What is Google Analytics?
Who uses Google Analytics?
Google Analytics Integrations
Why developers like Google Analytics?
Here are some stack decisions, common use cases and reviews by members of with Google Analytics in their tech stack.
Kubernetes Google Analytics
Most companies use Google Analytics via the web interface. And although it packs a lot of power and features, it still lacks integration with all other data sources. You can hook Search Console and Adwords but that would be it. However, there is a way you can tap into Google Analytics and overlap it with any other data source you have - from your backend or any other tool you might use. And that is by using the Google Analytics API. Lots of people are familiar with its existence but few are using it. The decision to move and use Google API was based on the shortcomings of the web interface and the ability to collaborate using the same data set, the same view.
So combining Chartio for a good sharable view with Google Analytics API and all the other data we have ( semrush, oncrawl and backend data from several sources) provides a quick view on the KPI we care about and a common view that can be discussed easily - especially for remote teams.
Here are some stack decisions, common use cases and reviews by companies and developers who chose Google Analytics in their tech stack.
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
We used Google Analytics to track user and market growth and Pushwoosh to send out push notifications by hand to promote new content. Even though we didn’t localize our pushes at all, we added custom tags to devices when registering with the service so we could easily target certain markets (e.g. send a push to German users only), which was totally sufficient at the time.
#WebPushNotifications #Analytics #GeneralAnalytics #Communications
I use Laravel because it's the most advances PHP framework out there, easy to maintain, easy to upgrade and most of all : easy to get a handle on, and to follow every new technology ! PhpStorm is our main software to code, as of simplicity and full range of tools for a modern application.
Google Analytics Analytics of course for a tailored analytics, Bulma as an innovative CSS framework, coupled with our Sass (Scss) pre-processor.
To deploy, we set up Buddy to easily send the updates on our nginx / Ubuntu server, where it will connect to our GitHub Git private repository, pull and do all the operations needed with Deployer .
CloudFlare ensure the rapidity of distribution of our content, and Let's Encrypt the https certificate that is more than necessary when we'll want to sell some products with our Stripe api calls.
Asana is here to let us list all the functionalities, possibilities and ideas we want to implement.
Google Analytics is a great tool to analyze your traffic. To debug our software and ask questions, we love to use Postman and Stack Overflow. Google Drive helps our team to share documents. We're able to build our great products through the APIs by Google Maps, CloudFlare, Stripe, PayPal, Twilio, Let's Encrypt, and TensorFlow.
Google Analytics's Features
- Analysis Tools- Google Analytics is built on a powerful, easy to use, reporting platform, so you can decide what data you want to view and customize your reports, with just a few clicks.
- Content Analytics- Content reports help you understand which parts of your website are performing well, which pages are most popular so you can create a better experience for your customers.
- Social Analytics- The web is a social place and Google Analytics measures success of your social media programs. You can analyze how visitors interact with sharing features on your site (like the Google +1 button) and engage with your content across social platforms.
- Mobile Analytics- Google Analytics helps you measure the impact of mobile on your business. Additionally, if you build mobile apps Google Analytics offers Software Development Kits for iOS and Android so you can measure how people use your app.
- Conversion Analytics- Find out how many customers you're attracting, how much you're selling and how users are engaging with your site with Google Analytics' range of analysis features.
- Advertising Analytics- Make the most of your advertising by learning how well your social, mobile, search and display ads are working. Link your website activity to your marketing campaigns to get the complete picture and improve your advertising performance.