What is Matomo and what are its top alternatives?
Top Alternatives to Matomo
- Piwik
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. ...
- Google Analytics
Google Analytics lets you measure your advertising ROI as well as track your Flash, video, and social networking sites and applications. ...
- 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. ...
- 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. ...
- 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. ...
- 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. ...
- Fathom
Fathom is an easy to use management reporting and financial analysis tool, which helps you to assess business performance, monitor trends and identify improvement opportunities. ...
- 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. ...
Matomo alternatives & related posts
- It's good to have an alternative to google analytics35
- Self-hosted27
- Easy setup10
- Not blocked by Brave2
- Great customs0
- Hard to export data2
related Piwik posts
- Free1.5K
- Easy setup926
- Data visualization890
- Real-time stats698
- Comprehensive feature set405
- Goals tracking181
- Powerful funnel conversion reporting154
- Customizable reports138
- Custom events try83
- Elastic api53
- Updated regulary14
- Interactive Documentation8
- Google play3
- Industry Standard2
- Walkman music video playlist2
- Advanced ecommerce2
- Medium / Channel data split1
- Easy to integrate1
- Financial Management Challenges -2015h1
- Lifesaver1
- Irina1
- Confusing UX/UI11
- Super complex8
- Very hard to build out funnels6
- Poor web performance metrics4
- Very easy to confuse the user of the analytics3
- Time spent on page isn't accurate out of the box2
related Google Analytics posts
We used to use Google Analytics to get audience insights while running a startup and we are constantly doing experiments to lear our users. We are a small team and we have a lack of time to keep up with trends. Here is the list of problems we are experiencing: - Analytics takes too much time - We have enough time to regularly monitor analytics - Google Analytics interface is too advanced and complicated - It's difficult to detect anomalies and trends in GA
We considered other solutions on a market, but found 2 main issues: - The solution created for analytic experts - The solution is pretty expensive and non-automated
After learning this fact we decided to create AI-powered Slack bot to analyze Google Analytics and share trends. The bot is currently working and highlights trends for us.
We are thinking about publishing this solution as a SaaS. If you are interested in automating Google Analytics analysis, drop a comment and you'll get an early access.
We will implement this solution only if we have 20+ early adaptors. Leave a message with your thought. I appreciate any feedback.
In order to accurately measure & track user behaviour on our platform we moved over quickly from the initial solution using Google Analytics to a custom-built one due to resource & pricing concerns we had.
While this does sound complicated, it’s as easy as clients sending JSON blobs of events to Amazon Kinesis from where we use AWS Lambda & Amazon SQS to batch and process incoming events and then ingest them into Google BigQuery. Once events are stored in BigQuery (which usually only takes a second from the time the client sends the data until it’s available), we can use almost-standard-SQL to simply query for data while Google makes sure that, even with terabytes of data being scanned, query times stay in the range of seconds rather than hours. Before ingesting their data into the pipeline, our mobile clients are aggregating events internally and, once a certain threshold is reached or the app is going to the background, sending the events as a JSON blob into the stream.
In the past we had workers running that continuously read from the stream and would validate and post-process the data and then enqueue them for other workers to write them to BigQuery. We went ahead and implemented the Lambda-based approach in such a way that Lambda functions would automatically be triggered for incoming records, pre-aggregate events, and write them back to SQS, from which we then read them, and persist the events to BigQuery. While this approach had a couple of bumps on the road, like re-triggering functions asynchronously to keep up with the stream and proper batch sizes, we finally managed to get it running in a reliable way and are very happy with this solution today.
#ServerlessTaskProcessing #GeneralAnalytics #RealTimeDataProcessing #BigDataAsAService
- Easy setup4
- Funnels3
- Great UI3
- Omni Channel2
- Custom Dashboards2
- Extensible via plugins2
- Custom Events1
- Secure1
- Extensible Product Analytics1
- Private Cloud1
- Cohorts1
- Push Notifications1
- Advanced Segmentation1
- User Profiles1
- Push Notifications1
- Crashes1
related Countly posts
Adobe Analytics
related Adobe Analytics posts
related Open Web Analytics posts
Mixpanel
- Great visualization ui144
- Easy integration108
- Great funnel funcionality78
- Free58
- A wide range of tools22
- Powerful Graph Search15
- Responsive Customer Support11
- Nice reporting2
- Messaging (notification, email) features are weak2
- Paid plans can get expensive2
- Limited dashboard capabilities1
related Mixpanel posts
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.
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!
related Fathom posts
- Can track any type of digital event7
- First-party tracking5
- Data quality5
- Real-time streams4
- Completely open source4
- Redshift integration4
- Snowflake integration3
- BigQuery integration3
related Snowplow posts
Trying to establish a data lake(or maybe puddle) for my org's Data Sharing project. The idea is that outside partners would send cuts of their PHI data, regardless of format/variables/systems, to our Data Team who would then harmonize the data, create data marts, and eventually use it for something. End-to-end, I'm envisioning:
- Ingestion->Secure, role-based, self service portal for users to upload data (1a. bonus points if it can preform basic validations/masking)
- Storage->Amazon S3 seems like the cheapest. We probably won't need very big, even at full capacity. Our current storage is a secure Box folder that has ~4GB with several batches of test data, code, presentations, and planning docs.
- Data Catalog-> AWS Glue? Azure Data Factory? Snowplow? is the main difference basically based on the vendor? We also will have Data Dictionaries/Codebooks from submitters. Where would they fit in?
- Partitions-> I've seen Cassandra and YARN mentioned, but have no experience with either
- Processing-> We want to use SAS if at all possible. What will work with SAS code?
- Pipeline/Automation->The check-in and verification processes that have been outlined are rather involved. Some sort of automated messaging or approval workflow would be nice
- I have very little guidance on what a "Data Mart" should look like, so I'm going with the idea that it would be another "experimental" partition. Unless there's an actual mart-building paradigm I've missed?
- An end user might use the catalog to pull certain de-identified data sets from the marts. Again, role-based access and self-service gui would be preferable. I'm the only full-time tech person on this project, but I'm mostly an OOP, HTML, JavaScript, and some SQL programmer. Most of this is out of my repertoire. I've done a lot of research, but I can't be an effective evangelist without hands-on experience. Since we're starting a new year of our grant, they've finally decided to let me try some stuff out. Any pointers would be appreciated!