How to track your newsletter performance with Google Analytics – part 2

We will go further into newsletter tracking and try to get all important stats from Google Analytics such as emails sent and emails openings. The advantage to doing this is that for most digital teams, the people creating the newsletters are not necessarily the ones analysing the data. This can help bring the teams a more in-depth view into their work and also a new angle in analysing the newsletter. Before you go ahead and implement this, you should be aware of a few aspects and make some important decisions. First, will you all be using the same Google Analytics account? Since the newsletter opens will send a lot of visits to your Google Analytics account and most of them will be bounces (a high percent of users will not click on the newsletter to go to the website), take into consideration that using the same account will interfere with your existing data from the website. Second, you can create a new, separate account. If you choose to create a new account you need to find out, if you use user tracking, how to link the user activity with the user activity on the website. For Google 360 users this is simpler because they can join views, but for regular Google Analytics users, this might be a struggle. The third option, which I recommend, is to create a second Google Analytics tracking code and run it in parallel with the one you're currently using for the newsletter. Now, let's dive into how you can track email opening and email clicks. The usual Google Analytics script will not work for email clients. However, Google Analytics also includes event tracking which can be used through an embedded image pixel within the email body. Implementing the Google Analytics pixel provides great information like real-time tracking, browser and operating system details and demographics. Insert this snippet in the body of your email like this: <html> <head> ... </head> <body> .... <img src = "Paste the URL here of the Google Analytics implementation"> </body> <footer> ... </footer> </html> Most of the newsletter platforms have an HTML editor, which you can find by searching the sign " <> " in the template. This will let you add <img src = URL> in the body of your email. The URL image pixel looks might like this: <img src="http://www.google-analytics.com/collect?v=1&tid=UA-12345678-1&cid=User_ID&t=event&ec=email&ea=open&el=recipient_id&cs=newsletter&cm=email&cn=Campaign_Name"> Building the URL of the Google Analytics implementation can be done with Google Analytics tool named: Hit Builder. You can also test the URL in the tool and see the hit in real time in Google Analytics. You have two options when sending the openings: as an event or as a custom metric.  Before you go ahead with the HIT Builder let's get familiar with the components of the URL: URL Component Explanation cm1=Custom metric This can be cm1,cm2 etc based on what you've created as a custom metric tid=UA-12345678-1 Your Google Analytics Tracking ID cid=User_ID A systematic tracking ID for the customer t=event Tells Google Analytics this is an Event Hit Type ec=email The Event Category helps segment various events ea=open The Event Action helps specify exactly what happened el=recipient_id Event Label specifies a unique identification for this recipient cs=newsletter Campaign Source allows segmentation of campaign types cm=email Campaign Medium could segment social vs. email, etc. cn=Campaign_Name Campaign Name identifies the campaign to you   To see openings as a custom metric, you should first create a new custom metric in the Google Analytics admin interface named Email Opens. Log in to Google Analytics, and click on Admin. Select the Account and Web Property, and click on Custom Definitions under the Web Property column. Then click on Custom Metrics. In the next window, click on the New Custom Metric button, and give your custom metric a name, formatting type, minimum and maximum value, and make sure the box is checked for Active. You may also find some other benefits to using Google Analytics tracking this way over most email service provider (ESP) tracking. It provides great system information like real-time tracking, browser and operating system details, demographic information including location, and will even tie in nicely with your web reports. How To Use Your Results The event tracking results can be seen in Google Analytics right away. Below are some examples of where you can see reports within Google Analytics. Real Time Events of openings for the newsletter: GA events This report shows the tracking for opens of the emails sent. You can now see how long it takes for people to start opening the newsletter after you've sent them. With this information, you can compare it with past newsletters and see if people are opening it faster or slower, which helps you determine if the subject of the message is motivating enough. Also, you can see what times of the day get the most opens and plan your newsletter schedule around that information. User location With the user location, you can see where in the world people are opening the message you're sending. This can help you determine who your most active audience is and if you should start tailoring your content towards different nations. If you have access to a translation service, this would also be helpful to determine what languages would be beneficial to add to your marketing content. Google Analytics also has a guide, which I recommend to read as well:  Email Tracking - Measurement Protocol.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-01-18

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

3 reasons you should be using Google Tag Manager for Shopify

Anyone running a Shopify store knows there are hundreds of Shopify apps, integrations and connections in the ecommerce world that can help you grow faster. But from Google Ads, DoubleClick, and Facebook Ads to custom plugins, many tools require you to insert scripts on the pages that need tagging, and for a lot of store owners, this can be a huge hassle without asking for developer help. Google Tag Manager (GTM) can launch new tags with just a few clicks. As the world's most popular enterprise-grade tag management solution, Google Tag Manager supports both Google and third-party tags. We've written quite a few articles on Google Tag Manager (including a full FAQ) and how to use it, but until now, we haven't dug deep into why you should use GTM. Here are 3 reasons why: 1. Reliable and accurate ecommerce data When your tags aren’t working properly, they can hurt your site performance, resulting in slow load times, website unavailability, or a loss of functionality. That’s why it’s critical to have a tag management solution in place that allows you to quickly determine the status of your tags. Easy-to-use error checking and speedy tag loading in Google Tag Manager means you know for certain that every tag works. You can rest assured knowing your mission-critical data is being collected reliably and accurately! Your IT team will also feel confident that the site is running smoothly, so everyone's happy, even during busy holidays or the launch of a new campaign. Large brands have implemented GTM to launch their tags exactly for this reason: reliable and accurate ecommerce data. PizzaHut, Made.com, AgeUK and many other brands running on Shopify use GTM to manage their tags for Google and third-party platforms. Setting up Enhanced Ecommerce via GTM In Google Analytics, the main benefit of using Enhanced Ecommerce tracking (EEC) over standard ecommerce implementation is the amount of valuable reports you have access to as a merchant with EEC. But that's not all—you can also segment data based on ecommerce events, such as: Which users visited your product pages Where your shoppers hit a roadblock in the customer journey (e.g. a shopper viewed a product but never added to cart) Which step of the checkout process a shopper abandoned cart This kind of data helps you zoom in on your sales funnel and update the parts of the process that either stall conversions or slow down the path to purchase. Enchanced Ecommerce implementation is no walk in the park, but it does depend on a few things: How large is your store? How many Google Analytics custom dimensions do you need to add? What type of custom dimensions? etc. Without question, Google Tag Manager is the easiest way to enable Enhanced Ecommerce in Google Analytics — and we can help with that! Do you already use GTM? If you already use GTM to track page views, you must send ecommerce data via Google Tag Manager. If you don't already use GTM...It’s a simple setup: activate EEC within your Google Analytics tags and use a dataLayer as an ecommerce data source. Just make sure the dataLayer contains all ecommerce data! 2. Quickly deploy Google and third-party tags With so many tracking tools out there, marketers need flexibility—whether that’s changing tags on the fly or having the ability to easily add tags from other sources. In GTM, marketers can add or change their own tags as needed. Google Tag Manager supports all tags and has easy-to-use templates for a wide range of Google and third-party tags for web and mobile apps. Don’t see a tag listed? You can add it immediately as a custom tag. With this much flexibility, your campaign can be underway with just a few clicks. Even if you are using Google Ads (Adwords), Adroll, Facebook, Hotjar, Criteo or your own script, you can implement it with Google Tag Manager. Even if you're a publisher as, let's say, nationalgeographic-magazine.com, sell furniture at Made.com, sell event tickets as eventbrite.com or organise courses as redcrossfirstaidtraining.co.uk, GTM is the best way to organise all the scripts your partners provides. 3. Collaborate across the enterprise and make tag updates efficiently Collaboration across a large team can be a challenge. Not having the proper tools can stall workflows, which decreases productivity and efficiency. Workspaces and granular access controls allow your team to work together efficiently within Google Tag Manager: Multiple users can complete tagging updates at the same time and publish changes as they’re ready Multi-environment testing lets you publish to different environments to ensure things are working as expected I don't know about you, but every time I need to add a new script on my website, I hesitate out of fear my website will break and I wouldn't know how to fix it. I wanted a solution where I could add a script on my own, test it and then publish it without any developer help. And then I found Google Tag Manager. GTM lets you collaborate and work independently, at the same time, on the same website. You can publish a tag at the same time your teammate is creating an A/B testing experiment, all in the same GTM container. Adding Google Tag Manager to Shopify will help increase the value of your store and the accuracy of your Shopify tracking. GTM is free, it's reliable, and you can find plenty of how-tos on online so you can start using it right away. Google Tag Manager currently provides out-of-the-box integrations with: Google Analytics AdWords Conversion Tracking AdWords Remarketing (aka Google Ads, which we integrate with for accurate marketing attribution) DoubleClick Google Optimize (which we have a connection for!) Google Surveys Website Satisfaction - Google Surveys AdRoll Crazy Egg Hotjar LinkedIn Yieldify and more This out-of-the-box integration doesn't require any special knowledge. And, for any other script that you might have, we can walk you through the process of integrating Google Tag Manager and Shopify. Questions about GTM? Get in touch with our team of Shopify experts and Google Analytics consultants!   Quick links Building funnels and triggering other marketing tags in GTM How to set up Enhanced Ecommerce tracking via GTM Google Tag Manager FAQ Connecting your Google Analytics store for accurate Shopify tracking

2016-12-13

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

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

Try the top-rated Google Analytics app for Shopify stores

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