areas that require attention, whether in individual performance, team dynamics, or organizational processes. However, criticism that stops at identification, without providing a roadmap for improvement, is incomplete. It can lead to frustration, demotivation, and a sense of aimlessness, akin to a patient knowing their ailment but having no means to cure it. The transition from merely diagnosing to offering a treatment plan in the business context involves providing actionable feedback. This step requires skill, empathy, and a deep understanding of the individual or the situation at hand. Actionable feedback is specific, achievable, and relevant. It not only points out the area of concern but also offers practical steps, resources, or guidance on how to rectify the issue. This approach transforms criticism from a potentially negative interaction into a constructive and empowering one. Incorporating actionable plans into criticism yields multiple benefits. For employees, it provides a clear path to improvement and facilitates growth. For teams, it encourages a culture of continuous improvement, collaboration, and open communication. And for organizations, it leads to improved results and a competitive edge. Implementing this approach is not without its challenges. It requires a culture that values open communication and continuous learning. Leaders and managers must be trained to provide balanced feedback that is both honest and constructive. Additionally, there must be an understanding that the 'treatment plan' might require adjustments and flexibility, as every professional scenario is unique. The takeaways ... [1] When offering criticism, accompany it with a specific, measurable action plan. For instance, if an employee's performance is lacking in a certain area, don't just highlight the problem; provide clear, achievable goals and a timeline for improvement. Offer resources, if needed. [2] Constructive criticism should not be a one-way street. Encourage employees to engage in the feedback process actively. This can be achieved by asking them for their input on potential solutions or improvements. Such an approach not only empowers the employees but also builds a culture of mutual respect and collaborative problem-solving. [3] Criticism and action plans are not a 'set it and forget it' scenario. Regular follow-ups are crucial to ensure that the action plan is being implemented and to assess its effectiveness. [4] Recognizing and acknowledging progress is equally important, as it reinforces positive behavior and outcomes, leading to sustained improvement and development. ✅ Share this to your network ✅ Follow me on LinkedIn for expert insights ★ DM me for a conversation to learn how we can help you grow & succeed #business #people #leadership #management #growth #success #feedback #communication

Most teams guess what to build. We talk to 100s of prospects a month and let them tell us exactly what’s broken. In the early days of Salesforge, we knew one thing: The company that talks to the most customers the fastest… wins. That’s why we book 10–20 meetings a day not just to sell, but to learn faster than anyone else in our category. Every single day, we hear what prospects hate, where their current stack fails, what gets them excited, and what they wish existed. That learning compiles. It compounds. And over time, it becomes your strategic edge. Here are 4 lessons we’ve learned by doing this at volume and how it’s shaped how we build: 1. Feedback isn’t optional Most teams try to prioritize based on opinions, roadmaps, or investor pressure. We don’t. We let volume of feedback decide what gets built and what doesn’t. When you’re on 100+ calls a week, patterns become undeniable. If 6 out of 10 people mention the same workflow friction — we tag it, push it to product, and ship fast. Sometimes within a week. Without this level of signal clarity, you risk overbuilding, building in the wrong direction, or even worse — building something nobody wants. Velocity of feedback → velocity of learning → velocity of execution. 2. The best ideas don’t live on a whiteboard We’ve never treated the roadmap as a fixed blueprint. It’s a living document that adapts with every conversation. Some of our biggest wins started out as throwaway questions from prospects: “Can you guys do this?” “What if your agent could also handle that?” When you hear something like that three, four, five times in a single week, it’s no longer a fluke. It’s a market pull. We’ve built entire products like Warmforge and Leadsforge based on patterns that showed up first in conversations. Too many teams fall in love with their own ideas. We fall in love with patterns. 3. Repetition forces clarity or it exposes fluff If you’ve ever delivered the same pitch 50+ times in a week, you know one thing: you can’t fake it. If your messaging isn’t sharp, people will tune out. That’s the beauty of repetition. It either breaks your narrative or forces you to tighten it. Every meeting becomes a stress test for your story. 4. Geography matters more than you think One of the most underappreciated lessons we’ve learned from talking to prospects globally is just how different buyer behavior is by region. → Southeast Asia and LATAM? WhatsApp. → US? Email + cold call + LinkedIn → Europe? Email + cold call + LinkedIn + Whatsapp Without the conversations, we’d be shipping the wrong thing into the wrong market. TAKEAWAY Most teams optimize for pipeline. We optimize for learning velocity. That’s how you ship products people want. That’s how you write copy that converts. That’s how you build an agent that actually works in the wild. And the only way to do it? Listen harder. Track everything. Move fast. It’s messy. It’s unscalable. And it’s the reason we’re winning.

Exciting New Research: Rec-R1 - A Breakthrough in Recommendation Systems Using Reinforcement Learning I just came across a fascinating research paper that introduces Rec-R1, a novel reinforcement learning framework that bridges large language models (LLMs) with recommendation systems through closed-loop optimization. Unlike traditional approaches that rely on prompting or supervised fine-tuning (SFT), Rec-R1 directly optimizes LLM generation using feedback from recommendation models without needing synthetic data from proprietary models like GPT-4o. >> How Rec-R1 Works Under the Hood: Rec-R1 creates a feedback loop where the LLM receives an input (like a user query or behavioral history), generates a textual output (such as a rewritten query), and then gets performance-based feedback from the recommendation system. This feedback is transformed into reward signals that optimize the LLM through reinforcement learning. The framework uses Group Relative Policy Optimization (GRPO) to train the LLM, which significantly reduces memory consumption while maintaining strong performance. Instead of relying on separate reward models, Rec-R1 uses rule-based reward functions derived from standard evaluation metrics like NDCG and Recall. >> Impressive Technical Results: - In product search tasks, Rec-R1 improved NDCG@100 scores by up to 21.45 points for BM25-based retrievers and 18.76 points for dense discriminative models - Showed remarkable cross-domain generalization ability on the Amazon-C4 dataset - Outperformed both prompting-based methods and SFT approaches - Preserved general-purpose capabilities of the base LLM while achieving strong task-specific performance The researchers from University of Illinois Urbana-Champaign and Amazon demonstrated Rec-R1's effectiveness across multiple recommendation scenarios, including product search and sequential recommendation. What makes this approach particularly promising is its cost-efficiency - the paper shows Rec-R1 can match or exceed SFT performance at less than 1/30 of the cost, requiring only about 210 seconds of training compared to hours for SFT pipelines. This research opens exciting possibilities for continual, reinforcement-based alignment of LLMs with evolving recommendation goals. The framework is model-agnostic and task-flexible, making it applicable across diverse recommendation paradigms. What do you think about this approach? Could reinforcement learning be the key to better recommendation systems?

My sense is that time is ripe for it a pragmatic advisory for pipeline development. Not outsourcing, not BCG or McKinsey repackaging Mary Lou Tyler, but a real operational model focusing on the inherent failings in outbound in 2025. Why now? For years, outbound sales development has been treated as a volume game—measured in activity, not outcomes. But as markets get noisier and buying behaviors shift, predictable revenue models have started to break down. I Have spent years working on this problem, observing firsthand how TOF selling lacked the adaptability and precision found in modern operational frameworks - having sat close to 700+ sales transformations in 5 years and observing why virtually all of them have failed somehow.  The breakthrough was applying agile execution—continuous iteration, real-time feedback, and structured compensation alignment—to transform outbound from a guessing game into a predictable system. It’s the most significant shift in TOF sales since Predictable Revenue, and it’s solving the pipeline problem that tech-first approaches have failed to fix. If your team isn’t consistently generating high-quality pipeline, or if outbound feels too reactive and inefficient, let’s connect. I’d be happy to walk through how we help teams bloated by tech and management burden implement a scalable, predictable system for growth. PipelineOS isn’t a tool—it’s a system for execution. We bring an agile, data-driven approach to outbound by: Embedding Agile into Sales Execution – Treating outbound as a structured, iterative process that continuously improves through rapid feedback loops. Aligning Compensation with Execution – Moving beyond static quotas to models that reward speed, efficiency, and conversion quality. Eliminating Bottlenecks at TOF – Providing clear playbooks and real-time execution insights so leadership can diagnose and correct gaps before they impact pipeline Help you maximise and engineer better use cases and system adoption of your tech investments Teach leaders how to lead in this system of excellence.

Do you swear by the sales methodology you use? It's just a guideline to supplement your approach with prospects. 🔸Challenger 🔸SPIN 🔸MEDDICC 🔸Sandler 🔸Winning by Design They are all great frameworks. But on their own, they’re not enough to meet today’s buyer expectations. Because the modern buyer has changed. — Modern buyers: 🔹would have already done their research before they talk to you. 🔹expect speed, transparency, and personalization. 🔹use various channels to make decisions. The way decisions are made post-Covid has shifted. And the landscape is still shifting as we speak. — Here’s how these methodologies fall short in today’s landscape: 📌 Challenger Selling: Great for reframing problems, but modern buyers need insights they can’t Google. 📌 SPIN: Effective at uncovering needs, but most buyers already know their pain points. 📌 MEDDIC: Brilliant for qualification but doesn’t account for the demand for speed and transparency. 📌 Sandler: Great for trust building for long sales cycles but lacks the speed and adaptability needed for digital-first buyers. 📌 Winning by Design: Great with data-driven, repeatable processes that focus on customers, but lacks flexibility for diverse buyer personas or complex cultural nuances in global markets. — Here's the solution: You don’t need to throw out frameworks. You don't need to start from scratch. You don't need to reinvent the wheel. You just need to enhance and supplement them with “The Trifecta of the Modern Seller”. --- The Trifecta isn’t a replacement - it’s a simple upgrade. It gives you the tools and the mindset shift you need to adapt your favorite methodologies to today’s buyer landscape. Three pillars set the foundation of the Trifecta: 1️⃣ Personal Branding Build trust before the first conversation. Share insights that make you the go-to expert in your industry. 2️⃣ Sales Strategy Apply the best parts of your methodology - like SPIN or Challenger - but adjust them for speed, personalization, and cultural intelligence. 3️⃣ Prospect Experience (PX) Make every touchpoint seamless and valuable, showing your buyer you’re invested in their success. The Trifecta makes you flexible, adaptable, and future-proof. --- What sales methodology are you using right now? And how are you enhancing it to meet today’s buyer demands? I’d love to hear your approach in the comments below 👇🏻 ==================== ♻️ Repost to inform your network 📬 DM me if you are stuck in B2B sales 🧠 Follow Sufi Rafa'ee for mindset shifts

✨Just wrapped up a coaching engagement that I can't stop smiling about 😄 When this sales rep first reached out to me, she was on a performance improvement plan 📉 and genuinely questioning whether she was cut out for sales. A veteran who'd been a top performer at previous companies, she was now missing quota for consecutive quarters at her new organization. "I'm doing everything the same way I always have," she told me during our first session. "But it's just not working here." That phrase – "the same way I always have" – was our first clue. After analyzing her approach, the pattern became clear. Her strengths had always been relationship-building and thorough discovery. Her previous companies sold complex solutions with long sales cycles where these skills shone 🌟. But her new company had a transactional offering with a shorter cycle, and her approach was creating friction rather than momentum. Instead of completely overhauling her style (which never works long-term), we identified specific micro-adjustments that would preserve her natural strengths while adapting to the new environment. 𝗪𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝘄𝗵𝗮𝘁 𝗜 𝗰𝗮𝗹𝗹 "𝗣𝗮𝗰𝗲 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴" 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀  – ways to maintain her thorough approach but calibrate it to her prospect's buying velocity. For instance, instead of a comprehensive discovery, we designed a "Quick Discovery" framework focused on just three critical questions  with optional deep-dive paths depending on the prospect's engagement signals 𝗪𝗲 𝗮𝗹𝘀𝗼 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗱 𝗮 "𝗩𝗮𝗹𝘂𝗲 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻" 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆  – ways to articulate complex value propositions in simpler, quicker formats without losing impact. This preserved her consultative approach while respecting the faster decision timelines. 𝙏𝙝𝙚 𝙢𝙤𝙨𝙩 𝙥𝙤𝙬𝙚𝙧𝙛𝙪𝙡 𝙘𝙝𝙖𝙣𝙜𝙚 𝙘𝙖𝙢𝙚 𝙬𝙝𝙚𝙣 𝙬𝙚 𝙢𝙖𝙥𝙥𝙚𝙙 𝙝𝙚𝙧 𝙣𝙖𝙩𝙪𝙧𝙖𝙡 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙩𝙮 𝙨𝙩𝙧𝙚𝙣𝙜𝙩𝙝𝙨 𝙩𝙤 𝙨𝙥𝙚𝙘𝙞𝙛𝙞𝙘 𝙢𝙤𝙢𝙚𝙣𝙩𝙨 𝙞𝙣 𝙝𝙚𝙧 𝙣𝙚𝙬 𝙘𝙤𝙢𝙥𝙖𝙣𝙮’𝙨 𝙨𝙖𝙡𝙚𝙨 𝙥𝙧𝙤𝙘𝙚𝙨𝙨 —𝙬𝙝𝙚𝙧𝙚 𝙩𝙝𝙚𝙮 𝙘𝙤𝙪𝙡𝙙 𝙗𝙚𝙘𝙤𝙢𝙚 𝙨𝙪𝙥𝙚𝙧𝙥𝙤𝙬𝙚𝙧𝙨 𝙧𝙖𝙩𝙝𝙚𝙧 𝙩𝙝𝙖𝙣 𝙤𝙗𝙨𝙩𝙖𝙘𝙡𝙚𝙨. She quickly turned things around, exceeding her targets and regaining her confidence. The lesson that keeps proving itself true: Sustainable sales success rarely comes from completely changing who you are. It comes from strategically adapting your natural style to the specific environment you're selling in. Have you ever found yourself in a new role or company where your tried-and-true approaches suddenly stopped working? How did you adapt? 🤔 #SalesCoaching #PerformanceImprovement #SalesSuccess

I used to feel so uncomfortable giving feedback to my peers and direct reports. Whilst I knew it would help them improve, it always seemed so difficult offering constructive criticism 😩 After chatting with the incredibly insightful Anne Lytle at Melbourne Business School, I left with a framework that now helps me confidently give feedback in a non-judgemental way whilst genuinely helping the other person positively change their behaviour 💙 It starts with Framing the Situation, so for example: “Ron, I would like for you to embody the values of our culture and be respectful of colleagues.” Next Stating the Facts, so for example: “Jenny said you looked at her as if her opinion didn’t matter and that only your solution is the right one.” Then Share the Outcomes of their behaviour, for example: “As a result of this, your colleagues feel offended and it affects our ability to provide our customers with quality service.” After that Highlight the Consequences, how it makes you feel, for example: “I feel disappointed and upset that our morale has been hurt in such a way.” Share your Recommendations, so for example: “I would like for you to work respectfully and collaboratively with other team members in order to support our culture and clients” Lastly, I’ve added: Ask them a question to get their input and feedback, so for example: “How does that sit with you?” or “What are your thoughts?” This framework alone has helped me turn a very uncomfortable conversation into a meaningful and transformative one. Thank you Anne! 🙏 How do you give feedback to others at work? 🤔 #corporate #feedback #workplace #managingpeople #career #professional #constructivecriticism

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.