Utilizing Software Features

You’re not a Salesforce org caretaker. You’re a software product owner. Act like one “Our Salesforce is a total mess” “Why?” “Things don’t really work well together” “How did that happen?” “Well… after a few years of just ‘doin stuff’ that everyone wanted, well here we are” This can happen to anyone because it sneaks up on you. You take care of day-to-day You build things You learn You build more things A few years later… * 2 apps that do the same thing—almost *Stuff not used but can’t get rid of *All those Sys Admin users *Users seeing records they shouldn’t *Apex code for what OOB can do *1000 reports and dashboards *100 record types—on one object That creaking sound? It’s your Salesforce structure bending under its own weight Avoid this by thinking like a commercial product software manager: Learn business outcomes needed (Product Value Proposition) Talk to users about wants, needs (Product Market Validation and Fit) Develop a Salesforce future vision (Product Vision) Create a feature plan (Product Roadmap) Establish solution standards (Product framework) Think scale,support,upgrades (Product Lifecycle) These are the things that product managers of commercial software think about. Why? Because if they don’t, the product doesn’t hit the mark. Then it doesn’t make money. Then it dies. Most of us don’t have to “make money” with our Salesforce org. But making it streamlined, extensible, upgradeable, and supportable is actually achieving the same thing: it drives your businesses’ productivity higher, which helps the bottom line So start acting like an owner today—a software product owner Start here: create a simple desired product feature roadmap for the next 12 months by quarter. I can show you how in 30 min Why do this? Because that old saying is true: “If you don’t know where you’re going, any road will do”

I opened Canva the other day and something caught my eye - 👀 A vibrant banner right on the home screen announcing "Droptober is coming." With a countdown, hyping up new features that are set to launch in a few days. It's a simple yet effective reminder for users that new and exciting tools are just around the corner that not only sparks curiosity but also creates anticipation. 👉🏻 𝗜𝘁'𝘀 𝗮 𝗯𝗿𝗶𝗹𝗹𝗶𝗮𝗻𝘁 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝘁𝗼 𝗯𝗼𝗼𝘀𝘁 𝗻𝗲𝘄 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝗰𝗮𝘂𝘀𝗲: ✅ Instead of relying on emails or external announcements that might get lost, a banner on the app's home screen ensures the message reaches active users. It’s an in-app reminder that stays top of mind. ✅ Adding a countdown creates a sense of urgency. It makes users feel like they’re part of something special, something they don’t want to miss out on. ✅ Visual elements like banners can capture attention faster than text-heavy announcements. 🔵 𝗪𝗵𝗮𝘁 𝗼𝘁𝗵𝗲𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗰𝗮𝗻 𝘄𝗲 𝘂𝘁𝗶𝗹𝗶𝘇𝗲 𝗳𝗼𝗿 𝗱𝗿𝗶𝘃𝗶𝗻𝗴 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻: 💡 𝗜𝗻-𝗮𝗽𝗽 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁𝘀: Like Canva, using banners or pop-ups within the product helps to keep users informed. It’s a great way to announce a new feature, offer tutorials, or even give a sneak peek. 💡 𝗚𝗮𝗺𝗶𝗳𝘆 𝘁𝗵𝗲 𝗹𝗮𝘂𝗻𝗰𝗵: Products like Duolingo have mastered gamification. What if you could create a mini-challenge for users to try out the new feature? Reward them with badges or exclusive access. 💡 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗿𝗼𝗹𝗹𝗼𝘂𝘁𝘀 𝘄𝗶𝘁𝗵 𝘂𝘀𝗲𝗿 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀: Netflix often tests new features with a small percentage of users before a full rollout. This helps gather feedback, refine the experience, and build buzz through word-of-mouth. 💡 𝗢𝗻𝗯𝗼𝗮𝗿𝗱𝗶𝗻𝗴 𝘄𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵𝘀: When Slack releases a new feature, they often integrate it directly into the product’s onboarding flow, guiding users step-by-step. It’s not just about telling users what's new, but how to use it. 👉🏻 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿 𝗣𝗠𝘀 - Invest in strategies that bring the message to where your users are most engaged i.e. within your product itself. Keep it simple, visually appealing, and engaging. And remember, the more excitement you build around a new feature, the higher the chances of driving adoption. So, next time you’re planning a launch, think about how you can create that “I can’t wait to try this!” moment. PS. What other strategies do you use as a PM for new feature launches? Do share in the comments!

The most overlooked startup growth strategy isn't the latest AI ads platform or improved funnel optimization. It's actually hiding in plain sight: how your product naturally spreads from one user to another. Teams that understand their product's inherent distribution mechanics outperform those relying solely on paid acquisition. This is less about forcing virality, and more about recognizing your product's natural sharing dynamics: - For communication tools, it's inviting collaborators - For design software, it's exporting and presenting work - For consumer apps, it's sharing results or achievements - For B2B platforms, it's onboarding team members At Gamma, we discovered our growth accelerator was reducing friction in how users share their presentations. And while that lever was specific to our product, the principle still applies universally: Identify where your product naturally creates opportunities for exposure, then systematically optimize that pathway. To this end, there are two questions worth asking: 1. When users get value from your product, how do others naturally see that value? 2. What's preventing that moment of visibility from happening more often? Every product category has different answers, but the approach is consistent: - Map out your product's natural exposure points - Measure how often those moments occur - Remove friction from that process - Build features that amplify visibility This thinking transformed our product roadmap. Features aren't just about utility; they're about enabling natural discovery. Your growth strategy might look completely different from ours, but the mindset remains the same: The best acquisition strategy is built into how your product is naturally experienced and shared.

Are you considering adding last-minute features to your product before production? Think twice! 🤔 Here's why: - Stability Risk: Late additions have not undergone the same rigorous testing as the rest of your application, increasing the chance of bugs and errors that can destabilize your product. - Quality Assurance Impact: Last-minute features can undermine the meticulous work of your QA team. With insufficient time to test the new functionalities, you risk deploying poorly tested features or delaying the release. - Developer Pressure: Already under immense pressure, your application team will face an increased workload and stress levels, potentially leading to oversights. - User & Stakeholder Impact: Unanticipated features can complicate the user experience and affect user satisfaction if not communicated effectively. - Support & Documentation Strain: Rapid updates to documentation and swift training for customer support teams often result in incomplete or inaccurate information, impacting the quality of support. Before you decide to introduce a last-minute feature, consider these risks. Is it worth disrupting the stability, quality, and experience of your product? What are your experiences with last-minute feature additions? Would you do it again? #SoftwareTesting #QualityAssurance #UserExperience #TestMetry

I’ve been working as a contractual Program/Project Manager on complex projects for the past 7 years, most of which followed Agile methodologies. While the Software Development Life Cycle (SDLC) is designed to reduce risk, poor implementation can have the opposite effect. If not executed properly, it significantly increases the risk of project failure. Here’s a quick ranking of critical failure points that commonly derail software projects: 🔴 1. Unclear or Changing Requirements Poorly defined needs or constant scope changes break alignment early and often. ✅ Fix: Involve stakeholders early, use user stories and clarify DoD (definition of done), and validate frequently; another advice: make sure to define change request in the initial contract with the client. 🔴 2. Inadequate Planning & Estimation Unrealistic timelines or budgets create pressure that leads to shortcuts and burnout. ✅ Fix: Buffer for unknowns, involve tech leads in estimation. 🟠 3. Ineffective Communication Team silos and misalignment cause costly rework and delays. ✅ Fix: Daily stand-ups, shared documentation, clear ownership. The tech team needs to understand the functional requirement to be able to implement it technically. 🟠 4. Weak Design & Architecture Hasty or shortsighted technical decisions lead to rework and scalability issues. ✅ Fix: Involving a software architect who could support drafting the best scalable architecture choices within the available projects needs, constraints and budget 🟠 5. Insufficient Testing & QA Testing cut short = bugs in production, bad UX, security holes. ✅ Fix: Invest in a QA strategy to identify tests to be run by type of release, and automate critical time-consuming tests 🟡 6. Lack of Stakeholder Involvement Software built in isolation rarely meets business goals. ✅ Fix: Demo regularly (ideally after each milestone), build feedback into the cycle. 🟡 7. Poor Change & Config Management Inconsistent environments and chaotic updates derail progress. ✅ Fix: Version control, CI/CD, and clear change protocols. 🟡 8. Inadequate Risk Management Unexpected issues become blockers when risks aren't flagged early. ✅ Fix: Ongoing risk logs, contingency planning. 🟢 9. Neglecting Post-Launch Support No plan for support = user churn and poor adoption. ✅ Fix: Monitor performance, address issues fast. 🟢 10. Lack of DevOps & Automation Manual processes delay releases and increase error rates. ✅ Fix: Embrace CI/CD and infrastructure-as-code. Strong software isn’t just about great code—it’s about clarity, communication, and continuous feedback. A strong Project Manager implements the right processes and follows each step methodically to spot weak links early and address them proactively. And when issues do arise (as they often do), they stay calm, communicate transparently, and ensure all stakeholders remain aligned throughout the journey. #SoftwareDevelopment #SDLC #TechLeadership #ProjectManagement #Agile #DevOps #ProductDelivery

Do you know how nearly impossible it is to break into India's payment app space? Let me give you a hint... two players control over half the market... and new entrants face astronomical customer acquisition costs. Still think you could succeed there? Most investors would laugh at the idea. When you look at how CRED managed to crack the top 5 payment platforms in India, you'll find three remarkable strategies: 1/ They dominated a specific niche - instead of competing for everyone, they laser-focused on users with high credit scores. While giants battled for mass market, they built deep loyalty with a premium segment that transacts more frequently and in higher values. 2/ Because payment apps face fierce competition, what set this app apart was exceptional UX design. They obsessively refined every screen, every interaction, creating India's most engaging payment experience while competitors settled for "good enough." 3/ And third is not some fancy technology - it's their gamification system. They created addictive reward mechanics that kept users coming back even when they reduced actual payouts. They understood that the feeling of winning is often more powerful than the prize itself. And guess what, this is exactly what defines winning in saturated markets: -Target a specific, underserved segment ruthlessly -Deliver an experience that's noticeably better, not just marginally so -Create habit-forming loops that make switching costs feel personally expensive Which Indian app do you think has cracked the perfect balance between utility and engagement? #cred #FinTech #UserExperience #GrowthHacking #MarketDisruption

Groundbreaking Research Alert: Making LLMs More Efficient with Smart Retrieval A fascinating paper from NAVER LABS Europe introduces a novel approach to optimize Large Language Models' retrieval mechanisms. The research shows how we can reduce retrieval operations by over 50% while maintaining or even improving performance. Key Technical Insights: - The system uses an "I Know" (IK) classifier that achieves 80% accuracy in determining when an LLM needs external knowledge - Only 32 tokens from the initial response are needed to make this determination - Training requires just 20,000 samples to achieve optimal performance - The approach works across multiple model families including Mistral, Llama, Gemma, and SOLAR Under the hood: - The system employs an LLM-as-judge architecture for training data generation - It uses adapters for fine-tuning larger models (7B+) - The IK score is computed using softmax on Yes/No token logits - Processing time is remarkably efficient: 3.7ms for IK classification, 8.3ms for generating 32 tokens Real-world Impact: - Reduces RAG processing time by up to 80% - Improves efficiency across various datasets including NQ, ASQA, HotpotQA - Particularly effective for general knowledge datasets like TriviaQA and SCIQ This research represents a significant step forward in making LLMs more efficient and practical for real-world applications. The ability to selectively activate retrieval mechanisms could be a game-changer for deployment at scale.

Common launch mistake: Rolling out new features to ALL customers. Pushing out a new feature to a sizable customer base comes with risks: - Higher support volume if things go south, affecting many. - Lost opportunity to refine the product with a focus group. - Difficulty in rolling back changes in certain cases. That's why products, especially those with huge customer counts, adopt a gradual rollout strategy to mitigate risk. There are multiple options here like: ✔️ Targeted roll-out Selective release to specific users or accounts. ✔️ Future-cohort facing Only new sign-ups get the feature, existing users keep legacy version ✔️ Canary release Test with a small group first, then expand after confirming it's safe. ✔️ Opt-in beta Users voluntarily choose to try new features before official release. ✔️A/B rollout Two different versions released to different groups to compare performance. ✔️Switcher Everyone gets new version by default but can temporarily switch back to old version. ✔️Geo-fenced Features released to specific geographic regions one at a time. Some factors to consider: ✅ User base capabilties How savvy is your user base? How adaptive would they be the change you're rolling out? If you need to ease them over time, think about a switcher or an opt-in beta. ✅ Complexity How complex is the product update and is it in the way of a critical path? If it's a minor update, a universal deployment will suffice. However, you might opt for an opt-in or canary release for more complex changes. ✅ Risk Assessment What's the risk profile of the update? Ex: If it's performance-intensive and could affect server load, consider using a phased release to observe patterns as you open the update upto more users. ✅ Objective Is this a revamped version of an existing product use case? Do you want to experiment which works better? Strategies like canary releases or A/B testing are valuable in this scenario. ✅ Target users Do you have different user behaviors or preferences across markets or geographies of operation? Do certain cohorts make more sense than others? Think about geo-fenced roll-outs (we used to use this a lot at Bayt when launching job seeker features). --- What rollout strategies do you use for your product?

I just analyzed HeyGen (one of the leading AI avatar apps) product experience through a behavioral lens. There are two found powerful UX lessons that apply to nearly any digital product and one big miss. ✅ Unpacking abstract value: Instead of vague promises about being an "AI platform," HeyGen shows specific use cases: create avatars, generate videos, make UGC ads, and translate content. Research shows unpacking complex ideas into concrete examples helps users understand your value proposition and envision specific outcomes. ✅ Templates reduce friction: Their 53+ ready-to-use templates make creation logistically easier while providing psychological scaffolding—showing what's possible and reducing decision paralysis. ❌ The vanity barrier: To create your avatar, you need to record yourself. But what if you're not camera-ready when signing up? I wasn’t :) This mirrors the challenge Airbnb faced when asking hosts to photograph their homes (solved by sending professional photographers). It mirrors what Google found when asking small businesses to share pictures of their store on Google Maps (solved by lots of nagging). Users will procrastinate rather than create something they're not proud of. The behavioral insight: Your users want to look good. This is a barrier. It’s your job to make them shine. Self-image concerns do impact user adoption of features. What other products have you seen that thoughtfully address psychological barriers to engagement? If you're building AI features and struggling with adoption, this teardown reveals principles you can apply immediately—whether your users are having a good hair day or not. Link to the full teardown in comments 👇 #AI #ProductDesign #BehavioralScience

We've all encountered it, or perhaps even inherited it: the Salesforce org that's become an unmanageable "Big Ball of Mud." It's easy to blame sloppy coding, but often the root cause is deeper—a gradual decay of architectural integrity over time. Think of it as the second law of thermodynamics applied to software. Every change, every quick fix, every workaround adds a tiny bit of disorder. Without active counter-measures, this disorder, or entropy, accumulates. The solution isn't just to "clean up the code" once in a while. It's about embedding continuous architectural attention into your development process. This means: • 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗿𝗲𝘃𝗶𝗲𝘄𝘀: Not just code reviews, but assessments that specifically evaluate the overall system structure and dependencies. • 𝗥𝗲𝗳𝗮𝗰𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗵𝗮𝗯𝗶𝘁: Make refactoring a continuous activity, not a "big bang" project that happens once a year (or never). • 𝗦𝘁𝗿𝗼𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Establish clear architectural guidelines and enforce them consistently. • 𝗙𝗶𝗴𝗵𝘁𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗰𝗿𝗲𝗲𝗽: Every new feature request should be evaluated not just for its business value, but also for its impact on overall system complexity. Architectural entropy is inevitable, but it's manageable. The key is to be proactive, not reactive. Want to learn more about preventing architectural decay and other common Salesforce pitfalls? Check out "Salesforce Anti-Patterns" for practical strategies and real-world examples.