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How Ecommerce Marketers can make the most out of Google Analytics

This is what every Business has always dreamt of:

To be sure that if it places an Ad, it will be successful. To invest in certainty. In order to be successful in this business, you must have great, precise decisions. Great decisions begin with one imperative: You need a lot of data.

Don’t worry if you do not have a lot of Data. This isn’t rocket science. You need to simply enable Enhanced Ecommerce Reporting in Your Google Analytics Account.

Everything a User does on your website can be watched, tracked and measured.

Because this is exactly what you need. A LOT of data about your User and your product to help you make great decisions. So, the question here is, have you upgraded your Ecommerce to Enhanced Ecommerce. “Any sufficiently advanced technology is indistinguishable from magic.” - Arthur C. Clarke

These top 7 Enhanced Ecommerce Reports can be put to use for this magic to happen with your Business and improve the conversion rate of your website.

1) Shopping Behavior report.

This will help you understand where users drop off from eCommerce funnel. We can analyze abandonments using dimensions or filters or segments at multiple levels - device, regions, source etc. And remarket to the drop of users like shopping cart abandonment.

By analyzing this you will eventually increase your Transactions and reduce drop off on your website

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Source: Google Analytics Demo Account


2) Checkout Funnel

In order to have seamless customer experience, you need to have a smooth payment process. With the help of checkout funnel, you can investigate the steps disrupting the checkout phase of the funnel.

The funnel will moreover look like shipping address->check delivery ->review the order and pay-out.

Remarket on several checkout steps will optimize the checkout page and increase CVR.

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Source: Google Analytics Demo Account


3) Product list performance reports

Use this report to analyze which product feed and features lead to conversions. We need to tracklist names like Catalogue search, wish list, related products and recently viewed. Figure out lists leading to conversions and maximise product list CTR for those lists. You can also increase Conversions from cross-selling and upselling. 

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Source: Google Analytics Demo Account


4) Internal promotions report

This will be helpful to measure the CTR of a banner on website, last-click transaction and sales measure. By analysing this report of banner names, ids and creatives also the position can lead us to actionable insights of restructure position of banner or remove banner with low CTR.

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Source: Google Analytics Demo Account


5) Product Performance reports

This powerful one measures the performance of products and brands’ categories by deep diving into purchase funnel in shopping behavior. This will help us decide to promote or stop selling a particular category. Three Powerful metrics in this report are

a) Buy-to-detail b) Cart-to-detail c) Average Quantity.

How to use these?

Use case 1:

  • Filter the product report by cart-to-detail less than your average value of cart-to-detail rate.
  • Potential products for optimizations are the one with decent revenue contribution but have a lower buy-to-detail rate.
  • The root cause of them having lower buy-to-detail rate can be because of the disconnect between Ad communication, technical issues, disturbed checkout process, shipping cost being more than product cost or perhaps competitive pricing.

Use case 2:

  • Filter with greater than average cart-to-detail. 
  • Potential products to be clubbed are products with decent revenue & higher average quantity, buy rates and cart rates can be clubbed together and sold in a pack of 3/6/8 etc.
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Source: Google Analytics Demo Account


6) Sales Performance report

You can use this report to evaluate the accuracy of transaction data in Google Analytics vs your CRM. Checkpoints can be revenue quantity, shipping charges and taxes. Potential Areas for optimization can be identified wherein transactions with shipping charges high for low average order value (AOV) products.

Create clusters of products for market basket analysis or cross-selling by the combination of product performance and sales performance report.

Eg: If the user is buying milk, showing him recommendations of Bread will eventually make the user purchase bread as well. Want to know how to create a basket of these products and find correlations between them?

Merkle Sokrati has a solution which can analyze and map the product recommendation model for you to cross-sell. From this analysis – you can create clustered userID of products categories brought together. Cross-sell to users who have brought one or more of the related products and probability of staying live resulting in Users having high lifetime value (LTV) and less churn rate.

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Source: Google Analytics Demo Account


7) LTV: Lifetime value also an industry buzz word

Myth: Needs rocket science, so marketers target bulk audiences in a basic way.

Some habits of high LTV customers - high order value, high profit, low retention cost, low acquisition cost, volume buyer, the frequent buyer.

We can get this data in Google Analytics under User Explorer in Audience reports.

The revenue LTV is mysterious, we do not know if it's based on historical data or a future prediction. Unfortunately, we are also not able to export this report. Don’t Worry!

Merkle Sokrati has a solution for you to find & target these high LTV customers. We can pull out this data from API and pass it on to an LTV prediction model. The model then returns us future predicted revenue, expected number of transaction and probability of a customer staying alive, Users of high LTV and churn rates for the same.

Actionable from this:

  • You can rank your users, acquisition, channel, campaign and many more.
  • You can also Analyze high LTV locations, source/medium, campaigns.

In order to figure out Campaign analysis, you can use a simple formula

CPA+ CPR < LTV.  if this is not the case the ROAS is negative and campaign needs serious optimization.

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Source: Merkle Sokrati RFM model

Model bucket your customers based on user previous behavior. By targeting each cohort with personalized communication will push the users to the bottom of the funnel.

Also, are you struggle with google LNDC attribution model? Try out our Markov attribution model, where you can analyze your campaign data and conversion and make the see a data-driven attribution to analyze the real effect of your Marketing Campaigns. 

Data helps you listen more to your customers. We hope this was helpful.


Merkle Sokrati offers Analytics Solutions, so if you would like to learn more, please contact us. If you are concerned about your Google Analytics account, we can perform a full Google Analytics Audit. Also, we can help you with LTV analysis and Market Basket mentioned above.