Difference Between Product Analytics and Business Intelligence
Last Updated :
08 May, 2024
Product Analytics involves examining the product features, quality, appearance, costs, and many other aspects. It is conducted by product managers and potential buyers. On the other hand, Business Intelligence involves strategies that are used by enterprises for data analysis and management of business information. This article focuses on discussing the difference between product analytics and business intelligence.
What is Product Analytics?
Product analytics is a set of tools that allows product managers and product teams to assess the performance of the digital experience of the product they build, i.e., analyze user interaction with digital products.
- It provides critical information to diagnose problems and optimize performance.
- It is user-centric and focuses on how the users interact with the product.
- Product analytics helps the product manager to determine what aspects of the product need improvement.
- It also helps to measure and optimize user experience.
- Product managers, user experience designers, and growth strategists rely on product analytics.
What is Business Intelligence?
Business Intelligence combines business analytics, data mining, data tools, and infrastructure to help organizations make more data-driven decisions.
- BI tools access and analyze data sets and present analytical results in the form of reports, summaries, graphs, and charts.
- These tools enable business users to access semi-structured data as well as unstructured data like social media.
- It provides business users access to analytical findings without having to contact analysts or IT.
- It helps business decision-makers to make informed decisions.
Product Analytics vs Business Intelligence
Factors
| Product Analytics
| Business Intelligence
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Definition
| Product Analytics is centered on the real-time analysis of how users interact with digital products.
| Business Intelligence is centered on analyzing historical data to guide business decisions.
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Objective
| The primary goal is to optimize product features and user experience.
| The primary goal is to support strategic decision-making.
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Scope
| It is focused and provides a user-centric view.
| It covers a broader spectrum and provides an organizational perspective.
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Focus
| It is focused on user behavior, product performance, feature usage, and user interactions.
| It is focused on business performance, business trends, and strategic decision-making.
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Example
| A SaaS company checks how much money they're making each quarter, how much it costs them to get new customers, and how smoothly their systems are running.
| An E-Commerce Platform looks at what pages people visit the most, makes it easier for them to buy stuff, and suggests things they might like based on what they've looked at before.
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Target Audience
| It is used by product managers, UX/ UI designers, and product analysts.
| It is used by executives, managers, and analysts across different departments.
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Data Nature
| It relies on real-time data about user behavior, and product interaction.
| It relies on historical, aggregated data from various sources for business analysis.
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Visualization
| It presents analytical results in the form of funnel charts, and user flows.
| It presents analytical results in the form of reports, summaries, charts, graphs, and dashboards.
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Tools
| Amplitude, Mitzu, Mixpanel.
| Power BI, Tableau, Qlik View.
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Conclusion
Product Analytics and Business Intelligence serve different but complementary purposes. Product analytics is useful for in-depth user interaction insight. On the other hand, business intelligence is an ideal choice for reporting business health metrics that use multiple data sources. Deciding between product analytics and business intelligence isn't a scenario but rather a strategy.
Frequently Asked Questions (FAQs) on Product Analytics vs Business Intelligence
1. What are common metrics used in Product Analytics?
Some of the common metrics used in product analytics include conversion rate, new customer growth rate, churn rate, cost per acquisition, and customer lifetime value.
Some of the tools that are used for Procust Analytics include Mixpanel, Amplitude, Heap, Google Analytics, and many more.
Some of the tools that are used for Business Intelligence include Microsoft Power BI, Tableau, Sisense, Domo, Zoho Corporation, and many more.
4. What type of data are analyzed in Product Analytics?
Product Analytics involves analyzing various types of data including user interactions, feature usage, product performance, feature adoption, and user feedback.
5. What type of data are analyzed in Business Intelligence?
Business Intelligence involves analyzing various types of data including customer interactions, website traffic, financial transactions, and social media activity.