Shopify Store Analysis: How To Analyze Shopify Data Exports

According to Shopify's 2023 Commerce Trend report, the pandemic led to a 77% year-over-year increase in online shopping within just a few months, effectively pushing the progress and acceptance of eCommerce forward by half a decade

No matter what you want to purchase, you can order it online and get it delivered to your doorsteps.

With an ever-increasing number of customers purchasing online and more than two million Shopify stores in existence, we’re seeing a significant shift in consumer behavior.

What has changed?

They have more options now than ever before.

Gone are the days when you could acquire new customers and retain already-existing ones by running ads, rolling out email newsletters, and promoting your products on social media.

Being strategic is the need of the hour.

And if you genuinely want to build strategic marketing campaigns, diving deep into your customer data is a must.

And that's what Gigasheet does best.

Analyze Shopify Data to Increase Your Sales & Margin

By analyzing Shopify data export, you’ll be opening up a world full of possibilities.

For example, you can use this data to understand how many customers have not purchased from you in the last 30 days or maybe 60 days.

Once you have the list, you can roll out a promotional email to offer them a coupon or discount and retain these customers.

But that’s not all.

You can study this data to:

• Predict trends: learn what kind of products people are purchasing or purchasing behavior, like seasonality or product popularity by region
• Build strategic marketing campaigns to increase your sales and retention
• Manage sprawling inventory, product pages and even optimize your drop-ship supply chain
• And so much more!

There are ‘n’ number of ways you can use Gigasheet to analyze your Shopify store’s data.

The more strategic you are and the more you try, test, measure, and optimize your strategies, the better will be the results you achieve. (For example, you can also use Gigasheet to analyze Shopify fraud.)

But how do you analyze and filter all of it?

How to Analyze & Filter Shopify Customer Data

The answer is – it depends. There are two major ways to analyze and measure Shopify store customer data:

• You can use an eCommerce customer data platform like Insider, or Klaviyo to unify all your eCommerce customer data in one place to analyze and filter it to understand your audience and deliver personalized experiences.
• Or if you prefer the good old-fashioned way (spreadsheets) – you can either use Microsoft Excel, Google Sheets, or an advanced spreadsheet analysis + filtering tool like Gigasheet.

The Problems Associated with Using an eCommerce Customer Data Platform

We’re not saying that you shouldn't use a Customer Data Platform.

These platforms are good.

But they're not for everybody. Some problems associated with using eCommerce CDP platforms are:

• CDPs have a steep learning curve.
• These platforms are good for processing large chunks of data – but if you’re not generating data at scale, then using a CDP doesn’t make sense. Customer Data Platforms are costly, and even if you subscribe to their free version – you’ll find yourself restricted.
• Some people like the good old way of analyzing and filtering data, i.e., spreadsheets over using CDPs.

You Can Use Microsoft Excel or Google Sheets, BUT...

If you like analyzing data using spreadsheets, Microsoft Excel or Google Sheets are great platforms.

BUT! They have their limitations.

First, if you’re generating huge chunks of data, then your spreadsheet file size may already be too big for Excel or Google Sheets to process. You may end up facing the “Excel Not Responding” or “Google Sheets Not Responding” error. You probably don't know this - but it’s hard for Microsoft Excel or Google Sheets to process large spreadsheet files smoothly.

Secondly, let’s say your data file is in JSON format – then you’ll have to manually import data from JSON to Microsoft Excel or Google Sheets. There are a few formats that Microsoft Excel or Google Sheets don’t fully support. So, figuring your way around is too much work.  

Lastly, the more you filter your data and perform calculations on Microsoft Excel or Google Sheets, you’ll find the file size getting larger, and if the file gets too large, then chances are that Excel or the browser that’s running Google Sheets crashes.

Use Gigasheet to Filter & Analyze Huge Shopify Store Data Exports

No matter how big your Shopify data export file is, Gigasheet users can easily filter and analyze it without facing any technical challenges. You don't need a database, code, or CDP. Gigasheet makes analyzing huge data files as easy as using a spreadsheet.

Simply upload your CSV (or zip multiple CSVs) and apply filters, use the search functionality, group your data by column, tap into the pivot mode and take full advantage of the analytic features. You can even load files directly from Google Drive, OneDrive, Dropbox, and more.

You can leverage Gigasheet to analyze Shopify store data like:

• Inventory and Product Pages - quickly filter all of your product pages, inventory and vendor data, and more
• Customer and Marketing Lists – information about your customers (name, email id, address, age, gender, and more)
• Transactional Data – easily aggregate and calculate sales totals by product, segment, vendor
• Behavioral Data – analyze how much time is being spent by your customers on your store (web and/or mobile application)
• SEO / SEM Data - analyze and optimize your web traffic and paid ad placements

We fetched a transactional Shopify store dataset from Kaggle – where we got out hands on every single transaction that has taken place on an eCommerce store. Here’s what the dataset looks like:

Shopify Store Analysis

This dataset comprises the following column groups:

• Invoice Number
• Stock Code
• Description (Name of Product)
• Quantity
• Invoice Date
• Unit Price
• Customer ID
• Country

Now, you can do literally anything with this data.

By simply clicking on a row, you can get an extended view of individual entries (see below). This is especially helpful with wide files (many columns) that are often exported from Shopify stores.

Shopify Store Data

Now, let’s say you want to remove the clutter and eliminate the Stock Code entry from your dataset. You can do this by unchecking the “Stock Code” column group as displayed in the screenshots below.

Gigasheet Hiding a Column

Now, let’s apply a filter or two to dive deep into the customer data.

Filter Data by Location

Let’s say you want to find out how many people from France purchased your product. To look at the number of transactions from France, we applied the following filter:

Filter Shopify Data Customer Data by Country

To apply a filter, click on “Filter,” as displayed in the screenshot below:

How to add a filter to Gigasheet

Here are the results upon applying the “Country” filter:

After applying country filter to Shopify store data

Let’s say you want to offer a special discount to your customers based in France, then you can apply this filter to get your hands on the Customer IDs of your customers based in France.

Now – you can use these Customer IDs to fetch their names and email addresses – which you can use to build strategic email marketing campaigns. You can even use these customer IDs to dive deep into the target demographic of your customers based in France like age, gender, and more.

Filter Data by Shopify Product Description (or Name)

Let’s say you want to understand how well a specific product is performing – like:

• How many quantities did your customers purchase?
• Or who purchased a specific product?
• Or customers from which country are purchasing a specific product?

Let’s apply the following filter (we want to understand the performance of the product “WHITE HANGING HEART T-LIGHT HOLDER”):

Filter Customer Data by Description

Here are the results:

Filter Shopify Customer Data by Description Results

We have over 2,369 entries, and as we dived deep, we found out that most customers who purchased the White Hanging Heart T-Light Holder product are based in the United Kingdom. Also, we got our hands on Customer IDs that purchased the White Hanging Heart T-Light Holder product – using which we can get our hands on further information about our customers.

Now, let’s apply the following “AND” filter to look at the transactions related to the “White Hanging Heart T-Light Holder” product from the“United Kingdom” -

Adding Multiple Filters

 Here are the results:

Adding Multiple Filters Result

There are over 2,271 entries – which means a majority of people who purchased the White Hanging Heart T-Light Holder product are from the United Kingdom.

That’s how you can filter your data using Gigasheet. You can narrow down your search and get your hands on very-specific data by using a combination of filters and applying the AND/OR conditions.

Now, let us show you the Group by feature – which allows you to group your data by column.

Let’s Group Our Data by Location

To group your data in Gigasheet, you can click on “Group” as displayed in the screenshot below:

Group Shopify Customer Data by Country

Let’s group our data by “Country.”

Grouping Data in Gigasheet

Here are the results:

Grouping Data Results

As you can see, most transactions are from the United Kingdom (495,478), followed by Germany (9,495), France (8,557), and so on. As you click on these countries, you’ll see a drop-down with their entries like this:

Grouping Data Results Gigasheet

Grouping your data is a great way of organizing your data.

Now – let’s say you want to find the average unit price – per country. So what we’ll do is click on the blank section under “Unit Price” as displayed in the screenshot and select “Average.”

Grouping Data Results Gigasheet Average

Here are the results:

Grouping Data Results Gigasheet Average Results

The average unit price of transactions are:

• United Kingdom – 4.532
• Germany – 3.967
• France – 5.029

Similarly, you can perform more calculations and do so much more!

We calculated the total quantity by country and here are the results:

The total quantity sold by country are:

• United Kingdom – 4,263,829
• Germany – 117,448
• France – 110,480

Let’s Further Group Our Data!

We first grouped our data by country. Now, let’s further group it by product.

Gigasheet Multiple Grouping

Yes – it’s possible to add multiple layers of grouping.

First, we grouped our data by country and then by product.

Here are the results:

Gigasheet Multiple Grouping Results

You can add another layer of grouping to organize your data even further. Now, you can get entries related to White Hanging Heart T-Light Holder product from the United Kingdom with ease like this:

Gigasheet Multiple Grouping Results Column Expansion

The magic of Gigasheet!

Now, what if we told you that’s not all?

Introducing Gigasheet’s Pivot Mode: The Ultimate Way To Analyze Your Shopify Store

You can turn on the Pivot mode from the right-hand panel.

Gigasheet Turn on Pivot Mode

We unchecked all the boxes to show you the full force of Pivot mode.

Gigasheet Pivot Mode

Next, we dragged the “Description” entry under Row Groups. As we did it, Gigasheet automatically added “Invoice Number” as a value under “Values.”

Results:

Gigasheet Using Pivot Mode to Group eCommerce Data

Now, let’s say you want to find the total quantity of products sold for every product you have in your inventory. So, we added “Quantity” under “Values.”

How to Use Pivot Mode in Gigasheet

Let’s say you want to find out how many transactions took place related to a specific product from people based in different countries.

So, here’s what our pivot values were:

Gigasheet Pivot Mode Settings

• Row Groups: Description
• Values – Sum (Quantity)
• Column Groups – Country

Here are the results:

Gigasheet Pivot Mode Results

Use Pivot mode once and we guarantee you’ll fall in love with it.

Use Gigasheet to Analyze & Process Your Shopify Store Data!

In this blog post, we showed you how to analyze Shopify store data. No matter what your dataset is, whether it's inventory, consumer transactions, or behavioral information, we highly recommend using Gigasheet to dive deep into your analysis.

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