Using Google Analytics to refine merchandising and product promotions

2018-02-23

The whole purpose of having Google Analytics tracking on your site is to find out how your website is performing and to use this data to improve your digital efforts. Yet many businesses miss the mark when it comes to taking action at the level of product listings, despite the fact that this can lead to huge revenue gains!

Why do they miss the mark? Two reasons: inaccurate tracking and unclear reporting. The Littledata app helps to fix these issues automatically, providing users with a reliable data stream and automated reporting based on Google Analytics data, but it’s still useful to drill down into Google Analytics itself to understand all of the details.

In this post I break down how to use Google Analytics to refine merchandising, product promotions and product listings in a way that can have a direct effect on both short-term and long-term revenue for your ecommerce site.

For this to work, you’ll need to have Enhanced Ecommerce set up on your website. You’ll also need some spreadsheet software (Excel, Google Sheets, etc.) so we can play with extracted data and drill down deep.

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Banners and creatives: getting users to see what we want them to see

A full enhanced ecommerce setup will enable you the power to see how much money each of the creatives on your site is bringing you. If your website is like most ecommerce sites, you have several creatives displayed, such as:

Homepage carousel
Homepage pods
Category main banner

Choosing which creative should get on your homepage might feel like just a preference, but it doesn’t have to be that way. You can use the ‘Internal Promotion’ menu in Google Analytics (Marketing > Internal Promotions) to make data-driven decisions about your homepage creatives.

Imagine an online store that sells scooters and accessories:

We have banners for categories like Helmets, Accessories, Mini Micro and Maxi Micro (different sizes of scooters).

We have 2 banners on the homepage with these two creatives: Safety (the first one) and Built for Adults (the second one).

We want to change one of the creatives on the carousel. Let’s analyze what is the best strategy here.

The first banner on the carousel was seen 24,404 times. It has a 5.01% click thru rate (CTR) and a £3.90 value per click.

The second banner on the carousel was seen 17,109 times. It has a 5.52% CTR a £2.02 value per click.

Now we can make a decision.

What to discard and what to keep

Even though we have a higher CTR on the second banner and this is an indicator that the message is more appealing, the reality is that the revenue that comes with that click is not even half of the revenue we get from a click on the first banner. If you want to make a 100% correct decision here you can analyze the margins on the product promoted by each of the banners. If you have double the margin for the products in the second banner you can get rid of the Safety banner and make the second banner primary. If your margin is the same for both categories then the best decision here is to replace the second banner with the first one.

How to populate the carousel

We already decided to keep the first banner, but now we need a replacement for the second one. So we need to find a creative in the website that had performed at least the same as the second banner. Based on the example above if we search by CTR higher than 5.52% we can see that we have a banner for Maxi Micro with 20% CTR and a value per click of £5.32. The action here is to replace the second slot of the carousel with this creative. After 1-2 weeks we can retake this process all over again and we may decide to reverse the creatives (Banner 1 will be Banner 2 and Banner 2 will be Banner 1 in the carousel).

This is not a one-time job. The analysis should be made every time you add a new creative or make a new promotion.–or even as a weekly task. Many Littledata clients run this type of analysis on a regular basis, whether or not they’ve launched a new promotion, to make sure they are optimizing sales and conversions. You should pay attention to the average click thru rate (CTR) based on creatives category, and also you should know what is your standard deviation for each category so that you can quickly spot which are over- or under-performing.

Based on the example above, the average CTR for a carousel banner on the site is 5.26% and the standard deviation is 0.25%. So I know that if I see a banner that has a CTR less than 5.01%, there is room to improve.

As per above for the category pages, we have an average of 10.92% CTR with a standard deviation of 6.28. This means that everything under 4.63% should be replaced ASAP and everything above 17.20% should be promoted.

List views: how to arrange products for ultimate engagement

One of the best Enhanced Ecommerce features in Google Analytics is the Product List Performance Report (Conversions > Ecommerce > Product List Performance). This report shows you how many views each list gets. Why does this matter? Because if you have a high margin on some products from a specific category, you should find out if that list (category) is being sufficiently promoted on your site.

From these reports, we can find out things like:

Most viewed categories (sort by Product List Views)
The category that has the biggest engagement (sort by Product List CTR)
The list that is bringing you the most money per view (Product Revenue divided by Product List Views)

Which categories are performing best — and which are most profitable?

Let’s say I have three categories in my store: categories 1, 2 and 3. And my margin for products in category 3 is three times the margin for those in category 1.

In the report above, we see that we don’t have a click thru for Category 2. This could mean:

The tracking is not working on that page
Users have issues clicking on the products
There is no call to action (CTA) on that page

So we can assume that Category 2 is not working. Moving forward we should analyze the performance of Category 1 vs Category 3.

	Views	Clicks	CTR	Revenue	Revenue / click	Margin at each $1 sold	Margin at 1000 clicks
Category 1	1,701,660	57,038	3.35%	$329,799.67	$5.78	0.23	$1,329.88
Category 3	46,895	3,175	6.77%	$23,881.37	$7.52	0.69	$5,189.97

We can see that even though we have a fraction of the views for Category 3, this category is for us almost 3 times more profitable per 1000 clicks. At this point, we should investigate how much marketing we’re doing around Category 3 to see if there are options to push harder on this highly profitable category, alongside whatever’s already working for promoting Category 1.

Order matters

The Product List Performance Report can also help us find out how customers progress from viewing a product in a list to clicking through for more information.

Let’s analyze the data in the above report. The table is sorted by Product List Views for Mobile devices. We know that the alignment for this website is one product under the other and for a product view to be sent the user needs to see it for at least one second. So we can draw these conclusions:

Position 2 and 3 are normally visually scanned by users. The fourth product in a list is seen in more detail but has a lower CTR than the second or third product in the list. We know that each page has 10 products so the average Product List CTR rate for page 1 is 1.36% and the standard deviation is 0.42. From this, we can see that position 2 has a good CTR and we need to change the photo and text of the listing to attract more attention — products placed in the second position in a product listing on this site tend to convert well. Position 4 gets attention but has low performance so we could try changing the photo and title of products in this position in order to increase the CTR.

If we are looking at this report as aggregate data then we can conclude that if we want to make a push for particular products, we should place them in position 1 or 4 for maximum visibility, or position 1 or 2 for maximum CTR.

How to monetize product list positions

We can take this analysis further by examining how list slots relate to product revenue, whether on your site or via affiliate programs. Looking at the report in aggregate and extracting the data, we can give a monetary value to each slot in the product listings.

Product List Position	Product List Views	Product Revenue	Revenue/view per slot
1	2,290,505	£183,207.00	£0.08
4	2,279,917	£99,830.00	£0.04
3	2,246,164	£117,096.00	£0.05
2	2,239,943	£157,605.00	£0.07
6	2,062,271	£73,183.00	£0.04
5	2,053,534	£94,889.00	£0.05
8	1,788,080	£58,585.00	£0.03
7	1,775,762	£60,603.00	£0.03
9	1,750,248	£52,366.00	£0.03
10	1,606,599	£50,913.00	£0.03

From the above example, we can see that each of the slots in the listing has a value per view. And the value is decreasing with the position. Using the known margin for a specific product in a list, you can improve your ROI just by positioning it in a slot with a higher CTR based on the model above.

Which photos should you show first in a listing?

If you offer a product in multiple colors, you should use an image and a default (primary) product selection in the most popular color. But how do you figure that out?

Product variants are too often left behind in analysis. The Product Variant field captures the specific variation of a product, e.g., XS, S, M, L for size; or Red, Blue, Green, Black for color. It is an Enhanced Ecommerce feature that can give you powerful insights into your users’ searches, interests and preferences. Paying attention to variant performance can have a big effect on shopping behavior and sales.

In the example above, we’re looking closely at the Product Variant dimension to figure out which color is most popular. We have a product with 4 colors: Black, Grey, Midnight Black and Persian Grey. There isn’t enough transaction data to make a decision based on purchases, but we can calculate the most popular variant (in this case, the most popular color) based on how often users have added items in each color to their shopping carts (Adds To Cart). For Black, we have a View to Add To Cart rate of 0.6% and for Grey 0.8%.

So in this case we should use the main Grey color for advertisements and main photos in listings pages. We might also try using the Persian Grey variant.

Note that in this example we can calculate for each product view because we’ve listed each color as a different product. If you’re listing only one product and you show variants on the product page, then you’ll need to divide the Adds To Cart for each variant by the total Product List Views.

What to do next

If you need help with Enhanced Ecommerce reporting, our analysts are ready to come to the rescue. You can either request a consultation or just sign up for a free Google Analytics audit and contact us directly from the app.

How are you using Enhanced Ecommerce reports in Google Analytics? Drop us a note below.

Alexandra

Previously led retail and digital marketing for LEGO Certified Stores and PANDORA. She’s experienced in PPC, affiliate, e-commerce, travel and news websites. She has a Master's degree in economic analysis.

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Google Analytics

How Grind boosted their sales by 50x

Grind had 11 brick and mortar stores in the UK before they dove into ecommerce on Shopify. It was not until the COVID lockdown that they moved from strictly brick and mortar to the ecommerce ecosystem. Before then, only half of a percent of their business was online! After they added subscriptions and built a data stack to inform the launch of new promotion methods, the business scaled to 50x revenue in just a couple of months. THE CHALLENGE Over the years, Grind built an incredible brand through their popular brick-and-mortar stores and newsletters, going beyond coffee to become a part of their customers' lives in a meaningful, authentic way. Grind is gaining awareness not just in their local region of London but celebrating subscribers from around the globe. After losing their offline (physical location) business practically overnight, Grind needed to quickly pivot to ecommerce and get an accurate picture of their online customer lifetime value (LTV) for “one-time” and “recurring” orders. They needed to see and report on customer behavior happening on their Shopify store to improve the checkout flow, build ideal customer profiles, retarget the right customers using dynamic social ads, and make crucial decisions using accurate data at the core of these efforts. THE SOLUTION Littledata's Recharge connection made it easy to get accurate sales data and marketing attribution across the subscriber journey. This smart technology connected Grind’s Recharge checkout with Google Analytics for accurate data about subscription revenue, including first-time payments, recurring transactions, and subscription lifecycle events. They were able to see LTV by channel and—critically—to predict where high- value subscriber growth was most likely to happen. It all came down to “measuring the difference in LTV for subscribers versus one-time purchasers” says Grind CMO Teddy Robinson. “Subscription revenue and return on ad spend (ROAS) were really the biggest top-line metrics for us.” RESULTS Subscription selling has created an exciting opportunity for the Grind ecommerce store to unlock potential revenue, build long-lasting relationships with their customers, and create a community among consumers. Building on their existing loyal customer base, Grind’s introduction of sustainable at home coffee pods— and tracking checkout events accurately with Littledata’s Recharge connection—Grind saw massive subscriber growth across paid and organic channels. They went from £10k to £500k monthly ecommerce revenue, and are have expanded internationally. A few takeaways of what Grind accomplished with Littledata: • 50x Subscription revenue in three months • 100% Recharge orders captured in Google Analytics • 28 Event types tracked in the checkout

by Blake

2023-05-09

Analytics Setup

GA4 Auto-migration: Why and how to opt-out

If you haven’t migrated to Google Analytics 4 (GA4) by now, you’re likely flooded with reminders from Google to get to it! But what does auto-migration mean? And can it break your Shopify data? With Universal Analytics’ July deprecation date coming soon, the pressure is on. To encourage UA users to make the switch, Google began auto-migrating UA properties to GA4 on March 1, 2023. While this may sound helpful, you actually do not want to rely on Google’s auto-migration to GA4. For data-driven ecommerce brands, Google’s “basic” configurations aren’t going to cut it, and Google’s auto-migration could actually hinder your data quality. The same goes for Shopify’s Google sales channel. With a few simple steps, you can opt out of GA4 auto-migration and set up your GA4 property correctly. There’s a lot of confusion in the marketplace right now, especially when it comes to tracking ecommerce stores in GA4. Whether you’re on Shopify, BigCommerce, or another ecommerce platform, it’s a must to track checkout steps and conversions, but auto migrations don’t cover that at all. So in this post, we’ll be clearing up: Why you should opt out of auto-migration (and how to do it) How to ensure data quality in GA4 How to track ecommerce events in GA4 Why you need to opt-out It’s important to note that GA4 is a completely new reporting tool, built from the ground up for faster reporting, increased flexibility, and streamlined audience building. GA4 relies on a new data model — event-based tracking, rather than session-based tracking — and doesn’t track data like-for-like with UA. To avoid any data disruptions and ensure data integrity, brands need to manually set up their GA4 properties. [tip] Watch our recent webinar to learn more about the differences between UA and GA4 [/tip] Problems with GA4 auto-migration Google’s auto-migration runs deep and migrates several parts of your account. If you’ve already set up a GA4 property, you may think this doesn’t apply to you, but even if you have manually created a GA4 property, it’s crucial that you switch off the auto-migration toggle to avoid further issues. Duplicated orders We’ve seen Shopify stores face duplicated orders and inaccurate data after missing this key step. Even with the proper data tech stack, brands are struggling with data quality issues because they have not opted out of auto-migration. Event naming If you’ve auto-migrated your UA events into GA4, your event naming can majorly skew conversion tracking, if not done properly. GA4 limits event names to 40 characters or less, so any UA events automatically migrated to GA4 that are 40+ characters will not be reported as a conversion because the appended “_c” will be missing. Managing users With great power comes great responsibility. There may be users you don’t want to migrate to GA4 so it’s best to review account and property users and the access they’re granted during migration. How to opt out of GA4 auto-migration It’s easy to opt out of auto-migration. Simply access the GA4 Setup Assistant via the Admin panel and scroll down to ‘Automatically set up a basic Google Analytics 4 property’ and ensure that the toggle is switched off, as pictured below. As an added measure, ensure that you have “Collect Universal Analytics events” toggled off in your data stream’s tag settings, as shown below. How to ensure data quality in GA4 Check that all pages and conversions are being tracked Mistakes and inconsistencies in your Google Tag Manager (GTM) tracking can result in missing data, with Littledata’s data layer, you won’t have to worry about that. Unlike alternatives, Littledata’s data layer works automatically, eliminating the risk of human error so you can finally trust your Google Analytics data. As mentioned above, GA4’s event naming requirements could interrupt your conversion tracking. To avoid any interruptions, make sure that your event names are below 40 characters, otherwise, you could face some issues in reporting. Check your transaction and purchase data Tracking complete transactions, revenue, and purchase data is essential for any ecommerce brand. Many of the apps that offer “GA4 setup” for Shopify or BigCommerce stores do not fix your tracking. Find out if your GA4 setup is up to speed with our free GA4 Conversions Checker. Connect your Google Analytics account and the Conversions Checker will automatically audit your GA4 property for any data discrepancies, explains why they exist, and how to get more accurate ecommerce reporting in just a few clicks. Build out your data tech stack Using the right Shopify or BigCommerce app can help make your transition to GA4 much easier. Littledata’s GA4 integration works seamlessly out of the box, automatically tracking the entire customer journey — from discovery at the source, through the checkout funnel and post-purchase events — pushing events into Google Analytics 4, Facebook Conversions API, Segment, and any connected reporting tool. How to track ecommerce events in GA4 Shopify’s GA4 integration is now available through the Google Sales Channel App, but as with Shopify’s native integration with UA, it comes with a few core limitations. Beyond this app, you have two other options: Google Tag Manager (GTM) GTM is a common tracking solution for ecommerce stores. And while GTM itself is free to use, it comes with a price — GTM is very time-consuming, complex, and often costly to maintain, especially for a lean team. Plus, Shopify’s recent updates removed the ability to add scripts to checkout.liquid, no longer permitting brands to track the checkout funnel with GTM. A trusted app, like Littledata Within minutes, fix your ecommerce tracking in GA4 — no implementation or developer needed. Littledata’s app uses a combination of client-side and server-side tracking to capture the entire customer journey, including complete marketing attribution and post-purchase events, like subscriptions and upsells. Start your GA4 journey on the right foot with hands-on support from our team of analytics experts. [tip] Read our step-by-step guide to tracking ecommerce conversions in GA4 [/tip] Next steps The good news is that there’s still time to fully migrate your ecommerce analytics to GA4 before the July deprecation date. But don’t delay — the sooner you set up a GA4 property that checks all the boxes, the better equipped you’ll be in the future with a treasure trove of historical data, custom-built reports to answer your business's top questions, and a BigQuery export to turn data into marketing magic.

by Gabrielle

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Using Google Analytics to refine merchandising and product promotions

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