Pros and cons of Google Analytics for ecommerce merchants

Particularly for newer users, Google Analytics is a complex platform. Because of this, Google Analytics users often have questions that either Aren't readily available in online content Poorly answered or over-complicated by resources and "data analytics experts" or Google Analytics consultants that claim to know the answers Today, we'll discuss: When ecommerce merchants should use Google Analytics The pros and cons of using Google Analytics [tip]Get free Google Analytics resources for Shopify with our GA content bundle[/tip] Why should I use Google Analytics? Have you ever questioned how websites know your location and redirect you to the page of that specific location? Or have you ever seen those ads that constantly appear after you visit a website, even for just a few seconds? That’s because of cookies, a set of parameters that get collected and interpreted. Cookies are a part of Google Analytics, which is Google's data measurement tool that helps online store owners, marketing managers and ecommerce managers understand who you should reach and how your website performs. "Website performance" is generally measured by Who is visiting your website How a user interacts with your website The set of decisions they take following those interactions Dynamic ecommerce data metrics like customer lifetime value, average order value, etc. Ultimately, ecommerce merchants want to use Google Analytics in order to increase the marketing and sales efforts of the platforms being used, whether that's Shopify, Magento, BigCommerce, Salesforce Commerce Cloud, or another platform. AnalyticsNinja takes an even deeper dive into Google Analytics and why it's an essential tool for your commerce business. [subscribe heading="Try Littledata free for 30 days" background_color="green" button_text="Start my free trial" button_link="https://www.littledata.io/app/get-free-trial"] Pros and cons of using Google Analytics Google Analytics is one of the most well-renowned ecommerce tracking tools. While there are certainly many pros (as it's considered an essential tool for marketers and store managers), there are also some drawbacks: Pros of Google Analytics It’s free of charge, so anyone can use it. You can use it on different digital environments such as websites, mobile applications, kiosks, or anything with an internet connection. There's a Google Analytics Academy, where you can get in-depth information on the platform. You can connect your Google Analytics account with your Google Ads account. You can collect data from different platforms and sources like commerce connections, industry benchmarks and more. You can create custom goals and track your ecommerce platform. You can create custom reports based on your needs. This way you can track specific information depending on your industry. It's simple enough to self-navigate, though some ecommerce managers seek help from a Google Analytics consultant. Cons of Google Analytics To understand the intricacies of Google Analytics, you need to "learn the language". Unfortunately, the right resources are often tough to find online, and instructions may be confusing, time-consuming or overwhelming to those without an intermediate analytics background. The overall feel of the platform may be overwhelming. There are many dashboards, settings, user views and metrics. The free version of Google Analytics suits even a rookie users almost anyone, but if your web traffic is high and you'd like to upgrade to the premium version, there's a hefty price point of $150,000. [tip]Get free Google Analytics resources for Shopify with our GA content bundle[/tip] If you'd like more information or have any questions about our Google Analytics connections for ecommerce, reach out to our team of Google Analytics experts. Next, check out part 2: What is the bounce rate in Google Analytics?

2017-03-22

6 reasons Facebook ads don’t match the data you see in Google Analytics

If you run Facebook Ads and want to see how they perform in Google Analytics, you may have noticed some big discrepancies between the data available in Facebook Ad Manager and GA. Both systems use different ways to track clicks and visitors, so let’s unpick where the differences are. There are two kinds of metrics you’ll be interested in: ‘website clicks’ = the number of Facebook users who clicked on an advert on your own site, and (if you do ecommerce) the transaction value which was attributed to that advert. Website Clicks vs Sessions from Facebook 1. GA isn’t picking up Facebook as the referrer If users click on a link in Facebook’s mobile app and your website opens in an in-app browser, the browser may not log that ‘facebook.com’ was the referrer. You can override this (and any other link) by setting the medium, source, campaign and content attributes in the link directly. e.g. www.mysite.com?utm_medium=social&utm_source=facebook.com&utm_campaign=ad Pro Tip: you can use GA’s URL builder to set the UTM tags on every Facebook campaign link for GA. In GA, under the Admin tag and then ‘Property settings’ you should also tick the box saying ‘Allow manual tagging (UTM values) to override auto-tagging (GCLID values)’ to make this work more reliably. 2. The user leaves the page before the GA tag fires There’s a time delay between a user clicking on the advert in Facebook and being directed to your site. On a mobile, this delay may be several seconds long, and during the delay, the user will think about going back to safety (Facebook’s app) or just closing the app entirely. This will happen more often if the visitor is not familiar with your brand, and also when the page contents are slow to load. By Facebook’s estimation the GA tracking won’t fire anywhere between 10% and 80% of clicks on a mobile, but fewer than 5% of clicks on a desktop. It depends on what stage in the page load the GA pixel is requested. If you use a tag manager, you can control this firing order – so try firing the tag as a top priority and when the tag container is first loaded. Pro Tip: you can also use Google's mobile site speed suggestions to improve mobile load speed, and reduce this post-click drop-off. 3. A Javascript bug is preventing GA receiving data from in-app browsers It’s possible your page has a specific problem that prevents the GA tag firing only for mobile Safari (or Android equivalent). You’ll need to get your developers to test out the landing pages specifically from Facebook’s app. Luckily Facebook Ad Manager has a good way to preview the adverts on your mobile. Facebook Revenue vs GA Ecommerce revenue 4. Attribution: post-click vs last non-direct click Currently, Facebook has two types of attribution: post-view and post-click. This means any sale the user makes after viewing the advert or clicking on the advert, within the attribution window (typically 28 days after clicking and 1 day after viewing), is attributed to that advert. GA, by contrast, can use a variety of attribution models, the default being last non-direct click. This means that if the user clicks on an advert and on the same device buys something within the attribution window (typically 30 days), it will be attributed to Facebook.  GA doesn't know about views of the advert. If another campaign brings the same user to your site between the Facebook ad engagement and the purchase, this other campaign takes the credit as the ‘last non-direct click’. So to match as closely as possible we recommend setting the attribution window to be '28 days after clicking the ad' and no 'after view' attribution in Facebook (see screenshot above) and then creating a custom attribution model in GA, with the lookback window at 28 days, and the attribution 'linear' The differences typically come when: a user engages with more than one Facebook campaign (e.g. a brand campaign and a re-targeting one) where the revenue will only be counted against the last campaign (with a priority for ads clicked vs viewed) a user clicks on a Facebook ad, but then clicks on another advert (maybe Adwords) before buying. Facebook doesn’t know about this 2nd advert, so will attribute all the revenue to the Facebook ad. GA knows better, and will attribute all (or part) of it to Adwords. 5. Facebook cross-device tracking The main advantage Facebook has over GA is that users log in to its platform across all of their devices, so it can stitch together the view of a mobile advert on day 1 with a purchase made from the user’s desktop computer on day 2. Here’s a fuller explanation. By contrast, unless that user logs into your website on both devices, and you have cross-device tracking setup, GA won’t attribute the sale to Facebook. 6. Date of click vs date of purchase In Facebook, revenue is attributed to the date the user saw the advert; in GA it is to the date of purchase. So if a user clicks on the advert on 1st September, and then buys on the 3rd September, this will appear on the 1st on Facebook – and on the 3rd in GA. 7. The sampling problem Finally, did you check if the GA report is sampled? In the top right of the screen, in the grey bar, you'll see that the report is based on a sample.  If that sample is less than 100% it means the numbers you see are estimates.  The smaller the sample size used, the larger the possibility of error.  So in this example, a 45% sample of 270,000 sessions could skew our results plus or minus 0.2% in the best case. As a rule of thumb, Google applies sampling when looking over more than 500,000 sessions (even if you select the 'greater precision' option from the drop-down menu). You can check your own sample using this confidence interval calculator. Conclusion Altogether, there’s a formidable list of reasons why the data will never be an exact match, but I hope it gives you a way to optimise the tracking. Please let us know if you’ve seen other tracking issues aside from these.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-08

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

Enhanced ecommerce tracking for travel booking sites

Every online business presence has a goal. These goals (bookings, donations, subscribers, events, or purchases) are the reason for our efforts. But how many of us really track how our goals really perform? In this article, you will find out how to take these business goals and track them on Google Analytics with an ecommerce approach. This article is not about how to set up goals in Google Analytics, but if you are interested in finding out more about the setup or what there are, then read: Setting up a destination goal funnel in Google Analytics. The advantage of using an ecommerce approach for non-ecommerce websites is that after the setup is done, you have a basis to develop correct marketing strategies. You will know what channels brings you money, you will know what channels interact with each other and you can adjust your budget to maximise the ROI. If you're in the business of selling tickets (planes, concerts, conferences), book medical exams or collect donations, this article concerns you! I will show you a step-by-step guide on where to implement the Enhanced Ecommerce features and I will provide links for each to find out how to implement them. Let's say you are Wizz Air. You sell flight tickets and book cars and so on. Promotion impressions and promotion clicks Each time Wizz Air displays a banner with some kind of marketing communication that banner can be tracked as a "promotion" in Google Analytics. In Google Analytics, you can see the performance of each banner and make decisions to replace them, change the order or even make them bigger based on the tracking you implement. The technicalities: implementing via Google Tag Manager or implementing via Google Analytics. After you implement the tracking and create the tags (for GTM) you will be able to see the data in Google Analytics under Ecommerce > Marketing > Internal Promotions Based on the position, click-thru-rate, and revenue gained for each, Wizz Air can then rearrange banners, eliminate some of them or boost their visibility. Ecommerce activities (catalogue views, service page views, click on call to actions) Wizz Air provides multiple sections on the website where you can search for flights. These sections can be mapped as product lists. For WizzAir, the product lists are in the homepage section, timetable section, and maps section. Typically, Google Analytics and Google Tag Manager requests the fields below when sending a product list view (product impressions). I will provide you with a schema that will capture the flight booking particularities but you can use your own business specific examples. When you click on a red point on the map, the customer can see the flights from a particular city. We will send all the flight information from that city as product impressions. 'id': 'LTN - PRG',                          // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',             // City name of departure - City name for arrival 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                      // If the page has the option to add the date we will add the date as a MMDDYY When the search button is present, you send the action "click". ga('ec:setAction', 'click', {                                    // click action. 'list': 'Maps'                                                          // Product list (string). }); After searching, the client can see the selection page from the product list. For Wizz Air customers, they can search the best price and see the package options. In the case of Wizz Air, these pages can be considered the product pages. The usual structure that needs to be sent to Google Analytics and Google Tag Manager is: 'id': 'LTN - PRG',                                    // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',          // City name of departure - City name for arrival 'category': 'Flight',                                 // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                               // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                             // If the page has the option to add the date we will add the date as a MMDDYY Each time the client changes the day a new detail view should be sent. Clicking on the price box will trigger an Add to cart action. The usual content of an Add To cart activity is: 'name': 'London Luton - Prague',    // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                               // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                         //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Flight number 'dimenstion2': 'WizzGO'              // Package option (Basic, Wizz Go, Wizz Plus) Check out steps and booking In the case of Wizz Air, each "continue" button will send a checkout step to Google Analytics. Sending the checkout steps will provide insights about where the customers drop off and what process steps can be improved. Wizz Air has a 4-steps checkout (choose flight, choose passengers, services, and payment). The final thing to send is the transaction (the booking). The structure and implementation details for Google Analytics and Google Tag Manager are in the links and the fields, in this case, will be: 'ecommerce': { 'purchase': { 'actionField': { 'id': 'T12345',                                           // Transaction ID. Required for purchases and refunds. 'affiliation': 'booking.com'                    // Affiliation agent, 'revenue': '35.43',                                 // Total booking value (incl. tax, airport fees etc) 'tax':'4.90', 'shipping': '5.99',                                 //can use this field to capture airport fees or thir party operators fees 'coupon': 'SUMMER_SALE'              //if a discount cupon was used }, 'products': [{                                      //if the flight has a return flight then two products will be sent 'name': 'London Luton - Prague',     // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                                // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                          //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Fligh number 'dimenstion2': 'WizzGO'               // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'         // Optional fields may be omitted or set to empty string. }, { 'name': 'Prague -London Luton',    // The departure airport code - The arrival airport code 'id': 'PRG -LTN',                               // City name of departure - City name for arrival 'price': '61.99',                                 // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '150117',                        //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN2143432',        // Flight number 'dimenstion2': 'WizzGO'             // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'        // Optional fields may be omitted or set to empty string. }] } } Sending all these steps to Google Analytics about the customer activity, on any kind of website, will provide you with information about return on marketing spends, improve page layout performance, improve conversion rate, find out insights about customer needs and a lot more. Having the full enhanced ecommerce setup is very powerful and can bring many advantages. You can test the full setup on the Google Analytics demo account. Have any questions or need some help? Please get in touch or comment below!  

2017-01-24

How to track your newsletter performance with Google Analytics - part 1

Newsletters are the most common form of digital marketing I have seen in the past years. I really don't know any website that doesn't send at least 1 newsletter a month, whether it's an ecommerce website, news website or a B2B presentation website. There are a lot of email marketing platforms, but the question is how profitable are these newsletters? Most platforms provide some form or analysis on the performance of each newsletter. Most providers can show you the numbers of emails sent, the number of users that opened your newsletter and the number of clicks in the email. Along with Google Analytics, you can see how impactful these newsletters are. I want to show you some hacks to dive deeper in analysing each part of your newsletter and improve your newsletter marketing. Analyse each section in the newsletter separate Most of the newsletter that I saw had several links in them so the best way to track them is to tag each link in a distinctive way using the Campaign Content parameter (utm_content). If you do not know what UTM parameters are, please take a moment to read this article: Why should you tag your campaigns? Using the blog post above create your tagged link and add the &utm_content=link1 OR &utm_content=second banner OR &utm_content=Discount banner (whatever works best for you when analysing the data) at the end. Here is an example: http://www.littledata.ro/?utm_source=newsletter&utm_medium=email&utm_campaign=20%25off&utm_content=banner1 Here is a newsletter as part of a campaign named: "black friday2" with 3 banners in it. You can see from the data bellow that the top banner had the most clicks, but, in fact, the second banner is the only one that converted. This means that in the future we should move the second banner as a primary banner to have a higher visibility and in this way increase the number of transactions. You can tag all your links in the newsletter (the logo, banners, hyperlinks, products and so on) And see how each section is performing and what is driving the customers to click in the email. In a real email marketing platform, I strongly recommend searching the provider blog to see if they already support this in any way. Here is MailChimp solution for tracking the newsletter performance in Analytics. If the platform you are using does not support Google Analytics at the moment you can just build the URL with Google's URL builder or our simple Littledata URL builder and add it as you normal do in the newsletter. Track users on how they get on your website from a particular newsletter We've tested some hypotheses and the first one is to make a group of users in Google Analytics that come from a newsletter. The standard way is just to tag the newsletter with UTM parameters and create an audience based on that traffic. But to be more precise and go further with the analysis, we can add a new UTM parameter to all the links in the newsletter that contained the User ID. So now this traffic is not random but it's from a customer we've engaged with already and I do have historical data. The benefit of doing so is that, in an era of mobile devices and cross-device interactions, people read newsletters on the move and react or buy on different devices at different times as a result of the same campaign. You, as a marketer need to understand the cross-device movement and so I recommend that you read about this in the blog post: User Tracking To be able to track the activity of each individual user in your newsletter, you need to build a URL with a User ID parameter in it. This step is similar to the one before so you can add on to the URL you already built for your banners and add the unique identifier number of each client like this: http://www.mywebsite.com/?utm_source=newsletter&utm_medium=email&utm_campaign=20%25off&userID=3D12345 The User ID is generated by the platform you're using, so please take your time and find out if your email marketing solution supports this, along with the email address you've imported and the User Id from your back end. We use Intercom, where you can just add it into the link with a simple click, like this: The platform you're using might be different but if there is an option to import the User Id along with the email address then it is likely that your platform supports this in some way. Once you've added this to the URL, you can then set up a URL variable in Google Tag Manager to pick it up and set up a field with the pageview that will be sent to Google Analytics. For more information, here's how to set a field in Google Tag Manager. Be sure to check back next week for part 2! If you have any questions or would like more help, please get in touch with one of our experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-01-12

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

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.  

2016-11-02

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?

2016-09-08
Try the top-rated Google Analytics app for Shopify stores

Try the top-rated Google Analytics app for Shopify stores

Get a 30-day free trial of Littledata for Google Analytics or Segment

Free Trial