5 (bad) reasons not to do a Google Analytics audit

Does this sound familiar? 'We know our data's bad, but we don't have the time or resources to fix it'. Or, even worse: 'I checked a bunch of other metrics and they didn't justify our current ad spend, so I think I'll just present that same old report at the meeting today...again. Luckily we haven't fixed our Google Analytics setup to track too much relevant data about other marketing channels, or to connect those channels directly to revenue, because then we might need to change our whole strategy!' There's still a lot of confusion out there about the role and scope of an analytics audit. With a free audit tool directly in the  app, Littledata is on a mission to change this. Here are some (slightly exaggerated) versions of common objections to doing an analytics audit, and how to overcome them. 1. You don't know what a Google Analytics audit is Okay, not to start this somewhat ironic post with an entirely un-ironic objection, but not understanding the process is probably the only good reason not to audit your analytics setup. Luckily an analytics audit is actually very straightforward: it's simply a check of your analytics configuration and implementation. Some consultants and last-gen apps can make the audit process seem confusing and disorienting. If that's been your experience, we're here to help. Our free Google Analytics audit tool explains the process in real time. Not only that, but many tracking and reporting issues can be fixed automatically by the app (hello, intelligent algorithm!). 2. You don't believe in marketing ROI There are a lot of fluffy tools out there. Google Analytics isn't one of them. It's not that all digital marketers take action based on analytics, but a majority of the top ones do. That's what makes them the best. If you need convincing that accurate data is the secret sauce behind higher marketing ROI (return on investment), check out the recent Google Analytics research with Econsultancy, where they found that '60% of leading marketers routinely take action based on analytics, and are also 48% more likely than mainstream marketers to say their strategy is strongly data-driven'. 3. You trust everything you read online Failing to audit your analytics setup is basically the same as believing that everything you read online is true, no matter the source. Why? Because bad data produces bad reports. This is true no matter how fancy your reporting templates might be, or how much time you've spent making spreadsheets of Google Analytics data look accessible. Unless you regularly audit your analytics setup, how do you know if you're tracking the right things in the right manner? This is especially true if you're using an otherwise awesome ecommerce platform like Shopify, which has notoriously questionable tracking that also happens to be easy to fix with the right analytics app. 4. You think that the customer is always wrong Customer happiness isn't just a buzzword, it's increasingly what's driving the growth and expansion of online businesses, especially in the ecommerce space. Big players like Amazon learned this early on, and they built an effective - and addictive - customer experience around heaps of data on everything from affiliate ads to repeat buying activity. Think you don't have access to those same tools? Think again. If you want to build a better customer experience, it's essential to start with the correct Google Analytics setup and end the guessing games about where your leads and customers come from, and how they act. That's where the audit comes in. 5. You're betting on failure Are you betting that your own company will fail? Unless you secretly run an ecommerce hedge fund and have shorted your own startup, this is probably a bad idea. Auditing your data tracking across the customer life cycle is a sure way to see what's working, what's not, and what can be improved. Otherwise you're stuck with bad data and revenue tracking that might not have much to do with the reality - or the future - of your online business. Is there a better way? Look, we get it. Change can be scary, but choosing to stay stuck in the same data rut isn't the way forward. We've helped over a thousand online businesses fix their Google Analytics setup to capture accurate, relevant data. Littledata's industry-leading automated audit tool is free to run as often as you'd like. Sign up today and start trusting your data.

by Ari
2017-09-07

Custom reporting for marketing agencies

Are you a digital marketing agency looking for new reporting solutions? As our agency partnerships continue to grow, we thought it would be useful to outline how Littledata's custom reporting helps forward-thinking agencies cut down on reporting time, visualise data and improve performance for their clients. The marketing landscape is complex, but your reporting doesn't have to be overly complicated. With such a wide range of channels and sites to track, many agencies struggle to find the best analytics tools. To you we say: Welcome, you've finally found a solution that both simplifies and enhances the reporting process. Smarter reporting and accurate analytics Do you produce regular campaign performance reports in Excel or Google Sheets for your clients? Have you rejected other reporting solutions as being too rigid or complex for your needs? Then Littledata’s custom reports could work well for you and your clients. We automate the data fetching and calculations you currently run manually, and display the results to clients in a streamlined web app. We'll even show you the most important metrics, and report on key changes - automatically. One key advantage over tools such as Tableau, Data Studio or Chartio is that you can define a template report and then roll it out for many different web properties (or segments of websites) with the click of a button. Compared with other solutions you may have considered we also offer: Full support in data setup, report design and client onboarding Branded report packs for your clients and customers Complete life cycle data on your clients' customers, from marketing attribution to repeat purchases (including for subscription-based businesses) 1st line support to end users Flexibility to calculate any metrics (using Google Sheets in our processing pipeline) Comparison to industry benchmarks for sales, marketing and web performance - or create private benchmarks amongst your own client base Actionable insights for any online business to improve marketing ROI and increase conversions, whether one large ecommerce site or a series of micro-sites Integration of Google Analytics with Google Search Console data for powerful SEO reports We’re also open to discussions about white-labelling the Littledata app. This type of partnership works best for agencies with at least 20 clients ready to take advantage of our intelligent analytics tools. Please contact us if you’d like a demo, to see how this has worked for existing customers, or to discuss a particular client’s needs. Get ready to love your analytics :)

2017-08-09

What to test with Google Optimize

So you’ve got a brand new tool in your web performance kit – Google Optimize – and now you want to put it to good use. What can you test with Optimize and how does it work? Firstly, what are the different options for setting up an experiment? AB Test Using the in-page editor you can create an altered version of the page you wish to test. This could be a change of text copy, different styling, or swapping in a different image. You can also add new scripts or HTML if you’re familiar with coding. The way this works is Optimize adds a script after the page loads to manipulate the page text, images or styles. I recommend not switching header elements or large images using this method as, depending on your website setup, there may be a noticeable flicker– try a redirection test below. You can create many versions with subtly different changes (C, D and E versions if you want) – but remember you’ll need a large volume of traffic to spot significant differences between lots of variations. You can also limit the test to a certain segment of users – maybe only first time visitors, or those on mobile devices. Multivariate Test Similar to an AB test, a multivariate test is used when you have a few different aspects of the page to change (e.g. image and headline text) and you want to see which combination is most engaging. To get a significant result, you'll need a large volume of traffic - even more than testing many options in AB tests.   Redirection Test This is where you have two different versions of a page – or a different flow you want to start users on. Optimize will split your visitors, so some see the original page and some are redirected to the B version. A redirection test is best when the page content or functionality is very different – perhaps using a whole different layout. The disadvantage is you’ll need a developer to build the B version of the page, which may limit the speed of cycling tests.   Personalisation Personalisation is not officially supported by Optimize right now, but we’ve found it to be a useful tool. You can assign 99.9% of the visitors who match certain criteria to see the alternative version of the page. An example is where you have a special offer or local store in a particular city - see our step-by-step local personalisation example. You can ensure that all the visitors from that city see a different version of the page. Unfortunately on the free version of Google Optimize you are limited to 3 concurrent ‘experiments’ – so it won’t be a good solution if you want to run similar personalisation across lots of cities or groups of users. Next the question is where to start with tests...   Start with the landing pages Landing pages get the greater volume of traffic, and are where small visual changes (as opposed to new product features) make the biggest difference to user engagement. This greater volume allows you to get a significant result quicker, meaning you can move on to the next test quicker. And keep on improving!   So what exactly could you test using Google Optimize? Here are six ideas to get you going.   1. Could call-to-actions (CTA) be clearer? Changing the colour or contrast of a key button or link on the page (within your brand guidelines) usually results in more visitors clicking it. This might involve changing the style of the CTA itself, or removing elements close by on the page – to give the CTA more space to stand out.   2. Are you giving the user too many choices? In Steve Krug’s classic Don’t Make me Think he explains how any small confusion in the user’s mind can stop them making any choice. Every choice the user has to make is an opportunity for them to give up. Try hiding one of the options and seeing if more users overall choose any of the remaining options.   3. Is the mobile page too long? As many sites move to responsive designs that switch layout on smaller screens, this has led to mobile pages becoming very long. User may get ‘scroll fatigue’ before then get to critical elements on the page. Try cutting out non-essential sections for mobile users, or editing copy or images to make the page shorter. You could also try switching sections so that the call-to-action is higher up the page on mobile – although this is harder to achieve without a redirection test.   4. Is localisation important to your users? You may have discussed providing local language content for your users, and been unsure if it is worth the costs of translation and maintenance. Why not test the benefits for a single location? As with the personalisation tests, you can show a different local language (or local currency) version of the page to half the users in the single location (e.g. Spanish for visitors from Mexico) and see if they convert better.   5. Does the user need more reassurance before starting to buy? It easier to build experiments which remove elements to the page, but you should also consider adding extra explanation messages. A common problem on ecommerce stores is that visitors are unsure what the shipping charges or timing will be before adding to cart. Could you add a short sentence at the start of the journey (maybe on a product page) to give an outline of your shipping policy? Or maybe some logos of payment methods you accept?   6. Changing header navigation If your site has a complex mix of products that has evolved over time it may be time to try a radical new categorisation – maybe splitting products by gender or price point rather than by type. For this test, you’ll want to target only new visitors – so you don’t confuse regular visitors until you’re sure it’s permanent. You will also need to make the navigation changes on all pages across the site.   Good luck! Littledata also offering consulting and AB testing support, so please contact us for any further advice.

2017-05-30

Shopify Marketing Events vs Google Analytics

At the Shopify Unite conference today I heard plenty of great ideas such as ShopifyPay but the most interesting for me as a data specialist was the marketing events API. Since we launched our Fix Google Analytics Shopify app earlier this year we’ve known that reporting was a weak spot in Shopify’s platform offering, and they admit that ‘understanding marketing campaign performance’ is one of the biggest challenges of Shopify merchants right now. The ability for other Shopify apps to plug their campaign cost and attribution data into Shopify (via the marketing events API) is a logical step to building Shopify’s own analytics capability, but I don’t believe it will be a substitute for Google Analytics (GA) anytime soon. Here’s why: 1. Google Analytics is the industry standard Every online marketer has used Google Analytics, and many have favourite reports they’ve learned to interpret. Moving them to use a whole new analysis platform will take time– and it’s taken GA 10 years to achieve that dominance. 2. GA provides platform-agnostic data collection For a store using Shopify as their only source of insights, moving away from Shopify would mean losing all the historic marketing performance data – so it would be very hard to make like-for-like comparisons between the old platform and the new. Many of our customers have used GA during and after a platform shift to get continuous historical data. Which ties into my first point that over 85% of businesses have a history of data in GA. 3. Incomplete marketing tagging will still cause issues Making valid analysis on multi-channel marketing performance relies on having ALL the campaigns captured - which is why our GA audit tool checks for completeness of campaign tagging. Shopify’s tracking relies on the same ‘utm_campaign’ parameters as GA, and campaigns that are not properly tagged at the time cannot be altered retrospectively. 4. Google is rapidly developing Google Analytics I’d like to see the Shopify marketing event collection evolve from its launch yesterday, but Google already has a team of hundreds working on Google Analytics, and it seems unlikely that Shopify will be able to dedicate resources to keep up with the functionality that power users need. 5. More integrations are needed for full campaign coverage Shopify’s marketing analysis will only be available for apps that upgrade to using the new API.  Marketing Events has launched with integrations for Mailchimp and Facebook (via Kit) but it won’t cover many of the major channels (other emails, AdWords, DoubleClick for Publishers) that stores use. Those integrations will get built in time, but until then any attribution will be skewed. 6. GA has many third-party integrations Our experience is that any store interested in their campaign attribution quickly wants more custom analysis or cuts of the data. Being able to export the data into Littledata’s custom reports (or Google Sheets or Excel) is a popular feature – and right now Shopify lacks a reporting API to provide the same customisations. You can only pull raw event data back out. That said, there are flaws with how GA attribution works. Importing campaign cost data is difficult and time consuming in GA – apart from the seamless integration with AdWords – and as a result hardly any of the stores we monitor do so. If Shopify can encourage those costs to be imported along with the campaign dates, then the return on investment calculations will be much easier for merchants. I also think Shopify has taken the right pragmatic approach to attribution windows. It counts a campaign as ‘assisting’ the sale if it happens within 30 days of the campaign, and also whether it was ‘last click’ or ‘first click’. I’ve never seen a good reason to get more complicated than that with multi-channel reports in GA, and it’s unlikely that many customers remember a campaign longer than 30 days ago. In conclusion, we love that Shopify is starting to take marketing attribution seriously, and we look forward to helping improve the marketing events feature from its launch yesterday, but we recommend anyone with a serious interest in their marketing performance sticks to Google Analytics in the meantime (and use our Shopify app to do so).

2017-04-21

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

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