Category : Custom Dimensions
Two ways to calculate customer lifetime value for ecommerce using Google Analytics data
Many of our customers come to us with a similar question: "how do I measure ecommerce lifetime value (LTV)?" The latest episode in our Learning Videos series shows you how to do just that for both your one-off purchasers and subscription customers. Our step-by-step tutorial covers two methods of calculating customer LTV using your Google Analytics (GA) data. You'll get to know Littledata’s custom dimensions in GA and learn how to visualize your calculations in Google Data Studio. https://www.youtube.com/watch?v=YOzHFN1ZjsA&t=21s During installation, Littledata automatically creates several custom dimensions in your connected Google Analytics property. These custom dimensions include: Lifetime Revenue, the sum total a customer has spent in your Shopify store (including one-time purchases and subscription orders)Shopify Customer ID, the unique identifier Shopify assigns to each customerLast Transaction DatePayment GatewayPurchase Count They offer better data to help you understand your customers' buying behavior, then calculate and visualize their LTV. To kick things off, you'll first need to export your data from GA to Google Sheets or another spreadsheet tool via CSV. Once you’ve enabled the GA add-on in Google Sheets, you're ready to get started. Method 1: Calculate LTV by Lifetime Revenue, Shopify Customer ID, and Transaction Count In the first method of calculating lifetime value, we’ll use Transactions as the metric. The dimensions we'll use—Shopify Customer ID and Lifetime Revenue—correspond with ga:dimension5 and ga:dimension3, respectively. Use the image below as a guide to set up your report: Next, set your Metrics Reference as Transactions and your Dimensions Reference as Custom Dimensions. After you run the report, Google Sheets should look something like this: Finally, use Google Sheets' built-in functions to calculate the average or median LTV of your customers. Method 2: Calculate LTV by Source/Medium, Transaction ID, Shopify Customer ID, and Transaction Revenue This second LTV calculation method helps you track which marketing channels bring in your most valuable customers: the ones who spend the most over time. In this method, use Transaction Revenue as the metric and Source/Medium, Transaction ID, and Shopify Customer ID as the metrics. These correspond with ga:sourceMedium, ga:transactionId, and ga:dimension1 respectively. This method requires the widest date range possible to capture the most transactional data possible—preferably since you started using Littledata. Before running the report, your Google Sheet should appear as follows: After exporting your data, your result will look like this—a list of transactions with source/medium and revenue data: Next, select all the data in your report to create a pivot table, aggregating by source/medium per customer. The result will reveal the total revenue per customer, per source. After completing the pivot table, you're ready to visualize your data in Data Studio. Build Reports in Google Data Studio Google Data Studio is one of our recommended reporting tools for ecommerce sites. Why? Because it's free, powerful, and works really well with Google Analytics. The first step in visualizing your data is to import your data into Google Data Studio by setting Google Sheets as your source. To do this, select your Google Sheets file followed by the pivot table you created in the previous method, and add it to your report in Google Data Studio. Change the data source by setting the aggregation to median so results yield the median lifetime revenue per traffic source. Your report dimension should be set to ga:sourceMedium and your metrics should be set to ga:transactionrevenue and ga:dimension1. Modify Shopify Customer ID from sum to count distinct to reveal the total unique customer IDs, which we'll use to sort our data. Sort by Shopify Customer ID to see the traffic source that brings the most customers to your site. The resulting report shows you the median lifetime revenue per traffic source, sorted by the total customers per source. References Quick Tips for Subscription Stores Using Custom Dimensions in GA 3 Deep Dives into Customer Lifetime Value for Ecommerce Sites LTV from GA vs LTV provided by Littledata How to Calculate Customer Lifetime Value in GA for Shopify Stores Custom Dimensions for Calculating Customer Lifetime Value Subscription Analytics Does Littledata work with my ecommerce reporting tool?
3 deep dives into customer lifetime value for ecommerce sites
Subscription ecommerce has continued to scale throughout the pandemic, with consumables like beverages and meal kits rising particularly fast in North America. Shopify sites selling by subscription have always been a core part of our customer base at Littledata, and with even traditionally brick-and-mortar retailers moving online and trying out subscription ideas, we've got our hands full with new subscription sites these days. It might be easier than ever to start a subscription box (companies like Bulu have transformed the market), but getting accurate data about marketing attribution and customer lifetime value is difficult without the right data setup. So with Black Friday Cyber Monday coming up, we thought it would be useful to share some of our top posts this year about subscription analytics. Here are our top three posts this year. Enjoy! 1. How to calculate LTV for ecommerce subscriptions Lifetime value is a core metric for many online companies these days. This is especially important for ecommerce sites, because whether you're focusing on repeat purchases or actual subscriptions, marketing to (and product alignment with) the highest LTV customers can make or break a DTC brand today. In this post, our founder breaks down the basics of LTV calculations for ecommerce, focused on Shopify stores using ReCharge for subscriptions. https://blog.littledata.io/2020/01/14/how-to-calculate-lifetime-value-ltv-for-subscription-ecommerce-in-google-analytics/ 2. How to use Custom Dimensions in Google Analytics to increase subscription sales Custom Dimensions are a remarkably powerful feature in Google Analytics, often overlooked because they can be complicated to set up manually (also, they "sound complicated"). But the truth is that with a little background research, custom dimensions are easy to understand, and -- more importantly -- to apply to your daily, weekly and monthly reports in order to get a clear view of different order types, repeat purchasing behavior, and more. https://blog.littledata.io/2019/09/19/quick-tips-for-subscription-stores-using-custom-dimensions-in-google-analytics/ In this detailed post, our lead analyst dives into the complications of modern DTC brands selling a mix of product types and subscriptions. He focuses on how to reconcile differences in different reporting tools, and how to create segments and reports to fit your unique business model. After all, talk is cheap. How can you put LTV calculations to work for your business? [note]Did you know? Littledata automatically tracks first-time and recurring purchases and ties them back to the original marketing source.[/note] 3. The ultimate guide to LTV tracking In this popular guest post on the Shogun blog, we take a look at everything you need to know about LTV. When you know your LTV, you can: Know which kinds of products your high-LTV customers want more of Know how much to spend to acquire a “similar” customer and still make a profit based on their projected buying habits Promote the products with the highest profitability Increase your marketing budget and inbound efforts to attract your most profitable types of customers [subscribe]
What's new for ReCharge tracking
Are you ready for ReCharge v2.3? The latest version of Littledata's popular ReCharge connection is more powerful and extensible than ever. Subscription ecommerce is booming right now, especially for consumables like wine and coffee. Many Shopify stores are even seeing Black Friday-level traffic. But there's also more competition than ever. ShipBob has noted that subscription discounts are especially popular right now, during the seemingly endless days of COVID-19, as a way to bring new subscribers to your brand. This is a major opportunity -- but it also means that there's a lot more competition. Data is more important than ever to understanding your store performance and benchmarking your site, choosing the best marketing channels for your products and targeting the best customers with a higher lifetime value (LTV). Data is more important than ever to understanding your store performance So what exactly can you track with Littledata's ReCharge integration? ReCharge integration for Google Analytics Our ReCharge connection has gone through a lot of updates over the years, based on feedback from our customers, including smaller Shopify merchants, larger DTC brands on Shopify Plus, and our agency partners around the world. Earlier this year, ReCharge v2 saw the addition of subscription lifecycle events. ReCharge v2.3 is now available to all merchants, with the addition of events to track the ReCharge checkout funnel -- and segment by product and marketing channel. So what's new? Clearer segmentation of first time vs recurring orders When you add Littledata's ReCharge connection we now add three Views in Google Analytics to help segment the data: One-time orders and first-time subscriptions - A good way to track initial purchases. We automatically filter out duplicate and recurring orders from this view. All orders - All orders placed on your store, including one-time orders, first-time subscriptions, recurring orders, and prepaid orders. Raw backup - A raw data backup with no filters! This separation enables stores to easily calculate Customer Acquisition Cost (CAC) on one-time orders and first-time subscriptions. Furthermore, for all the subscriptions that started after you installed Littledata’s ReCharge connection, you can group them by subscriber (Shopify customer ID) or by marketing channel or campaign for insightful Return on Investment (ROI) calculations. Read more about how Littledata works with Views and Filters. Checkout funnel events Starting from June 2020, stores on ReCharge v2.3 can see checkout step events to match the checkout events sent from the Shopify checkout. Littledata’s checkout tracking works without the need to add Google Tag Manager or other tracking scripts to the ReCharge checkout, simplifying implementation -- and reducing the risk that 3rd party script interrupt or intercept the sensitive payment details. Excluding prepaid subscriptions Stores generating prepaid subscriptions were seeing duplicate orders when that subscription eventually got processed. In the new One-time orders and first-time subscriptions view, we filter these duplicates out automatically. Custom dimensions for LTV and more Our ReCharge customers benefit from the same user-scope custom dimensions in Google Analytics that we have for all Shopify stores, allowing you to segment and retarget audiences based on data such as their lifetime spend, date of first subscription, or number of subscription payments. Marketing attribution All of these ReCharge v2.3 updates work with our smart tech for accurate marketing attribution. What's the real ROI on your Facebook Ads? Do customers who pick higher-value subscription bundles come from a particular channel? See how Littledata fixes marketing attribution automatically for Shopify stores, with a combination of client-side (browser) and server-side tracking. [tip]Read our reviews to see what ReCharge customers are saying about Littledata! [/tip] ReCharge integration for Segment Our ReCharge integration is now fully compatible with our Shopify to Segment connection, so if you want to send Shopify and ReCharge events to Segment, we've got you covered. This is a seamless way for ReCharge stores to get revenue and customer information into Segment's hundreds of destinations. Headless Shopify tracking for ReCharge ReCharge Connection v2.3 is fully compatible with Littledata's headless tracking solution. Stores using ReCharge's new Checkout API can use Littledata's headless demo to show you how to get the same seamless customer journey from storefront, through checkout to purchasing. Littledata is the only tracking solution compatible with headless ReCharge setups, including those built by our amazing tech partners like Nacelle. ReCharge in-app analytics ReCharge has also launched a powerful in-app analytics feature available to all users. ReCharge launched Enhanced Analytics for Pro customers in 2019 to allow cohort and metric tracking. This is a powerful feature, but it’s different from what Littledata does. The most successful brands are using both tools. ReCharge’s analytics feature offers easy ways to visualize your ReCharge data in the app, while Littledata fixes sales and marketing tracking and sends that data to Segment or Google Analytics. What you can do ReCharge Enhanced Analytics Littledata + Google Analytics Littledata + Segment Look at trends in subscription sign-ups and cancellations ✔ ✔ ✔ Analyze churn rate by cohort or product ✔ ✔ * ✔ * Visualize cohort retention ✔ Fetch last-click source and medium (UTM parameters) from subscription API ✔ Analyze multi-channel marketing contributions to subscription sales ✔ ✔ ✔ Attribute recurring orders back to marketing campaigns ✔ ✔ ✔ Analyze Customer Lifetime Value including non-ReCharge spend ✔ ✔ Track charge failures by any customer attribute ✔ ✔ Track subscription cancellations or upgrades by any customer attribute ✔ ✔ Track customer updates by any customer attribute ✔ ✔ Track usage of the customer portal on our site by any customer attribute ✔ ✔ See how any ReCharge customer event connects to the pre-checkout behaviour of the user ✔ ✔ Look at cancelation rate by marketing channel ✔ ✔ ✔ Trigger transactional emails based on changes to subscriptions ** ✔ Retarget segments of ReCharge audience in common marketing destinations ✔ * Requires additional analysis in a spreadsheet** In Segment destinations such as Iterable How do you get all this? If you're already a Littledata customer, you can update to ReCharge v2 directly in the app (just login and you'll be prompted to upgrade if you haven't already). New to Littledata? We now offer a 30-day free trial on all plans, and setup only take a few minutes. If you are looking for more support, like account management or analytics training, please contact us about enterprise plans.
Introducing Shopify Flow connectors for Google Analytics
Littledata has launched the first Shopify Flow connector for Google Analytics, enabling Shopify Plus stores to analyse customer journey using a custom event in Google Analytics. In addition to Littledata's native connections with Shopify, Shopify Plus, Facebook Ads, ReCharge, etc., we have now launched a beta version of a Flow connector for Google Analytics. What is Shopify Flow? Flow is an app included with Shopify Plus, which enables stores to define automation pathways for marketing and merchandising. Think of it as an ‘If This Then That’ generator just for Shopify. For example, after an order is marked as fulfilled in Shopify’s admin you might want to trigger an email to ask for a review of the product. This would involve setting a ‘trigger’ for when an order is fulfilled and an ‘action’ to send an email to this customer. How do you use Littledata Flow actions? You install Littledata's Shopify app along with Shopify Flow Every time an order is created in your store we send it to Google Analytics, along with information about which customer ID made the order (nothing personally identifiable) You add Littledata's actions to your Flow Every time the order or customer event is triggered, even for offline events, the event is linked back to Google Analytics In Google Analytics you can then: Segment the customer base to see if these actions influence purchasing behaviour Visualise when these events occurred Analyse the customers making these actions: which geography, which browser, which marketing channel (in GA 360) Export the audience to retarget in Google Ads (in GA 360) Export the audience to run a website personalisation for using Google Optimize How do you set the actions up in Flow? Google Analytics customer event – can be used with any customer triggers, such as Customer Created Google Analytics order event – can be used with any order triggers such as Order Fulfilled, Order Paid, How else could I use the events? You can now link any of your favourite Shopify Apps with Flow connectors into Google Analytics. Some examples would be: Analyse if adding a product review leads to higher lifetime value Retarget in Google Ads after a customer's order is fulfilled - Download .flow file Set up a landing-page personalisation for loyal customers (using Loyalty Lion connector) - Download .flow file How much does this cost? The Flow connectors are included as part of Littledata’s standard subscription plans. You’ll need Littledata’s app to be installed and connected to link the events back to a customer – and to get reliable data for pre-order customer behaviour. [subscribe] Can Littledata set up a flow for a specific app? Our Enterprise Plans offer account management to help you configure the Littledata Shopify connection, including the Shopify Flow connectors. Get in touch if you have a specific app you'll like to make this work with.
Tracking customers in Google Analytics
If your business relies on customers or subscribers returning to your site, possibly from different devices (laptop, smartphone, etc.) then it’s critical you start tracking unique customers rather than just unique visitors in Google Analytics. By default, Google Analytics tracks your customers by browser cookies. So ‘Bob’ is only counted as the same visitor if he comes to your site from the same browser, but not if he comes from a different computer or device. Worse, if Bob clears his cookies or accesses your site via another mobile app (which won't share cookies with the default browser) then he'll also be counted as a new user. You can fix this by sending a unique customer identifier every time your customer signs in. Then if you send further custom data about the user (what plan he / she is on, or what profile fields they have completed) you can segment any of the visits or goals by these customer attributes. There are 2 possible ways to track registered users: Using Google Analytics’ user ID tracker By storing the clientId from the Google cookie when a new user registers, and writing this back into the tracker every time the same user registers In both cases, we also recommend sending the user ID as a custom dimension. This allows you segment the reports by logged in / not logged in visitors. Let's look at the pros and cons. Session stitching Tracking customers involves stitching together visits from different devices into one view of the customer. Option 1, the standard User ID feature, does session stitching out the box. You can optionally turn ‘session unification’ on which means all the pageviews before they logged in are linked to that user. With option 2 you can stitch the sessions, but you can't unify sessions before the user logs in - because they will be assigned a different clientId. So a slight advantage to option 1 here. Reporting simplicity The big difference here is that with option 1 all of the user-linked data is sent to a separate 'registered users' view, whereas in options 2 it is all on the same view as before. Suppose I want a report of the average number of transactions a month for registered vs non-registered visitors. With both options, I can only do this if I also send the user ID as a custom dimension - so I can segment based on that custom dimension. Additionally, with option 1 I can see cross-device reports - which is a big win for option 1. Reporting consistency Once you start changing the way users are tracked with option 2 you will reduce the overall number of sessions counted. If you have management reports based on unique visitors, this may change. But it will be a one-time shift - and afterwards, your reports should be stable, but with a lower visit count. So option 1 is better for consistency Conclusion Option 1 - using the official user tracking - offers a better route to upgrade your reports. For more technical details on how this tracking is going to work, read Shay Sharon’s excellent customer tracking post. Also, you can watch more about customer tracking versus session tracking in this video. Have any questions? Comment below or get in touch with our team of experts! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
5 tips to avoid a metrics meltdown when upgrading to Universal Analytics
Universal Analytics promises some juicy benefits over the previous standard analytics. But having upgraded 6 different high traffic sites there are some pitfalls to be aware of. Firstly, why would you want to upgrade your tracking script? More reliable tracking of page visitors - i.e. fewer visits untracked More customisation to exclude certain referrers or search terms Better tools for tracking across multiple domains and tracking users across different devices Track usage across your apps for the same web property Ability to send up to 20 custom dimensions instead of the previous limit of only 5 custom variables If you want to avoid any interruption of service when you upgrade, why not book a quick consultation with us to check if Universal Analytics will work in your case. But before you start you should take note of the following. 1. Different tracking = overall visits change If your boss is used to seeing dependable weekly / monthly numbers, they may query why the number of visits has changed. Universal Analytics is likely to track c. 2% more visits than previously (partly due to different referral tracking - see below), but it could be higher depending on your mix of traffic. PRO TIP: Set up a new web property (a different tracking code) for Universal Analytics and run the old and new trackers alongside each other for a month. Then you can see how the reports differ before sharing with managers. Once this testing period is over you'll need to upgrade the original tracking code to Universal Analytics to you keep all your historic data. 2. Different tracking of referrals Previously, if Bob clicked on a link in Twitter to your site, reads, goes back to Twitter, and within 30 minutes clicks on a different link to your site - that would be counted as one visit and the 2nd referral source would be ignored. In Universal Analytics, when Bob clicks on the 2nd link he is tracked as a second visit, and 2nd referral source is stored. This may be more accurate for marketing tracking, but if Bob then buys a product from you, going via a secure payment gateway hosted on another domain (e.g. paypal.com) then the return from the payment gateway will be counted as a new visit. All your payment goals or ecommerce tracking will be attributed to a referral from 'paypal.com'. This will ruin your attribution of a sale to the correct marketing channel or campaign! PRO TIP: You need to add all of the payment gateways (or other third party sites a user may visit during the payment process) to the 'Referral Exclusion List'. You can find this under the Admin > Property > Tracking codes menu: 3. Tracking across domains If you use the same tracking code across different domains (e.g. mysite.co.uk and mysite.com or mysite.de) then you will need to change the standard tracking script slightly. By default the tracking script you copy from Google Analytics contains a line like: ga('create', 'UA-XXXXXXX-1', 'mysite.com');. This will only track pages that strictly end with 'mysite.com'. PRO TIP: It's much safer to change the tracker to set that cookie domain automatically. The equivalent for the site above would be ga('create', 'UA-XXXXXXX-1', 'auto');. The 3rd argument of the function is replaced with 'auto'. 4. Incompatibility with custom variables Only relevant if you send custom data already Custom variables are only supported historically in Universal analytics. That means you will need to change any scripts that send custom data to the new custom dimension format to keep data flowing. Read the developer documentation for more. PRO TIP: You'll need to set the custom dimension names in the admin panel before the custom data can be sent from the pages. You can also only check that the custom dimensions are being sent correctly by creating a new custom report for each dimension. 5. User tracking limitations We wouldn't recommend implementing the new user ID feature just now, as it has some major limitations compared with storing the GA client ID. You need to create a separate view to see the logged-in-user data, which makes reporting pageviews a whole lot more complex. Visits a user made to your site BEFORE signing up are not tracked with that user - which means you can't track the marketing sources by user PRO TIP: See our user tracking alternative. Got more tips on to setting up Universal Analytics? Please share them with us in the comments, or get in touch if you want more advice on how to upgrade! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
Measuring screen resolution versus viewport size
There’s a difference between the ‘screen size’ measured as standard in Google Analytics and the ‘browser size’ or ‘browser viewport’. Especially on mobile devices, there are pitfalls comparing the two. Browser viewport is the actual visible area of the HTML, after the width of scroll bars and height of button, address, plugin and status bars has been allowed for. Desktop computer screens have got much bigger over the last decade, but browser viewports (the visible area within the browser window) are not. The CSS tricks site found only 1% of users have their browser viewing in the full screen. While only 9% of visitors to his site had a monitor less than 1200px wide in 2011, around 21% of users have a browser viewport of less than that width. Simply put, on a huge monitor you don’t browse the web using your full screen. Therefore, 'screen resolution' may be much larger than 'viewport size'. The best solution is to post browser viewport size to GA as a custom dimension. P.S. Google Analytics does have a feature within In Page Analytics (under Behaviour section) to overlay Browser Size, but it doesn’t work for any of the sites I look at.
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