GA4: What Shopify stores should do TODAY to keep up with the new version of Google Analytics
Setting up Google Analytics 4 (GA4) on Shopify is easy with the right tools, but there is a lot of confusion in the marketplace right now. There are apps offering "GA4 setup" that can't actually help you with tracking (getting accurate data into Analytics), and there are agencies offering detailed GTM tag setup guides for GA4 without mentioning that there are automated solutions for GA4 conversion tracking. This is all very exciting...but also not necessary. The truth is that you don't need custom tagging or reporting, just the right Shopify tracking app for GA4. What is GA4? It's Google's answer to the modern data stack, in some ways a complement to it (eg. GA4's BigQuery connection, which used to be reserved for GA 360), and in others a replacement for multiple expensive tools that haven't always worked well together. The move toward GA4 started with Google's interest in offering better cross-device and cross-channel tracking, and has been refined with a focus on user privacy -- in other words a world without third-party cookies. As a result, using the right Shopify and GA4 connection now lets you start capturing data about your Shopify store performance that is by default more complex and dynamic than what you might be currently tracking in Universal Analytics (UA, the current version of GA). GA4 can save you time and money versus a complex analytics setup, while offering visibility into the entire customer lifecycle, from organic and paid channels through complex browsing behavior and -- essentially -- customer lifetime value (LTV) and purchase count. But at the very least you need to start capturing that data. [note]This doesn't only apply to Shopify stores! If you're on BigCommerce you can use our server-side BigCommerce to GA4 integration[/note] Google has also built in data-driven models for both comparative attribution reporting and predictive analytics, such as in-app purchase probability and overall purchase probability. But let's not get ahead of ourselves. First you need to capture the data. We expect some brands to just ignore GA4 until the last minute (I'm expecting some not-so-fun Memorial Day Weekend parties next year in NYC...), but we've also noticed that the top ecommerce managers and data scientists are all doing the same thing: tracking in parallel today, so they will have at least six months of data before making the full switch to GA4. Here's a quick guide to help you make the right moves too. 1. Stop procrastinating Is Google really getting rid of the old version of Google Analytics? The answer is a definitive yes. They are sunsetting the old version of Google Analytics in 2023. You need to be ready, but what does that mean exactly? Is there anything you can do today? Track in parallel today so you will have at least six months of data before making the full switch to GA4 Google formally announced the shift to a new version of Google Analytics back in November 2020, but many DTC brands are still putting off the shift to GA4. While moving to a different version of a tool most online marketers use weekly (if not daily) might sound a bit intimidating, there are two points to remember: Google is one of the most user-friendly companies on the planet and they have already added a bunch of functionality and default reporting templates in GA4 You need to capture data before you can analyze it! As our agency partner CXL writes in their ultimate guide to GA4: "Unlike previous upgrade iterations, GA4 is a brand-new product. This means starting afresh, with a new learning curve to navigate." But at the same time, as they say, "it promises to be the future of analytics, with cross-platform tracking, AI-driven data, and privacy-centric design." We couldn't agree more. Littledata's top 10 reasons to switch to GA4 include both custom funnels and predictive insights. This is especially important for ecommerce brands that want to building shopping funnel reports and LTV cohorts in GA4 that fit their particular business model and customer base. So what should you do today to take advantage of this powerful, free ecommerce reporting? First of all, create a GA4 property! 2. Create a GA4 property Google will not be allowing anybody to import historical data from UA into GA4, so you need to create a Google Analytics 4 (GA4) property today if you are serious about seeing performance over time. Luckily, adding a GA4 property is surprisingly easy. Current GA users (that's most of you) can just head to their Analytics accounts and use the setup assistant. You should add at least one data stream. (Don't worry, you can add more later.) Data Streams in GA4 replace Views in Universal Analytics, but they're a bit different . Data streams can be any website (or blog, microsite, country store, etc) or mobile app (iOS or Android), and they can be viewed in aggregate or individually. Adding a data stream might sound intimidating, but this can be as simple as adding the URL for your website (eg. "littledata.io"). [tip]Whether you're new to Google Analytics or a longtime user, we recommend turning on the Enhanced Measurement settings, which include useful defaults.[/tip] When you add a data stream, you will have the option to enabled Enhanced Measurement settings. This is highly recommended. Here's more info on what Littledata lets you track automatically in GA4 -- examples include product views, product list views, checkout funnel events and purchases -- and which events are tracked with Enhanced Measurement, such as page views, site search and form interactions. Now that you have set up a GA4 property, it's time to set up your ecommerce tracking. 3. Track in Parallel Tracking UA and GA4 in parallel means that you can send data to both destinations at the same time. This lets you capture browsing behavior and sales performance in both places, so you can analyze the data, build comparative attribution models and start to get a sense for how Universal Analytics and Google Analytics 4 are different -- as well as where they converge. The most accurate way to do this is to use an ecommerce data platform like Littledata to capture ecommerce events by default, including both sales/conversion tracking and marketing attribution (stitching sessions together). We send data directly to GA4. Because we have a pre-built GTM data layer, you don't need to add tags manually! Use a pre-built data layer for GA4 so you don't have to add tags manually Littledata's tracking schema works out of the box to capture both major and minor touch points in the ecommerce journey. When you install Littledata, we instantly start tracking all of the key ecommerce events for you in both UA and GA4, so you'll have the data you need when you're ready to dive into week-on-week and month-on-month analysis. Here's a quick video on tracking in parallel. To get something similar to Enhanced Ecommerce reporting, you'll need to build reports yourselves, so we've also put together a few videos on building ecommerce reports in GA4. These reports are more flexible and dynamic than anything available in UA. It's like having Google Data Studio within Google Analytics for complete reporting. There's even more free content available for subscribers in the app :) Wait, so GA4 is pretty different? GA4 is based on a different type of tracking called event-based tracking, which is is exactly what it sounds like: a more flexible and comprehensive way of tracking everything so you can build granular reports and predictive models based on the endless flow of events and attached parameters. The UA data model focused on sessions and pageviews. GA4 focuses on events, and sessions are no longer broken by a change in campaign "source" (GA4 continues tracking the same session as well as the change in source). But those sessions will not be stitched together automatically with purchase data and Shopify customer IDs. And many Google Tag Manager solutions for GA4 are missing out on the basics, like purchase events, revenue and conversion tracking. If you aren't capturing purchases, how are you supposed to know if your marketing is working? Using Littledata's solution is quick and easy, with both low-code and no-code options. Our ecommerce tracking is deep and comprehensive. When you start a free trial you can choose to send data to both UA and GA4 at no additional cost, with server-side tracking to guarantee accurate data. [subscribe heading="Top-rated GA4 tracking for $99 a month"] Want to know more? Book a free data audit with one of our Google Analytics experts today!
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5 myths of Google Analytics Spam
Google Analytics referral spam is a growing problem, and since Littledata has launched a feature to set up spam filters for you with one click, we’d like to correct a few myths circulating. 1. Google has got spam all under control Our research shows the problem exploded in May – and is likely to get worse as the tactics get copied. From January to April this year, there were only a handful of spammers, generally sending one or two hits to each web property, just to get on their reports. In May, this stepped up over one thousand-fold, and over a sample of 700 websites, we counted 430,000 spam referrals – an average of 620 sessions per web property, and enough to skew even a higher traffic website. The number of spammers using this tactic has also multiplied, with sites such as ‘4webmasters.org’ and ‘best-seo-offer.com’ especially prolific. Unfortunately, due to the inherently open nature of Google Analytics, where anyone can start sending tracking events without authentication, this is really hard for Google to fix. 2. Blocking the spam domains from your server will remove them from your reports A few articles have suggested changing your server settings to exclude certain referral sources or IP addresses will help clear us the problem. But this misunderstands how many of these ‘ghost referrals’ work: they are not actual hits on your website, but rather tracking events sent directly to Google’s servers via the Measurement Protocol. In this case, blocking the referrer from your own servers won’t do a thing – since the spammers can just go directly to Google Analytics. It's also dangerous to amend the htaccess file (or equivalent on other servers), as it could prevent a whole lot of genuine visitors seeing your site. 3. Adding a filter will remove all historic spam Filters in Google Analytics are applied at the point that the data is first received, so they only apply to hits received AFTER the filter is added. They are the right solution to preventing future spam, but won’t clean up your historic reports. To do that you also need to set up a custom segment, with the same source exclusions are the filter. You can set up an exclusion segment by clicking 'Add Segment' and then red 'New Segment' button on the reporting pages and setting up a list of filters similar to this screenshot. 4. Adding the spammers to the referral exclusion list will remove them from reports This is especially dangerous, as it will hide the problem, without actually removing the spam from your reports. The referral exclusion list was set up to prevent visitors who went to a different domain as part of a normal journey on your website being counted as a new session when they returned. e.g. If the visitor is directed to PayPal to pay, and then returns to your site for confirmation, then adding 'paypal.com' to the referral exclusion list would be correct. However, if you add a spam domain to that list then the visit will disappear from your referral reports... but still, be included under Direct traffic. 5. Selecting the exclude known bots and spiders in the view setting will fix it Google released a feature in 2014 to exclude known bots and spiders from reports. Unfortunately, this is mainly based on an IP address - and the spammers, in this case, are not using consistent IP addresses, because they don't want to be excluded. So we do recommend opting into the bot exclusion, but you shouldn't rely on it to fix your issue Need more help? Comment below or get in touch!
Setting up a destination goal funnel in Google Analytics
Destination goal funnels in Google Analytics track how well certain actions on your website contribute to the success of your business. By setting up a goal for each crucial activity you will get more focused reports on how visitors are using your website, and at what stage they are dropping out of the conversion funnel. The first time I tried to set up a destination goal was daunting, but after some practice, I am now seeing valuable information on how well visitors are interacting with our clients' websites. If like Teachable you have different subscription packages, then you might want to track how each subscription is converting. For this, set up the purchase confirmation page of each subscription plan as a goal, with a funnel to get additional insight into where people drop off. Step 1: Create a new goal To set up a destination goal go to Google Analytics Admin settings > View > Goals. Click ‘new goal.’ Step 2: Fill in destination goal details Google has some goal templates that provide set-up suggestions. They will only display if you have set your industry category in property settings. Selecting any of the given templates will only populate the name and type of the goal, but not the conversion details, which are more complicated for some. This is not very useful for me so I will ignore this: select ‘custom’ and click ‘next.’ Goal name Give your goal a descriptive name. You will later see it in various reports in Google Analytics so use whatever makes sense for you. Here I am going to use the name of the subscription plan I am tracking - Basic Subscription. Goal slot ID Goal slot ID is set automatically and you might want to change it if you want to categorise your goals. Select ‘Destination’ and click ‘next step.’ Step 3: define your destination goal Destination type You have a choice between 3 different match types. If you have an exact URL that does not change for different customers (without '?=XXX'), then use ‘Equals to’ for an exact match. If the beginning of your converting URL is the same, but there are different numbers or characters at the end of the URL for various customers, then choose ‘Begins with.’ Use ‘Regular expression’ to match a block of text within the URL. For example, if all your subscriber URLs have 'subscriber_id=XXX' somewhere then type 'subscriber_id=' into the text field. You can also use 'regular expression' if you need to match multiple URLs and know how to use special characters to build regex. One of our favourite tools to test regular expressions is Regex Tester. The match type you select here will also apply to the URLs in the funnel, if you choose to create one. Destination page Destination page is the URL where the conversion occurs. For Teachable, and most other websites that sell something online, the destination is usually a ‘thank you' page that is displayed after successful purchase. You might also have a thank you page for contact forms and newsletter signups, which you would track the same way as a payment thank you page. Here you insert the request URI, which is the URL part that comes after the domain address. It would look something like this: /invoice/paid /thank you.html /payment/success Step 4: Should you set a goal value? (optional) You can set a monetary value to your goal if you want to track how much it contributes. e.g. If the goal is visitors completing a contact form, and you know the average lead generates you £100, then you can put the value at 100. If you are an ecommerce site and want to track exact purchases, then set up enhanced ecommerce tracking instead. Step 5: Should you set up a funnel? (optional) If you have several steps leading up to the conversion, you should set up a funnel to see how many people move through each defined step and where they fall out. If you do not set the first step as 'required', Google Analytics will also track people coming into funnel halfway through. i.e. If the first stage of your funnel is the homepage, then it will still include visitors who land straight on your contact page. Verify Now that you have set up your destination goal, click ‘verify the goal’ to check it works. If all is set up correctly, you should see an estimation of the conversion rate your goal would get. If you do not get anything, then check each step carefully. Once all is well, click ‘create goal’ and check it is working after a few days or a week, depending on how much traffic you get. If you set up a funnel, you will see it in Conversions > Goals > Funnel Visualisation. This is what a typical funnel would look like. Because I did not set the first step as 'required' you can see people entering the funnel at various steps. Need more help? Get in touch or comment below!
Will a computer put you out of a job?
I see a two tier economy opening up in England, and it’s not as simple as the haves and have-nots. It’s between those that build machines, and those that will be replaced by them: between those that can code, and those that can’t. We’ve seen the massive social effects that declining heavy manufacturing jobs since 1970s have had on much of the North of England and Scotland, and I believe we’re at the start of a similar long-term decimation of service industry jobs – not due to outsourcing to China, but due to automation by computers. Lots of my professional friends in London would feel they’re beyond the reach of this automation: their job involves being smart and creative, not doing production-line tasks. But it is these jobs, which currently involve staring at numbers on a screen, which are most at risk from computer substitution. If your job involves processing a load of data into a more presentable format (analysts, accountants, consultants and some types of traders) then a computer will eventually - within the next 20 years - be able to do your job better than you. In fact, within 20 years computers will be much better than humans at almost every kind of data processing, as the relentless extension of Moore’s law means pound-for-pound computer processing will be 1 million times cheaper than it is now. As Marc Andreessen put it, ‘Software is eating the world’, and we’re only just beginning to work through the implications. This worries me. With the greater and greater levels of automation of the working world, what happens to employment? Last year we saw an incredible event in the sale of WhatsApp to Facebook: massive wealth creation ($17bn) accompanied by almost no job creation (33 employees at the time of sale). If a tiny number of highly skilled people can create a service with 300m paying customers, why do companies need to hire lots of people? In the utopian view of future work we give up all boring admin tasks to the machines, and focus on face-to-face interaction and making strategic decisions based on selected knowledge fed to us by our personal digital agents (like Google search on steroids). Lots more thinking space leads us to be more productive, and more leisure time makes us happier. But 30 years ago they thought computers would evolve into very capable personal assistants, when in fact office workers are chained to the screen for longer hours by the tyranny of email and real-time information flow. Look at Apple’s forecast from 1987 of what computing might look like in 2006: the professor is freed from the tedium of typing or travelling to the library. Yet they didn’t consider whether the professor himself might be needed in a world where students could get their lectures as pre-recorded videos. So the cynical view is that more volume of data will require more humans to interpret, and the technology will always need fixing. As companies become more automated there will be more and more jobs shifting into analysis and IT support; analogous to how, as postal mail has been replaced by email, jobs in the company post room have shifted into IT support. The problem is that there really are a limited number of humans that can set up and maintain the computers. I’d love to see society grappling with that limitation (see grass-roots initiative like CoderDojo) but there are some big barriers to retraining adults to code: limited maths skills, limited tolerance for the boredom of wading through code, and limited opportunities for people to test their skills (i.e. companies don’t trust this most critical of job roles to new apprentices). So those that have commercial experience in programming can command escalating day rates for their skills – and this is most apparent in London and San Francisco, while pay in other skilled areas is not even keeping up with core inflation. That leads us to the dystopian view: that the generation starting their working lives now (those 10 years younger than me) will see their prospects hugely diverge, based on which side of the ‘replace’ or ‘be replaced’ divide they are. If companies akin to Google and Facebook become the mainstay of the global economy, then they’ll be a tiny number of silicon sultans whose every whim is catered for – and a vast mass of technology consumers with little viable contribution to the workplace. Let’s hope our politicians start grasping the implications before they too are replaced by ‘democracy producing’ software!
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5 common Google Analytics setup problems
Can you rely on the data you are seeing in Google Analytics? If you use it daily in your business you should really give some time to auditing how the data is captured, and what glitches could be lurking unseen. The notifications feature in Google Analytics now alerts you to some common setup problems, but there are more simple ones you could check today. Here are 5 aspects of your Google Analytics account to check now. Are you running the latest Universal Analytics tracking code? Is your overall bounce rate below 10%? Are you getting referrals from your own website? Are you getting ‘referrals’ from your payment gateway? Have you got the correct website default URL set in GA? Are you getting full referring URL in reports? 1. Are you running the latest Universal Analytics tracking code? You may have clicked upgrade in the Google Analytics admin console, but have your developers successfully transferred over to the new tracker code? Use our handy tool to test for universal analytics (make sure you copy your URL as it appears in the browser bar). 2. Is your overall bounce rate below 10%? The 'bounce rate' is defined as sessions of only one page. It’s highly unlikely to be in single digits unless you have a very unique source of engaged traffic. However, it is possible that the tracking code is firing twice on a single page. This double counting would mean Google Analytics sees every single page view as two pages – i.e. not a bounce This is more common on template-driven sites like Wordpress or Joomla, where you may have one tracking script loaded by a plugin – and another pasted onto the main template page. You can check if you have multiple pageviews firing by using the Google Tag Assistant plugin for Chrome. 3. Are you getting referrals from your own website? A self-referral is traffic coming from your own domain – so if you are www.acme.com, then a self-referrals would be appearing as ‘acme.com’. Have a look at the (recently moved) referrals list and see if that is happening for you. This is usually caused by having pages on your website which are missing the GA tracking code, or have it misconfigured. You can see exactly which pages are causing the problem by clicking on your domain name in the list and seeing the referring path. If you are on universal analytics (please use our tool to check) you can exclude these referrals in one step with the Referral Exclusion list. For a fuller explanation, see the self-referral guide provided by Google. 4. Are you getting ‘referrals’ from your payment gateway? Similar to point 3: if you have a 3rd party payment service where customers enter their payment details, after they redirect to your site – if you are on Universal analytics – they will show up as a new visit… but originating from ‘paypal.com’ or ‘worldpay.com’. You need to add any payment gateway or similar 3rd party services to that referral exclusion list. Just add the domain name - so PayPal would be 'paypal.com' 5. Have you got the correct website default URL set in GA? When Google Analytics was first set up for your website you may have set a different domain name than what you now use. Or maybe you have switched to run your site on https:// rather than http://. So you need to change the default URL as set up in the admin page. For this go to Admin > Property > Property Settings. Once that is setup correctly, the ‘All Pages’ report becomes a lot more useful – because you can click through to view the actual page using the open link icon. Advanced: Are you getting full referring URL in reports? If you run your website across different subdomains (e.g. blog.littledata.co.uk and www.littledata.co.uk) then it can be difficult to tell which subdomain the page was on. The solution to this is to add the hostname to the URL using a custom filter. See the guide on how to view full page URLs in reports. What other setup issues are you experiencing? Let us know in the comments or by tweeting @LittledataUK.
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Agriculture in Uganda: Measure and Improve
I had a truly inspiring day visiting Send a Cow project near Masaka in Uganda. A group of 30 farmers underwent 4 years of training, supported by weekly visits from a social worker and agricultural trainer. From a group living in absolute under-a-dollar-a-day poverty, there are now farmers owning thousands of dollars worth of livestock and selling export crops like coffee. This education and support, plus the capital grant of one animal per household, has transformed their community. Although the success relied on a solid base of family and group cohesion, organised labour and animal husbandry, I want to focus on three aspects which have ongoing potential for the community. 1. Record keeping Yep, data to you and I. Writing daily details of milk yields, crop inputs, market sale prices and even visitor numbers enabled the farmers to measure and improve. Data also allows farmers to forecast and be inspired. Selling a regular surplus of milk from two cows (after family consumption – yes, they have great teeth!) gave the farmer a regular income of US$3.50 per day at the farm gate. That is more than a teacher’s salary in Uganda. With tender care and back-breaking forage harvesting, they now have a calf being reared – and can count just how much that will mean in further milk and profits. Maybe in 10 years they will be entering yields into a smartphone app, and have market prices forecast automatically. 2. Organic agriculture Oil derivatives (like diesel and fertiliser) are nearly as expensive in Uganda as the UK – in ridiculous contrast to the local market prices for vegetables. Efficient farming therefore has to rely on minimal imported inputs, and maximise the local bounty of sun, rain … and manure. Every precious drop of animal urine is captured – to mix with ash and chilli as an insect repellant for plants – or used neat as a fertiliser. In dry season, every rainfall is maximised, with lots of mulching of vegetables to prevent evaporation; and with a permaculture approach of shading coffee bushes with banana plants, and vegetables under the coffee. I am a fan of organic farming for health and environmental reasons, but out here I just do not see an alternative, cost-effective way to increase crop yields. 3. Peer-to-peer lending Developed-to-developing country lending networks, like Kiva.org, have grown rapidly – but with inevitable problems in vetting funding applications at distance. What farmers need are equivalents of 19th century Europe’s co-operative societies – where savers and lenders from the same area are brought together. These farmer groups operate a very effective local system. All members pledge to save every month: from just 1 cent a week. Then any member can ask for a short term (maximum 3 month) loan from the fund – which is now $2000. The default rate is low – around 2% - as members know the debtors ability to repay, and can monitor progress in person. Plus every debtor has savings in the scheme – so wants to preserve their share of the capital. Three month loans (and flat 10% interest) make repayments easy to predict – and work in a country where planting to harvest is only 3 months. Uganda’s government abolished co-operatives in the 1990s when they started sponsoring political campaigns. But if these lending clubs can grow they could go some way to unlocking the capital that Africa needs to grow. This post was written by Edward Upton, Founder of Littledata, @eUpton
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