Google Analytics

For any project that anyone creates these days (myself included) you want to see how many visitors your site gets, where they come from, what they clicked etc. The easiest and cheapest way to do this is obviously Google Analytics I think that 99% of websites out there have Google Analytics embedded into their code.

I am no different, I wanted to see how the users were getting to my site, what they clicked, how long they were on there for etc. etc. So I added Google Analytics to my site too... Which was really cool and free, but the longer I used Google Analytics I felt that I wanted more insight. Like what if I could actually see how users interacted with my site.

I came across FullStory and this is exactly what I wanted, it will record all your user sessions and you get to replay them in full. So you can see exactly how a user uses your site, what they click, and see if your site might benefit from some A/B tests or changes. - I am on the free plan, so it doesn't need to cost you a fortune.

Both Google Analytics and FullStory allow you to login with your G Suite account which helps you keep your accounts all linked.

With FullStory and Google Analytics I feel that I have the perfect insight into how the users are using my site, and all the analytics around how they get there. Its a perfect combo

We chose Webflow to build up websites faster and to make possible for particular employees to fix some misspellings or add an easy element to the page on their own - it is like Adobe Photoshop. To work with the incoming traffic we use our own product, that I can't pin here. It helps to make nurture visitors from the first session into the signing up and further activation into the product. In addition to @Carrrot we use Google Analytics to traffic source awareness, to monitor customers inside the product FullStory helps is a lot with its fury clicking and abandoned links. Activation and retention are done by our own product through the pop-ups, live chat, and emails that all based on customer behavior.

Adopting Amplitude was one of the best decisions we've made. We didn't try any of the alternatives- the free tier was really generous so it was easy to justify trying it out (via Segment). We've had Google Analytics since inception, but just for logged out traffic. We knew we'd need some sort of #FunnelAnalysisAnalytics solution, so it came down to just a few solutions.

We had heard good things about Amplitude from friends and even had a consultant/advisor who was an Amplitude pro from using it as his company, so he kinda convinced us to splurge on the Enterprise tier for the behavioral cohorts alone. Writing the queries they provide via a few clicks in their UI would take days/weeks to craft in SQL. The behavioral cohorts allow us to create a lot of useful retention charts.

Another really useful feature is kinda minor but kinda not. When you change a saved chart, a new URL gets generated and is visible in your browser (chartURL/edit) and that URL is immediately available to share with your team. It may sound inconsequential, but in practice, it makes it really easy to share and iterate on graphs. Only complaint is that you have to explicitly tag other team members as owners of whatever chart you're creating for them to be able to edit it and save it. I can see why this is the case, but more often than not, the people I'm sharing the chart with are the ones I want to edit it 🤷🏾‍♂️

The Engagement Matrix feature is also really helpful (once you filter out the noisy events). Charts and dashboards are also great and make it easy for us to focus on the important metrics. We've been using Amplitude in production for about 6 months now. There's a bunch of other features we don't use regularly like Pathfinder, etc that I personally don't fully understand yet but I'm sure we'll start using them eventually.

Again, haven't tried any of the alternatives like Heap, Mixpanel, or Kissmetrics so can't speak to those, but Amplitude works great for us.

#analytics analyticsstack

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