Google Analytics for eCommerce- Growth Explained

Google Firebase has many use cases that can benefit your business in many ways by making application of the analytics and followed by the Google Firebase growth which they provide.

In the previous article, we saw how Google Firebase Analytics can be effectively implemented for eCommerce apps. In this article, I’ll give you a brief introduction to Google Firebase Growth for eCommerce Apps.

The predictions

Predictions are one best features of Google firebase. Just as it sounds, Predictions let you know how your business will be in the coming days. The primary data that is fed into this is users’ behavior.

If you own an eCommerce App, Then this is your cup of coffee. Google Firebase interprets all most of the data that is fed into it. Like how your users get engaged with eCommerce Apps.

Why your eCommerce store need predictions?

A look into your business ahead of time can help you systematically resolve issues.

Google Firebase provides predictions:

• Churn
• Not churn
• spend
• Not Spend

Each prediction mentioned above are automatically generated and is provided by the Firebase.

Additional to this, You can add more predictions.

(Read on to know about setting up predictions.)

NOw, Lets look look into individual prediction cases.

For this, I’ll be taking an Online WooCommerce Store’s analytics which in further I’ll be addressing as ‘E_Store.’

Churn

The Churn Rates of an eCommerce app matters, because it lets you know how many of your eCommerce shoppers are going to be inactive in the next few coming days.

The Churn rates in different use cases is provided, like for high risk tolerance, Medium risk tolerance and High risk tolerance.

To understand what are risk tolerance is, you can read this article.

For every prediction that is made has different risk tolerance conditions.

For example, the churn rate for the E-Store for the day is like this each risk tolerance

• Low-risk tolerance: The prediction is inactive since the values predicted is below the quality threshold
• Medium risk tolerance: 18% (about 3.5k) of daily users can churn
• High-risk tolerance: 28% which is 5.7k users can churn

No churn

The no churn prediction tells you about the customers who have been using your app and will continue using your eCommerce app for on the coming days.

You can see predictions for each risk tolerances.

Spend

With the spend prediction, you can learn about the users that will spend their time within the app.

Just like all other prediction, this prediction also about the next 7 days from the day the prediction is made

Not spend

This prediction will tell you about the number of users who will not be using your eCommerce app for the following week.

More predictions to add

All the four above mentioned prediction are automatically generated by the Firebase.

Information like the churn, not churn, spend and not spend are fundamental for all sort of apps.

For an eCommerce store, you can use Firebase predictions for better purposes, like know the purchase rates, app installs and uninstalls rates and more.

Additional to the above-discussed predictions you can add new projections and input the conditions.

Let me tell you an example

Will uninstall the app

This a prediction that I am going to create for the eCommerce store – E_Store to know how many users of E_store app will uninstall the app in the coming days.

Creating new Prediction

In order to create a new prediction

Google Firebase Analytics< Growth< Predictions> Add new Prediction

For example, here I am adding a new prediction ‘ will uninstall the app‘

To categorize the users who uninstall the app, You have to give the conditions in the next step.

You will be provided with all the events that can cause the conversion and not.

Since the prediction that I am going to create her is ‘Will uninstall the app,’ the event that ‘will’ be taken by the users is ‘app_remove.’

After you add a prediction, The results will be generated after 24 hours.

What after predictions?

Learning the user’s behavior in advance alone is not enough for an eCommerce store.

You need remedies and solutions to avoid a situation like the bulk of users leaving the app, not completing a purchase.

Select an Action

Based on the predictions you can assign actions.

Looking into the predictions and taking actions helps you grow business and boost sales in a very adequate manner.

#1 Send Push notification:

When you find you are loosing a crowd from your eCommerce app, you can send a customized push notification to that particular group alone using Google Firebase.

Scheduling Push notification:

The studies say that sending push notification between 10 Am and 1 am can be effective for your online business.

With Google Firebase, you can schedule your push notifications.

For example, you want to send a push notification to notify the users about the flash sale at your store at certain time, you can enter the time in schedule notification and it will be send automatically when the time reaches.

The E_Store has witnessed a 47% increase in conversion and engagement by sending push notification to the crowd generated by google predictions

Brushing Up

With Google Firebase Growth you can create predictions and get a brief idea about how your user’s behavior in the coming few days.

With the predictions, you get to target a group of shoppers within your eCommerce app. And you can send them customized push notification to retain the business.

Thinking about building an eCommerce app with Google firebase integration?