Why do I need Google Analytics with Shopify?

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

2016-12-07

Tracking customers in Google Analytics

If your business relies on customers or subscribers returning to your site, possibly from different devices (laptop, smartphone, etc.) then it’s critical you start tracking unique customers rather than just unique visitors in Google Analytics. By default, Google Analytics tracks your customers by browser cookies. So ‘Bob’ is only counted as the same visitor if he comes to your site from the same browser, but not if he comes from a different computer or device. Worse, if Bob clears his cookies or accesses your site via another mobile app (which won't share cookies with the default browser) then he'll also be counted as a new user. You can fix this by sending a unique customer identifier every time your customer signs in. Then if you send further custom data about the user (what plan he / she is on, or what profile fields they have completed) you can segment any of the visits or goals by these customer attributes. There are 2 possible ways to track registered users: Using Google Analytics’ user ID tracker By storing the clientId from the Google cookie when a new user registers, and writing this back into the tracker every time the same user registers In both cases, we also recommend sending the user ID as a custom dimension. This allows you segment the reports by logged in / not logged in visitors. Let's look at the pros and cons. Session stitching Tracking customers involves stitching together visits from different devices into one view of the customer. Option 1, the standard User ID feature, does session stitching out the box. You can optionally turn ‘session unification’ on which means all the pageviews before they logged in are linked to that user. With option 2 you can stitch the sessions, but you can't unify sessions before the user logs in - because they will be assigned a different clientId. So a slight advantage to option 1 here. Reporting simplicity The big difference here is that with option 1 all of the user-linked data is sent to a separate 'registered users' view, whereas in options 2 it is all on the same view as before. Suppose I want a report of the average number of transactions a month for registered vs non-registered visitors. With both options, I can only do this if I also send the user ID as a custom dimension - so I can segment based on that custom dimension. Additionally, with option 1 I can see cross-device reports - which is a big win for option 1. Reporting consistency Once you start changing the way users are tracked with option 2 you will reduce the overall number of sessions counted. If you have management reports based on unique visitors, this may change. But it will be a one-time shift - and afterwards, your reports should be stable, but with a lower visit count. So option 1 is better for consistency Conclusion Option 1 - using the official user tracking - offers a better route to upgrade your reports. For more technical details on how this tracking is going to work, read Shay Sharon’s excellent customer tracking post. Also, you can watch more about customer tracking versus session tracking in this video. Have any questions? Comment below or get in touch with our team of experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-12-06

Comparing 3 time ranges in Google Analytics

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

2016-12-01

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

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

2016-11-30

The referral exclusion list: what it is and how to update it?

The referral exclusion list is only available for properties using Universal Analytics ... so please make the jump and take advantage of the benefits! Let's find out how excluding referral traffic affects your data and how you can correct some of the wrong attributions of sales. By default, a referral automatically triggers a new session. When you exclude a referral source, traffic that arrives to your site from the excluded domain doesn’t trigger a new session. Because each referral triggers a new session, excluding referrals (or not excluding referrals) affects how sessions are calculated in your account. The same interaction can be counted as either one or two sessions, based on how you treat referrals. For example, a user on my-site.com goes to your-site.com and then returns to my-site.com. If you do not exclude your-site.com as a referring domain, two sessions are counted, one for each arrival at my-site.com. If, however, you exclude referrals from your-site.com, the second arrival to my-site.com does not trigger a new session, and only one session is counted. Common uses for referral exclusions list in Google Analytics: Third-party payment processors Cross-subdomain tracking If you add example.com to the list of referral exclusions, traffic from the domain example.com and the subdomain another.example.com are excluded. Traffic from another-example.com is not excluded. Only traffic from the domain entered in the referral exclusions list and any subdomains are excluded. Traffic from domains that only have substring matches are not excluded. How to add domains in the referral exclusion list: Sign in to your Gooogle Analytics account. Click admin in the menu bar at the top of any page. In the account column, use the drop-down to select the Google Analytics account that contains the property you want to work with. In the property column, use the drop-down to select a property. Click tracking info. Click referral exclusion list. To add a domain, click +add referral exclusion. Enter the domain name. Click create to save. The referral exclusion list used contains matching. For example, if you enter example.com, then traffic from sales.example.com is also excluded (because the domain name contains example.com). Need help with these steps? Get in touch with one of our experts and we'd be happy to assist you!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-29

4 common pitfalls of running conversion rate experiments from Microsoft

At a previous Measurefest conference, one of the speakers, Craig Sullivan, recommended a classic research paper from Microsoft on common pitfalls in running conversion rate experiments. It details five surprising results which took 'multiple-person weeks to properly analyse’ at Microsoft and published for the benefit of all. As the authors point out, this stuff is worth spending a few weeks getting right as ‘multi-million-pound business decisions’ rest on the outcomes. This research ultimately points out the importance of doing A/A Testing. Here follows an executive overview, cutting out some of the technical analysis: 1. Beware of conflicting short-term metrics Bing’s management had two high-level goals: query share and revenue per search. The problem is that it is possible to increase both those and yet create a bad long-term company outcome, by making the search algorithm worse. If you force users to make more searches (increasing Bing’s share of queries), because they can’t find an answer, they will click on more adverts as well. “If the goal of a search engine is to allow users to find their answer or complete their task quickly, then reducing the distinct queries per task is a clear goal, which conflicts with the business objective of increasing share.” The authors suggest a better metric in most cases is lifetime customer value, and the executives should try to understand where shorter-term metrics might conflict with that long-term goal 2. Beware of technical reasons for experiment results The Hotmail link on the MSN home page was changed to open Hotmail in a separate tab/window. The naïve experiment results showed that users clicked more on the Hotmail link when it opened in a new window, but the majority of the observed effect was artificial. Many browsers kill the previous page’s tracking Javascript when a new page loads – with Safari blocking the tracking script in 50% of pages opening in the same window. The “success” of getting users to click more was not real, but rather an instrumentation difference. So it wasn’t that more people were clicking on the link – but actually that just more of the links were being tracked in the ‘open in new tab’ experiment. 3. Beware of peeking at results too early When we release a new feature as an experiment, it is really tempting to peek at the results after a couple of days and see if the test confirms our expectation of success (confirmation bias). With the initial small sample, there will be a big percentage change. Humans then have an innate tendency to see trends where there aren’t any. So the authors give the example of this chart: Most experimenters would see the results, and even though they are negative, extrapolate the graph along the green line to a positive result and four days. Wrong. What actually happens is regression to the mean. This chart is actually from an A/A test (i.e. the two versions being tested are exactly the same). The random differences are biggest at the start, and then tail off - so the long term result will be 0% difference as the sample size increases. The simple advice is to wait until there are enough test results to draw a statistically significant conclusion. That generally means more than a week and hundreds of individual tests. 4. Beware of the carryover effect from previous experiments Many A/B test systems use a bucketing system to assign users into one experiment or another. At the end of one test the same buckets of users may be reused for the second test. The problem is that if users return to your product regularly (multiple times daily in the case of Bing), then a highly positive or negative experience in one of the tests will affect all of that bucket for many weeks. In one Bing experiment, which accidentally introduced a nasty bug, users who saw the buggy version were still making fewer searches 6 months after the experiment ended. Ideally, your test system would re-randomise users for the start of every new test, so those carryover effects are spread as wide as possible. Summary For me the biggest theme coming out of their research is the importance of A/A tests – seeing what kind of variation and results you get if you don’t change anything. Which makes you more aware of the random fluctuations inherent in statistical tests. In conclusion, you need to think about the possible sources of bias before acting on your tests. Even the most experienced analysts make mistakes! Have any comments? Let us know what you think, below!    

2016-11-27

5 tips to avoid a metrics meltdown when upgrading to Universal Analytics

Universal Analytics promises some juicy benefits over the previous standard analytics. But having upgraded 6 different high traffic sites there are some pitfalls to be aware of. Firstly, why would you want to upgrade your tracking script? More reliable tracking of page visitors - i.e. fewer visits untracked More customisation to exclude certain referrers or search terms Better tools for tracking across multiple domains and tracking users across different devices Track usage across your apps for the same web property Ability to send up to 20 custom dimensions instead of the previous limit of only 5 custom variables If you want to avoid any interruption of service when you upgrade, why not book a quick consultation with us to check if Universal Analytics will work in your case. But before you start you should take note of the following. 1. Different tracking = overall visits change If your boss is used to seeing dependable weekly / monthly numbers, they may query why the number of visits has changed. Universal Analytics is likely to track c. 2% more visits than previously (partly due to different referral tracking - see below), but it could be higher depending on your mix of traffic. PRO TIP: Set up a new web property (a different tracking code) for Universal Analytics and run the old and new trackers alongside each other for a month. Then you can see how the reports differ before sharing with managers. Once this testing period is over you'll need to upgrade the original tracking code to Universal Analytics to you keep all your historic data. 2. Different tracking of referrals Previously, if Bob clicked on a link in Twitter to your site, reads, goes back to Twitter, and within 30 minutes clicks on a different link to your site - that would be counted as one visit and the 2nd referral source would be ignored. In Universal Analytics, when Bob clicks on the 2nd link he is tracked as a second visit, and 2nd referral source is stored. This may be more accurate for marketing tracking, but if Bob then buys a product from you, going via a secure payment gateway hosted on another domain (e.g. paypal.com) then the return from the payment gateway will be counted as a new visit. All your payment goals or ecommerce tracking will be attributed to a referral from 'paypal.com'. This will ruin your attribution of a sale to the correct marketing channel or campaign! PRO TIP: You need to add all of the payment gateways (or other third party sites a user may visit during the payment process) to the 'Referral Exclusion List'. You can find this under the Admin > Property > Tracking codes menu: 3. Tracking across domains If you use the same tracking code across different domains (e.g. mysite.co.uk and mysite.com or mysite.de) then you will need to change the standard tracking script slightly. By default the tracking script you copy from Google Analytics contains a line like: ga('create', 'UA-XXXXXXX-1', 'mysite.com');. This will only track pages that strictly end with 'mysite.com'. PRO TIP: It's much safer to change the tracker to set that cookie domain automatically. The equivalent for the site above would be ga('create', 'UA-XXXXXXX-1', 'auto');. The 3rd argument of the function is replaced with 'auto'. 4. Incompatibility with custom variables Only relevant if you send custom data already Custom variables are only supported historically in Universal analytics. That means you will need to change any scripts that send custom data to the new custom dimension format to keep data flowing. Read the developer documentation for more. PRO TIP: You'll need to set the custom dimension names in the admin panel before the custom data can be sent from the pages. You can also only check that the custom dimensions are being sent correctly by creating a new custom report for each dimension. 5. User tracking limitations We wouldn't recommend implementing the new user ID feature just now, as it has some major limitations compared with storing the GA client ID. You need to create a separate view to see the logged-in-user data, which makes reporting pageviews a whole lot more complex. Visits a user made to your site BEFORE signing up are not tracked with that user - which means you can't track the marketing sources by user PRO TIP: See our user tracking alternative. Got more tips on to setting up Universal Analytics? Please share them with us in the comments, or get in touch if you want more advice on how to upgrade!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-26

Widget Tracking with Google Analytics

I was asked recently about the best way to track a widget, loaded in an iframe, on a third-party site with Google Analytics. The difficulty is that many browsers now block 3rd party cookies (those set by a different domain to the one in the browser address bar) – and this applies to a Google Analytics cookie for widgets as much as to adverts. The best solution seems to be to use local storage on the browser (also called HTML5 Storage) to store a persistent identifier for Analytics and bypass the need to set a cookie – but then you have to manually create a clientID to send to Google Analytics. See the approach used by ShootItLive. However, as their comment on line 41 says, this is not a complete solution - because there are lots of browsers beyond Safari which block third party cookies. I would take the opposite approach and check if the browser supports local storage, and only revert to trying to set a cookie if it does not. Local storage is now possible on 90% of browsers in use and the browsers with worst 3rd party cookie support (Firefox and Safari) luckily have the longest support for local storage. As a final note, I would set up the tracking on a different Google Analytics property to your main site, so that pageviews of widgets are not confused with pageviews of your main site. To do list: Build a script to create a valid clientID for each new visitor Call ga('create) function, setting 'storage' : 'none', and getting the 'clientID' from local storage (or created from new) Send a pageview (or event) for every time the widget is loaded. Since the widget page is likely to be the same every time it is embedded, you might want to store the document referrer (the parent page URL) instead Need help with the details? Get in touch with our team of experts and we'd be happy to help!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-25
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