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Your team is facing a product analytics challenge. What tools can help you overcome it?

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You have a great product idea, but how can you know if it will be successful in the market? Product analytics is the process of collecting, analyzing, and visualizing data from your product and its users to inform your product decisions and strategy. However, product analytics can also present some challenges for your team, such as deciding which metrics and KPIs to use for your product goals and stages, setting up a reliable and scalable data pipeline and infrastructure, using data effectively and communicating insights to stakeholders and users, as well as balancing data-driven and intuition-based decisions to avoid analysis paralysis. Fortunately, there are tools available to help with different aspects of product analytics, such as data collection, storage, analysis, visualization, and experimentation. With the right tools in place, you can make product analytics easier and more efficient.

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Basit khan

General Manager @ AAI | AI, Agile Methodologies
Dr. Venkateswaran PS

??Technical Leader - Academics, CADFEM India | Bridging Industry & Academia | CAE in Curriculum | Research & Development
Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology…

1 Data Collection Tools

Data collection is a crucial initial step of product analytics, which involves capturing data from your product and its users, such as events, actions, attributes, and feedback. To automate this process and ensure consistent and accurate data, you can use various data collection tools. Google Tag Manager allows you to manage and deploy tags (code snippets) on your website or app without modifying the code. It also helps track events such as clicks, form submissions, page views, and conversions. Segment acts as a data hub, collecting data from different sources and sending it to multiple destinations like analytics platforms, marketing tools, or data warehouses. Amplitude specializes in product analytics, allowing you to track user journeys, cohorts, retention, engagement, and more. It also helps create custom events and properties to measure what matters for your product.

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Basit khan

General Manager @ AAI | AI, Agile Methodologies
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In facing a product analytics challenge, data collection tools like Google Analytics for user interaction insights and Mixpanel for detailed user behavior tracking are essential in gathering comprehensive data to inform strategic decisions.

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Dr. Venkateswaran PS

??Technical Leader - Academics, CADFEM India | Bridging Industry & Academia | CAE in Curriculum | Research & Development
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When tackling a product analytics challenge, some of the tools that could aid streamlining the process, derive actionable insights, and aid data-driven decision making would be as follows - Data Collection - Google Analytics, Mixpanel, Segment, etc. Date Warehousing - Amazon Redshift, Google BigQuery, Snowflake, etc. Data Visualization - Tableau, Power BI, Looker, etc. Data Modeling - Python, R, TensorFlow, PyTorch, etc. Communication - Google Workspace, Slack, Jupyter Notebooks, etc. Hope these tools help to improve your product's performance and user experience.

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Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology roadmaps | R&D Management | IP Strategy
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Data collection is a crucial step in product analytics, involving capturing events, actions, and feedback. Tools like Google Tag Manager automate this process by managing tags without code modification. Segment acts as a data hub, collecting and sending data to various destinations. Amplitude specializes in product analytics, tracking user journeys, cohorts, retention, and engagement, allowing customization for specific product metrics.

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Vamsidhar Ambatipudi, FRM, FIAI, CERA, MBA (IIM Indore)
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To tackle a product analytics challenge, you can use tools like Google Analytics and Mixpanel. Imagine you run a small bakery, and you want to know which cakes are the most popular among your customers. Google Analytics can help by tracking how many people visit your bakery's website and which cake pages they spend the most time on. Mixpanel can further assist by analyzing customer behavior, showing you which cake flavors are frequently added to shopping carts. These tools gather data about your website and customer interactions, making it easier to make informed decisions and improve your cake offerings based on real-time information.

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2 Data Storage Tools

Data storage is an essential part of product analytics, which involves storing and organizing data in a way that makes it accessible and queryable. To do this, you can use data storage tools such as Google BigQuery, Amazon S3, and MongoDB. BigQuery allows you to create a cloud-based data warehouse, where you can store and query large amounts of data using SQL. S3 allows you to create a cloud-based data lake, where you can store and access data in its original format. MongoDB helps you create a database that uses a document-based model, where you can store and query your data as JSON-like documents. These tools enable you to integrate your data from different sources, such as Google Analytics, Firebase, web logs, social media, or IoT devices. Additionally, they allow you to perform fast and scalable analysis on your data and use their flexible and dynamic schema to adapt to your data needs.

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Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology roadmaps | R&D Management | IP Strategy
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Data storage is vital in product analytics, and tools like Google BigQuery, Amazon S3, and MongoDB offer distinct benefits. BigQuery creates a cloud-based data warehouse for large datasets, S3 establishes a cloud-based data lake for data in its original format, and MongoDB provides a document-based model for storing and querying data as JSON-like documents. These tools integrate data from various sources, ensuring flexibility and scalability for fast and scalable analysis in product analytics.

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3 Data Analysis Tools

Data analysis is the third step of product analytics, and it involves processing and transforming your data into meaningful insights and recommendations. By using data analysis tools, you can perform various types of analysis on your data, such as descriptive, diagnostic, predictive, and prescriptive analysis. For example, Google Data Studio allows you to create interactive dashboards and reports to visualize and explore your data from different sources. With Tableau, you can create visualizations and stories to analyze and communicate your data from different sources. And with Python, you can write and run code to perform complex analysis on your data from different sources, as well as use machine learning and artificial intelligence techniques.

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Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology roadmaps | R&D Management | IP Strategy
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Data analysis is a crucial step in product analytics, involving the use of tools like Google Data Studio for interactive dashboards, Tableau for visualizations and stories, and Python for complex analysis, including machine learning and AI techniques. These tools help transform data into meaningful insights and recommendations from various sources.

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Vamsidhar Ambatipudi, FRM, FIAI, CERA, MBA (IIM Indore)
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You can harness the power of data analysis tools like a magnifying glass for detectives. Just like detectives use a magnifying glass to closely examine clues and solve mysteries, you can use tools like Microsoft Excel or Google Sheets to dig deep into your data, uncover patterns, and gain valuable insights. Imagine you're running a small ice cream shop, and you want to know which flavors are the most popular. You can use these tools to analyze your sales data, compare flavor preferences, and make informed decisions about which ice cream flavors to offer more often, just like a detective cracking the case!

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4 Data Visualization Tools

Data visualization is an important step in product analytics, which involves presenting and communicating data in a way that is easy to understand and actionable. To do this, you can use data visualization tools like Google Charts, D3.js, and Plotly. Google Charts allows you to create and embed charts and graphs on your website or app, from different sources such as Google Sheets, JSON, or SQL. You can make various types of charts such as line, bar, pie, scatter, or geo charts with customization options like colors, fonts, or tooltips. D3.js enables you to make data-driven documents on your website or app with data from CSV, JSON, or XML. This tool also lets you create interactive visualizations like networks, trees, maps, or animations with features like data binding, transitions, or events. Lastly, Plotly helps you create and share interactive and responsive visualizations on your website, app, or platform with data from Python, R, or Excel. You can make various types of visualizations such as line, bar, pie, scatter or box plots with features like annotations, hover effects or zooming.

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Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology roadmaps | R&D Management | IP Strategy
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Data visualization is a crucial aspect of product analytics, making data understandable and actionable. Google Charts, D3.js, and Plotly are recommended tools for this purpose. Google Charts facilitates the creation of charts and graphs on websites or apps with customization options. D3.js allows the creation of data-driven documents on websites or apps, supporting interactive visualizations like networks, trees, maps, or animations. Plotly enables the creation and sharing of interactive visualizations with various types of plots and features like annotations and hover effects, using data from Python, R, or Excel.

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5 Data Experimentation Tools

Data experimentation is the fifth step of product analytics, and it involves testing and validating your data insights and recommendations with real users and scenarios. To help with this, there are a variety of data experimentation tools available, such as Google Optimize, Firebase Remote Config, and LaunchDarkly. Google Optimize allows you to create A/B tests, multivariate tests, or redirect tests that measure the effect of content, layout, design, or functionality on conversions, revenue, or engagement. Firebase Remote Config allows you to create A/B tests, staged rollouts, or feature flags that measure the effect of app parameters on retention, sessions, or in-app purchases. Lastly, LaunchDarkly allows you to create feature flags, canary releases, or kill switches that measure the effect of code versions on errors, latency, or user satisfaction. All these tools have integrations with various analytics tools for analyzing results.

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Dr. Jefferson do Rosário

Head of Business Development Office @ DESY - the German Electron Synchrotron | Development of new markets | Technology roadmaps | R&D Management | IP Strategy
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Data experimentation, the fifth step in product analytics, involves testing insights with real users. Google Optimize, Firebase Remote Config, and LaunchDarkly are key tools for this. Google Optimize enables A/B tests for content, layout, or functionality impact. Firebase Remote Config conducts A/B tests for app parameters, and LaunchDarkly uses feature flags for code version impact. Integration with analytics tools ensures thorough result analysis.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Your team is facing a product analytics challenge. What tools can help you overcome it?

1

2

3

4

5

6

1 Data Collection Tools

2 Data Storage Tools

3 Data Analysis Tools

4 Data Visualization Tools

5 Data Experimentation Tools

6 Here’s what else to consider

Product R&D

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Your team is facing a product analytics challenge. What tools can help you overcome it?

1

2

3

4

5

6

1 Data Collection Tools

2 Data Storage Tools

3 Data Analysis Tools

4 Data Visualization Tools

5 Data Experimentation Tools

6 Here’s what else to consider

Product R&D

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