Product Siddha

Product Siddha

Product Siddha

Case Studies

AI Automation, Case Studies

From Lead to Site Visit – Voice AI Automation for a Real Estate Platform

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From Lead to Site Visit – Voice AI Automation for a Real Estate Platform Client Confidential (Fast-growing Property Management / Real Estate Aggregator in South India) Service Provider Product Siddha Industry Real Estate Service AI Automation Services / Voice AI for Real Estate The Problem: Too Many Inquiries, Not Enough Humans Our client is a rapidly growing real estate aggregator in South India. They receive thousands of property inquiries every month from their website, WhatsApp, 99acres, and Magicbricks. But here was the real challenge: Human agents were overwhelmed by repetitive questions “Is this available?” “What is the rent?” “2BHK in HSR?” – same questions all day Agents missed calls and delayed responses Many leads came at odd hours Too much time was spent filtering serious buyers from casual ones The result was lost leads, slow replies, and too much manual effort. They needed a way to automate qualification and bookings without losing a personal touch. The Solution: A Digital Leasing Assistant That Handles Everything Product Siddha designed a Voice AI system that works like a real leasing agent. Not a bot. Not a script. A smart Digital Leasing Assistant that understands context and responds naturally. Here’s how it works: 1. Context-Aware Conversations The AI knows where the lead came from and acknowledges it: Website WhatsApp Property listing links It instantly recognizes the property and starts the conversation. 2. Smart Interruption Handling In the test video, the customer suddenly asked: “Do you have anything in Sipani Viveza?” The AI immediately switched context and spoke about that exact building. 3. Real-Time Database Lookup It checks availability in real-time and even suggested alternatives: HSR Layout Marathahalli Other matching properties This removes the back-and-forth humans struggle with. The Wow Moment: The AI Negotiated and Upsold This was the most powerful part of the interaction. The customer had a budget of ₹40,000. The property they wanted was ₹45,000. Instead of rejecting the lead, the AI said: “The rent for this property is ₹45,000, which is slightly above your budget. Would you still be interested?” The customer said yes and accepted the price difference. No human intervention. No negotiation stress. This showed the AI could sell, not just support. The Conversion: Appointment Booked Automatically Once the customer showed interest, the Digital Leasing Assistant moved into conversion mode: Confirmed date & time Collected name, phone, email Booked the site visit Sent confirmation by SMS/Email It handled the complete pipeline from question → price discussion → qualification → booking. All without a human. The Outcome: Faster Responses, Better Conversion This Voice AI became the first touchpoint and the qualification engine. Key Wins 24/7 agent availability Human workload is drastically reduced Instant answers for availability, pricing, and alternatives Serious buyers only – filtered before they reach the sales team Professional, polite, and consistent tone every time Business Impact No missed leads Faster conversions Zero wait time High-quality appointments Pull Quotes (From the Conversation) “I found a 2BHK… However, the rent is 45,000, which is slightly above your budget. Would you still be interested?” “I have noted your preferred time. I will now proceed to book this site visit for you.” Conclusion: AI That Automates Real Estate Conversions This project proves how Product Siddha’s Voice AI Automation can turn inbound inquiries into qualified site visits with zero effort. From natural conversations to smart negotiation and perfect scheduling, the Digital Leasing Assistant removes the human bottleneck and boosts conversions. If you want to automate real estate leasing workflows, Product Siddha can do it for you.

AI Automation, Case Studies

Building a Lead Engine After Apollo Shut Us Out

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Building a Lead Engine After Apollo Shut Us Out Client Internal Project (Product Siddha) Service Provider Product Siddha Industry B2B / Marketing Automation Service AI Automation Services / Lead Generation Workflow Automation The Problem: Apollo Went Down, and Our Lead Flow Stopped Like many agencies, Product Siddha relied on Apollo for fast prospect lists and outbound campaigns. It worked well – until Apify’s Apollo scraping access was suddenly banned. Overnight, our outbound system stopped working. No more fresh leads. No more automated contact lists. We tried a few alternatives like Ample Leads and Scraper City. They worked, but most of the data felt old or repeated. We needed something fresh, real-time, and under our control. The goal was clear: Pull live business data from Google Maps Find key decision-makers via LinkedIn Automate everything through n8n Keep it cheaper than Apollo or Sales Navigator scraping The Solution: A Smart, Lightweight Lead Engine Instead of switching to another expensive tool, Product Siddha decided to build its own lead generation system – powered by open tools like Google Maps, Apify, and n8n. Here’s how our new lead engine works: Google Maps Scraper → Fresh Business Data The system starts by pulling live business listings directly from Google Maps (for example: “clinics in Melbourne”). This ensures that every lead is from a real, active business. Google Sheets → Clean Data Storage The scraped data – business name, website, phone, and address – is automatically added to a Google Sheet for easy use. SERP API → Decision Maker Discovery Using Google’s free Search API, the system finds decision-maker names and LinkedIn profile URLs based on each company’s website. Apify LinkedIn Scraper → Profiles & Emails Next, Apify’s LinkedIn automation fetches profile data, roles, and professional emails for each contact. n8n → Full Automation n8n ties everything together. It schedules scrapes, merges data, and updates Google Sheets, all without any manual effort. The Outcome: Reliable Leads Without the Heavy Price Our internal automation system now runs smoothly without relying on Apollo or similar databases. Key Wins: Fresh business data from Google Maps Real-time decision-maker insights from LinkedIn Fully automated workflow via n8n Lower cost than any paid lead database Zero manual upkeep once built Measurable Results: Accuracy: ~60% (strong for live scraped data) Time saved: Hours each week Cost: Significantly lower than Apollo or Sales Navigator Maintenance: Zero ongoing management Output: Google Sheet with business name, contact, title, email, LinkedIn URL, company, website, phone, and address Lessons Learned Live data beats bulk databases every time. Automation doesn’t have to be perfect, just consistent. Building your own tools can often be faster (and smarter) than finding new ones. Now, this same workflow helps us: Test new Ideal Customer Profiles (ICPs) Feed AI tools for personalized outreach Run cleaner, higher-quality cold campaigns The Stack Tools Used: n8n, Google Maps API, Google SERP API, Apify LinkedIn Scraper, Google Sheets Watch the Walkthrough: Product Siddha Case Video Conclusion: Own Your Data, Own Your Growth This project shows how Product Siddha uses AI automation to solve real business problems, quickly and cost-effectively. By creating our own lightweight lead system, we now have control, flexibility, and accuracy without paying for outdated data. Want a similar setup for your sales or marketing team? Reach out to Product Siddha – we’ll help you build it, or share the JSON file so you can tinker with it yourself.

AI Automation, Case Studies

Built an AI Stock Advisor That Tracks, Analyzes, and Remembers, Cutting Manual Research by 75%

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Built an AI Stock Advisor That Tracks, Analyzes, and Remembers, Cutting Manual Research by 75% Client Confidential (High-Net-Worth Individual Investor) Service Provider Product Siddha Industry Wealth Management / Fintech Service AI-Powered Investment Assistant for Indian Equity Markets The Problem: Too Many Tools, Too Much Time A high-net-worth investor came to Product Siddha with a clear problem: They were spending too much time switching between apps, websites, and spreadsheets just to track their portfolio. Here’s what wasn’t working: Manual research across platforms like Groww and Screener No smart system to track financial ratios or market trends No way to remember personal investment preferences No personalized advice based on real portfolio data Repetitive tasks and API overuse, leading to unnecessary costs What the client wanted was simple: A smart, memory-aware AI stock advisor that could save time, reduce research work, and give custom investment insights based on their risk profile. The Solution: A Personalized AI Investment Assistant Product Siddha designed a lightweight, cost-efficient AI system built specifically for the Indian equity market. It was more than just a dashboard; it was a true assistant that remembered, learned, and adapted to its users’ needs. Here’s how we did it: Live Portfolio Tracking via Groww API We connected the client’s brokerage account to pull real-time data on holdings, stock prices, and order history, automatically and securely. Fundamentals via Screener.in Scraper To track stock health, the system pulled data like: P/E Ratio ROCE Debt-to-Equity Dividend Yield This allowed smart filtering of high-risk or low-return stocks. Built-In Technical Analysis Engine Custom code to calculate: RSI (Relative Strength Index) MACD (Moving Average Convergence Divergence) SMA (Simple Moving Average) These helped identify overbought or oversold conditions, giving timely signals for entries and exits. These helped identify overbought or oversold conditions, giving timely signals for entries and exits. Conversational AI Using OpenAI 4.1 We trained the AI to understand: The client’s risk tolerance Investment goals (capital preservation + dividend income) Preferences for industry sectors and stock types This made every recommendation personal and context-aware. This made every recommendation personal and context-aware. Smart Memory via Supabase (Postgres) Every user interaction, stock preference, and past recommendation was stored securely.That way, the assistant could say things like:”You previously reduced Tata Motors due to cyclicality. Do you want to review that decision again based on new signals?” Automation Layer with n8n We added automation to: Orchestrate workflows Control API usage (to avoid overbilling) Trigger insights only when market conditions change This kept the system efficient, scalable, and low-cost. Real Use Cases: Smart Actions, Not Just Alerts The AI assistant didn’t just pull data; it gave smart, actionable suggestions. Reduce exposure to Tata Motors & JSW Steel Both were showing cyclical risk and overbought signals (via RSI/MACD) AI flagged and suggested gradual exits Increase allocation to Power Grid, HDFC Bank, and ITC Low debt, stable income, and consistent dividends Matched the client’s conservative investment goals Adaptive re-entry alerts If technical indicators improved, the AI suggested when to consider buying back, while reminding the user of their original risk profile The Outcome: Research Time Cut by 75%, Confidence Boosted With this system in place, the investor no longer needed to: Jump between platforms Track charts manually Second-guess buy/sell decisions Key Wins: Daily stock insights customized to client holdings Tailored recommendations for lower-risk, high-dividend picks Clear visibility into stock fundamentals and technical indicators 75% reduction in time spent on portfolio research Minimal API usage, low cost, high performance History tracking: See how and why decisions were made over time Bonus: Ready for Other Markets Too This AI stock advisor was built for Indian stocks, but the same system works in other markets. For U.S. Markets, we can plug in: Broker APIs like Robinhood, Alpaca, and Interactive Brokers Data from Yahoo Finance, Alpha Vantage, and Finviz News and fundamentals via Seeking Alpha The architecture remains the same, making it easy to adapt for global equity, ETFs, or crypto. Conclusion: Personalized Investment Automation That Learns With Product Siddha’s smart AI setup, this investor gained: More time More confidence Smarter decisions All without hiring a portfolio manager. If you’re an investor looking to automate your stock research, reduce manual work, and get insights tailored to you, Product Siddha can help. Let’s build an AI stock advisor that works like your second brain.

Case Studies, Product Analytics

Driving Growth for a U.S. Music App with Full-Stack Mixpanel Analytics

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Driving Growth for a U.S. Music App with Full-Stack Mixpanel Analytics Client Kingfisher Digital Product Snobs (Swipe-based music discovery app) Service Provider Product Siddha Industry Music / Mobile Apps / Consumer Tech Service Mixpanel Integration, Analytics Strategy & Dashboard Setup The Problem: Swipes, Streams… but No Direction Snobs is a swipe-based app that helps people in the U.S. discover music from over 150+ subgenres. Users explore short music clips and swipe right to add artists to their favorites. The app was growing, but the team didn’t have a clear picture of what was working. Here’s what was missing: No tracking of how often users engaged with swipes No clear signal for user activation Couldn’t tell who the power users were Drop-offs in the onboarding journey were a mystery Trial-to-paid conversions weren’t well understood Teams relied too much on analysts for reports Snobs needed a smarter system to track product usage, understand behavior, and drive real growth. The Solution: Full-Stack Mixpanel Analytics Setup Product Siddha rolled out a complete analytics solution using Mixpanel, designed specifically for a swipe-based music discovery experience. Here’s what we did to unlock product growth: Mapped the Entire User Journey in Mixpanel We set up tracking across all the key touchpoints inside the app: First swipe Right vs left swipe count Artist follows Time spent per session Playlist creations In-app trial activations Paid plan signups These events gave the Snobs team full visibility into how users explored music inside the app. These events gave the Snobs team full visibility into how users explored music inside the app. Built Custom Dashboards by Stage To make the data usable, we created dashboards for each part of the user lifecycle: Activation Dashboard How many users swiped at least X times in the first 30 days Helped define a clear activation benchmark Showed which users were exploring music vs those who churned early Conversion Dashboard Compared free trial users to paying subscribers Helped spot what actions led to paid upgrades Led to better CTAs and trial experience tweaks Engagement Dashboard Tracked average swipes per session Measured time spent and session frequency Helped identify power users and top features Retention Curves Showed how long users stayed active Identified patterns among users who returned after a gap Allowed for better re-engagement strategy planning Onboarding Funnel Tracked every step from app open → first swipe Found drop-off points and improved onboarding screens Empowered All Teams with Self-Serve Analytics We trained the product, growth, and marketing teams to: Explore dashboards without coding Run weekly product experiments Compare cohorts over time Now, they no longer rely on analysts. They could act fast and test ideas weekly. Now, they no longer rely on analysts. They could act fast and test ideas weekly. The Outcome: Swipe-Based Growth, Backed by Data With Product Siddha’s full-stack analytics setup, Snobs moved from guessing to growing, using real user behavior. Key Wins: Activation insights: Swipe thresholds tied to long-term retention Conversion optimization: Improved trial-to-paid journey Experimentation speed: New features are tested every week Power user focus: Features shaped around the top 10% of users No analyst needed: PMs and marketers owned the data Measurable Results: Clear engagement metrics tied to feature usage 100% visibility into onboarding and drop-off stages Faster release cycles with real-time data Smarter personalization based on user patterns Conclusion: From Music Discovery to Data-Driven Growth Snobs is more than a music app; it’s a swipe-powered experience built on curiosity and sound. With help from Product Siddha, they now have a powerful analytics engine behind that experience. From user onboarding to retention, everything is tracked, tested, and improved. Whether you’re building a music app, social platform, or mobile product, real growth starts with real data. 📞 Let Product Siddha help you turn user behavior into business results.

Case Studies, MarTech Implementation

HubSpot Marketing Hub Setup for a Growing Fintech Brand

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HubSpot Marketing Hub Setup for a Growing Fintech Brand Client Fast-Growing Fintech Company Service Provider Product Siddha Service MarTech Implementation – HubSpot Automation Industry Fintech / SaaS The Problem: No Easy Way to Track or Nurture Leads A fast-growing fintech company wanted to get more business partners. But they had a big problem – there was no clear system to track deals or talk to leads the right way. The marketing team didn’t have the tools to send personalized messages. The sales team couldn’t see how close a deal was to closing. Everyone was working with different tools, and that caused delays, missed follow-ups, and lost deals. They needed one powerful system to manage it all. The Solution: HubSpot Marketing Hub + Smart Automation That’s when Product Siddha stepped in. The team used HubSpot Marketing Hub to build a full system that helped both marketing and sales teams work together better. Lead Nurturing Workflows Product Siddha created custom email workflows that sent messages based on: Lead’s risk profile Where they were in the sales journey The emails were tailored to each lead so they felt personal and helpful. As leads moved through the pipeline, HubSpot updated their status automatically – so the team didn’t have to do it by hand. Sales Pipeline Setup Next, Product Siddha built a sales deal pipeline that matched the fintech brand’s full sales cycle. This pipeline: Tracked over 400 deals in real time Showed which deals were open, closed, or stuck Calculated average deal age (about 85 days) Helped sales teams know which leads to focus on first It turned the sales dashboard into a smart tool that saved time and improved follow-ups. Marketing and Sales Alignment Before, the handoff between marketing and sales wasn’t smooth. Leads were falling through the cracks. Now with HubSpot automation, every marketing-qualified lead (MQL) moved seamlessly into the sales process—with all the right lifecycle stages and triggers in place.No more guessing. No more delays. The Outcome: Smarter Sales, Stronger Marketing In just a short time, the fintech brand saw real results: A fully automated lead nurturing system that engaged the right people with the right message A clear, visual deal pipeline that helped close more deals faster Better teamwork between marketing and sales with no leads missed Real-time performance insights from the dashboard HubSpot became the single source of truth for both teams. Conclusion: HubSpot Automation That Powers Fintech Growth With help from Product Siddha, this fintech brand now runs a smarter, faster partner acquisition process. Thanks to the HubSpot Marketing Hub setup, lead engagement and deal tracking became seamless. This is how modern MarTech implementation should work – clear, simple, and powerful.

Case Studies, MarTech Implementation

Boosting Email Revenue with Klaviyo for a Shopify Brand

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Boosting Email Revenue with Klaviyo for a Shopify Brand Client Leading Shopify Brand (EU Region) Service Provider Product Siddha Sevice MarTech Implementation – Email Marketing Automation Industry E-commerce / Shopify The Problem: Email Marketing Was Not Working Well A popular Shopify brand in Germany was struggling to get results from their email marketing. They had: Poor email open and click rates Outdated email designs Unorganized and messy contact lists No behavior-based automation flows Even though they had many customers, their email revenue was low. They knew they needed a better system to reach more people and make more sales through email. The Solution: Smart Klaviyo Setup and Email Flow Strategy That’s where Product Siddha came in. The team started with the German store and used Klaviyo, a top tool for email automation for Shopify, to fix and improve everything. Better Email Designs and List Cleanup Re-designed emails to look more modern and user-friendly Removed inactive or fake emails to improve deliverability Created clear segments based on customer actions like purchase history and product views Behavior-Based Automation Flows Set up targeted email flows that were based on what users actually did, such as: Welcome series for new customers Abandoned cart reminders Product view follow-ups Win-back emails for old customers These flows helped the brand stay connected with shoppers and bring them back to the store at the right time. Scaling to Other EU Markets After strong results in Germany, Product Siddha rolled out the same setup for three more Shopify stores in: France The Netherlands Spain Each store had localized content and email strategies tailored to their market. The Outcome: Big Growth in Email Revenue The results were impressive and fast: 55% increase in email-attributed revenue for the German store within 6 months (year-over-year) Product Siddha now manages a €2.5 million email marketing portfolio across 4 countries Higher open rates, click rates, and customer return rates Better customer experience with personalized, timely emails Product Siddha turned email marketing into one of the brand’s top growth channels. Conclusion: Smart Email Automation That Grows EU Shopify Brands By using Klaviyo and focusing on behavior-based flows, Product Siddha helped this Shopify brand grow fast across Europe. With better design, smarter segments, and market-specific strategies, email became a powerful tool again. This case shows how the right MarTech implementation can turn email into a money-making machine.

Case Studies, Product Management

Building the World’s First AI-Powered Networking Assistant

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Building the World’s First AI-Powered Networking Assistant Client Capivara – AI Networking App Service Provider Product Siddha Service Product Management & MVP Development Industry Career Tech / AI / Professional Networking The Problem: Turning a Big AI Idea into a Real Product Capivara had a big vision: create the world’s first AI-powered networking assistant. This app would help people connect with others in their industry, find career opportunities, and grow their network – automatically. But there were some tough challenges: The idea was still very early. There was no working product – only a concept. The Capivara team needed help with everything: planning, design, and building the app from scratch. They had to move fast to test the idea and attract users. The Solution: Full Product Management by Product Siddha Product Siddha stepped in as the end-to-end product partner, working closely with Capivara from the very first idea through the MVP launch. Product Management from Day One Product Siddha took the lead in planning the project from scratch. This included: User journey mapping to understand how users would move through the app Writing a clear Product Requirements Document (PRD) Designing wireframes for the first version of the product Managing the project timelines and team collaboration MVP Development and Deployment The team built and launched the Minimum Viable Product (MVP) with a smart focus on core features: AI-powered matchmaking for professional networking Easy onboarding for users looking for job leads or connections A simple, user-friendly web platform that worked from day one Product Siddha worked with developers to make sure the product was delivered on time, met technical needs, and was ready to go live. The Outcome: 500+ Signups in Just 6 Weeks Thanks to the fast and focused product work: Capivara launched the MVP quickly, getting it into users’ hands for real-world feedback The platform gained over 500 professional signups in just six weeks The AI networking assistant started connecting users to headhunters and experts in their domain based on their interests and skills Early traction helped the startup attract attention from partners and potential investors The project proved the concept worked—and that there was a real need for AI-powered tools in the networking space. Additional Information: PRD CapiVara (1) (1) Conclusion: Product Management That Brings AI Ideas to Life By guiding Capivara from idea to MVP, Product Siddha turned a bold vision into a working product with real users and real results. With smart product planning, strong execution, and clear communication, Product Siddha shows how the right product management support can launch the next big thing in AI and career networking.

Case Studies, Product Management

Product Management for UAE’s First Lifestyle Services Marketplace

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Product Management for UAE’s First Lifestyle Services Marketplace Client Pointy – A SaaS Platform for Salons & Gyms Service Provider Product Siddha Industry Health, Fitness & Beauty Tech Service Full-Cycle Product Management The Problem: Building a New Marketplace from Scratch A startup called Pointy wanted to build a first-of-its-kind marketplace in the UAE for fitness and beauty services. The goal was to create a platform where users could easily find salons, spas, and gyms in their area – and where vendors could showcase and manage their services online. But the challenges were big: There were no similar platforms in the UAE market to learn from. Pointy needed to build three different products at the same time: A vendor website for business sign-ups A B2C mobile app for users to explore and book services An AI-powered co-pilot that helped users with fitness and beauty goals They needed a product management partner who could lead from idea to execution, fast. The Solution: End-to-End Product Management by Product Siddha Product Siddha became the full product management partner for Pointy, owning the process from start to finish. Led Technical Strategy & Product Roadmap Product Siddha worked closely with the founders to define a clear product vision, roadmap, and go-to-market strategy. They made sure every team, from developers to designers, was aligned on goals and timelines. Built 3 Core Products in Sync Vendor Website: Made it easy for salon and gym owners to join, upload their info, and manage bookings. B2C App: Designed for users to search, filter, and book services quickly. It focused on great UX and mobile-first design. AI Co-Pilot: A new tool that acted like a smart guide—helping users with recommendations on fitness and beauty routines. It was the first of its kind in the region. Achieved Early Market Validation Within just one month of launching, the platform had already signed up 5 active salons. This early traction showed that there was strong demand in the market. It gave the startup proof that their idea worked—and gave investors confidence too. The Outcome: A Solid Start for a Big Vision With Product Siddha as their PM partner, Pointy was able to: Launch faster by aligning teams and focusing on MVP features Get real user feedback early to shape future updates Create a complete digital ecosystem with vendor tools, customer app, and AI innovation Validate product-market fit by securing salon sign-ups and building initial brand trust The foundation built during this phase set the stage for future growth, funding, and expansion in the UAE’s wellness and beauty tech space. Conclusion: Product Management That Turns Vision into Real-World Traction By guiding product development from idea to early market success, Product Siddha helped Pointy build a strong launchpad for its lifestyle services platform. With smart strategy, fast execution, and a user-first mindset, Product Siddha proves that the right product management partner can make all the difference.

Case Studies, Product Analytics

Product Analytics for a Ride-Hailing App with Mixpanel

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Product Analytics for a Ride-Hailing App with Mixpanel Client Ride-Hailing Mobile App Service Provider Product Siddha Industry Transportation / Mobile App Service Product Analytics Implementation using Mixpanel The Problem: No Clear View of How Users Book Rides The client runs a ride-hailing app. They wanted to better understand how users moved from installing the app to completing a ride. But there were several problems: Missing event tracking: There was no intuitive way to see what users were doing inside the app, like where they dropped off or how long it took to book a ride, without having to visit the app logs/backend. No organized data structure: The teams couldn’t trust the numbers because there was no proper naming or structure. Lack of funnel visibility: Product/Marketing team couldn’t track the full ride journey – install, sign-up, payment, and ride completion. Slow decision-making: Product and marketing teams were guessing instead of using real data to improve the app. They needed a strong analytics system to track user actions clearly and help teams make smarter, faster decisions. The Solution: Smart Mixpanel Setup for End-to-End Tracking Product Siddha stepped in to create a complete product analytics solution using Mixpanel, a powerful tool for tracking user behavior. Here’s how they did it: Full Event Tracking Plan The team designed and set up a complete event tracking plan. This plan captured all the key actions users took in the app—like signing up, selecting rides, making payments, and completing rides. Built a Scalable Data Taxonomy To keep everything clean and easy to manage, a data taxonomy and schema was created. This included: Ride lifecycle (start to finish) Payments and cancellations User actions and traits (such as device type, location, etc.) With this system, teams could easily track and compare data across all parts of the app. Funnel Visibility from Start to Finish Mixpanel was set up to track the entire user journey, from app install to the first completed ride. The analytics showed exactly where users dropped off and which parts of the ride process needed fixing. Dashboards for Everyone Custom Mixpanel dashboards were built for each team—marketing, product, and operations. These dashboards made it easy to see real-time results and understand user behavior at every step. The Outcome: Clear Data, Faster Growth, Better Rides The new analytics setup gave the ride-hailing app a complete view of how users behaved. In just 30 days, the team saw big results: +13.5% increase in funnel completion rate – More users finished the ride booking process because drop-off points were fixed. Faster decision-making – Teams used dashboards to track real-time trends and test new ideas. Improved user experience – With better data, the app was updated to match how users actually used it. Scalable tracking – The clean schema and taxonomy made it easy to add new features without breaking the system. Conclusion: Clear Analytics That Drive Better Rides and Results This case study shows how Product Siddha used Mixpanel to bring smart product analytics to a fast-moving ride-hailing app. With better tracking, real-time data, and easy-to-use dashboards, the company made quick improvements that boosted user engagement and completed rides. If your mobile app needs clear insights and smarter funnels, Product Siddha can build the right system for you.

Case Studies, Product Analytics

Product Analytics & Full-Funnel Attribution for a SaaS Coaching Platform

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Product Analytics & Full-Funnel Attribution for a SaaS Coaching Platform Client SaaS Platform for Coaching Businesses Service Provider Product Siddha Industry SaaS / Coaching Services Service Product Analytics & Full-Funnel Attribution The Problem: Hard-to-Track User Behavior and Marketing Performance The client is a SaaS company that provides tools for coaching businesses. They had trouble knowing how their users moved through the platform. In simple terms, they needed to understand how visitors became active users, stayed engaged, and eventually paid for the service. The challenges were clear: Unclear user journey: It was hard to track all the actions that users took on different parts of the platform. No full-funnel attribution: The company could not see the complete path from when a user clicked a Meta ad to when they signed up or paid. Gaps in data flow: There was a disconnect between different systems. Data from Meta Ads, HubSpot CRM, and the client app did not flow together smoothly. Lack of real-time insights: The client did not have live dashboards to see daily active users (DAUs), feature engagement, or where users dropped off in the funnel. Without clear and connected data, it was very hard for the company to make smart decisions that could boost growth. The Solution: A Powerful, End-to-End Analytics and Attribution System Product Siddha stepped in to build a complete solution that connected every step of the user journey and brought all the data together. Here’s how the solution worked: Amplitude Implementation from Scratch They set up Amplitude, a top product analytics tool, from the ground up. This tool was used to map how users interacted with various features on the platform. By tracking every action from sign up to subscription, the system provided clear insights into activation, retention, and conversion. Seamless Data Flow with Custom API Integration The team created a custom API solution that tied together different systems. Data from Meta ads, Hubspot and client’s application was smoothly connected with Amplitude, without any instance of duplicate user profiles. This seamless data flow allowed the company to track the entire customer journey. Every ad click, trial signup, and subscription was now linked in one complete picture. Live Dashboards for Real-Time Insights Live dashboards were set up to show key performance metrics. These dashboards displayed: Daily Active Users (DAUs) The split between trial users and paid subscribers Engagement with platform features Drop-off points along the user journey These live views meant the client could see what was working and what needed improvement right away. Insight-Led Growth and Campaign Optimization The product analytics solution helped the client identify the top behaviors among their user base. They found the top five actions that led to higher conversion rates. With this insight, the company was able to optimize their marketing campaigns and product features to boost growth. It was no longer guesswork; it was data-driven decision-making. The Outcome: Full-Funnel Visibility and Measurable Growth After implementing Product Siddha’s end-to-end analytics solution, the client achieved significant results across key business metrics: 92% of user behavior tracked: The analytics setup provided visibility into nearly all meaningful user actions, helping the team pinpoint exactly how users activated, engaged, and converted. 30% increase in paid conversions: By identifying the top five behaviors that led to subscriptions, the team improved onboarding and feature usage, resulting in a measurable boost in paid sign-ups. 18% higher retention rate: With clearer insight into feature engagement, the platform optimized product experiences that encouraged users to stick around longer. 35% improvement in marketing ROI: Connecting Meta Ads to HubSpot and Amplitude allowed precise attribution, helping the team focus spend on high-performing campaigns. 3x faster decision-making: Live dashboards enabled leadership and marketing teams to react in real time to user trends and campaign results—reducing decision cycles from days to hours. 40% growth in active user base (QoQ): With better data and continuous optimization, the company scaled its coaching user base and expanded its reach more efficiently. Conclusion: Data-Driven Insights That Power Smarter SaaS Growth This case study shows how Product Siddha leverages cutting-edge product analytics and full-funnel attribution techniques to drive clarity, efficiency, and growth for SaaS companies in the coaching space. If you need help mapping your customer journey and turning data into action, Product Siddha is ready to help you succeed.