The whole purpose of having Google Analytics tracking on your site is to find out how your website is performing and to use this data to improve your digital efforts. Yet many businesses miss the mark when it comes to taking action at the level of product listings, despite the fact that this can lead to huge revenue gains! Why do they miss the mark? Two reasons: inaccurate tracking and unclear reporting. The Littledata app helps to fix these issues automatically, providing users with a reliable data stream and automated reporting based on Google Analytics data, but it's still useful to drill down into Google Analytics itself to understand all of the details. In this post I break down how to use Google Analytics to refine merchandising, product promotions and product listings in a way that can have a direct effect on both short-term and long-term revenue for your ecommerce site. For this to work, you'll need to have Enhanced Ecommerce set up on your website. You'll also need some spreadsheet software (Excel, Google Sheets, etc.) so we can play with extracted data and drill down deep. [subscribe] Banners and creatives: getting users to see what we want them to see A full enhanced ecommerce setup will enable you the power to see how much money each of the creatives on your site is bringing you. If your website is like most ecommerce sites, you have several creatives displayed, such as: Homepage carousel Homepage pods Category main banner Choosing which creative should get on your homepage might feel like just a preference, but it doesn't have to be that way. You can use the 'Internal Promotion' menu in Google Analytics (Marketing > Internal Promotions) to make data-driven decisions about your homepage creatives. Imagine an online store that sells scooters and accessories: We have banners for categories like Helmets, Accessories, Mini Micro and Maxi Micro (different sizes of scooters). We have 2 banners on the homepage with these two creatives: Safety (the first one) and Built for Adults (the second one). We want to change one of the creatives on the carousel. Let's analyze what is the best strategy here. The first banner on the carousel was seen 24,404 times. It has a 5.01% click thru rate (CTR) and a £3.90 value per click. The second banner on the carousel was seen 17,109 times. It has a 5.52% CTR a £2.02 value per click. Now we can make a decision. What to discard and what to keep Even though we have a higher CTR on the second banner and this is an indicator that the message is more appealing, the reality is that the revenue that comes with that click is not even half of the revenue we get from a click on the first banner. If you want to make a 100% correct decision here you can analyze the margins on the product promoted by each of the banners. If you have double the margin for the products in the second banner you can get rid of the Safety banner and make the second banner primary. If your margin is the same for both categories then the best decision here is to replace the second banner with the first one. How to populate the carousel We already decided to keep the first banner, but now we need a replacement for the second one. So we need to find a creative in the website that had performed at least the same as the second banner. Based on the example above if we search by CTR higher than 5.52% we can see that we have a banner for Maxi Micro with 20% CTR and a value per click of £5.32. The action here is to replace the second slot of the carousel with this creative. After 1-2 weeks we can retake this process all over again and we may decide to reverse the creatives (Banner 1 will be Banner 2 and Banner 2 will be Banner 1 in the carousel). This is not a one-time job. The analysis should be made every time you add a new creative or make a new promotion.--or even as a weekly task. Many Littledata clients run this type of analysis on a regular basis, whether or not they've launched a new promotion, to make sure they are optimizing sales and conversions. You should pay attention to the average click thru rate (CTR) based on creatives category, and also you should know what is your standard deviation for each category so that you can quickly spot which are over- or under-performing. Based on the example above, the average CTR for a carousel banner on the site is 5.26% and the standard deviation is 0.25%. So I know that if I see a banner that has a CTR less than 5.01%, there is room to improve. As per above for the category pages, we have an average of 10.92% CTR with a standard deviation of 6.28. This means that everything under 4.63% should be replaced ASAP and everything above 17.20% should be promoted. List views: how to arrange products for ultimate engagement One of the best Enhanced Ecommerce features in Google Analytics is the Product List Performance Report (Conversions > Ecommerce > Product List Performance). This report shows you how many views each list gets. Why does this matter? Because if you have a high margin on some products from a specific category, you should find out if that list (category) is being sufficiently promoted on your site. From these reports, we can find out things like: Most viewed categories (sort by Product List Views) The category that has the biggest engagement (sort by Product List CTR) The list that is bringing you the most money per view (Product Revenue divided by Product List Views) Which categories are performing best -- and which are most profitable? Let's say I have three categories in my store: categories 1, 2 and 3. And my margin for products in category 3 is three times the margin for those in category 1. In the report above, we see that we don't have a click thru for Category 2. This could mean: The tracking is not working on that page Users have issues clicking on the products There is no call to action (CTA) on that page So we can assume that Category 2 is not working. Moving forward we should analyze the performance of Category 1 vs Category 3. Views Clicks CTR Revenue Revenue / click Margin at each $1 sold Margin at 1000 clicks Category 1 1,701,660 57,038 3.35% $329,799.67 $5.78 0.23 $1,329.88 Category 3 46,895 3,175 6.77% $23,881.37 $7.52 0.69 $5,189.97 We can see that even though we have a fraction of the views for Category 3, this category is for us almost 3 times more profitable per 1000 clicks. At this point, we should investigate how much marketing we're doing around Category 3 to see if there are options to push harder on this highly profitable category, alongside whatever's already working for promoting Category 1. Order matters The Product List Performance Report can also help us find out how customers progress from viewing a product in a list to clicking through for more information. Let's analyze the data in the above report. The table is sorted by Product List Views for Mobile devices. We know that the alignment for this website is one product under the other and for a product view to be sent the user needs to see it for at least one second. So we can draw these conclusions: Position 2 and 3 are normally visually scanned by users. The fourth product in a list is seen in more detail but has a lower CTR than the second or third product in the list. We know that each page has 10 products so the average Product List CTR rate for page 1 is 1.36% and the standard deviation is 0.42. From this, we can see that position 2 has a good CTR and we need to change the photo and text of the listing to attract more attention -- products placed in the second position in a product listing on this site tend to convert well. Position 4 gets attention but has low performance so we could try changing the photo and title of products in this position in order to increase the CTR. If we are looking at this report as aggregate data then we can conclude that if we want to make a push for particular products, we should place them in position 1 or 4 for maximum visibility, or position 1 or 2 for maximum CTR. How to monetize product list positions We can take this analysis further by examining how list slots relate to product revenue, whether on your site or via affiliate programs. Looking at the report in aggregate and extracting the data, we can give a monetary value to each slot in the product listings. Product List Position Product List Views Product Revenue Revenue/view per slot 1 2,290,505 £183,207.00 £0.08 4 2,279,917 £99,830.00 £0.04 3 2,246,164 £117,096.00 £0.05 2 2,239,943 £157,605.00 £0.07 6 2,062,271 £73,183.00 £0.04 5 2,053,534 £94,889.00 £0.05 8 1,788,080 £58,585.00 £0.03 7 1,775,762 £60,603.00 £0.03 9 1,750,248 £52,366.00 £0.03 10 1,606,599 £50,913.00 £0.03 From the above example, we can see that each of the slots in the listing has a value per view. And the value is decreasing with the position. Using the known margin for a specific product in a list, you can improve your ROI just by positioning it in a slot with a higher CTR based on the model above. Which photos should you show first in a listing? If you offer a product in multiple colors, you should use an image and a default (primary) product selection in the most popular color. But how do you figure that out? Product variants are too often left behind in analysis. The Product Variant field captures the specific variation of a product, e.g., XS, S, M, L for size; or Red, Blue, Green, Black for color. It is an Enhanced Ecommerce feature that can give you powerful insights into your users' searches, interests and preferences. Paying attention to variant performance can have a big effect on shopping behavior and sales. In the example above, we're looking closely at the Product Variant dimension to figure out which color is most popular. We have a product with 4 colors: Black, Grey, Midnight Black and Persian Grey. There isn't enough transaction data to make a decision based on purchases, but we can calculate the most popular variant (in this case, the most popular color) based on how often users have added items in each color to their shopping carts (Adds To Cart). For Black, we have a View to Add To Cart rate of 0.6% and for Grey 0.8%. So in this case we should use the main Grey color for advertisements and main photos in listings pages. We might also try using the Persian Grey variant. Note that in this example we can calculate for each product view because we've listed each color as a different product. If you're listing only one product and you show variants on the product page, then you'll need to divide the Adds To Cart for each variant by the total Product List Views. What to do next If you need help with Enhanced Ecommerce reporting, our analysts are ready to come to the rescue. You can either request a consultation or just sign up for a free Google Analytics audit and contact us directly from the app. How are you using Enhanced Ecommerce reports in Google Analytics? Drop us a note below.

In the previous blog post, how to improve your landing pages using Google Analytics, we started analysis what makes a good landing page. Some of the ideas were related to call to actions. Your landing page must have a call to action (CTA) correlated with the marketing campaign and the full content of the page. Clear and unambiguous CTA(s) If you are offering app access, go with "Get Started" or "Create account" and don't say “Get your free ebook” or “go” or “submit”. Say short and clear what you want them to do. Don't mislead the users and don't use fancy words. When you're choosing the CTA for your landing page you should consider these three: what you say how your customer will interact with it where to place it What to say is the wording. If you want the customer to subscribe to the newsletter say "subscribe to the newsletter", if you want them to buy say "buy", if you want them to call say "call". Keep it short and clear. If the customer needs to subscribe you need to provide them with the field were to add their email address; If you want them to call you then you should use a dial function for mobile users or show the number for the desktop users; If you want them to buy then the press of the button should redirect them to a page where they can choose the option for delivery and payment. Where to place the call to action in your landing page is simple - where the customers will see it first. I presume you already have event tracking, in place (if no, find out how to set up in this blog post: Set up event tracking in GTM ). Based on some numbers from Google Analytics, let's see how good and bad engagement looks like for a landing page. Find out the level of engagement with the page Bounce rate: This will show you the number of people that entered this page and left without taking any other action (like seeing the second page or clicking on the call to action). The bounce rate will tell you how your whole landing page is engaging with the audience. In the example above, the landing page, /find-more has a bounce rate of 98,8%. This is very bad! On the other side, we have the landing page apps.shopify.littledata with 0% bounce rate. This is the holy grail of landing pages. These means that from an engagement point of view your landing page is perfect. As a rule: You should aim for at least the same bounce rate as you have on the entire website as a medium. Find out if your call to action performed Method 1 - Deducting from landing page report Go into Google Analytics -> in the search bar search landing page -> Choose Site content - Landing pages. Click on your landing page name and now add a second dimension: Second page. Find the link where your call to action redirects and analyse all elements in this report. If you don't have events in place, you will still be able to see how your traffic is clicking through the links on your landing page. If your landing page has more than 1 action then you can add a second dimension on the landing page report and see what was the second page they visited. In the example above, the call-to-action redirected them to the apps.shopify.com/littledata. From the numbers of sessions, we can see that only 10% of the users clicked the call-to-action button. 89% of the people wanted to find more about the product before purchasing. This is the example of bad engagement. The fact that 89% of the people wanted to find more means that we need to provide more details on the landing page and maybe have a clearer call-to-action. Method 2 - Deducting from Top Events report For this, go to Google Analytics and search for Top Events and add a second dimension to the report "Page". You can also build a custom report so you see the number of people that saw the page and the number of people that took the call-to-action. Have any questions? Comment below or get in touch!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

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.

In every retail business, knowing your customers is vital to succeeding. All decisions you make about business and marketing strategies must begin from the user's perspective. Let's find out how we can build the user persona with the data that lies in Google Analytics. Even though Google's user profile is not as fancy as Facebook's, you can still have a pretty good idea about your customers. Let's start with the basics, and ask the most basic questions: How many of my customers are men or women? What is the age range of my customers? What devices do they use to access my website? How often do they visit my website? What are their interests? What makes them convert? For the first two questions, you should already have enabled Demographics and Interest reports in your Google account. If not, go to Admin > Property Settings > Enable Demographics and Interest reports. The split of age and sex can be seen in Audience > Demographics. The most interesting thing here is that you can add a second dimension to compare and see how people are different based on more than one vector. If you add a second dimension, such as Device Category, you will get a split like this: You can see from the above screenshot that females prefer mobile and are the majority user. Also when females are on desktop, they are more likely to spend more time on the website. You can go into more depth and analyse the conversion rate also. You can find out the behaviour of new vs. returning customers from the report, New vs. Returning under Audience. Add a second dimension "Gender" and you will see who's more likely to come back to your website. Based on the above screenshot, women are returning about 25% of the time, while men return about 21% of the time. Also, men have a higher bounce rate. Under Audience, you will also find the Frequency & Recency report and the Engagement report. If you create two new segments by gender: female and male, you will find who your most loyal visitors are. The interests (Google reads them from the user behaviour online) can be found under Audience > Interests. It is best to split the report based on females and males. You will now have a full view of your customers. And for the final and most important question: what makes them convert?, you can find this out by going to Aquisition > Channels. Add a second dimension by gender, age or interests and analyse the traffic for each channel. Find out what channel brings the most important users. In the screenshot below, you can see that woman are more likely to buy if the website is referred. This means that the reputation of the website is a big factor in their decision. Don't be shy when creating custom reports because you can drill down the data to multiple levels of understanding. Applying second dimensions has its limitations and you can see only a part of the information at once. If you still need a more detailed view of what each customer does on the website, we strongly recommend the User Explorer menu. We found it useful to find out how different touch points are important and how long the path is for a valuable customer. Also, it was useful in debugging and creating a marketing strategy based on the customer's flow. The bottom line is that you can answer "who is your customer?" with Google Analytics through its reports if you learn to see the report from different perspectives. Feel free to drop us a line if you use any other report that is relevant to this article!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

In May 2014 Google Analytics introduced a new feature: Enhanced Ecommerce tracking. If you run an ecommerce operation, this gets you much more detailed feedback on your checkout process. What will I see? Shopping behaviour: how are people converting from browsers to purchasers? Checkout behaviour: at what stage of your checkout do buyers abandon the process Product performance: which products are driving your sales, and which have a high return rate Real campaign returns: see your real return on marketing investment including promotional discounts and returns How do I set this up? The bad news is it definitely requires an experienced software developer for the setup. The reports require lots of extra product and customer information to be sent to Google Analytics. You can read the full developer information on what you can track, or our own simpler guide for tracking ecommerce via Tag Manager. However, if you already have standard ecommerce tracking and Google Tag Manager, we can set Enhanced reports up in a couple of days with no code changes on your live site - so no business disruption or risk of lost sales. Is it worth implementing? Imagine you could identify a drop-off stage in your checkout process where you could get a 10% improvement in sales conversion or a group of customers who were unable to buy (maybe due to language or browser difficulties) – what would that be worth? Many businesses have that kind of barrier just waiting to be discovered…   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

Google Analytics engagement reports can provide great insight into user behaviour on your site. However, it’s not obvious how to read them - and when you figure out how to read them, it’s not always obvious what’s good! The visitor frequency report is found under Audience > Behaviour > Frequency and recency in the Google Analytics menu. The report shows the sessions, by the number of visits for each visitor. Google’s explanation of how to read the frequency graph is nice and clear. However, their simplified example leaves out something important: returning users. In the graph, the visit count shown is for the whole user history, not just the period of the report. So if a visitor has come three times before the period their session will show in the three band. Similarly, if a user has visited the site five times in the past and then visits the site twice during the period, they will in count of sessions as 6 and 7. The example below is from a newly released site, where nearly all the visits are from the developers, who have been many times before - so there are no visitors in the 1,2,3,4 or 5 visit bands for this site in this period. The fact that the banding is based on the whole user history, not just behaviour during the period, can make the report much harder to interpret - you can't easily see the drop off in repeat visitation if an unknown number of return visitors at some point in their many visits history are also coming in. Fortunately, Google Analytic's segmentation capability comes to the rescue! For example, you can find out about the returning behaviour of visitors who came the first time during the period in question, with the segment - the settings are in the screenshot below. Note that you need to change the segment to filter users, not sessions, otherwise you will just create a more complex version of the built-in new users segment! Here’s an example of the sort of thing you might see with the segment, showing 3 segments: All Session (built in) 'User sessions = 1'  - custom segment for users having their first session in the period New Users (built in) Note: For the visit count = 1 band, the session count is the same across all three segments, For the visit count = 2 band, (and above) the number for all sessions is higher, because it includes all the users who came in on their second visit during the period. The number for the user sessions = 1 segment, is lower because it includes only the users who had their first visit during the period. The number for new users is zero because users are not new on their 2nd visit As you can see, the custom segment makes it possible to see the real return rate of new visitors in the period, narrowing to visitors who came the first time in the period in question. From this example, you can also see how misleading it would be to naively interpret the default 'all sessions' segment for, say, four sessions as the number who returned four times during the period - clearly there is a large number who have previous visits outside the period of the report. Note that none of the segments in the example actually gives the number of users who returned four times during the period - this is actually really difficult to obtain. Leaving that question aside for now, to extract some real insight from the approach of segmenting in the frequency report, combine that segment condition with a goal, say ‘transactions per user > 0’ - then you can see how many new users went on to a transaction, and how many visits they made during the period. Need help to set this up or have any questions? Get in touch with our team of experts and we'd be happy to answer any questions! This is a valuable segment to monitor and analyse - how many users have gone from first visit to a transaction this week, and how many sessions did they make along the way?

We’ve got some exciting news! We’ve launched some great updates on our web app, which will make your lives a little easier. Find out how the navigation has improved and new in-app messaging will help you find out more, get a glimpse into our trend detection algorithm and new reports on mobile devices! Our mission is to make the way you gain access to important analytics, an all-around easier process and we know we’re heading in the right direction with these updates. We already give you actionable and easier to understand insights of your Google Analytics and now we’ve made the experience more friendly based on your invaluable feedback! Find your reports quicker We’ve improved the navigation of the web app, giving you one new category, and two updated categories on the left-hand side of your profile, which are now simpler to find and easier to understand. There are currently three categories: Dashboard, Benchmark, and Reports, which will be visible to you depending on your Littledata package. Instead of having them in separate locations, we brought them together into one navigation panel so that you can find specific reports and findings quickly based on your current questions or company needs. Under the reports category, we have changed types of reports into tags. Now you can select one or multiple tags, and decide how you prefer to view the different types of insights you get. For example, if you want to view your trends reports with tips you’re getting, then all you need to do is select those two. The benchmark category brings together all the benchmark metrics available for your site, and to see more detail click on the individual benchmark you’re interested in. You can still see the category you are being benchmarked against just above your benchmarks. If your current category is ‘all websites’ then you should make this more specific by updating the category in the settings. The Dashboard is the latest addition to these categories, which we added to be able to provide a flexible and customised solution that is perfect for reporting needs that go beyond standard Google Analytics reports. See below for more detail. Get our custom dashboard This is a new feature, available to clients who are also receiving consulting services on top of our Pro package. Please contact one of our lovely experts if you’d like to know more about these features, and how they can give you the results you strive for. The dashboard category is completely customisable, which we develop through consulting services by going over what your goals and needs are, and then creating these reports for simple and actionable insights of your data. These reports are completely flexible and allow you to see metrics that are difficult to view in Google Analytics, which include: Calculations, such as performance changes in percentages and conversion rates Combined metrics and dimensions from different reports Custom visualisations of trends based on how you prefer to see the data. Want to include a pie or bar chart? Not a problem. A custom schedule for dashboard data refresh. If your reporting requires weekly, quarterly or annual updates, we’ll set it up for you. Customised reports based on your formatting preferences, so if you'd like to include your brand colours, it's a possibility! Our smarter algorithm When we started Littledata, we developed a trend detection algorithm to find significant changes in your data and send you alerts, reducing the time spent wading through data in Google Analytics. But as times change and data gets busier, we needed a better way to serve your reporting needs. So recently we collaborated with mathematicians to improve the algorithm, which is now sensitive enough to pick up small changes in low traffic website, but also specific enough to ignore the random noise of daily traffic. Want to hear more about this intriguing story? Find out more in our blog post: Making the detection of significant trends in your traffic easier to see! Are mobile devices losing you customers? Analytics from mobile devices is extremely important. Through our web app, you will find out how many transaction or users you lost due to poor experience on mobile devices. According to Dave Chaffey at Smart Insights, 80% of internet users own a smartphone. A growing number of people are searching through their phones and as a result, we’ve incorporated mobile devices reports. They will spot and highlight potential issues around responsiveness, layout or bugs. Finding out which devices are the worst will allow you to optimise your website and campaigns to capture all of these individuals. Your personalised communication We completely agree with Intercom’s belief that “customers today want to communicate with the people behind the business, not with a faceless brand”! This is why we’ve integrated their messenger into our web app so that you can chat with us directly and quickly. There’s a great deal of custom features available, including formatting, delivery, and most importantly the different ways to respond. You can choose your own way to chat and react, with images, audio, emojis, video, and more. If you want to know more about the expert you’re talking to, you can view their profile within the app. Our customer experience is key in our business model and we hope this function delivers that. If you have any questions regarding any of the new features, please contact us, or use the in-app messenger!   Image credit: Image courtesy of Smart Insights and Intercom

On most websites, the conversion journey involves many different routes and across many sessions: few customers buy from the first advert. You may have heard of the ‘rule of 7’. In reality, it varies from maybe 2 or 3 touches for a $20 purchase and definitely more than 10 for an enterprise business service. Your company is buying prospects (or traffic) from a number of online channels, and in many cases, it will be the same potential customer coming from different sources. To be able to report on this in Google Analytics, we need to get the basic setup correct. Tagging campaigns for attribution The first step is to make sure that the different traffic sources can be compared in a multi-channel report are consistent and have complete inbound link tagging. Be sure to tag your campaign correct with our URL Builder. Some tools (such as Bing or Mailchimp) have options to turn on link tagging for GA - although it's buried in the settings. With many others, you will have to add the necessary ‘UTM’ parameters onto the link. Without this tagging, many sources will be misattributed. For example, affiliate networks could send referrals from any of thousands of websites which will appear under the ‘referrals’ channel by default. Facebook ads, since the majority come from the Facebook’s app, will appear under the ‘direct’ (or ‘unknown’) channel. From when full tagging is in effect, the channels report will start to reflect your genuine traffic acquisition source. But don’t expect a 100% match with other tracking tools – see our article on Facebook – GA discrepancies. Importing cost data The cost for any Google AdWords campaigns can be imported automatically, by linking the accounts, but for any third party campaigns, you will need to upload a spreadsheet with your costs on. The benefit is that now you can see the return on investment calculation update in real-time in the multi-channel reports. Model attribution The final step is to decide how you will attribute the value of a campaign if it forms part of a longer conversion pathway. The default for Google Analytics (and most others) is ‘last non-direct click’. That means that the most recent TAGGED campaign gets all the credit for the sale. If the user clicks on 5 Facebook ads, and then eventually buys after an abandoned basket email reminder, that email reminder will get all the sales (not Facebook). This attribution is what you’ll see in all the standard campaign and acquisition reports. You may feel that it is unfair on all the work done by the earlier campaigns, so ‘linear’ (sale equally credited to all tagged campaigns) or ‘time decay’ (more recent campaigns get more credit) may be a better fit with your businesses’ goals. Conclusion Multi-channel marketing performance attribution is not a luxury for the largest companies. It’s available to you now, with the free version of Google Analytics. It will require some setup effort to get meaningful reports (as with any measurement tool) but it has the power to transform how you allocate budget across a range of online marketing platforms. But if this still is not working for you then you may have a problem with cross domain tracking. Need a bit more advice or have any questions? Get in touch with our experts or leave a comment below!