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

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

It’s Black Sunday – not Black Friday

The biggest day for online retail sales among Littledata’s clients is the Sunday after Black Friday, followed closely by the last Sunday before Christmas. Which is more important - Black Friday or Cyber Monday? Cyber Monday saw the biggest year-on-year increase in daily sales, across 84 surveyed retailers from the UK and US. In fact, Cyber Monday is blurring into the Black Friday weekend phenomenon – as shoppers get used to discounts being available for longer. We predict that this trend will continue for 2016, with the number of sales days extending before and after Black Friday. Interested in what 2016 will bring? Stay tuned for our upcoming blog post! Want to see how you did against the benchmark? Sign up for a free trial or get in touch if you have any questions!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-23

The Black Friday Weekend of 2015

Shoppers on Black Friday are becoming more selective – with a decrease in the number of retailers seeing an uplift in Black Friday sales, but an increase in the purchase volumes seen at those selected stores. Littledata looked at the traffic and online sales of 84 ecommerce websites* over the Black Friday weekend (four days from Friday to the following Monday), compared with the rest of the Christmas season (1st November to 31st December). 63% of the surveyed retailers saw a relative increase in traffic on Black Friday weekend 2015 versus the remainder of the season, compared with 75% of the same retailers seeing traffic rise on Black Friday 2014. This implies some decided to opt out of Black Friday discounting in 2015 or got less attention for their discounts as other retailers spent more on promotion. The same proportion of retailers (60% of those surveyed) also saw a doubling (on average) in ecommerce conversion rate** during Black Friday 2015. In 2014, over 75% of retailers saw an improved conversion rate during Black Friday, but the median improvement over the rest of the season was just 50%. 61% of websites also saw an increase in average order value of 16% during Black Friday 2015, compared with only 53% seeing order values increase the previous Black Friday. We predict that this trend will continue in 2016, with a smaller number of websites benefiting from Black Friday sales, but a greater increase in ecommerce conversion rate for a select few. Be sure to check back for what the actual trends will be for 2016! Let us know what you think below or get in touch! * The surveyed websites were a random sample from a group which got a majority of their traffic from the UK or the US. The data was collected from Google Analytics, and so represents real traffic and payments. ** The number of purchases divided by the total number of user sessions   Image credit: HotUKDeals   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-18

What are custom dimensions in Google Analytics?

By default, Google Analytics allows you to segment traffic by standard dimensions such as visitor location, screen size, or traffic source. You can view smarter reports by adding custom dimensions specific for your business. Give me an example Let's say when your members register they add a job title. Would you like to see reports on the site activity for a particular job title, or compare conversion for one job title versus another? In which case you would set a custom dimension of 'Job Title' and then be able to filter by just the 'Researchers' for any Google Analytics report. Or if you run a blog / content site, you could have a dimension of 'author' and see all the traffic and referrals that a particular author on your site gets. How do I set this up? First, you need to be on Universal Analytics, and then you need to tag each page with one or more custom dimensions for Google Analytics. This is more easily done with Google Tag Manager and a data layer. It may be that the information is already on the web page (like the author of this post), but in many cases, your developer will need to include it in the background in a way that can be posted to Google Analytics. Then you will need to set up a custom report to split a certain metric (like page views) by the custom dimension (e.g. author). Please contact our specialists if you want more advice on how to set up custom dimensions!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-16

Exclude fake 'bot' traffic from your site with Google Analytics

Ever wondered why so few visitors convert on your site? One answer is that a big chunk of your traffic is from search engine spiders and other web 'bots' which have no interest in actually engaging with you. Google Analytics has a great new feature to exclude this bot traffic from your site. All you need to do is check a box under the Admin > View > View Settings. The new option is down the bottom, underneath currency selection. It uses the IAB /ABC Bots and Spiders list, which is standard for large publishers, and updated monthly. Warning: you will see a dip in traffic from the date you apply the setting. If you're looking for a more comprehensive method to exclude spam and ghost referrals, check out our how-to guide! Have some questions about this? Get in touch with our Google Analytics experts!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-11-15

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