Mobile Analytics Insights

🚀 Google Play Console just changed the game for ASO & User Acquisition. They've introduced a new metric alongside "Acquisitions": 👉 "Opens" – the number of users who actually opened your app from the store listing after install. 🔍 Why this matters more than ever: Until now, we were optimizing based solely on store listing visits → installs (Acquisitions). But let’s be honest: 📱 An install ≠ usage. An app can be installed but never opened. That’s a dead install—it adds to your numbers but does nothing for your growth or LTV. Now, with Opens, we can track the next step in the user journey, and that brings immense value for: 📈 1. App Store Optimization (ASO): More data = smarter testing Earlier: ✔️ Test new creatives, icons, videos ✔️ Measure success via install rate uplift Now: ✅ Track which creative or keyword not only drives installs, but actual opens ✅ You can now align creative optimization with downstream activation This is a big step towards connecting ASO to real app usage and value delivery. 💰 2. Paid UA + Organic Growth: Quality > Quantity Let’s say you run two campaigns: Campaign A brings 10,000 installs, 3,000 opens Campaign B brings 8,000 installs, 5,500 opens Which one’s truly performing? 👉 Campaign B. Because users are actually engaging post-install. This update allows UA managers to allocate budgets smarter, and measure the true impact of every acquisition channel. 📊 3. A new metric for retention proxy We know Day 1 Retention is the gold standard for early engagement. But until now, Play Console didn’t give any proxy metric close to that within the store listing funnel. With “Opens,” we now get a pre-retention insight. This is especially useful for apps with: - Large install volumes but low activation - App previews or store listing videos that overpromise 🤝 4. Product, Marketing, and ASO alignment This update allows ASO, Product, and Growth teams to: - Correlate store-level metrics (visits, installs, opens) - Track funnel drop-offs from impression → install → open - Align messaging, onboarding, and app promise better 🌟 This isn’t just a metrics update—this is a mindset shift. From installs to actual intent. From growth hacks to value. It pushes us toward optimizing not for vanity metrics but for impact. Let’s move beyond installs. Let’s optimize for value-driven user journeys. #ASO #GooglePlay #playstore #PlayConsole #MobileGrowth #UserAcquisition #AppMarketing #ProductAnalytics #AppStoreOptimization #Retention #AppEngagement #GrowthStrategy #growthmarkting #google

Hanish Bhatia joins Recon Analytics through the acquisition of Atom Insights Most device analysts work in silos. They forecast smartphone shipments without accounting for macroeconomics, consumer sentiment, or network performance. The result? Forecasts that miss badly. Case in point: major agencies predicted 1-2% smartphone market growth through 2028-30, then scrambled to revise to negative 10% for 2026 when the memory price crisis hit. That's not analysis. That's guessing with a spreadsheet. Here are key takeaways from this week's episode of "The Week with Roger": 1. Recon Analytics acquired Atom Insights, and the combination is unlike anything else in market. Hanish Bhatia's device shipment tracking across 18 countries at model level (covering 1.1-1.2 billion devices sold annually) now merges with Recon's consumer experience data serving 25,000+ respondents weekly. The result: end-to-end visibility from purchase to network experience to next device decision. No other firm connects these dots. 2. Device-level intelligence now goes deeper than consumers themselves know. Recon passively captures hardware IDs down to the SKU level. We can distinguish between international SKUs of the same Samsung Electronics America phone and map 22 NPS dimensions to specific device components. That means we can tell you how a device with a particular baseband chip performs differently on AT&T versus T-Mobile versus Comcast. Not all devices behave the same across networks, and now there's data to prove exactly where and why. 3. Semiconductor companies finally get a live demand signal. Chipset makers have always struggled with one question: how is my silicon actually performing in consumers' hands? By combining weekly NPS data mapped to hardware components with shipment volumes, Recon can now provide semiconductor clients with real-time consumer sentiment tied directly to their chips. Does it matter if you have a Qualcomm or a MediaTek modem? Our data says yes. Our carrier clients know. Wouldn't you want to know what they know? 4. The AI infrastructure behind this is serious. Investment this year: north of $2.5 million on the platform alone. One carrier client told us our live demonstrations surpassed every canned demo they'd seen from competitors. Several have bought into our solution. The device intelligence market has been fragmented for years: shipment trackers over here, experience analysts over there, network performance somewhere else. This acquisition connects all three layers into a single analytical framework. What would your organization do differently if you could trace the full arc from component to purchase to daily network experience? Listen to "The Week with Roger" for the full discussion. Here is the link: https://bit.ly/4lFuhmb ---

It’s been four months since we launched Auditzy™ RUM (Real User Monitoring) for websites, aimed at tracking website speed and performance. Today, I’m excited to share some key insights we've gathered from monitoring Indian E-commerce websites and user behaviour's. ▶ We've captured over 1 billion data points across various E-commerce websites to understand how performance impacts user experience. ▶ 60% of visitors access websites from mobile devices using a 4G network. ▶ A whopping 85% of mobile website visitors in India use the Chrome browser. ▶ Approximately 30% of traffic on e-commerce websites comes from in-app browsers (browsers that open when clicking ads on Instagram, Facebook, YouTube, etc.). ▶ In-app browser conversions are 1.5 times lower than native browser conversions, indicating that users are less likely to make purchases directly from in-app browsers. (It's a Big Pain, we are also solving this, announcing Soon! 💪 ) ▶ Bounce rates increase by nearly 10% when visitors use a 3G network compared to a 4G network. ▶ 60% of unique "Add to Cart" actions occur when website speed is less than 3 seconds throughout the user journey. ▶ Operating Systems: In India, 70% of mobile website visitors use Android OS, while around 30% use iOS. ▶ A 3-second improvement in load time led one of our customers to witness an 18% growth in conversions. (Detailed Case Study Coming Soon!) 🔥 ▶ 55% of Indian users have devices with less than 8 GB of RAM when visiting websites from mobile devices. ▶ About 15% of traffic on Indian e-commerce websites comes from desktop browsers. These insights highlight the critical importance of optimising website performance to enhance user experience and conversions. If you found this post insightful, feel free to reshare it! 🙌 Start measuring meaningful web performance metrics rather than chasing a perfect 100/100 on PageSpeed Insights! 😅 #Pagespeed #CoreWebVitals #AuditzyInsights

It has been 213 days, still no leads. "We’ve launched, but why aren’t we converting?" the team lead asked, frustration evident in his voice. The mobile app was live, but they had no idea what was happening on the user side. Users were complaining and losing interest but they had no way to know why. This could be your reality before utilizing the power of product analytics. Without insights, it will feel like navigating a maze blindfolded. Here are my best picks of product analytics tools: Google Analytics: Tracks who visits your site and what they do. Mixpanel: Looks closely at user activities and how often they come back. Hotjar: Shows where users click and how they move on your site. Amplitude: Gives detailed insights into user behavior. Looker: Helps visualize data and trends. Dear Friends and fellow PMs, Here’s how you can use Amplitude for a simple lead conversion website as an example. 1. Identify Key Actions First, figure out the important steps users take: - Landing Page Viewed: When someone visits the homepage. - Form Started: When someone starts filling out the lead form. - Form Submitted: When someone finishes and sends the form. 2. Understand User Paths Then, Amplitude’s “User Flows” feature shows how users move through your site. This helps see where they go and where they might leave. 3. Track Conversion Steps Next you set up a simple user path to see how users turn into leads: - Step 1: Landing Page Viewed - Step 2: Form Started - Step 3: Form Submitted This helps you see how many people complete each step and where you might lose them. 4. Compare User Groups You can then use Cohort Analysis to compare groups of users. For example, see if people who submit forms behave differently from those who don’t. 5. See Who Returns Check how many users come back to your site with Retention Analysis. This shows if people are interested enough to return. 6. Test Different Ideas You can also try different versions of your website’s features, like forms or buttons, to see which ones work best. Amplitude helps compare these tests easily. Tools like Amplitude use AI and machine learning to enhance it analytics capabilities and it can be used to uncover key insight and improve results. P.S.: Has using data improved your operations? Share your experience in the comments below. Let’s discuss how analytics can enhance your strategies. #ProductManagement #AI #Analytics #mystorymyvoice Amaka Ifeduba #Elevateyourprofessionalpresencewith_Amaka

Mobile Data 101: 7 Things Retailer Should Know With Math Examples. Here is how mobile data companies do the magic 👇. 1️⃣ How is the data collected There are very different sources, with different reliability: • SDK data (best): From apps that 𝘯𝘦𝘦𝘥 location. • Bidstream data (weaker): Lat/long from ad clicks. 2️⃣ How visits are attached to stores Phones are accurate to ~3–5 meters. Platforms draw polygons around stores to decide what counts as a visit. Bad polygons = bad data - Include parking lots, roads, or neighbors and your visits inflate fast. Particularly relevant when you have a super small parcel size or are in a mall. 3️⃣ How demographics are estimated Legally devices have to be anonymous. So demographics are inferred from the census block group where devices sit at night, not the actual owner. Example: • 80 visits from a $100K block group • 20 visits from an $80K block group → Estimated visitor income ≈ $96K 4️⃣ How visits are extrapolated No provider sees every phone. My best guess is companies are getting between 3 - 7% of visits. Before you freak out consider 5% of the population is akin to a 16.5 million person survey - damn good. Observed visits are: • Tied to home block groups • Weighted by population • Adjusted by panel penetration Example: 100 observed visits + 10% panel coverage → 1,000 estimated visits Every provider does this differently (and proprietary). 5️⃣ How are panels weighted and corrected for bias? Panels don’t represent everyone evenly. Your source might over represent families (family tracking apps), singles (dating apps), lower income (deals apps). You get where I am going here. Does that invalidate it. NO! Remember we have a sample "survey" of 16.5m devices. Just like the block group example you re-weight re-weight devices to census benchmarks. If your source panel under-samples a high income - they you re-weight those higher income block groups accordingly. 6️⃣ How Trade Areas Are Drawn Mobile-derived trade areas respect rivers, highways, and traffic patterns and expand and contract based on 𝘢𝘤𝘵𝘶𝘢𝘭 𝘷𝘪𝘴𝘪𝘵𝘰𝘳𝘴 Platforms typically draw areas capturing 60%, 70%, or 80% of visitor home locations—shown as block groups or contoured polygons. Simplified Example: If a store has 300 visitors and 210 of them come from three nearby block groups, the platform defines those as the 70% trade area. On a map, that trade area can be shown either as the three block groups shaded in or as a contoured polygon that overlaps them. 7️⃣ What can break mobile data Watch out for: • Urban canyons (GPS bounce) • Multi-level malls (no elevation) • Small parcels (store bleed) • Drive-bys & employees (no dwell filter) • Poorly drawn default polygons With this knowledge in hand you are at the 99th percentile of all users. Most people see dots. You understand the engine. Onwards.

Pacvue takes it a step further with "𝗦𝗵𝗮𝗿𝗲 𝗼𝗳 𝗩𝗼𝗶𝗰𝗲" of Voice" insights. Users are now able to track 𝗦𝗢𝗩 𝗱𝗮𝘁𝗮 𝗼𝗻 𝗺𝗼𝗯𝗶𝗹𝗲. In the past SOV data was only based on desktop data. SOV dashboards and reports can be segmented by desktop, mobile, or hybrid of both. What's the value prop?  Due to limited space on mobile devices, the Amazon SERP is significantly different on mobile vs. desktop. Tracking SOV data just on the desktop doesn't tell the complete story. Mobile traffic makes up a high portion of traffic on Amazon. Gathering SOV data on mobile and desktop allows users to get the full story of their presence on the Amazon SERP. Where to find this feature?  The option to track SOV on mobile can be selected during the SOV setup. Throughout Pacvue SOV reports and dashboards can be segmented by desktop, mobile or hybrid of both. #amazonadvertising #amazonppc #amazonads

90% of mobile time is in apps. Is your marketing strategy keeping up? Most marketers miss out by not analyzing mobile user behavior deeply. Phones are central to daily life. Understanding user interactions on your campaigns can reveal insights that personalize and improve marketing strategies. How to evaluate campaign effectiveness? ↪️ Monitor engagement metrics like opens, clicks, and interaction times. ↪️ Track conversion rates from actions such as app installs and purchases. ↪️ Gather user feedback through surveys and reviews to refine your campaigns. Here are some basics: ➡️ Segment data to tailor marketing efforts based on demographics and usage patterns. ➡️ Conduct A/B testing to continually optimize different aspects of your campaigns. ➡️ Employ analytics tools for deeper insights into user behavior and campaign performance. Are you maximizing mobile analytics to enhance your marketing?

"Is AppLovin stealing credit from Meta, or actually driving incremental new customer revenue?" This question came up in three separate customer calls last week. I just recorded a 3-minute demo showing exactly how we answer this using our upcoming multi-touch attribution dashboard. The insights from real customer data might surprise you. 🎯 The Problem: Most brands run Meta + AppLovin but can't tell if they're cannibalizing each other or truly incremental. Platform reporting shows conflicting numbers. Third-party MTA tools give you black-box answers you can't verify. 🔍 What We Built: Instead of adding another pixel, we aggregate your existing data—GA4, CAPI, Shopify, plus zero-party attribution surveys—into one unified stream. In the demo, you'll see exactly how we: → Layer first-touch, last-touch, and linear attribution models → Cross-reference with post-purchase survey data ("How did you hear about us?") → Isolate true incrementality between channels The verdict for this brand? When we filtered to customers who said they heard about the brand from "mobile games," it was overwhelmingly AppLovin. When filtered to "Facebook/Instagram," Meta dominated. Where there are overlaps, it's less than 10%. This took 3 minutes to analyze. Previously would have required weeks of spreadsheet gymnastics or expensive consulting. Our philosophy: 70% attribution you can verify beats 100% modeled data you can't trust. Especially when making million-dollar budget decisions. Full transparency = full BigQuery access to audit every calculation. Want early access to what we're building? We'll be releasing the waitlist next week.

Weekly KPI reporting is broken. GenAI is rewriting the playbook. Every Monday, teams scramble to explain what changed: > Why did conversion dip? > Was it the new onboarding flow? > Did someone ship something that broke sign-up? Most KPI reports are reactive. Static dashboards, crowded charts, and guesswork disguised as insight. Gen AI changes all of that. And, this isn’t just about automation- it’s causality. From what changed to WHY it changed - active explanation through causal insight. Examples: 💡 “Weekly Active Users dropped 14% last week. Likely Driver: seasonality - holidays" 💡 "Activation dropped 12% after Thursday’s onboarding update. Likely driver: mobile load time on step 3 increased 400ms." 💡 "Conversion increased for paid traffic, 85% of the decline attributed to experiment test B and Android users" With causal inference under the hood, GenAI handles the heavy lifting: > Detects anomalies > Surfaces causal drivers > Ranks insights by impact Gen AI tells the story in an easy-to-digest way. No more pulling charts into slides. No more guessing what matters. Just a clean, contextual narrative that helps the whole team align and act - faster. For analysts? Less time building reports. More time doing deep research that drives new insights and growth. This is the shift in Offensive and Defensive Analysis that Elena Verna and I collaborated on in her latest Elena's Growth Scoop article. For product and analytics leaders? Significant value. ✅ You stop losing money on things you’ve discovered too late ✅ Discover deep research-fueled levers for strategic growth. ✅ Build a truly data-fluent culture - everyone understands how the company is growing and why. Insights are available through truly self-served analytics. Instead of sifting through dashboards, Gen AI-powered tools, like Loops, deliver real-time summaries of what changed, why it happened, and what to do next all in plain language, directly in Slack, Ms-Teams, email or wherever your team works. Bottom line: Gen AI compresses the time between signal and action. These new tools are showing us the future of Product Analytics. It’s not just a reporting upgrade. It’s a competitive advantage. And once you’ve seen a causal insight summary show up in Slack, there’s no going back. #productanalytics #AIinAnalytics #CausalInference

Quo vadis mobile gaming?  love analyzing data from multiple perspectives to gain a clearer understanding of its implications. I examined trends in the mobile gaming industry (using Sensor Tower data) to understand growth patterns in different markets. To gain a broader perspective, I analyzed data not only from the first three quarters of 2024 (Q1-Q3), comparing it to the same period in 2023, but also compared it with the growth of those countries between 2022 and 2023. This provides a comprehensive view of the 'journey' of these countries in the mobile gaming landscape. It is quite visible that:  ▶️ iOS is very strong in last quarters (both in case of T1 and T2/T3 markets) ▶️ US, although big, is gaining on importance in the last months (how long yet?)  ▶️ majority of Asian markets perform much better in last months than before  ▶️ we see much lower (often negative) downloads growth rate YoY both in T1 and T2/T3 markets. Any surprises, comments? Once you: ✅ find it valuable and you would like to see more such materials, "like it" ✅ find it helpful and useful, please reshare it ✅ would like to add something, understand deeper, feel free to do it in comments. #data #mobilegaming #gaming #games #marketopportunities #insights