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Author Archives: Ravi Pathak

 About Ravi Pathak

Ravi is the co-founder of Tatvic and expert at managing different web analytics tools. Ravi actively works on conversion optimization projects to improve conversion rate and test newer hypothesis with e-commerce companies.

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What is Shapley Value Fractional Attribution Model?

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In the last blog, we have discussed the various attribution models available in most of the sophisticated analytics tools and we now know that considering the position and the frequency of occurrence of channels can be instrumental in designing an optimal attribution model. 

While there are many approaches to design this attribution model that leverages historical data for attributing conversion to channels, I have found the following two approaches to be the most efficient - 

  1. Fractional Attribution Using Shapley Value
  2. Multi-touch Attribution Using Markov Chain

In this article, we will be discussing the Fractional Attribution Using Shapley Value in detail.

Fractional Attribution Using Shapley Value 

As the name suggests the Fractional Attribution model uses the Game Theory principle - Shapley Value for attributing conversions to channels.

To understand the model in more detail, let us consider some more examples of channel sequences. For ease of understanding, we are only considering the total number of conversions from each channel. Later, we will extend the analysis to include the drop-offs from each channel sequence as well.

Here, the second column represents the sequence of the channels while the third column represents the total number of conversions from that sequence. 

If we analyze the given data now using the Shapley Value principle, For case 1, all the 100 conversions will be attributed to Google Search Ads and 0 to Facebook Ads. Similarly, for case 2, all the 80 conversions will be attributed to Facebook Ads and 0 to Google Search Ads. 

Now let us consider case 3, for this case, Google Search Ads is the first channel that the user interacts with and Facebook Ads is the second channel and there are 160 conversions for this channel sequence. Shapley Value will remember that Google Search Ads was able to bring 100 conversions on its own (from case 1) consequently, 100 conversions will be attributed to Google Search Ads and the remaining 60 conversions to Facebook Ads. A similar approach will be followed for case 4 as well and for this case, 80 conversions will be attributed to Facebook Ads and 70 to Google Search Ads out of the total 150 conversions.

From the table, we can see that for Case 3 and Case 4, even though we have the same channels appearing in the sequence and the total number of conversion are also the same. The conversions attributed to each channel is different for the two cases. Moreover, the attribution leverages data from Case 1 and Case 2 for assigning conversions in Case 3 and Case 4.

Creating A More Sophisticated Fractional Attribution Model 

In the above example, we have only considered the conversions from each channel sequence, a more sophisticated model would also consider the number of drop-offs (non-conversions) from each channel sequence. 

Let us try looking at the same example by considering the drop-offs from each channel as well.

With this data, we will be able to calculate the efficiency or the probability of conversion for each channel sequence.

We may now again use the Shapley Value model and calculate the relative efficiency of each channel in the channel sequence -

Normalize the marginal efficiencies from multiple channels to calculate the fractional attribution to each channel in the channel sequence

From the table above, we now have the Fractional Conversion Attribution for Google Search Ads and Facebook Ads should they appear in a channel sequence. 

Let us now see how we can use these attribution values for assigning conversions to channels. Assume that from the latest data in our analytics tool, we have observed 500 conversions from channel sequence - ‘Google Search Ads > Facebook Ads’ and 300 conversions from the channel sequence ‘Facebook Ads > Google Search Ads’. The following table shows how conversions will be attributed to the two channels - 

Please note that we may also use the revenue from each channel sequence for calculating the ROI from each channel/campaign. 

There are some tools/packages already available in the market that works on the Shapley Value principle for attributing conversions to channels, some of these tools are listed below - 

  1. Data Driven Attribution Model in Google Analytics 360
  2. The Fractribution R Package By Google 
  3. Media Spends Attribution By Tatvic

Concluding Thoughts 

We have seen how the Fractional Attribution Model can help us in assigning conversions to multiple channels and multiple campaigns. Concludingly, I would like to highlight the key features of the Fractional Attribution Model -

  1. Fractional Attribution Model uses data for assigning conversions to channels, it is not a rule-driven model
  2. It considers the frequency of occurrence as well as the position of occurrence of various channels 
  3. It is based on a Game Theory Principle - ‘Shapley Value’ and is not a machine learning model 
  4. There are attribution tools/packages in the market that works on the Fractional Attribution Model 

With these thoughts, I would like to conclude this article. In an upcoming article, we will be discussing the Markov Model in detail.

References 

Answering Every Marketer’s Dilemma - Which attribution model to choose? All about Fractional Attribution - Part 1

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Working as a Digital Marketer, you would have definitely faced the above situation. Marketers in an organization will be running multiple campaigns across various channels, some of these campaigns will be aimed at driving brand awareness while others will be aimed at driving engagement or direct conversions.

A user typically interacts with multiple channels or campaigns before converting, consequently it is unjust to attribute the entire conversion to a single channel. 

Most of the sophisticated analytics tools in the market allows you to track the entire journey of your customers. These tools also have predefined campaign attribution models. Let us try and analyze some of the most commonly used models with the following sample user journeys - 

The Last Click(LC) or the Last Non-Direct Click(LNDC) is the most common attribution models used nowadays. The LC model attributes the entire conversion to the last channel whereas the LNDC model attributes the entire conversion to the Last Non-Direct Channel.

If we analyze the six user journeys using the LC or the LNDC models, we might end up reducing our spends on the Youtube Video Ad even though the campaign has been very efficient in driving acquisitions. Consequently, using LC or LNDC does not seem to be an optimal strategy. 

Another commonly used attribution model is the First Click Model(FC) which attributed the entire conversion to the first channel. We analyze the user journeys using the FC model, we might end up spending only on the Youtube Video Ads. Though the Youtube Video Ads have been effective in acquiring new users, the Ads have not been instrumental in driving conversion. Concludingly, leveraging FC model does not seem to be an optimal strategy either.

A Linear Attribution Model distributes the conversion equally among all the channels in a conversion path. If we try analyzing the journey for User F using the linear attribution model, The fractional contribution of Facebook Ad, Google Search Ad, and Youtube Video Ad will only be  (1/110) each in the conversion (110 is the total number of channels is the channel sequence for User F). The contribution of the direct channel will be (107/110) as the user has used the directly channel 107 times to navigate to the website. 

Even if we consider other attribution models such as The Position-Based Model or The Time Decay Model, all these models are driven by rules based on human intuition and not data. Moreover, none of the models considers the position or the frequency of occurrence of different channels.

Please refer to the article to know more about the various attribution models. 

We now know that considering the position and the frequency of occurrence of channels can be instrumental in designing an optimal attribution model.

In the next blog in this series, we will be talking about one of the most effective mechanisms for assigning conversions to channels based on the position and the frequency of occurrence.

 

 

Tracking AMP Pages with Google Analytics & Google Tag Manager

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Tracking AMP Pages with Google Analytics

Accelerated Mobile Pages (AMP) are not a new concept. You must have heard about it, and worked on implementing it for your website as well in the last year and a half. For all those who are still discovering AMP, let me give you a very brief idea of what it exactly is. AMP is an open-source technology that allows your website’s static content to be rendered instantly on mobile devices. In simpler language it means that this technology is basically a stripped-down form of HTML that is meant to help the mobile pages load really fast, improving the user experience on mobile.

What makes it so fast? That is something that a lot of digital marketers and companies have talked about. Moz has done a great job explaining all that you need to know about AMP in one of their Whiteboard Fridays.

At the Google I/O 2017 conference, Google announced that more than 2 billion AMP pages have been published by 900,000 domains and these pages are now loading twice as fast resulting in a significant improvement in the traffic as well as the conversions.

Measuring the performance of such pages to be able to make constant improvements and derive actionable insights is critical and hence, in this blog post, we are going to focus on setting up the tracking of these Accelerated Mobile Pages in Google Analytics.

AMP & Google Analytics:

AMP allows us to track user activity in google analytics through the amp-analytics tag. You can track your AMP pages and user actions accurately in Google Analytics with and without GTM. The post is therefore further divided into the following two parts:

  1. Using AMP analytics library [Without GTM]

  2. Using GTM for Tracking AMP [With GTM]

Using AMP analytics library [Without GTM]

AMP doesn’t support javascript and so we, unfortunately, cannot use the analytics.js library to track AMP pages. Although, we can use the amp-analytics library which is provided by amp project.

<script async custom-element=”amp-analytics” src=“https://cdn.ampproject.org/v0/amp-analytics-0.1.js“></script>

Let us take a look at basic pageview tracking setup and event tracking setup for AMP pages in Google Analytics without any dependency on GTM:

1. Basic page view tracking:

To implement basic pageview tracking, the following code needs to be implemented on all the AMP pages:

Basic page view tracking

The above-given snippet should be implemented after the AMP analytics library code snippet. All you have to do is insert the property id of your Google Analytics and the page title value in the snippet. Once this tracking code is set up, your basic pageview tracking is implemented and you can start analyzing the basic performance of your AMP pages.

Additional tip: We can also take advantage of custom dimensions in Google Analytics for tracking AMP pages.

For e.g, I want to pass the author’s name with every pageview. As we do normally in Google Analytics, using the index value of Custom Dimension, we can start capturing the author name as desired. In the below-given code replace the author’s name with the actual author’s name.

Basic page view tracking

2. Event tracking:

Custom event tracking allows us to track detailed user behavior on the AMP Pages. To track events in AMP, use the tracking code given below:

Event tracking

Define the eventCategory and event action values as per your reporting requirements that you are looking to track in your Google Analytics. Furthermore, we shall also have to replace the

value of selector with CSS selector of that specific element which we want to track and if you want to track multiple elements, you can do so by inserting selectors with semicolons. We can also send noninteractive events in Google Analytics while setting up the tracking for AMP pages. To do so, we need to add the below JSON in the JSON of event tracking right after the vars key:

extra url parameter tracking

Using GTM for Tracking AMP Pages:

To get started, create a new container for AMP in Google Tag Manager.
new container for AMP in Google Tag Manager
Now, to add GTM on the amp, we need to install two code snippets on all amp pages as you can see in the screenshot below:
AMP Analytics tracking code integration
Now, let us take a look at the basic pageview tracking setup and event tracking setup for AMP pages in Google Analytics with Google Tag Manager.

1. Basic page view tracking:

Connect a GTM tag with addition of the GTM code to start with pageview tracking as shown in the screenshot below:

Add your Google Analytics property ID as the tracking ID in the UA tag. Add default All Pages pageview trigger as a trigger of this pageview tag and voila, you are all set to start tracking the AMP pages with the help of Google Tag Manager

2. Event tracking:

Event tracking on the AMP page is the same as the normal page event tracking. We just need to configure one UA tag with track type as event and have to provide category, action, and label for same.

Going further, we need to add a trigger to be able to send event data in Google Analytics and set triggers like click trigger, scroll trigger, and visibility of elements so as to successfully fire this event tag.

By creating and adding triggers as per your tracking requirements, the tags will start sending data to Google Analytics when the trigger conditions are met.

Conclusion:

We’ve covered the basic implementation of tracking AMP Pages in Google Analytics. Over the next couple of weeks, we will be focusing on various analytics quick hacks and advanced AMP tracking in Google Analytics and the benefits of doing so, with a series of blogs. So stay tuned to this space to read up more on this topic.

If you are facing any issues in setting up your AMP tracking in Google Analytics or if there is a topic in particular that you’d like us to cover in our series of blogs on AMP tracking in Google Analytics, please feel free to let us know in the comment section below.

Campaign Attribution in Firebase Analytics for Tracking Mobile App

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Firebase tool is like a dream come true for product managers owing to its smooth app development capabilities and the complimentary app analytics features for analysis & insights.

In my previous blog on Firebase analytics for mobile app I had shared a few tips on how to enhance Firebase analytics reporting, identified its limitations & later discover how swiftly we can overcome them.

One of the actionable items in that blog was to attain the General Campaign Attribution data for Android app users in Firebase Analytics Reporting and we are going to cover the same in detail in this blog post.

So what’s missing in Firebase Reporting?

Undoubtedly the analytics reporting aspect of Firebase covers a wide range of important events that add value to your business. But unfortunately, when it comes to the “Attribution” section of reporting, it has its limitations and what is missing is the following:

  • No custom campaign attribution is available for App Installs.
  • The re-engagement campaigns attribution data is missing.

Elaborating on the above problem statement, currently, Firebase Analytics tracks Attribution for a limited number of Advertisers listed here. For any other platform or source/medium apart from those, the data tracked will be attributed to “direct”, which is a major problem.

Workaround to Track Custom Campaigns

In this blog, I will walk you through a solution that is a workaround developed by our team at Tatvic and will enable you to track custom campaigns through deep links for re-engagement attribution. The solution offered here that for the second problem statement mentioned above.

Let’s get an understanding of how the Campaign Attribution in Firebase Analytics will be done:

1. Deep link with UTM tagging

A prerequisite to being able to track custom campaign data is that all the links through which the users land on your app should have proper UTM tagging to it. Below is a sample URL:

https://shop.tatvic.com/?utm_campaign=test_dynamic&utm_medium=internal_campaign&utm_source=tatvic


2. Fetching UTM parameters

When the user lands on the app through the link, we fetch the UTM parameters form the link.

The below code describes how to fetch the UTM parameters from a deep link in Android.  

Note: The above code is for Android only. For similar implementation in iOS, you can refer to the iOS document and get the UTM parameters.

Also, Read Firebase Analytics Reporting

3. Sending campaign data along with an event

Being aware of the fact that Firebase Analytics is event-based, the campaign attribution is viewed against the “first_open” event only in Attribution Report of Firebase.

And we have no control or access to this event(first_open) and when it is fired. Thus, we define or use alternative event and track the campaign parameters along with it.

In our solution, we will use the suggestive event of Firebase Analytics i.e. “app_open”.

FirebaseAnalytics.Event.APP_OPEN


4. Enabling event as attribution in Firebase Reports

Since we are now tracking the campaign data, it is necessary to view the same in the reporting section. However, to view the campaign data with events, your very first job is to

“mark the event as conversion in the event report”

After enabling the toggle button, very soon you will be able to see your attribution report getting populated with fine campaign data.

Note:

The attribution will be only done for the count of events, only after they have been marked as true for conversion in the Event Report.

Comprehensively with the above set of actions, you will be able to start tracking the custom campaign data for deeplinks or re-engagement campaign in Firebase Reporting successfully. The above approach tracks source/medium in Firebase, with a custom event (“app_open”) and not with the default attribution of the “first_open” event that we send.

Below is the glimpse of a report with custom campaign attribution data:

Conclusion

Although we have achieved tracking Custom Campaign Attribution for Re-engagement Campaigns, unfortunately, we still have no sight of Install Campaign data in Firebase Analytics.

Are we really losing hopes on Install Referrer Data in our Analytics Reports?

Absolutely not! We are not going to give up so soon.

We have an in-house solution for tracking Install’s Campaign data in Firebase. The solution that we offer is quite savvy, it allows you to track the Install Referrer data in both Firebase and Google Analytics Platform. But not limited to ourselves we can further track these Install Data in other Analytics platforms too. For further assistance do reach us out and we will be glad to serve you!

How to Customize Google Analytics Campaign Timeout For Affiliates

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As a marketer, we all should have used the feature in Google Analytics to set the Campaign Timeout period. By default, campaign timeout settings in Google Analytics are set to 6 months. What it means is, GA will stop giving attribution to a specific campaign after 6 months. We also have the option to change this setting to customize the campaign timeout period ranging from 1 minute to 24 months depending on the business need.

                                       Image: Campaign Timeout Settings in Google Analytics

The idea behind customizing the campaign timeout period is to adjust it according to the campaign duration. Or, the duration for which we think the campaign could be relevant or should get the attribution.

For example, brand campaigns run for user acquisition tend to be relevant for months. Whereas, micro-campaigns like one-day offers have an impact on users for a few days.

Problem:

Here, we may start having a problem with setting up a campaign timeout period. As the setting is applied at the GA property level, the same configuration will be applicable to all the GA views and thus to all the campaigns.

Many times, we are running many campaigns with different goals - user acquisition, user retention, etc.

We may want to set up a different campaign timeout period. Going by the default settings in Google Analytics, all the campaigns will keep getting attribution for six months.

Businesses that acquire users from affiliates face a bigger challenge. If we are getting users from certain affiliates for only two weeks, we would not want to pay that affiliate after the contract is over. Hence, another possible problem is the fact that relationships with different affiliates vary a lot.

Possible Solution:

Looking at the severity of the problem, Tatvic came up with workarounds. The solution allows us to set up different campaign timeout periods for different affiliates or campaigns.

There are constraints in GA configurations. To overcome it, we create a new Google Analytics property for the affiliate with a new campaign timeout period.

For sending the user behavior data in both the GA properties without impacting current reporting, we make some modifications from Google Tag Manager. This way, we are able to track affiliate traffic as per the specific need and also make sure that rest of the reporting is intact.

Conclusion:

Now that we are able to customize the campaign timeout period for affiliates, we have a clear picture of which campaigns are performing better and can compare affiliate performances.

To make the reporting simple across different GA properties, we use another fantastic product from Google - Looker Studio. Google Data Studio allows us to easily build reporting dashboards from many data sources.

This way, we get all the numbers related to Affiliate traffic in one place.

What’s Next:

The affiliate marketing industry is expected to reach $6.8 billion by 2020. If you are already working with affiliates or planning to do so in the future make sure you customize the campaign timeouts appropriately.

We hope the blog proved to be insightful to you and helped you in understanding the importance and know-how of customizing campaign timeouts for affiliates. If you have any doubts or are looking for clarifications, please drop in comments and we will surely get back to you.

Firebase Analytics tips to Control Mobile Application Limitations

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Introduction
Firebase is one of the popular products from the Google Analytics 360 product family that has rapidly gained an affinity as a go-to tool for App development & analytics. Firebase SDK provides a bagful of features to make the end-to-end process of mobile app development run as smoothly as a hot knife through butter. After its acquisition by Google Analytics, Firebase is now a wholesome platform for your app development & app data analytics requirements. Today we are going to focus on Firebase Analytics and the limitations we face while using the tool for data analysis & insights. In this blog, we will also look at workarounds for each of the major limitations & discover how swiftly we can overcome them.

Why we love Firebase Analytics

  • No additional cost for analytics data collection - yes it is totally free of cost.
  • Unlimited data collection - can collect up to 500 unique events.
  • Crucial events’ data collection is done automatically. This includes events like App installs, App uninstalls, In-app purchases, App updates, etc.

There are quite a few other bundled-up features of Firebase Analytics such as Crash Reports, Notifications Setup that targets the Audience generated by Analytics, and diverse sets of events.

Firebase Analytics Limitations: Our wishlist for Firebase Analytics & a few basic workarounds to overcome the limitations and achieve better reporting

  1. Restricted Reporting :
    • Firebase reporting console barely offers any course of action to view data in diverse ways. We have the one and only “Events” Report to view all the tracked events at once. Elaborating further, the Events Report provides no major cardinality on reporting apart from the option to apply a basic filter on either “Event Name” or “User Property”.
    • Another major limitation of the Event Report worth highlighting is the inability to view the extra parameters tracked with one single event in the said report. Note: Firebase Analytics can report data only on 10 text parameters and 40 numeric parameters in the Event Report.

      Image 1 : Setting Up Parameter - Limitation.

    • Firebase also falls short of API support so as to access the collected analytical data.

    Workaround: Your best bet to access the raw analytics data is to link your Firebase Analytics data with BigQuery.  And when it comes to BigQuery, performing analysis on massive datasets is simply more straightforward. This will solve a major issue of limited data access, and, at the same time, we also gain the liberty to perform analysis on diverse datasets in a single pane.

    Note: Linking to BigQuery will cost us a few pence!

  2. Event Limitations :
    • Despite the fact that Firebase offers unlimited event collection for each event type for your App, it has confined us to the range of the said event types. This means we can only have 500 unique events for an individual app in the Firebase project. To begin with, this limit does not seem much of a hurdle, but if we revamp our app tracking or if we keep updating the event tracking process, without a doubt we are going to meet the dead end of the reporting platform.

    Thus, precautionary steps to manage the event limitations are:

    • To wisely design the event names before they are implemented.
    • To define a standard structure, identify the events and the required parameters for event tracking in an app.

    For Example:

    • Navigation Event: This event is templatized for all the navigations that a user can perform within an app.
      “navigation_<from_to>” - here “from” and “to” are navigation sections defined in-app.
      Parameter1 - intermediate of navigations to be tracked in this parameter.
      Eg. navigation_home_help, navigation_home_mens-shirt.
    • Product Interactions: This event is templatized for user interaction with the product.
      “product_<interaction>” - various product actions(i.e. View, click, addtocart) can be tracked.
      Parameter1: “Category”
      Parameter2: “product name”
      Eg. product_view, product_detailed.
    • For Articles:  This event is templatized for tracking articles.
      “article_<action>” - various article actions (i.e.  viewed, click, read_began, read_incomplete) can be tracked.    Eg. article_clicked, article_read_began,article_read_complete.
  3. Campaign Attribution :
    • The attribution of Install referrers in Firebase is controlled by Firebase SDK itself. Currently, Firebase tracks attribution for a limited number of Advertisers which are defined in this link. For any other platform or source/medium attribution, other than the listed Advertisers, the default attribution of source/medium will be done to direct.

    Workaround: Knowing the fact that we are missing out on a major custom campaign attribution, we need a quick fix on it. For this, we have worked out a solution which I shall share with you as a separate blog post.  Our custom solution includes tracking both; install referrers and custom campaign data. Below is a glimpse of the Attribution Report for Custom Attributions.

      Image 2: Attribution Report with custom “source” value.

Conclusion
Here, we have discussed how data can be analyzed in a better way by linking BigQuery as compared to normal reporting. Also, for reporting it is necessary that we collect meaningful data and as mentioned in point 2 above, do not reach the limitation of the event type that is getting tracked. We have already seen a few examples of how to structure and design the events in this blog, and we hope you find them resourceful.

Quick Tip: If using Firebase Analytics, do plan out the tracking approach and efficiently design the events for the same. This will yield absolute data in reports and help you analyze it.

The limitations discussed above cover a large ground of Firebase Analytics. However,  if we have missed out on any titbits on the same, do comment in the below section and we will be happy to acknowledge it!

4-Steps Tip to Detect Facebook in App Browse

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Identify FaceBook In-App Browser

 

Google Analytics offer a dynamic platform to track and analyze your website traffic in various ways. Talk about the hidden gem of Google Analytics, talk about Acquisition Report. This blog explores the Acquisition Report to understand the diverse channels that identifies the source of origin from where users lands on to your website.

Since it is crucial to know the source of your website visitors, the Acquisition Report helps in comprehending the right channel, referral, and source performances.

However, the challenge in Google Analytics is to track a web page that is displayed in Facebook in-app browser. The implausible result can be any of the following:

  • Difficulty in identifying the actual origin of traffic.
  • Inadequate estimation of the ad campaign(s) that are active on social networking sites.

To bridge the above gap in Google Analytics, we will here understand how to track a web page that is rendered in a Facebook in-app browser.

Following are the prerequisites to track the actual in-App browser in Google Analytics:

  1. A Google Analytics Custom Dimension (named - browserType) preferable of Session Scope.
  2. Google Tag Manager(GTM) implemented on web page.

We will now move to the implementation steps :

  1. Create one Data Layer variable “browserType” in GTM

     2. Create a Custom HTML tag in GTM and add the following code within it

NOTE : Make sure that the Custom HTML tag triggers before or along with the the pageview in GTM implementation.

3. Set the browserType in a Page view Tag

NOTE : The index number set in custom dimensions is equal to the index number of the custom dimension in which you desire to view the data

    4. You can also track the browser type with an event. Below is the Event Tag configuration:

 

That’s that! You are now all set to track the Facebook in-app browser.

Below image is just a glimpse of the report you would see post atoning to the above-mentioned changes.

Leave your comments to further offshoot the In-App Browser Tracking and we will be glad to assist you further.  

How to setup Google Analytics site tagging & tracking with gtag.js?

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Since the last few weeks, you must have heard and read quite a lot about gtag. Have you been wondering about analytics.js? Do you think it’s a game-changing method for implementing Google Analytics across your websites?

In this blog, I have curated and laid out some general information about gtag.js and as a cherry on the cake, we have a few tips for you so that you can use gtag.js optimally.

Google Analytics has released a new implementation method, called the Global Site Tag (gtag.js) and it is yet in beta.

First of all, let’s address a rumor that’s been going around and get it out of the way. I have come across quite a few comments and questions where there seems to be a misconception that gtag.js is a newer version of GTM. But as a matter of fact, that is not true. gtag.js is not a newer version of GTM but rather, a newer version for analytics.js

gtag.js global tracking snippet

 

Why gtag.js?

The ga.js was developed mainly for measuring websites way back in June 2006 with ga.js & dc.js. After some time, Google Analytics came up with a new tracking library analytics.js in 2013, which measures the website but is much more user-centric. Hence, with this update, we can track the user’s journey throughout multiple devices using User Id and so on.

Now, here we are at the end of the year 2017. Analytics.js is more than 4 years old. Google Analytics has come up with another update in the tracking library. This time around, the main reason for making this change is to achieve clean tagging and unification across Google’s site measurement, conversion tracking, and remarketing products.

Setting up gtag.js:

To find the gtag.js code snippet follow these steps:

Step 1: Sign in to your Google Analytics account

Step 2: Click on Admin

Step 3: Select an account from the menu in the ACCOUNT column.

Step 4: Select a property from the menu in the PROPERTY column.

Step 5: Under Property, Click Tracking Info > Tracking code

gtag.js

Note: Put gtag.js tracking code snippet right after opening <head> tag on every page.

Tracking Pageviews:

You just need to put the following line for pageview.

Tracking Events:

Note:

This event will send to the default configured UA id in gtag(‘config’,’<UA ID>’).

Cross-domain tracking in gtag.js:

If you have an eCommerce website or such a website from where your user can travel from one domain to another (not subdomain), you need to set up cross-domain tracking in gtag.js

What is cross-domain?

If you have a website set up as www.source.com and you also own www.destination.com - this kind of setup is called cross-domain. If this is the case then you need to set up the said cross-domain tracking.

Note: www.website.com is the main domain, shop.website.com is the sub-domain and hence for this you don’t need to set up cross-domain tracking.

Setup cross-domain tracking in gtag.js:

If your is able to travel to destination.com from source.com and return to source.com from destination.com, place the following snippets on respective domains as mentioned.

The following snippet needs to be put on source.com

The following snippet needs to be put on destination.com

Tips for Using gtag.js Optimally:-

1. For configuring Google Optimize using gtag.js:

To load an optimized container using gtag.js, one just needs to put the following line With valid GA_Tracking_id and optimize_container_id for configuring Google Optimize.

For more detailed instructions:

Google support link for deploying google optimize using gtag.js

2)  Firing website conversion tracking tag:

Note:

  • For conversion tracking, you need to add your AdWords id in gtag(‘config’,’Adwords Id’)
  • If there is already gtag snippet exists on your page then put gtag(‘config’,’Adwords Id’) after gtag(‘config’,’GA_Tracking_Id’)

gtag tracking

Event Snippet:

3) Client Id Implementation:

In analytics.js, one who wants to send a client id in a custom dimension has to follow some steps instead of that gtag.js gives one step solution.

Concluding Thoughts:

As of now, gtag.js is in beta and it will be rolled out in the near future, complete with all features which are yet not available. Even though, it provides many features, sending data to multiple UA Tracking IDs is one of my favorites.

Using gtag.js is simple as well as beneficial since it lets us use the latest tracking features. Have you had the chance to know gtag.js yet? If yes, we would love to know your opinion on it, and in case of any queries too, please leave a comment and tell us about it.

How Tracking Internet Speed with Google Analytics Can Enable You to Improve Your Website’s User Experience

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Tracking Internet Speed


Tracking Internet Speed

Internet connection speeds are on the rise globally. According to reports from Akamai, world’s leading cloud service provider, global average internet speed increased by 15% year on year to 7.2 Mbps1. South Korea continues to have the highest average internet speed 28.6Mbps, followed by Norway and Sweden. A major shift in internet ecosystem is also observed in Indian markets with 4G revolution recently.

We have observed a tremendous change in the way content delivery happens due to the availability of better internet speed. This has also paved a path for user interaction happening in a more in-depth manner than ever before.

According to studies1, the digital video audience is expected to grow at 8.2% in 2017. User experience has improved dramatically for the new age apps. The inherent restrictions caused by slow internet speeds is no more a roadblock. Businesses need to update their content (website/app) to match to the latest technology trends since the market has moved on from static websites to instant personalized content to users based on their past behavior.

With better infrastructure, fast internet and increasing user base, diverse markets like India have a huge potential for telecom companies. While making the changes, we also need to keep in mind that 40% of mobile internet subscribers are still using 2G network. Though the number of 2G internet users is declining in the country, we have to make sure content is accessible on 2G network as well.

To make the decisions based on users network types, it is imperative to track internet speed for every user we have.

Internet Speed Tracking solution developed by Tatvic team is pretty handy here.

It helps us understand the following:

  • True download speed at specific time
  • Network Type - 2G, 3G, 4G

These reports will be available in Google Analytics for every individual user along with acquisition and behavior data. One can create reports like Correlation between Internet connection speeds and content consumption rates by geography, network type across time.

User Experience
Use Cases:

Content Optimization:

If we observe that most of the users (let’s say more than 60%) are coming from 4G network, we can focus on better designs, adding animations to improve user experience. To increase the user engagement, we can also consider adding more video content as well. But on the other hand, if more users are from slower connections like 2G, the focus would naturally be on content optimization. Less content and fewer user interactions would be key to drive more conversions in such a scenario. In such cases, it’s better to store content on users devices and thus achieve faster content delivery, for which, Progressive Web Apps is the perfect solution.

Personalization:

In today’s dynamic world, ‘one size fits all’ doesn’t work anymore. We cannot have same strategy/design for both slow connection and fast connection users. Personalization tools enable us to serve different user experience to different segments. In above analysis, if we are having users coming from different network types and correlation in conversion rates and app designs by network type is higher, we should consider personalizing content by network type.

What’s Next:

If you are looking to personalize user experience based on network type being used, Google Optimize can be helpful. Its one of the best tools for running AB tests and personalization solutions. It natively integrates with Analytics. So we can use integrations with other Google products like AdWords, BigQuery, DoubleClick For Publishers, etc. to deeply analyze the metrics. Tatvic has posted an informative webinar on Personalization using Google Optimize.

We hope the blog was insightful for you and helped you in understanding the benefits, as well as the use cases of tracking internet speed and network types for your website/app. If you have any doubts or are looking for clarifications, please drop in comments and we shall be sure to get back to you.

Tracking Push Notifications in (UA3) Google Analytics to Interact Users with Brands

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Most businesses nowadays are leveraging technology to make it easier for their customers to interact with them. Technological strides such as the launch of Mobile Apps, Accelerated Mobile Pages, and Progressive Web Apps have been key in facilitating a stronger connection between these 2 entities.

There are over 2.8 Million apps in the Play Store and 2.2 Million apps in the App Store, it is highly likely that a customer is using mobile apps from competing businesses. So, can there be ways to drive customers to use your app over others, once the customer has been acquired? How do you change the status of a user from an active user to a daily or weekly active user?

The answer lies in the glorious solution of Push Notifications. I am sure you are already aware of what a push notification is. As a refresher, let’s go through the definition once again.

A push notification is a pop-up message that looks like an SMS text message or a mobile alert. The app publishers can send push notifications to the user once the user has installed the app. The user need not have the app open to receive the notifications.

How can push notifications help increase user engagement?

An app publisher can send notifications to users at different times. Below are a few examples of when you can send notifications to drive engagements:

  • Notifying users about the sale that the publishers would be hosting in the coming few days. A user is less likely to miss push notifications as compared to emails and SMS text messages.
  • Retargeting users based on their recent activities. For example: Suppose a user has recently booked a flight to New York using your app. Later, you can send notifications to this user with details on hotels in New York during the user’s visit
  • Nudging users drive them to complete a purchase funnel. For example, if you are an E-commerce business and one of your users has added a few items to cart and exited. You send notifications to remind this user to complete a purchase for the products.
  • Sending regularly timed daily/weekly/monthly notifications to users to keep them updated on the new releases on your app.

There can be many such similar use cases where you can leverage push notifications to drive more engagement from the users.

Tracking Push Notifications:

While push notifications can help increase user engagement,  tracking the performance of different types of notifications helps in determining how your users are reacting or interacting with your push notifications.

Analytics Best Practices for Tracking Push Notifications

For gaining insights from push notifications, it is recommended to track the following events:

  • Push Received:

“Push Received” event should be fired whenever a user receives a push notification. But, this event does not indicate that the user has interacted with the notification

  • Push Clicked:

The “Push Clicked” event should be fired whenever the push notification has been clicked by the user. This indicates that the user has interacted with the notification and is interested in the information provided

  • Push Discarded:

The “Push Discarded” event should be fired whenever the user discards the notification sent. This indicates that the user has interacted with the notification. However, the information provided under the notification has not been useful to the user.

The occurrence of events - Push Received and Push Discarded does not indicate that the app has been opened by the user. These two events should be configured as non-interactive events in Google Analytics. The event Push Clicked should be marked as an interactive event as it indicates that the user has shown interest in the information sent.

The events would be displayed as shown below in Google Analytics reports -

Events tracking insights in Google AnalyticsImage 1: Push Notification Tracking Events Report

Insights from Tracking Push Notifications with Google Analytics:

  • The behavior of Returning Visitors:

You can build a mechanism to understand the behavior of your returning visitors. You could also use it to ascertain the app’s usability. A high ratio of Push Discarded to Push Clicked events for all types of push notifications sent could indicate problems in App’s usability.

  • Engaging Push Notifications:

Tracking the aforementioned 3 events for all types of Push Notifications will give you insights as to which notification types perform better.

  • Uninstall Tracking:

If users receive too many notifications from an app, they are more likely to uninstall that app. An indication of the same would be a high number of Push Discarded events. In such a scenario, you can cite the high number of push notifications as a probable reason for the uninstall.

  • The audience for Adwords Remarketing:

Push notifications can help you create an audience for remarketing. Users with a higher number of Push Clicked events tend to engage more with the app. They also have a higher probability of transacting. With this data, you can create segments for this cohort of users and re-market effectively.

Concluding Thoughts:

We all accept that push notifications can drive greater user engagement. But tracking these engagements actually helps businesses to allot resources towards the right type of push notifications. It also gives us an optimal frequency at which notifications are to be sent. Following this practice keeps any negative impact on your user experience at bay.

Have more insights for push notification tracking? Tell us about it in the comment section below and also feel free to leave your feedback for us. We would love to hear from you.

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