Recommendation systems have been around us for quite some time now. Youtube, Facebook, Amazon, and many others provide some sort of recommendations to their users. This not only helps them show relevant products to users but also allows them to stand out in front of competitors.
Thinkfic is a platform to make it easy to create, market & sell online courses. Everything you need to run your education business under one roof.
However, one thing it lacks is the ability to track the session duration for each student. You can’t monitor how much time students spent on courses.
We can leverage Mixpanel analytics for this purpose. It allows analyzing user's (students in our case) behavior across your sites with a real-time cohort.
The best part is Thinkfic has direct integration to Mixpanel.
Here is how it will look in the Mixpanel
Capturing data from multiple sources is the key to creating a rich data warehouse. And it becomes even more crucial when it comes to online businesses as the data is scattered across a plethora of platforms used to run the business.
SendGrid is one such marketing platform that delivers transactional and marketing emails to help keep users informed and engaged.
We will go over different methods to store SendGrid email data to our data warehouse tool.
SendGrid provides several ways to export its data out. One such way is to use SendGrid webhooks. Webhooks are automated messages sent from apps…
Cloud SQL is a database service from Google Cloud Platform that makes it easy to set up, maintain, and manage relational databases.
However, It isn’t built for heavy analytics operations. You need a warehouse service such as BigQuery to do advanced analytics and machine learning tasks such as performing RFM analytics using BigQuery ML. For this, you need to move your cloud SQL data to BigQuery.
In this guide, we will build a data pipeline to send the cloud SQL data to BigQuery in an automated fashion.
For our data pipeline, we will make the use of two GCP components…
An e-commerce company reached out to you to build a recommender system to suggest three items on their product detail page. Their analytics systems have around 30% of the user’s purchase history and only 20% of the products have users rating history. What method will you use for this type of problem? What will be your evaluation technique?RMSE, Precision, MRR, nDCG, or any other?
In this article, we will build a recommender system for the product detail page to recommend items to users based on their rating history.
We will evaluate Eight different models to get ourselves the best working…
Finding similar products to suggest is a tough nut to crack, especially when we have a large number of products. We want to suggest similar products in our number of marketing and product activities, such as in promotional email marketing.
Here is the super art, I build using autodraw, illustrating a marketing email having suggestions for similar products with the main product.
Firebase Dynamic Links help to control where a user lands depending on whether they have an app installed or not. Having such behaviour gives a richer user experience when interacting with marketing campaigns.
In this guide, we will go through different Firebase Dynamic Links components that you need to consider when creating a dynamic link. These help to ensure that users are directed to the desire landing page/app section and that marketing channels remain intact throughout the conversion.
There are four components of a dynamic link to keep in mind when creating a Firebase Dynamic Link:
More and more mobile applications are utilizing power WebView to customize the user experience on runtime. And it is all the more important to track users’ activities on the WebView.
However, Firebase analytics’s SDK doesn’t support sending events from WebView pages of the mobile app. So to use Analytics in WebView, its events must be forwarded to native code before they can be sent to analytics.
First, we have to pass the events from WebView to native code.
Second, fetch the passed events in the native environment and send it to Firebase Analytics.
Bigquery is a fantastic tool! It lets you do really powerful analytics works all using SQL like syntax.
But it lacks chaining the SQL queries. We cannot run one SQL right after the completion of another. There are many real-life applications where the output of one query depends upon for the execution of another. And we would want to run multiple queries to achieve the results.
Here is one scenario, suppose you are doing RFM analysis using BigQuery ML. Where first you have to calculate the RFM values for all the users then apply the k-means cluster to the result…
Suppose you want to remarket your users for conversions and reactivations by creating segments based on their website app activities using the google analytics data stored in BigQuery.
In this guide, I will show how to implement one of the popular techniques to segment users, and visualize the results to help make important marketing decisions.
The technique we will go through is RFM analysis. We will be using BigQuery ml to create segments and the data studio to visualize the results. Here is how it will look.
In this guide we will: