Cross Domain tracking for Eventbrite using Google Tag Manager (GTM)

Are you using Eventbrite for event registrations? And would you like to see the marketing campaign which drove that event registration correctly attributed in Google Analytics? Then you've come to right place! Here is a simple guide to adding a Google Tag Manager tag to ensure the correct data is sent to Eventbrite to enable cross-domain tracking with your own website. Many thanks to the Lunametrics blog for their detailed solution, which we have adapted here for GTM. Before this will work you need to have: links from your site to Eventbrite (including mysite.eventbrite.com or www.eventbrite.co.uk) the Universal Analytics tracking code on both your site and your Eventbrite pages. only have one GA tracking code on your own site - or else see the Lunametrics article to cope with this 1. Create a new tag in GTM Create a new custom HTML tag in GTM and paste this script: [code language="javascript"] <script> (function(document, window) { //Uses the first GA tracker registered, which is fine for 99.9% of users. //won't work for browsers older than IE8 if (!document.querySelector) return; var gaName = window.GoogleAnalyticsObject || "ga" ; // Safely instantiate our GA queue. window[gaName]=window[gaName]||function(){(window[gaName].q=window[gaName].q||[]).push(arguments)};window[gaName].l=+new Date; window[gaName](function() { // Defer to the back of the queue if no tracker is ready if (!ga.getAll().length) { window[gaName](bindUrls); } else bindUrls(); }); function bindUrls() { var urls = document.querySelectorAll("a"); var eventbrite = /eventbrite\./ var url, i; for (i = 0; i < urls.length; i++) { url = urls[i]; if (eventbrite.test(url.hostname) === true) { //only fetches clientID if this page has Eventbrite links var clientId = getClientId(); var parameter = "_eboga=" + clientId; // If we're in debug mode and can't find a client if (!clientId) { window.console && window.console.error("GTM Eventbrite Cross Domain: Unable to detect Client ID. Verify you are using Universal Analytics."); break; return; } url.search = url.search ? url.search + "&" + parameter : "?" + parameter; } } } function getClientId() { var trackers = window[gaName].getAll(); return trackers[0].get("clientId"); } })(document, window); </script> [/code]   2. Set the tag to fire 'DOM ready' Create a new trigger (if you don't have a suitable one) to fire the tag on every page at the DOM ready stage.  We need to make sure the Google Analytics tracker has loaded first. 3. Test the marketing attribution With the script working you should see pageviews of the Eventbrite pages as a continuation of the same session. You can test this by: Opening the 'real time' reporting tag in Google Analytics, on an unfiltered view Searching for your own site in Google Navigating to the page with the Eventbrite link and clicking on it Looking under the Traffic Sources report and checking you are still listed as organic search after viewing the Eventbrite page Need more help? Comment below or get in touch!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-07

WWI Codebreaking and Interpretation

Reading Max Hasting’s excellent book on The Secret War, 1939-1945, I was struck by the parallel between the rise of radio communications in the 1930s and the more recent rise in internet data. The transmission of military and diplomatic messages by radio in the 1930s and 1940s provided intelligence agencies with a new gold mine. Never before had so much potential intelligence been floating in the ether, and yet it threatened to flood their limited manpower with a tide of trivia. The bottleneck was rarely in the interception (trivial with a radio set) or even decryption (made routine by Bletchley Park with the Enigma codes), but rather in filtering down to the tiny number of messages that contained important facts – and getting that information in real time to the commanders in the field. The Ultra programme (Britain’s decryption of German radio intercepts) was perennially understaffed due to the fact that other civil servants couldn’t be told how important it was. At Ultra’s peak in 1943, only around 50% of the 1,500 Luftwaffe messages a day were being processed – and it is unknown how many of those were in time to avert bombing raids. The new age of technology provided an almost infinitely wide field for exploration, as well as the means of addressing this: the trick was to focus attention where it mattered. The Secret War, page 203 The ‘new age of technology’ in the last two decades poses much the same problem. Data on internet behaviour is abundant: there are countless signals to listen to about your website performance, and the technology to monitor users is commonplace. And the bottleneck is still the same: the filtering of useful signals, and getting those insights to the ‘commanders’ who need them in real time. I started Littledata to solve this modern problem in interpreting website analytics for managers of online businesses. There is no decryption involved, but there is a lot of statistics and data visualisation know-how in making billions of data points appreciable by a company manager. Perhaps the most important aspect of our service is to provide insights in answer to a specific question: Group-Captain Peter Stewart, who ran the Royal Air Force’s photo-reconnaissance operations, was exasperated by a senior offer who asked for ‘all available information’ on one European country. Stewart responded that he could only provide useful information if he knew roughly what intelligence the suppliant wanted – ‘naval, military, air or ecclesiastical’. The Secret War, page 203 In the world of online commerce, the question is something like whether the client needs insights into the checkout conversion rate of all customers (to improve site design) or for a specific marketing campaign (to improve campaign targeting). So by focusing on insights which are relevant to the scale, stage or sector of the client company, and making these accessible in a real-time dashboard, Littledata can feed into decision making in a way that raw data can never do. Want to discuss this further? Get in touch or comment below!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-01

Don’t obsess over your homepage – its importance will decrease over time

Many businesses spend a disproportionate amount of time tweaking copy, design and interactive content for their homepage. Yet they miss the fact that the action is increasingly elsewhere. Homepage traffic has traditionally been seen as a proxy for ‘brand’ searches – especially when the actual search terms driving traffic are ‘not provided’. Now, brand search traffic may be finding other landing pages directly. Our hypothesis was that over the last 2 years the number of visits which start at the homepage, on the average website, are decreasing. To prove this, we looked at two categories of websites in Littledata’s website benchmarks: Websites with more than 20,000 monthly visits and more than 60% organic traffic (227 websites) Large websites with more than 500,000 monthly visits (165 websites) In both categories, we found that the proportion of visits which landed on the homepage was decreasing: by 8% annually for the smaller sites (from 16% of total visits to 13% over two years), and 7% annually for the larger sites (from 13% to 11%). If we ignore the slight rise in homepage traffic over the November/December period (presumably caused by more brand searches in the Christmas buying season), the annual decline is more than 10%. From the larger websites, only 20% showed any proportionate increase in homepage traffic over the 2 years – and those were mainly websites that were growing rapidly, and with an increasing brand. I think there are three different effects going on here: Increased sophistication of Google search usage is leading to more long-tail keywords, where users want a very specific answer to a question – usually not given on your homepage. The increase in mobile browsing, combined with the frustrations of mobile navigation, is leading more users to use search over navigation – and bypass your homepage That Google’s search-engine result page (SERP) changes have made it less likely that brand searches (searching for your company or product names) will navigate to your landing page – and instead browse social profiles, news, videos or even local listings for your company. In conclusion, it seems that for many businesses the homepage is an increasing irrelevance to the online marketing effort. Spend some time on your other content-rich, keyword-laden landing pages instead! And would you like to see if you are overly reliant on your homepage traffic, compared with similar websites? Try Littledata’s reporting suite.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-01-26

Online reporting: turning information into knowledge

Websites and apps typically gather a huge flow of user behaviour data, from tools such as Google Analytics and Adobe Analytics, with which to better target their marketing and product development. The company assumes that either: Having a smart web analyst or online marketer skim through the reports daily will enable management to keep tabs on what is going well and what aspects are not Recruiting a ‘data science’ team, and giving them access to the raw user event data, will surface one-off insights into what types of customers can be targeted with which promotions Having worked in a dozen such companies, I think both assumptions are flawed. Humans are not good at spotting interesting trends, yet for all but the highest scale web businesses, the problem is not really a ‘big data’ challenge. For a mid-sized business, the problem is best framed as, how do you extract regular, easy-to-absorb knowledge from an incomplete online behavioural data set, and how do you present / visualise the insight in such a way that digital managers can act on that insight? Littledata is meeting the challenge by building software to allow digital managers to step up the DIKW pyramid. The DIKW theory holds that there are 4 levels of content the human mind can comprehend: Data: the raw inputs; e.g. the individual signals that user A clicked on button B at a certain time when visiting from a certain IP address Information: provides answers to "who", "what", "where", and "when" questions Knowledge: the selection and synthesis of information to answer “how” questions Wisdom: the extrapolation or interpretation of this knowledge to answer “why” questions Information is what Google Analytics excels at providing an endless variety of charts and tables to query on mass the individual events. Yet in the traditional company process, it needs a human analyst to sift through those reports to spot problems or trends and yield genuine knowledge. And this role requires huge tolerance for processing boring, insignificant data – and massive analytical rigour to spot the few, often tiny, changes. Guess what? Computers are much better at the information processing part when given the right questions to ask – questions which are pretty standard in the web analytics domain. So Littledata is extending the machine capability up the pyramid, allowing human analysts to focus on wisdom and creativity – which artificial intelligence is still far from replicating. In the case of some simpler insights, such as bounce rates for email traffic, our existing software is already capable of reporting back a plain-English fact. Here’s the ‘information’ as presented by Google Analytics (GA). And here is the one statistically significant result you might draw from that information: Yet for more subtle or diverse changes, we need to generate new ways to visualise the information to make it actionable. Here are two examples of charts in GA which are notoriously difficult to interpret. Both are trying to answer interesting questions: 1. How do users typically flow through my website? 2. How does my marketing channel mix contribute to purchasing? Neither yields an answer to the “how” question easily! Beyond that, we think there is huge scope to link business strategy more closely to web analytics. A visualisation which could combine a business’ sales targets with the current web conversion data, and with benchmarks of how users on similar sites behave, would give managers real-time feedback on how likely they were to outperform. That all adds up to a greater value than even the best data scientist in the world could bring. Have any questions? Comment below or get in touch with our team of experts! Want the easier to understand reports? Sign up!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-12

Why do I need Google Analytics with Shopify?

If the lack of consistency between Shopify’s dashboards and the audience numbers in Google Analytics is confusing, you might conclude that it’s safer to trust Shopify. There is a problem with the reliability of transaction volumes in Google Analytics (something which can be fixed with Littledata’s app) - but using Shopify’s reports alone to guide your marketing is ignoring the power that has led Google Analytics to become over by over 80% of large retailers. Last-click attribution Let’s imagine your shoe store runs a Google AdWords campaign for ‘blue suede shoes’. Shopify allows you to see how many visits or sales were attributed to that particular campaign, by looking at UTM ‘blue suede shoes’. However, this is only capturing those visitors who clicked on the advert and in the same web session, purchased the product. So if the visitor, in fact, went off to check prices elsewhere, or was just researching the product options, and comes back a few hours later to buy they won’t be attributed to that campaign. The campaign reports in Shopify are all-or-nothing – the campaign or channel sending the ‘last-click’ is credited with 100% of the sale, and any other previous campaigns the same customer saw is given nothing. Multi-channel attribution Google Analytics, by contrast, has the ability for multi-channel attribution. You can choose an ‘attribution model’ (such as giving all campaigns before a purchase equal credit) and see how much one campaign contributed to overall sales. Most online marketing can now be divided into ‘prospecting’ and ‘retargeting’; the former is to introduce the brand to a new audience, and the latter is to deliberately retarget ads at an engaged audience. Prospecting ads – and Google AdWords or Facebook Ads are often used that way – will usually not be the last click, and so will be under-rated in the standard Shopify reports. So why not just use the analytics reports directly in Google AdWords, Facebook Business, Twitter Ads etc.? Consistent comparison The problem is that all these different tools (and especially Facebook) have different ways of attributing sales to their platform – usually being as generous as possible to their own adverting platform. You need a single view, where you can compare the contribution of each traffic source – including organic search, marketing emails and referrals from other sites – in a consistent way. Unfortunately, Google Analytics needs some special setup to do that for Shopify. For example, if the customer is redirected via a payment gateway or a 3D secure page before completing the transaction then the sale will be attributed to a ‘referral’ from the bank - not the original campaign. Return on Advertising Spend (ROAS) Once you iron out the marketing attribution glitches using our app, you can make meaningful decisions about whether a particular form of marketing is driving more revenue that it is costing you – whether there is a positive Return on Advertising Spend. The advertising cost is automatically imported when you link Adwords to Google Analytics, but for other sources, you will need to upload cost data manually or use a tool like funnel.io . Then Google Analytics uniquely allows you to decide if a particular campaign is bringing more revenue than it is costing and, on a relative basis, where are the best channels to deploy your budget. Conclusion Shopify’s dashboards give you a simple daily overview of sales and products sold, but if you are spending more than hundreds of dollars a month on online advertising – or investing in SEO tactics – you need a more sophisticated way to measure success. Want more information on how we will help improve your Shopify analytics? Get in touch with our experts! Interested in joining the list to start a free trial? Sign up! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-07

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.

2016-12-06

Comparing 3 time ranges in Google Analytics

Selecting time ranges for comparison in Google Analytics can trip you up. We find comparing 28-day or 7-day (one week) periods the most reliable method. Gotcha 1: Last 4 days with previous 4 days This is comparing the same time periods (4 days) so shouldn't they be comparable? No! Most websites show a strong weekly cycle of visits (either stronger or weaker on the weekend), so the previous four days may be a very different stage of the week. Gotcha 2: Last month compared with the previous month Easy - we can see traffic has gone up by 5% in March. No! March has 11% more viewing time (3 extra days) than February. So the average traffic per day in March has actually dropped by 5.5%. Gotcha 3: Last week compared with the previous week You can see what's coming this time... Certain weeks of the year are always abnormal, and the Christmas period is one of them. But most business / educational sites it is a very quiet period. The best comparison would be with the same week last year. Have any questions? Let us know by commenting below or get in touch with our lovely experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-01

Top 5 Google Analytics metrics Shopify stores can use to improve conversion

Stop using vanity metrics to measure your website's performance! The pros are using 5 detailed metrics in the customer conversion journey to measure and improve. Pageviews or time-on-site are bad ways to measure visitor engagement. Your visitors could view a lot of pages, yet be unable to find the right product, or seem to spend a long time on site, but be confused about the shipping rates. Here are the 5 better metrics, and how they help you improve your Shopify store: 1. Product list click-through rate Of the products viewed in a list or category page, how many click through to see the product details? Products need good images, naming and pricing to even get considered by your visitors. If a product has a low click-through rate, relative to other products in the list, then you know either the image, title or price is wrong. Like-wise, products with very high list click-through, but low purchases, may be hidden gems that you could promote on your homepage and recommended lists to increase revenue. If traffic from a particular campaign or keyword has a low click-through rate overall, then the marketing message may be a bad match with the products offered – similar to having a high bounce rate. 2. Add-to-cart rate Of the product details viewed, how many products were added to the cart? If visitors to your store normally land straight on the product details page, or you have a low number of SKUs, then the add-to-cart rate is more useful. A low add-to-cart rate could be caused by uncompetitive pricing, a weak product description, or issues with the detailed features of the product. Obviously, it will also drop if you have limited variants (sizes or colours) in stock. Again, it’s worth looking at whether particular marketing campaigns have lower add-to-cart rates, as it means that particular audience just isn’t interested in your product. 3. Cart to Checkout rate Number of checkout processes started, divided by the number of sessions where a product is added to cart A low rate may indicate that customers are shopping around for products – they add to cart, but then go to check a similar product on another site. It could also mean customers are unclear about shipping or return options before they decide to pay. Is the rate especially low for customers from a particular country, or products with unusual shipping costs? 4. Checkout conversion rate Number of visitors paying for their cart, divided by those that start the process Shopify provides a standard checkout process, optimised for ease of transaction, but the conversion rate can still vary between sites, depending on payment options and desire. Put simply: if your product is a must-have, customers will jump through any hoops to complete the checkout. Yet for impulse purchases, or luxury items, any tiny flaws in the checkout experience will reduce conversion. Is the checkout conversion worse for particular geographies? It could be that shipping or payment options are worrying users. Does using an order coupon or voucher at checkout increase the conversion rate? With Littledata’s app you can split out the checkout steps to decide if the issue is shipping or payment. 5. Refund rate Percent of transactions refunded Refunds are a growing issue for all ecommerce but especially fashion retail. You legally have to honour refunds, but are you taking them into account in your marketing analysis? If your refund rate is high, and you base your return on advertising spend on gross sales (before refunds), then you risk burning cash on promoting to customers who just return the product. The refund rate is also essential for merchandising: aside from quality issues, was an often-refunded product badly described or promoted on the site, leading to false expectations? Conclusion If you’re not finding it easy to get a clear picture of these 5 steps, we're in the process of developing Littledata’s new Shopify app. You can join the list to be the first to get a free trial! We ensure all of the above metrics are accurate in Google Analytics, and the outliers can then be analysed in our Pro reports. You can also benchmark your store performance against stores in similar sectors, to decide if there are tweaks to the store template or promotions you need to make. Have more questions? Comment below or get in touch with our lovely team of Google Analytics experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-30

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