Alternatives to Google Tag Manager logo

Alternatives to Google Tag Manager

Google Analytics, Mixpanel, Facebook Pixel, Segment, and Ensighten are the most popular alternatives and competitors to Google Tag Manager.
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What is Google Tag Manager and what are its top alternatives?

Google Tag Manager is a powerful tool offered by Google that helps marketers and website owners manage various tracking tags on their website without having to modify the site's code. Key features include tag templates, version control, built-in tag testing, and a user-friendly interface. However, some limitations include the learning curve for new users, potential data discrepancies, and limited support for customization.

  1. Tealium: Tealium offers a robust tag management solution with features like real-time data enrichment, audience segmentation, and privacy compliance. Pros include comprehensive data governance and customer support, but cons may include higher pricing compared to GTM.
  2. Adobe Launch: Adobe Launch is part of Adobe Experience Cloud and provides advanced tag management capabilities, including cross-device tracking and AI-powered recommendations. Pros include seamless integration with other Adobe products, while cons may include complexity for beginners.
  3. Segment: Segment is a customer data platform that includes tag management as one of its features. Key features include data integration, audience segmentation, and analytics. Pros include flexible data routing options, while cons may include additional costs for advanced features.
  4. Matomo Tag Manager: Matomo Tag Manager is an open-source alternative that offers privacy-focused tag management solutions with features like user consent tracking, event tracking, and data ownership. Pros include data privacy compliance, while cons may include limited support compared to GTM.
  5. Ensighten: Ensighten provides enterprise-level tag management solutions with features like data governance, compliance, and real-time personalization. Pros include high-level security measures, but cons may include pricing for larger organizations.
  6. Signal: Signal offers a customer data platform with tag management capabilities, allowing for real-time data collection, cross-channel insights, and audience targeting. Pros include seamless data integration, while cons may include limited customization options.
  7. Google Analytics 4: Google Analytics 4, the latest version of Google Analytics, includes enhanced tag management features, event tracking, and cross-device measurement. Pros include seamless integration with Google products, while cons may include potential data discrepancies compared to GTM.
  8. Piwik PRO: Piwik PRO is an enterprise-level tag management solution that focuses on data privacy and compliance, with features like consent management, data ownership, and data residency options. Pros include strict data privacy regulations compliance, while cons may include higher pricing.
  9. Trepn Analytics: Trepn Analytics is a tag management platform designed for mobile apps, providing features like app event tracking, crash reporting, and user segmentation. Pros include mobile-specific features, while cons may include limited web tracking capabilities.
  10. Dynamic Tag Management by Adobe: Dynamic Tag Management by Adobe offers a comprehensive tag management solution with features like automated tag placement, rule-based triggers, and data mapping. Pros include seamless integration with Adobe products, while cons may include complexity for beginners.

Top Alternatives to Google Tag Manager

  • 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. ...

  • Facebook Pixel
    Facebook Pixel

    A code that you place on your website. It collects data that helps you track conversions from Facebook ads ...

  • Segment
    Segment

    Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch. ...

  • Ensighten
    Ensighten

    Ensighten is a comprehensive website security company, offering next generation compliance, enforcement and client-side protection against data loss, ad injection and intrusion. ...

  • Hotjar
    Hotjar

    See how visitors are really using your website, collect user feedback and turn more visitors into customers. ...

  • Google Search Console
    Google Search Console

    It is a web service, provided free of charge for webmasters, which allows them to check indexing status and optimize visibility of their websites. Its tools and reports help you measure your site's Search traffic and performance, fix issues, and make your site shine in search results. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

Google Tag Manager alternatives & related posts

Google Analytics logo

Google Analytics

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Alex Step

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.

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Tim Specht
‎Co-Founder and CTO at Dubsmash · | 14 upvotes · 952.3K views

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

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Mixpanel logo

Mixpanel

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Max Musing
Founder & CEO at BaseDash · | 8 upvotes · 351.2K 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 · | 7 upvotes · 368.7K 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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Facebook Pixel

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      Iva Obrovac
      Product Marketing Manager at Martian & Machine · | 8 upvotes · 76.1K views

      Hi,

      This is a question for best practice regarding Segment and Google Tag Manager. I would love to use Segment and GTM together when we need to implement a lot of additional tools, such as Amplitude, Appsfyler, or any other engagement tool since we can send event data without additional SDK implementation, etc.

      So, my question is, if you use Segment and Google Tag Manager, how did you define what you will push through Segment and what will you push through Google Tag Manager? For example, when implementing a Facebook Pixel or any other 3rd party marketing tag?

      From my point of view, implementing marketing pixels should stay in GTM because of the tag/trigger control.

      If you are using Segment and GTM together, I would love to learn more about your best practice.

      Thanks!

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      Segment logo

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        Cleanest API
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        Free
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        Mixpanel Integration
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        Segment SQL
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      Principal Software Engineer at Tophatter · | 16 upvotes · 3.1M views

      Back in 2014, I was given an opportunity to re-architect SmartZip Analytics platform, and flagship product: SmartTargeting. This is a SaaS software helping real estate professionals keeping up with their prospects and leads in a given neighborhood/territory, finding out (thanks to predictive analytics) who's the most likely to list/sell their home, and running cross-channel marketing automation against them: direct mail, online ads, email... The company also does provide Data APIs to Enterprise customers.

      I had inherited years and years of technical debt and I knew things had to change radically. The first enabler to this was to make use of the cloud and go with AWS, so we would stop re-inventing the wheel, and build around managed/scalable services.

      For the SaaS product, we kept on working with Rails as this was what my team had the most knowledge in. We've however broken up the monolith and decoupled the front-end application from the backend thanks to the use of Rails API so we'd get independently scalable micro-services from now on.

      Our various applications could now be deployed using AWS Elastic Beanstalk so we wouldn't waste any more efforts writing time-consuming Capistrano deployment scripts for instance. Combined with Docker so our application would run within its own container, independently from the underlying host configuration.

      Storage-wise, we went with Amazon S3 and ditched any pre-existing local or network storage people used to deal with in our legacy systems. On the database side: Amazon RDS / MySQL initially. Ultimately migrated to Amazon RDS for Aurora / MySQL when it got released. Once again, here you need a managed service your cloud provider handles for you.

      Future improvements / technology decisions included:

      Caching: Amazon ElastiCache / Memcached CDN: Amazon CloudFront Systems Integration: Segment / Zapier Data-warehousing: Amazon Redshift BI: Amazon Quicksight / Superset Search: Elasticsearch / Amazon Elasticsearch Service / Algolia Monitoring: New Relic

      As our usage grows, patterns changed, and/or our business needs evolved, my role as Engineering Manager then Director of Engineering was also to ensure my team kept on learning and innovating, while delivering on business value.

      One of these innovations was to get ourselves into Serverless : Adopting AWS Lambda was a big step forward. At the time, only available for Node.js (Not Ruby ) but a great way to handle cost efficiency, unpredictable traffic, sudden bursts of traffic... Ultimately you want the whole chain of services involved in a call to be serverless, and that's when we've started leveraging Amazon DynamoDB on these projects so they'd be fully scalable.

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      Robert Zuber

      Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

      We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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      Ensighten logo

      Ensighten

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            In order to fix this, we had to set up our own content delivery service. We chose Amazon CloudFront and Amazon S3 to do the job because it has a good synergy with Heroku PaaS we are already using.

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            Cofounder at FeaturePeek · | 7 upvotes · 165.8K views

            Segment has made it a no-brainer to integrate with third-party scripts and services, and has saved us from doing pointless redeploys just to change the It gives you the granularity to toggle services on different environments without having to make any code changes.

            It's also a great platform for discovering SaaS products that you could add to your own – just by browsing their catalog, I've discovered tools we now currently use to augment our main product. Here are a few:

            • Heap: We use Heap for our product analytics. Heap's philosophy is to gather events from multiple sources, and then organize and graph segments to form your own business insights. They have a few starter graphs like DAU and retention to help you get started.
            • Hotjar: If a picture's worth a thousand words, than a video is worth 1000 * 30fps = 30k words per second. Hotjar gives us videos of user sessions so we can pinpoint problems that aren't necessarily JS exceptions – say, logical errors in a UX flow – that we'd otherwise miss.
            • Bugsnag: Bugsnag has been a big help in catching run-time errors that our users encounter. Their Slack integration pings us when something goes wrong (which we can control if we want to notified on all bugs or just new bugs), and their source map uploader means that we don't have to debug minified code.
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                  Javascript is the New PHP
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                  No GitHub
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                But yeah, things are different now. I still love Rails, and still use it for a lot of apps I build. But it's that silly rich apps phrase that's the problem. Users have way more comprehensive expectations than they did even five years ago, and the JS community does a good job at building tools and tech that tackle the problems of making heavy, complicated UI and frontend work.

                Obviously there's a lot of things happening here, so just saying "JavaScript isn't terrible" might encompass a huge amount of libraries and frameworks. But if you're like me, yeah, give things another shot- I'm somehow not hating on JavaScript anymore and... gulp... I kinda love it.

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                Conor Myhrvold
                Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 10.2M views

                How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

                Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

                Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

                https://eng.uber.com/distributed-tracing/

                (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

                Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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