Shopify has readily available data that is hard to access in other tools like Google Analytics. Some of those data are customer data, like the customer type that marks if the transaction is from a first-time or returning buyer. Other data that are immediately available that could be useful are discounts, returns, products bought per customer, etc.

Here are three analyses you can do with immediately available Shopify data:

1. Assess long-term sales impact from customers who took discounts on their first purchase, compared to people who have not taken a discount.

Many businesses offer discounts for the first purchase, so it is important to see if customers will return and buy more after getting the deal. You can easily set up a report with the right data to analyze to answer this business question. However, the tricky part is setting up the data so it is easy to examine the impact of the cohort of customers.

One approach we have used to tackle this is by querying the following data from Shopify, creating a Tableau data prep flow to create a desirable data output for analysis, and then using Tableau to visualize the data.

The hypothesis is that business owners or marketers are concerned that they’re giving out too many discounts, which could hinder their long-term sales. The action we usually envision from this analysis is determining whether to continue with the deal or scale back.

2. Cohort analysis of the campaign’s effectiveness in driving repeat purchases.

This is a common question that many marketers need to answer, and Shopify data makes it easier to do so. You can quickly generate a report with data from campaigns (for instance, Google CPC, paid social ads, etc.) and the number of purchases from returning customers.

You can go one step further and create customer segments based on those returning customers. For instance, if multiple campaigns send traffic to the store, you can segment customers based on which campaign they came from and compare how each segment performs in terms of repeat purchases.

You can see from this data, Facebook ads that acquired first-time buyers in January and March had a descent repeat purchase rate when compared to February. So now we can look into what campaigns were effective.

The typical hypothesis the business has, which leads to this type of analysis, is to understand better if the ad campaigns should focus on certain messaging or an offer, or shift their budget elsewhere. Then determine if they can double down on a strategy that drives new customers and increases sticky customers that buy again.

3. Campaigns that drove first-time customers or repeat customers using both Google Analytics and Shopify data.

Using Google Analytics and Shopify data together may allow you to get further insights on campaigns that drove first-time or repeat customers. By combining your Google Analytics data with the customer list from Shopify, you can figure out how effective your campaigns are in driving new versus returning customers.

As I wrote earlier, Shopify has good data that marks up the customer type that tells us if the transaction is from a first-time or returning buyer.

If you have your Google Analytics setup to track transaction id or customer id, you can pull a Google Analytics report with utm_source, utm_medium, utm_campaign, transaction id, customer id, and transaction data. With Google Analytics attribution data, marketers will have more flexibility in analyzing beyond Shopify’s last click attribution data in their reporting.

You can certainly use Google Analytics only. However, many businesses may not have data like the customer id or transaction id integrated with Google Analytics, and they may not have detailed transaction data readily available from Google Analytics. Also, many marketers are much more confident with the analysis when we reference the usage of Shopify transaction data in the research.

Under a data environment with partial data, marketers could not understand the full exposure and impact the ads have on sales. So the typical business question tries to better understand if ads are effective, but taking into account attribution is not limited to the last click model. (Or even looking at certain attribution types only.)

In Summary

Leveraging Shopify data, you can use customer segmentation to gain deeper insights into customer behavior and make more informed decisions about marketing campaigns, discounts, product offerings, and more. Quickly and accurately analyzing Shopify data can help marketers understand what drives their sales performance and provide valuable guidance for making sound business decisions.

Ideally, the business stakeholders should have a good hypothesis and data environment that allows us to analyze the data before taking further actions. Without a good business question, there are so many ways to analyze the data, and it may not be an effective way to spend anyone’s time brainstorming without having a possible idea on addressing the issue.

Happy analyzing!