Alternatives to Piwik logo

Alternatives to Piwik

Google Analytics, Mixpanel, Matomo, Open Web Analytics, and Adobe Analytics are the most popular alternatives and competitors to Piwik.
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What is Piwik and what are its top alternatives?

Matomo (formerly Piwik) is a full-featured PHP MySQL software program that you download and install on your own webserver. At the end of the five-minute installation process, you will be given a JavaScript code.
Piwik is a tool in the General Analytics category of a tech stack.
Piwik is an open source tool with 15.6K GitHub stars and 2.2K GitHub forks. Here’s a link to Piwik's open source repository on GitHub

Top Alternatives to Piwik

  • Google Analytics

    Google Analytics

    Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...

  • Mixpanel

    Mixpanel

    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. ...

  • Matomo

    Matomo

    It is a web analytics platform designed to give you the conclusive insights with our complete range of features. You can also evaluate the full user-experience of your visitor’s behaviour with its Conversion Optimization features, including Heatmaps, Sessions Recordings, Funnels, Goals, Form Analytics and A/B Testing. ...

  • Open Web Analytics

    Open Web Analytics

    It is open source web analytics software that you can use to track and analyze how people use your websites and applications. It provides website owners and developers with easy ways to add web analytics to their sites using simple Javascript, PHP, or REST based APIs. ...

  • Adobe Analytics

    Adobe Analytics

    It is a web analytics service used in the measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. It makes hard things easy. Its AI and machine learning brings hidden opportunities and answers to everyone with the click of a button. ...

  • Countly

    Countly

    Countly is a product analytics solution and innovation enabler that helps organizations track product performance and user journey and behavior across mobile, web, and desktop applications. ...

  • Snowplow

    Snowplow

    Snowplow is a real-time event data pipeline that lets you track, contextualize, validate and model your customers’ behaviour across your entire digital estate. ...

  • Elasticsearch

    Elasticsearch

    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack). ...

Piwik alternatives & related posts

Google Analytics logo

Google Analytics

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Enterprise-class web analytics.
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PROS OF GOOGLE ANALYTICS
  • 1.5K
    Free
  • 925
    Easy setup
  • 888
    Data visualization
  • 696
    Real-time stats
  • 403
    Comprehensive feature set
  • 180
    Goals tracking
  • 154
    Powerful funnel conversion reporting
  • 137
    Customizable reports
  • 83
    Custom events try
  • 53
    Elastic api
  • 13
    Updated regulary
  • 8
    Interactive Documentation
  • 3
    Google play
  • 2
    Advanced ecommerce
  • 2
    Industry Standard
  • 2
    Walkman music video playlist
  • 1
    Medium / Channel data split
  • 1
    Financial Management Challenges -2015h
  • 1
    Lifesaver
  • 1
    Easy to integrate
CONS OF GOOGLE ANALYTICS
  • 9
    Confusing UX/UI
  • 6
    Super complex
  • 5
    Very hard to build out funnels
  • 3
    Poor web performance metrics
  • 2
    Very easy to confuse the user of the analytics
  • 2
    Time spent on page isn't accurate out of the box

related Google Analytics posts

Tassanai Singprom

This is my stack in Application & Data

JavaScript PHP HTML5 jQuery Redis Amazon EC2 Ubuntu Sass Vue.js Firebase Laravel Lumen Amazon RDS GraphQL MariaDB

My Utilities Tools

Google Analytics Postman Elasticsearch

My Devops Tools

Git GitHub GitLab npm Visual Studio Code Kibana Sentry BrowserStack

My Business Tools

Slack

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Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 127.2K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

See more
Mixpanel logo

Mixpanel

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Powerful, self-serve product analytics to help you convert, engage, and retain more users
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PROS OF MIXPANEL
  • 143
    Great visualization ui
  • 107
    Easy integration
  • 76
    Great funnel funcionality
  • 58
    Free
  • 22
    A wide range of tools
  • 15
    Powerful Graph Search
  • 11
    Responsive Customer Support
  • 1
    Nice reporting
CONS OF MIXPANEL
  • 2
    Messaging (notification, email) features are weak
  • 2
    Paid plans can get expensive

related Mixpanel posts

Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 127.2K views

Functionally, Amplitude and Mixpanel are incredibly similar. They both offer almost all the same functionality around tracking and visualizing user actions for analytics. You can track A/B test results in both. We ended up going with Amplitude at BaseDash because it has a more generous free tier for our uses (10 million actions per month, versus Mixpanel's 1000 monthly tracked users).

Segment isn't meant to compete with these tools, but instead acts as an API to send actions to them, and other analytics tools. If you're just sending event data to one of these tools, you probably don't need Segment. If you're using other analytics tools like Google Analytics and FullStory, Segment makes it easy to send events to all your tools at once.

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Yasmine de Aranda
Chief Growth Officer at Huddol · | 6 upvotes · 92.3K views

Hi there, we are a seed-stage startup in the personal development space. I am looking at building the marketing stack tool to have an accurate view of the user experience from acquisition through to adoption and retention for our upcoming React Native Mobile app. We qualify for the startup program of Segment and Mixpanel, which seems like a good option to get rolling and scale for free to learn how our current 60K free members will interact in the new subscription-based platform. I was considering AppsFlyer for attribution, and I am now looking at an affordable yet scalable Mobile Marketing tool vs. building in-house. Braze looks great, so does Leanplum, but the price points are 30K to start, which we can't do. I looked at OneSignal, but it doesn't have user flow visualization. I am now looking into Urban Airship and Iterable. Any advice would be much appreciated!

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Matomo logo

Matomo

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A free and open source web analytics application
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PROS OF MATOMO
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    CONS OF MATOMO
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      related Matomo posts

      Open Web Analytics logo

      Open Web Analytics

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      Open source web analytics software
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      PROS OF OPEN WEB ANALYTICS
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        CONS OF OPEN WEB ANALYTICS
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          related Open Web Analytics posts

          Adobe Analytics logo

          Adobe Analytics

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          A marketing analytics and reporting platform
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          PROS OF ADOBE ANALYTICS
            Be the first to leave a pro
            CONS OF ADOBE ANALYTICS
              Be the first to leave a con

              related Adobe Analytics posts

              Countly logo

              Countly

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              Product Analytics and Innovation. Build better customer journeys.
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              PROS OF COUNTLY
              • 3
                Funnels
              • 3
                Easy setup
              • 3
                Great UI
              • 2
                Custom Dashboards
              • 2
                Extensible via plugins
              • 2
                Omni Channel
              • 1
                Cohorts
              • 1
                Custom Events
              • 1
                Advanced Segmentation
              • 1
                Push Notifications
              • 1
                Secure
              • 1
                Extensible Product Analytics
              • 1
                Private Cloud
              CONS OF COUNTLY
              • 1
                User Profiles
              • 1
                Push Notifications
              • 1
                Crashes

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              Snowplow logo

              Snowplow

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              The enterprise-grade event data collection platform
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              PROS OF SNOWPLOW
              • 6
                Can track any type of digital event
              • 4
                Data quality
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                First-party tracking
              • 3
                Real-time streams
              • 3
                Redshift integration
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                Completely open source
              • 2
                Snowflake integration
              • 2
                BigQuery integration
              CONS OF SNOWPLOW
                Be the first to leave a con

                related Snowplow posts

                Elasticsearch logo

                Elasticsearch

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                Open Source, Distributed, RESTful Search Engine
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                PROS OF ELASTICSEARCH
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                  Powerful api
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                  Great search engine
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                  Open source
                • 213
                  Restful
                • 200
                  Near real-time search
                • 96
                  Free
                • 83
                  Search everything
                • 54
                  Easy to get started
                • 45
                  Analytics
                • 26
                  Distributed
                • 6
                  Fast search
                • 5
                  More than a search engine
                • 3
                  Great docs
                • 3
                  Awesome, great tool
                • 3
                  Easy to scale
                • 2
                  Document Store
                • 2
                  Nosql DB
                • 2
                  Great piece of software
                • 2
                  Great customer support
                • 2
                  Intuitive API
                • 2
                  Fast
                • 2
                  Easy setup
                • 2
                  Highly Available
                • 1
                  Not stable
                • 1
                  Scalability
                • 1
                  Open
                • 1
                  Reliable
                • 1
                  Github
                • 1
                  Elaticsearch
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                  Actively developing
                • 1
                  Responsive maintainers on GitHub
                • 1
                  Ecosystem
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                  Easy to get hot data
                • 1
                  Potato
                • 0
                  Community
                CONS OF ELASTICSEARCH
                • 6
                  Resource hungry
                • 6
                  Diffecult to get started
                • 5
                  Expensive
                • 3
                  Hard to keep stable at large scale

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                Tim Abbott

                We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

                We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

                And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

                I can't recommend it highly enough.

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                Tymoteusz Paul
                Devops guy at X20X Development LTD · | 23 upvotes · 4.7M views

                Often enough I have to explain my way of going about setting up a CI/CD pipeline with multiple deployment platforms. Since I am a bit tired of yapping the same every single time, I've decided to write it up and share with the world this way, and send people to read it instead ;). I will explain it on "live-example" of how the Rome got built, basing that current methodology exists only of readme.md and wishes of good luck (as it usually is ;)).

                It always starts with an app, whatever it may be and reading the readmes available while Vagrant and VirtualBox is installing and updating. Following that is the first hurdle to go over - convert all the instruction/scripts into Ansible playbook(s), and only stopping when doing a clear vagrant up or vagrant reload we will have a fully working environment. As our Vagrant environment is now functional, it's time to break it! This is the moment to look for how things can be done better (too rigid/too lose versioning? Sloppy environment setup?) and replace them with the right way to do stuff, one that won't bite us in the backside. This is the point, and the best opportunity, to upcycle the existing way of doing dev environment to produce a proper, production-grade product.

                I should probably digress here for a moment and explain why. I firmly believe that the way you deploy production is the same way you should deploy develop, shy of few debugging-friendly setting. This way you avoid the discrepancy between how production work vs how development works, which almost always causes major pains in the back of the neck, and with use of proper tools should mean no more work for the developers. That's why we start with Vagrant as developer boxes should be as easy as vagrant up, but the meat of our product lies in Ansible which will do meat of the work and can be applied to almost anything: AWS, bare metal, docker, LXC, in open net, behind vpn - you name it.

                We must also give proper consideration to monitoring and logging hoovering at this point. My generic answer here is to grab Elasticsearch, Kibana, and Logstash. While for different use cases there may be better solutions, this one is well battle-tested, performs reasonably and is very easy to scale both vertically (within some limits) and horizontally. Logstash rules are easy to write and are well supported in maintenance through Ansible, which as I've mentioned earlier, are at the very core of things, and creating triggers/reports and alerts based on Elastic and Kibana is generally a breeze, including some quite complex aggregations.

                If we are happy with the state of the Ansible it's time to move on and put all those roles and playbooks to work. Namely, we need something to manage our CI/CD pipelines. For me, the choice is obvious: TeamCity. It's modern, robust and unlike most of the light-weight alternatives, it's transparent. What I mean by that is that it doesn't tell you how to do things, doesn't limit your ways to deploy, or test, or package for that matter. Instead, it provides a developer-friendly and rich playground for your pipelines. You can do most the same with Jenkins, but it has a quite dated look and feel to it, while also missing some key functionality that must be brought in via plugins (like quality REST API which comes built-in with TeamCity). It also comes with all the common-handy plugins like Slack or Apache Maven integration.

                The exact flow between CI and CD varies too greatly from one application to another to describe, so I will outline a few rules that guide me in it: 1. Make build steps as small as possible. This way when something breaks, we know exactly where, without needing to dig and root around. 2. All security credentials besides development environment must be sources from individual Vault instances. Keys to those containers should exist only on the CI/CD box and accessible by a few people (the less the better). This is pretty self-explanatory, as anything besides dev may contain sensitive data and, at times, be public-facing. Because of that appropriate security must be present. TeamCity shines in this department with excellent secrets-management. 3. Every part of the build chain shall consume and produce artifacts. If it creates nothing, it likely shouldn't be its own build. This way if any issue shows up with any environment or version, all developer has to do it is grab appropriate artifacts to reproduce the issue locally. 4. Deployment builds should be directly tied to specific Git branches/tags. This enables much easier tracking of what caused an issue, including automated identifying and tagging the author (nothing like automated regression testing!).

                Speaking of deployments, I generally try to keep it simple but also with a close eye on the wallet. Because of that, I am more than happy with AWS or another cloud provider, but also constantly peeking at the loads and do we get the value of what we are paying for. Often enough the pattern of use is not constantly erratic, but rather has a firm baseline which could be migrated away from the cloud and into bare metal boxes. That is another part where this approach strongly triumphs over the common Docker and CircleCI setup, where you are very much tied in to use cloud providers and getting out is expensive. Here to embrace bare-metal hosting all you need is a help of some container-based self-hosting software, my personal preference is with Proxmox and LXC. Following that all you must write are ansible scripts to manage hardware of Proxmox, similar way as you do for Amazon EC2 (ansible supports both greatly) and you are good to go. One does not exclude another, quite the opposite, as they can live in great synergy and cut your costs dramatically (the heavier your base load, the bigger the savings) while providing production-grade resiliency.

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