How to set up ecommerce tracking with Google Tag Manager
Enhanced ecommerce tracking requires your developers to send lots of extra product and checkout information in a way that Google Analytics can understand. If you already use GTM to track pageviews you must send ecommerce data via Google Tag Manager Step 1 Enable enhanced ecommerce reporting in the Google Analytics view admin setting, under 'Ecommerce Settings' Step 2 Select names for your checkout steps (see point 4 below): Step 3 Get your developers to push the product data behind the scenes to the page 'dataLayer'. Here is the developer guide. Step 4 Make sure the following steps are tracked as a pageview or event, and for each step set up a Universal Analytics tracking tag: Product impressions (typically a category or listing page) Product detail view (the product page) Add to basket (more usually an event than a page) Checkout step 1 (views the checkout page) Checkout step 2 etc - whatever registration, shipping or tax steps you have Purchase confirmation Step 5 Edit each tag, and under 'More Settings' section, select the 'Enable enhanced ecommerce features' and then 'use data layer' options: Of course, there's often a bit of fiddling to get the data layer in the right format, and the ecommerce events fires at the right time, so please contact us if you need more help setting up the reports! Step 6 - Checking it is working There is no 'real time' ecommerce reporting yet, so you'll need to wait a day for events to process and then view the shopping behaviour and checkout behaviour reports. If you want to check the checkout options you'll need to set up a custom report: use 'checkout options' as the dimension and 'sessions' and 'transactions' as the metrics. Need some more help? Get in touch with our lovely team of experts and we'd be happy to answer any questions! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
Android users buy 4x more than Apple users. Why?
Looking at a sample of 400 ecommerce websites using Littledata, we found mobile ecommerce conversion rates vary hugely between operating systems. For Apple devices, it is only 1% (and 0.6% for the iPhone 6), whereas for Android devices the conversion rate is nearly 4% (better than desktop). It’s become accepted wisdom that a great ‘mobile experience’ is essential for serious online retailers. As 60% of all Google searches now happen on mobile, and over 80% of Facebook ad clicks come from mobile, it’s highly likely the first experience new customers have of your store is on their phone. So is it because most websites look worse on an iPhone, or iPhone users are pickier?! There’s something else going on: conversion rate on mobile actually dropped for these same sites from July to October (1.25% to 1.26%) this year, even as the share of mobile traffic increased. Whereas on desktop, from July (low-season) to October (mid-season for most retailers), the average ecommerce conversion rate jumped from 2% to 2.5%. It seems during holiday-time, consumers are more willing to use their phones to purchase (perhaps because they are away from their desks). So the difference between Android and iOS is likely to do with cross-device attribution. The enduring problem of ecommerce attribution is that it’s less likely that customers complete the purchase journey on their phone. And on an ecommerce store you usually can’t attribute the purchase to the initial visit on their phone, meaning you are seriously underestimating the value of your mobile traffic. I think iPhone users are more likely to own a second device (and a third if you count the iPad), and so can more easily switch from small screen browsing to purchase on a large screen. Whereas Android users are less likely to own a second device, and so purchase on one device. That means iPhone users do purchase – but you just can’t track them as well. What’s the solution? The only way to link the visits on a phone with the subsequent purchases on another device is to have some login functionality. You can do that by getting users to subscribe to an email list, and then linking that email to their Google Analytics sessions. Or offering special discounts for users that create an account. But next time your data tells you it’s not worth marketing to iPhone users, think again. Need help with your Google Analytics set up? 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.
Personalising your site for a local event with Google Optimize
Google Optimize versus Optimizely
Why your business shouldn't go freemium
I took part in an interesting discussion about when the popular ‘freemium’ model for web apps is viable. Startup dogma says that scalable software/web products must be (at least partly) free, so it's worth reiterating the downsides of this approach. Here’s how I see the economic realities: 1. Free brings you the wrong sort of users UK supermarkets are currently locked in a price war to try to grab market share, so let’s imagine Tesco were crazy and desperate enough to offer free cases of beer to all shoppers. Do you think their overall sales would suddenly rise that day? Or would freeloaders come from around town to collect free beer… and then buy their groceries from Asda or Sainsbury’s, as usual? Giving away free products, including free web apps, discourages users from thinking about whether they value the product – and would ever pay for it. Often free attracts the kind of time-rich, cash-poor user who will go to extreme lengths to avoid paying – so frustrating the ‘pay for extra convenience’ premium package of many freemium models. Conversely, offering a free trial and taking payment options up front confirms that customers can and will be able to pay. GoToMeeting’s CFO found their conversion rate of ACTIVE trial users to paying customers was 45% when credit card details were taken up front – as opposed to 3% when they were not. 2. Low payment conversion rates need massive active user bases Most freemium products report less than 5% of active users paying for the service, with the conversion rate generally closer to 1% if you look at all users (including inactive). So with a standard consumer subscription fee of $20/month you would have an Average Revenue Per User (ARPU) of $20 x 12 x 1% = $2.40. To get to a respectable turnover of $10m per year, (I'd suggest the minimum aim for an Angel or seed funded startup) you would need over 4m users. And to get to VC-exit/IPO scale you would need 50m users. That is achievable for mass-adoption products like Evernote and Dropbox but really isn’t if you’re in a niche market like schools. There are less than 4m teachers in the whole of the USA, and expecting to get more than 10% of them adopting your product is cuckoo-land talk. For example, Busuu (a language learning marketplace) boasted 45m million users last year but only $12m in revenue – under 30 cents per user. Busuu is probably just safe with a huge market, but for an app with impressive global traction that is a very small reward: Facebook makes $6 per user per year just from advertising. 3. Low customer lifetime values limit your marketing options If your lifetime value per user is $3 - since the average subscription length is likely to be around a year – there are very few marketing options beyond SEO or word-of-mouth. You can’t justify affiliate deals, pay-per-click or display advertising if you can’t afford to buy customers. e.g. If you buy a keyword for even $0.30 you would need a bullish 10% signup rate even to break even on the marketing spend. Freemium businesses have genuinely got to sell themselves, and so must focus on the product first. The exact opposite was ScreenSelect (which merged into LoveFilm and then Amazon film rental) – customer growth could be bought with the predictable recurring revenue. All LoveFilm did, was promise a marketing agency the first few months of a customer’s subscription payments, and the agency worked out which tactics would be profitable at that level. See William Reeve’s great explanation of the economics. But that only worked when the customer lifetime value was $100 or more. 4. Freemium generates huge buzz but little sustainable profit A big trap in startup business model innovation is following pundits saying ‘freemium is really working for X company’ when ‘working for’ means they are getting loads of users and publicity. In itself publicity is a good thing – ‘product as the message’ – but it will only translate into profit if there is a massive market to capture. Here's the dirty secret: many venture-capital funded businesses are more focused on user growth than revenue because hyper-growth of users increases their valuation faster than moderate revenue growth. So don’t copy them if you plan to grow your company without massive VC funding! How many truly big businesses make the majority of their revenue from premium subscriptions backed by free software? From an article in 2011 here is a decent list who are still going strong: LogMeIn (switched to free trial) Dropbox Skype Spiceworks Lookout Eventbrite Zendesk Evernote SurveyMonkey GitHub What they have in common is mass-market, simple tools with massive international business audiences, and masses of venture funding behind them. But there are plenty of other former tech darlings who flamed out before making the critical mass of users – e.g. Helpstream in 2010. Conclusion: freemium is not for bootstrapped companies I’m not saying freemium can’t work for you, but be very wary of following the freemium orthodoxy unless you have: A potential user base of more than 10m (and preferably 100m users) A product so simple it doesn’t need training or support to get set up Substantial funding – it takes a long time to build enough users to generate cost-covering income For many businesses, the fabled networked effects of all those free users will be out-weighted by the opportunity cost of not charging some of them from the start. To profitability and beyond! Have any questions? Comment below or get in touch! (Chart: How Much Is the Average Facebook User Worth? | Statista) Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
Making the detection of significant trends in your traffic easier to see
Our core belief at Littledata is that machines are better at spotting significant changes in your website’s performance than a human analyst. We’ve now made it easier for you to get specific alerts, reducing the time spent wading through data. This is the story of how we produced the new trend detection algorithm. Enjoy! Back in 2014, we developed the first version of an algorithm to detect if today or this week’s traffic was significantly different from previous periods. This allows managers to focus in on the aspects of the traffic or particular marketing campaigns which are really worthy of their attention. Although the first version was very sensitive, it also picked up too many changes for a single person to investigate. In technical language, it was not specific in enough. In June and July, Littledata collaborated with a working group of mathematicians from around Europe to find a better algorithm. The European Study Group with Industry (ESGI) originated in the University of Limerick’s mathematics department in Ireland and has helped hundreds of businesses link up with prominent mathematicians in the field to solve real-world problems. Littledata joined the latest study group in University College, Dublin in July, and was selected by a dozen mathematicians as the focus for their investigation. Andrew Parnell from the statistics department at University College, Dublin helped judge the output from the four teams that we split the group into. The approach was to use an algorithm to test the algorithms; in other words, we pitted a group of statistical strategies against each other, from clustering techniques to linear regression, through to Twitter’s own trend detection package, and compared their total performance across a range of training data sets. Initially, the Twitter package looked to be doing well, but in fact, it had been developed specifically to analyse huge volumes of tweets and perform badly when given low volumes of web traffic. In between our host’s generous hospitality, with Guinness, Irish folk music, and quite a lot of scribbling of formulas on beer mats, myself and our engineer (Gabriel) worked with the statisticians to tweak the algorithms. Eventually, a winner emerged, being sensitive enough to pick up small changes in low traffic websites, but also specific enough to ignore the random noise of daily traffic. The new trend detection algorithm has been live since the start of August and we hope you enjoy the benefits. Our web app allows for fewer distractions and more significant alerts tailored to your company’s goals, which takes you back to our core belief that machines are able to spot major changes in website performances better than a human analyst. If you’re interested in finding out how our web app can help you streamline your Google Analytics’ data, please get in touch! Further reading: 7 quick wins to speed up your site analysis techniques Online reporting turning information into knowledge Will a computer put you out of a job?
Setting up common email software for Google Analytics
Many of the popular email providers make it easy to automatically tag up links in your emails to allow Google Analytics to track them under the 'Email' channel. Without this, the traffic from email links will be dispersed under 'Direct' and 'Referral' channels, and you won't be able to see which emails really drive engagement or sales. Here are the links to set up some common email services: MailChimp Campaign Monitor ActiveCampaign Benchmark Email ConstantContact iContact Emma MadMimi GetResponse Mail Jet If your email provider is not in the list, or you send emails from your own platform, you'll need to manually paste in tagged up email links. Still need some help? Contact us and we'll be happy to answer any questions!
Personally Identifiable Information (PII), hashing and Google Analytics
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