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UTM Parameters

Popular UTM Parameters | Gain insights from where your customers are coming from the most at each segment in your Shopify Store.

Last Update:
Aug 12, 2024

In today’s data-driven e-commerce environment, leveraging UTM parameters offers a distinct advantage by providing clear visibility into the effectiveness of different marketing strategies. The insights gleaned from ECPower's "Popular UTM Parameters" report equip Shopify store owners with the necessary data to refine their customer acquisition and retention tactics effectively.

Detailed Insights from the "Popular UTM Parameters" Report

What's 'UTM parameters' and how do they work on Shopify?

UTM parameters, often referred to as UTM codes or tags, are snippets of code added to the end of a URL. They play a crucial role in tracking the effectiveness of online marketing campaigns across traffic sources and publishing media. Shopify store owners can use UTM parameters to see precisely how visitors interact with their site, helping to pinpoint the success of specific campaigns.

These parameters include:

  • utm_source: Identifies the source of the traffic, such as Google, Facebook, or an email campaign.
  • utm_medium: Specifies the medium the link was used on, like email or social media.
  • utm_campaign: Describes the specific campaign associated with the link, like a seasonal sale or product launch.
  • utm_term: Highlights which keywords were effective in PPC campaigns.
  • utm_content: Helps distinguish between links that go to the same URL from the same campaign, like text link or logo link.

An example of a URL with UTM parameters might look like this:

https://example.com/?utm_source=newsletter&utm_medium=email&utm_campaign=20230101

📢 UTM Parameters Popularity Share Among Segment Members

🔍 Curious about which UTM parameters are most popular among your customer segments?

Let’s dive into some basic insights! For example, within the "24SS Repeater Customer Segment," a whopping 80% of customers have made purchases via 'UTM medium: campaign-email'. 📧

By applying these insights, you can analyze the most popular UTM parameters for other segments like “New Customers in the Last Month” or “Loyal Customers with High Purchase Frequency” and compare them to your overall store data. 🔗

👀 You can view up to the top 100 items in a table format and download the data in CSV format!

📢 Monthly UTM Parameters Popularity Among Segment Members

📈 Track the number of purchasers per month for each UTM parameter across your customer segments.

For example, purchases from 'flow-email' spiked in May 2024. What could have caused this change? 🧐

Leverage these trends to gain insights into what’s driving your sales each month. You can view the data in a table format for the top 100 items and download it in CSV format! 📊

📢 UTM Parameters Popularity for Initial vs. Repeat Purchases Among Segment Members

🛒 Discover which UTM parameters are most popular for customers’ first, second, third, and repeat purchases.

For example, in the data below, more customers made their first purchase via 'display' but then returned via 'cpc' for subsequent purchases. 📊

Understanding these trends can help you fine-tune your marketing strategy for repeat customers! View the data for the top 100 items in a table format and download it in CSV format. 💾

Advanced Insights: Filtering by First Purchase UTM Parameters

🔎 Let’s explore an advanced analysis: filtering customer segments by the UTM parameters used during their first purchase.

For instance, check out the "Second Purchase" column to see which UTM parameters were popular among those who initially bought via UTM Parameter X. 📈

This insight can help you understand the journey of repeat customers and how to optimize their experience from the start!

📢 UTM Parameters Repeat Purchase Frequency Among Segment Members

🔄 Gain insights into the UTM parameters that have the highest percentage of repeat customers in your segments. Compare the proportion of customers who made only one purchase with those who made two or more.

In the example below, the gold-colored area represents the percentage of customers who made only one purchase, while the right side shows those who made two or more. 🏆

Notice how sessions via 'newsletter' often lead to repeat purchases, making it a popular choice for loyal customers. You can view up to the top 100 items in a table format and download the data in CSV format! 💼

Why 'Popular UTM parameters' Insights Matter

While session-based reporting can sometimes be a black box, especially with the increasing use of private browsers that hinder data capture, UTM parameters provide a powerful way to obtain accurate information about user journeys. They are especially valuable for understanding which campaigns drive conversions among loyal customers, which ads contribute to new customer acquisition, and how effective your email marketing strategies are at encouraging repeat purchases.

Use Cases - Recommended Segments Filters

  • Repeat or Loyal Customer Segments: By studying the journey of your current loyal customers, you can better understand what retention marketing strategies work. Analyzing the routes through which they make their first and subsequent purchases provides actionable insights.
  • Segments Based on Initial Purchase behaviors: By examining the purchase paths of different customer segments—whether defined by the product initially purchased or the acquisition channel—you can understand how they transition to repeat purchases.

By understanding the specific sources, mediums, and campaigns that attract and retain customers, merchants can optimize their marketing efforts to not only capture more leads but also enhance customer loyalty. Ultimately, these insights enable Shopify store owners to make more informed decisions, aligning their marketing strategies with actual customer behavior and preferences to drive sustainable business growth.

Author
ECPower Product Manager

Edited and supervised by Product Manager of ECPower - Shopify Customer Segment & Journey Management, supporting Shopify merchants' CLV growth, CRM strategy and data analytics.

Change log

22 Apr. 2024 Article Published

12 Aug 2024 Updated

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