Online Analytics for Product Launches

We didn’t fail because the product sucked. We failed because we were looking at the wrong numbers. One of our best-looking product launches quietly started leaking cash within 3 months. Sales were good. Reviews were solid. Site traffic was up. But under the surface? Margins shrinking Return rates rising Repeat purchases… flat Turns out we were too busy watching vanity metrics the ones that make you feel good in a pitch deck and ignoring the ones that actually shape the health of the business. So we rebuilt our dashboard. And I now swear by these 4 KPIs 👇 1. Product-Specific NPS Not general CSAT. Not site feedback. We track NPS per product, every 90 days. If it dips, we investigate. FAST. 2. Warranty Claims per 1,000 Units It’s the quietest indicator of product quality. We aim for <5%. Above that, your cost of support and margin pain kicks in. 3. 60-Day Repurchase Rate 20–40% is solid in most DTC categories. We’ve seen how this drives word-of-mouth, not just retention. If people love it, they’ll buy again (or send friends). 4. Checkout Completion % by Device This helped us uncover a massive drop-off on mobile. Fixing that UX bump raised conversions by 14% in a week. These aren’t always the sexiest metrics. But they tell the truth. And when you're scaling, the truth is more useful than dopamine. What 3–4 KPIs do you actually look at every week? ♻️Repost if you think more founders should obsess over the right metrics, not just the pretty ones.

We just wrapped our second annual study of the most successful Amazon product launches. We tracked 30,000 products launched in January and tracked them through April to determine the most impactful drivers of success. Some of this year’s findings surprised us compared to 2024, while others might challenge assumptions about what drives early success. The biggest shift from 2024? Star ratings lost their predictive power for launch success. When we look at star ratings alone, both top performers and average launches achieved 4.5+ star ratings at nearly identical rates after three months. Here’s what top performers figured out that others did not: 🌟 Beyond star rating, review velocity is everything. Winning products averaged 262% more written reviews than their category mean. Meanwhile, typical (not best in class) launches actually trailed by 2%. And this creates a pretty clear cycle… When you drive initial volume through advertising, you generate more reviews, which then drives organic visibility and more sales. 📈 Search visibility compounds quickly. Winners generated 605% higher organic search volume and 356% more paid appearances than their peers within 90 days. You need to track not just where you show up, but where your competitors appear and you don't. That's where to focus your ad spend. 💵 Premium pricing works. This surprised us. Top launches were priced at nearly 2X their category average. Amazon shoppers will pay for quality, even for unknown products. The spotlight is squarely on your ability to communicate value effectively through content, messaging, and positioning. There is also a lesson that instead of competing on baseline price, you can use limited-time discounts to drive early sales. There you have it! For brands asking "How long before we know if this launch is working?", you should expect signals within 60-90 days through review accumulation and search visibility growth. And for teams planning Q2 launches, set your sights on driving review velocity above category benchmarks in those first 90 days. Amazon Vine is one lever for unlocking review velocity. What else is working for you to get those reviews flowing early and often?

Why do some products sell out instantly? It’s not luck. It’s data. After 5 years of scaling DTC products, Here are 8 data-backed moves I wish I used sooner: 1. Dig into top search terms These are real-time signals of what people want, use them to inform product names, bundles, or new SKUs. 2. Study repeat customer products Which products do your loyal buyers come back for? That’s your sticky gold, build variations or upgrades of them. 3. Analyze abandoned carts Don’t just recover them. Reverse-engineer why customers didn’t convert: price, shipping, sizing? Fix that. 4. Monitor “Notify Me” requests If people ask for restocks, that’s demand data. Build urgency-based restock campaigns around it. 5. Use quiz & review feedback Ask why they chose the product. Patterns reveal what people value most, bake those into your offers. 6. Segment by LTV High LTV segments = your most profitable personas. Build products tailored only to them. 7. Watch email click heatmaps Which content or product sections attract clicks? That’s what excites them, focus your roadmap there. 8. Analyze what customers gift Products bought as gifts hint at broader appeal. These are ready-made virality vectors. The most underrated on this list? 👉 What customers gift. Because gifting is free brand exposure and a trust signal. What’s working now: Brands using customer data for launch sequencing are seeing sell-outs in <48 hours. Not luck. Data. Save this if you’re: – A DTC founder launching new products – A retention marketer optimizing LTV – A growth strategist building demand loops Which one of these surprised you most?

𝗖𝗿𝘂𝗰𝗶𝗮𝗹 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗟𝗮𝘂𝗻𝗰𝗵 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆 𝗣𝗠 Launching a product is thrilling, but measuring success is key. Here's a precise guide created by PM School on the 10 crucial metrics every Product Manager should monitor: 𝗣𝗿𝗲-𝗟𝗮𝘂𝗻𝗰𝗵 𝗕𝘂𝘇𝘇: - Website Traffic: Monitor traffic growth on your landing page or product sign-up during the prelaunch phase. - Social Media Engagement: Track metrics like shares, likes, comments, and click-through rates on your pre-launch social media content. - Waitlist Signups: Analyze the growth rate of your waitlist to gauge pre-launch interest and potential user demand. 𝗨𝘀𝗲𝗿 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: - Cost per Acquisition (CPA): Track CPA for different marketing channels to identify the most cost-effective ones for acquiring new users. - Customer Acquisition Rate (CAR): Monitor CAR at various launch stages to understand how quickly you're acquiring new users. 𝗨𝘀𝗲𝗿 𝗔𝗰𝘁𝗶𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁: - Activation Rate: Measure the percentage of users who complete the onboarding process and begin using core features. - Daily/Weekly Active Users (DAU/WAU): Track DAU/WAU to understand user engagement. - Feature Adoption Rate: Analyze which features users are exploring and using most. 𝗨𝘀𝗲𝗿 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗮𝗻𝗱 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁: - App Store Ratings and Reviews: Monitor user reviews to understand initial impressions and identify pain points. - Net Promoter Score (NPS): Measure NPS to gauge user loyalty. - Customer Support Inquiries: Analyze recurring themes in inquiries to identify usability issues or unmet user needs. 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻: - Churn Rate: Track the percentage of users abandoning your product after a specific timeframe. - Retention Rate: Monitor the percentage of users who stick with your product over time. 𝗨𝘀𝗲𝗿 𝗟𝗶𝗳𝗲𝘁𝗶𝗺𝗲 𝗩𝗮𝗹𝘂𝗲 (𝗟𝗧𝗩): - Customer Lifetime Value (CLTV): Estimate the average revenue a single customer generates throughout their relationship with your product. - Customer Acquisition Cost (CAC Payback Period): Divide CLTV by CAC to understand how long it takes to recoup the cost of acquiring a customer. By tracking these metrics, you can gain valuable insights and make data-driven decisions throughout the launch process to ensure a successful product launch. ✍ I regularly share free product resources, tips, and tricks. If it is relevant to you or your network, feel free to copy, share, and follow Rahul Rawat #productmanagement #productmanagers #productmetrics

2 common mistakes product marketing teams are making with launches: 1) Selecting the same metrics for every launch they carry out. 2) Failing to set a different metrics for each GTM motion used within a launch. Let's break it down. Let's say Hubspot is launching Service Hub, a customer support tool, to expand on their CRM offering. Segment: Hubspot will likely position Service Hub for new prospects to act as a wedge for their ecosystem. Parameters: → B2B sales-led motion → Goal: acquisition (Yes, they have a PLG play as well but let's keep that aside for now) In this scenario, measuring aspects like: - discovery (landing page visits) and - interest via sales inquiries (MQLs) work better to assess launch efficacy, than how many end up "using" the feature. Segment 2: Service Hub can also be a upsell hook for existing customers: → B2B sales-led motion → Target: existing Sales Hub users → Goal: upsell In this scenario, measuring visits to their in-product landing page and CTAs allowing them to hand-raise to their account managers work better. But, hang on, they also offer free trials. In that case, More classic adoption metrics like tracking onboarding completion and aha moments make sense. There are several other variations to take into account. Ex: Some features like Slack Lists are meant more for retention than acquisition. Usage by existing customers work better. In summary, Launch metrics should be "crafted" based on a number of factors like: - launch goals/scope - GTM motion - target segment Then, select the right leading indicator from the feature adoption funnel. -- Do you vary your metrics from launch to launch?