Utilizing Software Features

Ever wonder why some pricing pages convert effortlessly while others fall flat? After auditing over 200+ pricing pages, I’ve discovered there’s a science to getting it right. Here are 3 key lessons and 6 breakdowns to optimize your pricing page for clarity and conversions: — 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟭: 𝟯 𝗞𝗲𝘆 𝗟𝗲𝘀𝘀𝗼𝗻𝘀: 1. Simplify the Decision-Making Process: The best pricing pages make it easy for customers to understand their options quickly and without confusion. Guide them by recommending a plan or narrowing down their choices. Keep it simple, and they’ll pick faster. Principle: Hick's Law – The more choices people have, the longer it takes them to decide. 2. Highlight Key Features and Benefits: Don’t just list features—emphasize the benefits of each tier. Make it clear what customers gain as they move up the pricing ladder. By showcasing the tangible value of upgrades, you make it easier for users to understand why a more expensive plan is worth it. Principle: Value Proposition Design — Your brand positioning should revolve around what people want, not what you “think” they want. 3. Address Objections Early: Many customers come to the pricing page with concerns about affordability, commitment, or value. Address them directly on the page by offering guarantees, social proof, flexible payment options, or highlighting low-risk entry points. Principle: Risk Reversal — The more you mitigate the risk, the easier it is for them to make a decision. — 𝗖𝗵𝗮𝗽𝘁𝗲𝗿 𝟮 – 𝟲 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗣𝗮𝗴𝗲 𝗕𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 Let’s start with Figma: Figma’s page makes it easy to distinguish between plans using simple color schemes. The call-to-action (CTA) for each plan also stands out. Instead of a generic button, each plan has its own, like “Choose Starter” or “Contact Sales” for enterprises. Each plan progressively highlights more features which keeps things clear and shows exactly what you’re getting as you move up.The design is optimized for visibility—everything important is right above the fold on most desktop screens. You don’t have to scroll endlessly to find out the basics. Unlike most companies, Figma is upfront about the price of its enterprise plan. You still have to contact sales to buy it, but at least the cost isn’t hidden. — If you want to read the in-depth breakdowns of 5 other companies including Monday, Apple, and Fortnite, check the breakdown available in the comments below.

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”

Excited to share our production guide for building RAG-based LLM applications where we bridge the gap between OSS and closed-source LLMs. - 💻 Develop a retrieval augmented generation (RAG) LLM app from scratch. - 🚀 Scale the major workloads (load, chunk, embed, index, serve, etc.) across multiple workers. - ✅ Evaluate different configurations of our application to optimize for both per-component (ex. retrieval_score) and overall performance (quality_score). - 🔀 Implement LLM hybrid routing approach to bridge the gap b/w OSS and closed-source LLMs. - 📦 Serve the application in a highly scalable and available manner. - 💥 Share the 1st order and 2nd order impacts LLM applications have had on our products and org. 🔗 Links: - Blog post (45 min. read): https://lnkd.in/g34a9Zwp - GitHub repo: https://lnkd.in/g3zHFD5z - Interactive notebook: https://lnkd.in/g8ghFWm9 Philipp Moritz and I had a blast developing and productionizing this with the Anyscale team and we're excited to share Part II soon (more details in the blog post).

If you’re building anything with LLMs, your system architecture matters more than your prompts. Most people stop at “call the model, get the output.” But LLM-native systems need workflows, blueprints that define how multiple LLM calls interact, how routing, evaluation, memory, tools, or chaining come into play. Here’s a breakdown of 6 core LLM workflows I see in production: 🧠 LLM Augmentation Classic RAG + tools setup. The model augments its own capabilities using: → Retrieval (e.g., from vector DBs) → Tool use (e.g., calculators, APIs) → Memory (short-term or long-term context) 🔗 Prompt Chaining Workflow Sequential reasoning across steps. Each output is validated (pass/fail) → passed to the next model. Great for multi-stage tasks like reasoning, summarizing, translating, and evaluating. 🛣 LLM Routing Workflow Input routed to different models (or prompts) based on the type of task. Example: classification → Q&A → summarization all handled by different call paths. 📊 LLM Parallelization Workflow (Aggregator) Run multiple models/tasks in parallel → aggregate the outputs. Useful for ensembling or sourcing multiple perspectives. 🎼 LLM Parallelization Workflow (Synthesizer) A more orchestrated version with a control layer. Think: multi-agent systems with a conductor + synthesizer to harmonize responses. 🧪 Evaluator–Optimizer Workflow The most underrated architecture. One LLM generates. Another evaluates (pass/fail + feedback). This loop continues until quality thresholds are met. If you’re an AI engineer, don’t just build for single-shot inference. Design workflows that scale, self-correct, and adapt. 📌 Save this visual for your next project architecture review. 〰️〰️〰️ Follow me (Aishwarya Srinivasan) for more AI insight and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

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.

Have you observed lately that many agentic AI applications fail because they rely directly on raw LLM calls without a gateway to handle context routing, model orchestration, caching, rate-limiting, and fallback strategies? You must need an LLM gateway or a layer of such kind that acts as a middleware layer that sits between your application and multiple LLM providers. Hence, an LLM gateway is essential for building scalable, safe, and cost-effective agentic AI applications in the enterprise. An LLM gateway essentially functions as a central control panel to orchestrate workloads across models, agents, and MCP servers (the emerging protocol connecting AI agents to external services). Core functions and concepts of an LLM gateway include: ➤ Unified Entry Point: It provides a single, consistent interface (API) for applications to interact with multiple foundational model providers. ➤ Abstraction Layer: It hides the complexity and provider-specific quirks of working directly with individual LLM APIs. This means developers can use the same code structure regardless of which model they call. ➤ Traffic Controller: It intelligently routes requests to the most suitable LLM based on specific criteria like performance, cost, or policy. ➤ Orchestration Platform: It improves the deployment and management of LLMs in production environments by handling security, authentication, and model updates from a single platform. LLM gateways are becoming essential, particularly for enterprises building production-ready and scalable agentic AI applications, because they address multidimensional challenges related to vendor lock-in, complexity, costs, security, and reliability. Know more about LLM gateways through below resources: https://lnkd.in/gimgJ4hD https://lnkd.in/gawvkzGw https://lnkd.in/g-377ESP

As more enterprises integrate LLMs into their workflows, one question dominates: How do we scale inference efficiently and securely? This article explores how two powerful open-source tools—Apache Kafka and Apache Flink—can be used to: ✅ Queue and manage real-time LLM inference requests ✅ Enrich, orchestrate, and route requests dynamically ✅ Enable resilient, low-latency, and observable AI pipelines 🔍 We also walk through a real-world example of LLM-powered financial chatbots and how asynchronous processing enables compliance-ready, intelligent responses. 🧠 https://lnkd.in/g4iDzrpG #Kafka #Flink #LLM #AIArchitecture #StreamingAI #MachineLearning

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!

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

There’s been a lot of discussion about how Large Language Models (LLMs) power customer-facing features like chatbots. But their impact goes beyond that—LLMs can also enhance the backend of machine learning systems in significant ways. In this tech blog, Coupang’s machine learning engineers share how the team leverages LLMs to advance existing ML products. They first categorized Coupang’s ML models into three key areas: recommendation models that personalize shopping experiences and optimize recommendation surfaces, content understanding models that enhance product, customer, and merchant representation to improve shopping interactions, and forecasting models that support pricing, logistics, and delivery operations. With these existing ML models in place, the team integrates LLMs and multimodal models to develop Foundation Models, which can handle multiple tasks rather than being trained for specific use cases. These models improve customer experience in several ways. Vision-language models enhance product embeddings by jointly modeling image and text data; weak labels generated by LLMs serve as weak supervision signals to train other models. Additionally, LLMs also enable a deeper understanding of product data, including titles, descriptions, reviews, and seller information, resulting in a single LLM-powered categorizer that classifies all product categories with greater precision. The blog also dives into best practices for integrating LLMs, covering technical challenges, development patterns, and optimization strategies. For those looking to elevate ML performance with LLMs, this serves as a valuable reference. #MachineLearning #DataScience #LLM #LargeLanguageModel #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gvaUuF4G