Time-to-Value Analysis

⏱️ Product Time‑to‑Value (PTV) is the new North Star. If your product delivers value slower than users can brew a coffee, they won't stick around. Let's put some physics around this idea so every builder can see the bottlenecks and crush them. Every time I speak to someone about product, the conversation almost always lands on money or time. 🧾 How to show better ROI 💸 How to improve margins ⏳ How to save time for the user 🧠 How to buy time for the team But the more I sat with it, the more I felt that time is the real currency. Not just time saved — but time to value. That's where I started seeing this pattern. A quiet but fundamental shift across all breakout AI products. They weren't just doing things faster. They were compressing the entire journey from intent → value. And that's when the idea clicked: Product Time-to-Value (PTV) is the real lever. 🔬 Here's the (tiny) math: PTV = α × T_setup  + β × T_interact   + γ × T_system  + δ × T_cognitive + ε × T_feedback Each one is a friction: T_setup: how much config before I can even try? T_interact: how many clicks/fields to express intent? T_system: how long does the machine take to respond? T_cognitive: how much do I need to "get" before I use it? T_feedback: how fast can I see and refine the result? See example in the image. The best AI-native tools are not just fast — they annihilate one or more of these terms entirely. So if you're building in 2025 and beyond, don't ask "what features should we build?" Ask: Where is the PTV highest in our flow — and can we crush it? Can value be felt before the user deserves it? Because we're no longer in the era of complex workflows and heavy dashboards. We're in the age of compressed value. Of instant outcomes. Of invisible interfaces. And if you track PTV the same way you track revenue or engagement, you might just build something people fall in love with — not over months, but in seconds. Did I just cracked the code on why Anysphere, Perplexity, Gamma, Lovable, and Replit feel magical while most tools feel like work? At Plane, we're obsessing over this metric. How does your PTV look like?

It's easy for PMs to mismeasure time-to-first-value. It's not because of them. It's because of how users typically behave. Just so we're aligned, time-to-first-value (TTFV) is defined as the time customers take to get initial value from a product. TTFV is used to measure how well the first mile of the user journey is performing, particularly onboarding experiences. A low TTFV often closely correlates with retention too. But why is it tricky to set? Because there's a tendency to set the completion of a main user flow as the "aha moment" to measure. But that may well be the pre-requisite to value as opposed to the value itself. Let's look at an example. Here's Loom's co-founder, Shahed Khan: (("The team thought that signing up to Loom and recording a video would be the “aha” moment to the product’s value. Data and feedback showed that it wasn’t. In actuality, people’s first videos were just a test to play around with what the product could do. From there, they began measuring adoption metrics starting from a user’s second video. Consequently, they learned that it wasn’t until someone received their first view on their Loom video that users would see the value in their product and continue using it.)) This happened to me on an applicant tracking system I was leading as well. It was thought that a recruiter experienced their first value when they published their first job. But similar to Loom's case, most recruiters were just trying out the job posting experience the first time around. It wasn't until a job seeker application from an external domain was submitted that true value was registered. How are you setting your first time-to-value for your product?

Why Time to First Value Is the Strongest Predictor of Retention Clients don’t stay because of potential. They stay because of immediate value. ⸻ Time to First Value (TTFV) is the gap between: First use And meaningful result ⸻ Research across product and SaaS industries shows: Reducing TTFV can increase retention by 30–50% Users who experience value quickly are 2x more likely to continue using a product Delayed value is one of the top drivers of early churn ⸻ In sexual wellness, this is even more critical. Because clients are already evaluating: Did this work Did I choose correctly Is this worth continuing ⸻ If value is delayed, doubt increases. If value is immediate, confidence builds. ⸻ High-performing products reduce TTFV through: Intuitive design Clear onboarding Immediate usability Aligned expectations ⸻ There is also a psychological effect. Fast value creates: Momentum Positive reinforcement Emotional validation ⸻ Another key factor is consistency. When early value is delivered, clients: Use the product more frequently Develop habits faster Trust the system more ⸻ At V For Vibes, speed to value is a priority. Because retention is not built over time. It starts with how quickly value is delivered. And in this category, the faster clients feel the benefit, the stronger the relationship becomes. #SexTech #UserExperience #CustomerRetention #Ecommerce #ProductStrategy

Nearly 80% of free trial users never convert to paying customers. The culprit? SaaS products often take too long to deliver value. Time-to-value is the hidden killer of SaaS products. If users don't quickly experience how software improves their life, they'll delete the app or abandon their account. Accelerating time-to-value should be a top priority for SaaS companies. Reducing the time to value can boost customer satisfaction by 10-30%, directly impacting retention. Strategies like optimizing onboarding, personalizing user experiences, and implementing quick wins can dramatically improve time-to-value. The key is identifying your product's "Aha!" moments... those eye-opening experiences that turn casual users into lifelong customers. Here's how: ↳ Personalization is crucial Different user segments will find value in different aspects of your product. Tailor the experience to get each group to their "Aha!" moment faster. ↳ Don't overwhelm new users with every feature Focus on their specific objectives and guide them step-by-step to experiencing core value. ↳ Quick wins build momentum Help users complete small, meaningful actions to boost their confidence and engagement with your tool. ↳ Continuously measure and iterate on your onboarding process There's always room for improvement in accelerating time-to-value. Want to learn more about optimizing your SaaS product's time-to-value? Read the full article for in-depth strategies and insights. Link in comment 👇

Fast features are useless if users don’t feel value. AI is shrinking production timelines. That means teams can now build faster than ever, but here’s the catch: most don’t know if they’re building the right thing. Feedback loops are slowing, and validation is getting harder, not easier, with AI outputs. Over the last week, I’ve swapped 10 emails with product and design leaders, all pointing to the same problem. So what really matters? Two strong signals decide if your product survives in the AI era: 1. Time to Create Value (TTCV): internal metric. How fast your team can design, build, and ship. It’s efficiency, coordination, and resource use. 2. Time to Value (TTV): customer metric. How quickly a user feels meaningful benefits. It’s clarity, onboarding, and usability. →  If TTCV is short but TTV is long, you ship fast but frustrate customers. (Think of that feature launch that tanks because onboarding was a mess.) → If TTV is short but TTCV is long, customers love the ideas, but you’ll lag behind competitors and miss opportunities to refine the concepts. When you optimize both, you accelerate innovation, adoption, and growth. Finding the right signals will make this happen. That’s how SaaS teams win. We built our open-source framework, Helio Glare, to help product and design leaders shorten these loops, surface the right UX metrics, and ship decisions with confidence. It’s already used by teams managing millions of users. If you’re a founder or product leader struggling with stalled feedback loops, I’ll show you how to align TTCV + TTV with signals so your launches create value fast. 👉 Want your next release to delight customers and cut release friction? DM me for a free assessment if you’re a product or design leader focused on building better products.

By now, the "95% failure rate" of GenAI financial returns (ref MIT's Project NANDA) is part of all consulting decks. The report blames the incorrect approach as the primary reason, rather than model maturity, etc. The key is to understand what #ROI metrics are used to determine the financial returns. I asked #Copilot on this, and here's what it told me: --- Here are three examples of ROI frameworks that enterprises are using to evaluate and scale GenAI adoption effectively: 1. Business Outcome-Based ROI Framework (Gartner) Summary: Gartner recommends aligning GenAI initiatives with measurable business outcomes such as cost reduction, revenue growth, or productivity gains. For example, a retail company using GenAI for automated product descriptions tracked a 22% increase in conversion rates and a 15% reduction in content creation costs. The framework emphasizes setting baseline metrics before deployment and tracking improvements post-implementation. 🔗 https://lnkd.in/dER7cTeF 2. Time-to-Value and Efficiency Metrics (BCG) Summary: Boston Consulting Group suggests using time-to-value (TTV) and operational efficiency as key ROI indicators. In one case, a logistics firm used GenAI to optimize routing, reducing delivery times by 18% and fuel costs by 12%. BCG’s framework includes pre/post comparisons, automation impact, and employee productivity metrics to quantify GenAI’s contribution. 🔗 https://lnkd.in/da2zcSfW 3. Model Performance vs. Business KPIs (McKinsey) Summary: McKinsey advocates for linking GenAI model performance directly to business KPIs. For instance, a financial services firm used GenAI for customer support automation and tracked resolution time, customer satisfaction scores, and call deflection rates. The framework includes continuous monitoring of model accuracy, relevance, and business impact. 🔗 https://lnkd.in/dA6zEGuS 🔑 Key Message Summary Effective GenAI ROI frameworks combine technical performance metrics with business impact indicators. Leading approaches include tracking cost savings, productivity gains, time-to-value, and alignment with strategic KPIs. Enterprises that define success upfront and monitor outcomes continuously are more likely to scale GenAI successfully. --- The direction taken seems to be well-intentioned. However, the measure of success is not quite what might lead to real solid business outcomes! Individual productivity improvements are just that! They don't scale across the organization unless "vertically scaled" top-to-down an entire process delivering bottomline improvements, which then need to be further "horizontally scaled" end-to-end across the entire value chain of the firm to deliver topline value! My forthcoming book on Cognitive Chasm provides actionable guidance to practitioners on this.