Leanplum vs Mixpanel
Overview
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Detailed Comparison
Mixpanel helps companies build better products through data. With our powerful, self-serve product analytics solution, teams can easily analyze how and why people engage, convert, and retain to improve their user experience. | It is a mobile engagement platform that helps forward-looking brands like Grab, Tinder, and Tesco meet the real-time needs of their customers. |
Interactive reports to answer more product questions;
Team dashboards & alerts for better visibility into how the things you ship move the numbers that matter; Limitless segmentation to analyze why metrics change; Group analytics to calculate metrics at the account or company level; Scalable infrastructure; 50+ Data integrations to connect your stack; Data governance to keep data clean and usable; Security & privacy compliance to keep customer data safe | Messaging - Deliver messages across multiple channels and increase relevancy by coordinating with in-app content.; Automation - Respond to in-app activities at scale during every stage of the customer journey.; App Editing - Take full control of your app experience without developer resources or App Store resubmissions.; Personalization - Unlock the power of mobile with context and personalization enabled by a rich user profile.; A/B Testing - Test everything, in and out of your app, from mobile messaging to UI content.; Analytics - Collect a real-time understanding of user activity with insights designed to drive marketing action. |
Statistics | |
Stacks 7.0K | Stacks 42 |
Followers 3.7K | Followers 41 |
Votes 438 | Votes 0 |
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Integrations | |
What are some alternatives to Mixpanel, Leanplum?
Google Analytics
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Intercom
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Piwik
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Amplitude
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Customer.io
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Clicky
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CleverTap
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Countly
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Plausible
It is a lightweight and open-source website analytics tool. It doesn’t use cookies and is fully compliant with GDPR, CCPA and PECR.
Databricks
Databricks Unified Analytics Platform, from the original creators of Apache Spark™, unifies data science and engineering across the Machine Learning lifecycle from data preparation to experimentation and deployment of ML applications.
