Category : Google Analytics
Increase ecommerce conversion rates with segments in Google Analytics
Are you generating enough site traffic? Are there enough visitors each month that engage with your content and spend time on your site? Those are good things, but the important question is how you are doing with conversions, as that is where the magic happens! For many businesses, there will be slumps where conversions are not where you need them to be and increasing conversions is tougher than bringing in visitors. The reasons for not converting are many, which could include a poorly designed landing page or frustration with a slow page load time. Fortunately, the technical aspects of your site are somewhat clear cut and influence all users to the site. Either it loads quickly or it does not. It responds to mobile devices or does not. But there are principles that are about the groups visitors to a site. What do they search for and can you provide it? How are they different from each other? How are they similar? In short, you need smart segmentation if you want to continue to increase conversions. Here's a quick guide to using segments in Google Analytics. Segments vs personas In this post, we will build on some of the work you have hopefully done to create personas and highlight the value of segments when optimizing for conversions. Personas help you be empathetic to your customers. Visualizing a 35-year old professional female makes it easier to create the right message for her rather than general messages to all women. This is not about stereotypes. Personas help you hypothesize about similarities in how people behave. So how is that different from segments? There is confusion with segments versus personas and you want establish a definition for your team so you all work from the same framework. In the simplest terms, you segment your audience with existing data and create campaigns based on personas. Start with your segments. With Google Analytics, you can use segmentation to group people by identifying criteria such as location. Think of segments as the somewhat objective view of your audience based on raw data. (There is still some subjectivity when deciding the makeup of segments). Personas are very subjective - based more how a person thinks or feels. [subscribe] Get to know your audience with Google Analytics Google Analytics provides a lot of data that helps us understand our segments if we go beyond basic metrics, such as pageviews. Below are a few ways to learn more about your segments with the goal of increasing conversions and adding depth to your personas. Pages per Session: This is a basic metric in Google Analytics but you can go beyond scenarios, such as users visiting two pages compared to those visiting seven pages. Look at which pages they visited. Did they visit the intro offerings (probably a newcomer) or the help section (probably an existing customer)? Did they read the entire section about a topic (more methodical) or buy on the first visit (maybe more impulsive)? Note these are assumptions about motivations but you can develop hypotheses based on behavior. Content Grouping: Content grouping categorizes your site content based on rules created in the Admin section of your Google Analytics account. Once you have these rules, you can view content groups for different scenarios, such as where people are the journey, how they flow through content, how they came into your site (traffic source), and how much time they spent on there. For sites with thousands of pages, this makes it more manageable than viewing individual pages. You can analyze conversions on the categories of your site rather than a specific page. Cohort Analysis: Found in the Audience section of Google Analytics, this is used to examine the behavior and performance of groups of users related by common attributes. It allows you to view a group of visitors based on a shared acquisition date. If you have a drip campaign scheduled for May, you may want a Cohort Date Range of May 1 to May 7 to target people who first visited the site during that time period. You can learn if people who visited on a specific day were more inclined to visit again than other members of that group. User Login: Custom Variables can be fired when users login. That provides additional data for more advanced segments by identifying the behavior of different customer types. Site visitors self-segment when they log into the site to take an action. With Custom Variables, you can see how behavior is different for those who log-in versus those who do not. Bounce Rate: We all get hung up on this metric. People see a site bounce rate of 78% and begin to panic but you need to drill in to see if that matters. Do existing customers and regular visitors bounce from a blog post? That is expected. However, if new people regular bounce from the site, look at the landing pages. There could be a message mismatch with the source that sent them to a particular page. Affinity Segments: Use affinity and in-market segments in Google Analytics to help define your personas. They are broad classifications about users which may be helpful when layered on top of other characteristics. For example, you may discover segments that prefer one content grouping over another. Collect metrics that matter When there is a difference in the conversion rate and user journey among segments, it indicates your identified segments truly represent distinct types of users. Read that again because whether your segments make sense determines whether your data is any good. With the right segments, you can determine which groups to cultivate or which ones to not pursue with limited resources. For example, if one segmented group regularly buys add-ons for product, that might justify allocating more advertising dollars. With target segments identified, you can also look at which marketing effort attracted them to your site. Some of this is obvious. If users in their 30s never respond to a CTA on your site from Facebook, you may not want to pay for ads on that channel or even post to it regularly. So yes, we all care about who converts compared to those who do not. But remember there are stages leading up to a conversion and this Facebook audience could still have a role, so watch where in the process people drop off. And hopefully by now you realize that non-converters are more than just non-converters. View this by segment too to identity what non-converters may have in comment. As data comes in, additional segmenting can be done by on locations, time of conversion, brand search terms versus early stages searches. But do not collect data for the sake of collecting data. Although it is easy to do with the abundance of data available in Google Analytics, it does not guarantee a return for your efforts. Want to know more? Get in touch with Tina’s agency, 360 Internet Strategy, and follow her on LinkedIn.
Google Analytics Data Retention policy - which reports does it limit?
From 25th May 2018 Google allowed you to automatically wipe user-level data from the reporting from before a cut-off date, to better comply with GDPR. We made the change for Littledata's account to wipe user-level data after 26 months, and this is what we found when reporting before February 2016. Reports you can still view before the user data removal Audience metrics Pageviews ✓ Sessions ✓ Users X Bounce rate ✓ Audience dimensions Demographics X OS / Browser X Location X User Type X Behaviour Pageviews ✓ Custom events X
How to implement a successful mobile marketing strategy
Mobile as a marketing strategy isn’t a new idea to anyone, but the landscape is changing quickly. Back in 2015, Google told us it would be expanding its use of mobile-friendliness as a ranking signal. More recently, in early 2018, they stated that page speed will be a ranking factor for mobile searches middle of this year. As consumers change their behavior on mobile devices, this greatly impacts our strategy as marketers. We now need to be visible on all devices, all the time. What do all these changes mean for marketers? Whether you're a solo AdWords consultant or a member of a digital agency, it's essential to stay on top of consumer trends in a way that is measurable and repeatable. In this post I break down how to develop a data-driven mobile marketing strategy that can easily scale with your online business. Mobile search has changed As consumers, we are research-obsessed. We want to know everything we can about an ecommerce product or service so we can make informed decisions. And as more of us search for seemingly minor things and do so on a small device, advertisers have the opportunity to be present in those micro moments. With an increase of searches on mobile devices (and with mobile searches already having bypassed desktop searches several years ago) we need to be present across the entire consumer experience, making the customer experience a business priority regardless of our brand or business size by providing a seamless experience on every device. Analyzing data with a last-click attribution model misses some of these mobile moments. Assumptions have changed along with search behaviors. In September 2015, Google shared that “near me” or “nearby” searches on Google had grown 2X in the previous year, but the use of that phrase has since declined. People still want results that are near them, but the assumption of today’s searchers is that Google knows the location of the searchers and where to find what was searched because people are using their devices throughout the day. Increase of use for “open now” and “tonight" and “today” travel-related terms indicate people are seeking information on their device. [subscribe] What this means for brands Does your strategy consider these trends and adjust to changes in consumer behavior? A mobile experience leads to a brand impression. People expect a consistent experience every time they interact with a brand. If your site does not deliver and does not deliver quickly, they will quickly leave. Regardless of which channel they used to get to your site, the mobile experience must be as seamless as the desktop experience. What this means for Google AdWords As mobile use continues to increase and consumer behavior changes, we need to better align our PPC efforts and use an attribution model that addresses all steps of the journey. With AdWords, we can align our marketing strategy to mobile use with mobile search ads, mobile display ads and app ads on mobile devices. Each option offers slightly different features. Text ads can display on any device. The primary difference with ads on mobile vs desktop is more ads per page on a desktop and only a couple on a mobile device. Because the first couple ads take up most of the screen on a smartphone, advertisers need to be in the first or second position because that is all that will display. Impatient searchers will not scroll down on their device to your ad in position four. On the Display Network, you can be more creative with ads, adding images and videos to the mix. Although image sizes that work on desktop computers will also work on mobile devices, aim for a smaller size of 320 x 50 when possible, keeping the layout of smaller screen sizes in mind. The third option for mobile ads are appearing on mobile apps, which are part of the Display Network. App promotion ads have a goal of driving downloads. Campaigns with only app promotion ads are eligible for phones and tablets; they are not on desktop computers. Bid adjustments With your AdWords campaigns, set bids on mobile devices that are aligned with your goals. As mentioned above, many will not scroll down the search results page on a smartphone to view ads so may want to increase these bids. This is also important for branding goals; you need to be at the top to be seen. When determining mobile bids based on ROI, identify ROI for desktop versus tablets and devices. That way, your adjustment is based specifically on the mobile value of conversions. Keywords In any AdWords campaign, the key to success is selecting the correct keywords. But you can go a step further and use the keyword tool to also see mobile trends for your selected keyword over the previous year. Use these findings to inform your bidding strategy. A subjective approach is to view your keywords in the eyes of your users. Are the keywords in your campaigns ones that you would type into your mobile device? Although more people use voice recognition to search, there are still those who type in their request. Since typing on a small screen results in typos, you want broad match keywords in your campaign when targeting mobile users. Make sure these keywords include action-oriented terms. Some people may surf their device out of boredom while standing in line, but many search to find information to make a decision. You can capture these early clicks with an attribution model other than last-click. Mobile URLs Google provides an option of using mobile URLs in ads to customize the mobile experience, but if the mobile URL is the same as the Final URL in AdWords, adding it does not impact mobile performance. This is designed for people who have different pages for mobile users. AMP pages An open source initiative, Accelerated Mobile Pages (AMP) solve the issue around slow landing pages to make them faster for mobile. Business that have used them find a much quicker loading time and a more engaging experience. You can also use the AMP version of your website in this option for final URL Bid strategy Take advantage of machine learning with a Smart Bidding strategy in your AdWords campaigns. It considers the multiple signals around device type and browser for auction-time changes, offering more targeting than we could do manually as an AdWords account manager with simple bid adjustments. Monitor device performance with this strategy and prioritize mobile traffic if it does particularly well on devices. Attribution models In all AdWords campaigns, regardless of device, many advertisers use the last-click attribution model, which is not ideal for any campaign, including those targeting mobile. It gives all the credit for a conversion to the last touchpoint - the last click - which misses out on how other interactions influenced the decision to convert. If you have enough data in your account, utilize the Data-Driven Attribution Model. If it is not available to you, consider one of the other options besides last-click attribution. The right reporting for mobile marketing Before you target mobile users with advertising, check first that your site performs well on mobile devices if you do not plan to have a mobile specific URL. Start with a quick test for mobile speed to see if you are at risk of losing traffic. Next do a quick SEO check of your site which is based on Google’s guidelines, which is also relevant to paid traffic. For all your campaigns, not just AdWords, you need to consider metrics such as sessions by device type for general site behavior and conversions once a campaign is running for a while. To minimize manual work for reporting and analysis, use a Littledata report pack which pulls in data from Google Analytics to offer automated reporting on customer touch points, providing data you need without the manual labor. And remember your mobile users are on the go, so any advertising needs to cater to them in the moment! Want to know more? Get in touch with Tina's agency, 360 Internet Strategy, and follow her on LinkedIn.
Google Analytics 360 versus the free version
We often receive questions about what customers get when they upgrade from the free version of Google Analytics to Google Analytics 360. The quick answer is that you get a lot - the possibilities are literally endless - as long as you're a big, data-driven company willing to put energy into customer engagement and marketing. Google emphasises that their enterprise analytics are designed to help large companies, like major ecommerce sites, create better customer experiences. But what does that mean in practice? There are a lot of details to understand if you're thinking of transitioning to the big paid version of Google Analytics. The main differences lie in how each product deals with the volume of data and integrations that they have available by default. I've broken those differences down into three categories: Data Collection, Data Sampling and Data Sources. Data collection In short, Google Analytics 360 allows for a faster, smarter, larger data collection. With unlimited hits per month and up to 200 custom dimensions per web property. Features Google Analytics (free) 360 Suite (paid) Hits per Month up to 10M unlimited Custom Dimensions/Metrics 20 Per Property 200 Per Property Calculated Metrics 5 Per View 50 Per View Properties per Account 50 50+ Views per Property 25 25+ Roll-Up Properties No Yes Data Freshness 24 – 48 hours 4 Hours [subscribe] Data sampling and limits As your web traffic grows, Analytics 360 lets you get more out of both sampled and unsampled data sets. Compared with the standard version of GA, you get better reporting on large amounts of data. Understanding how data is sampled in Google Analytics will help you scale the smart way. Features Google Analytics (free) 360 Suite (paid) Report Row Limit per Day Yes Yes Standard Reports Pre-Aggregated 50K 75K Sampling in Ad-Hoc Reports 500K Sessions per Property 100M Sessions per Property Custom Tables No 100 Custom Table Report Row Limit per Day No 1M Rows Unsampled Reports No Yes Unsampled Report Row Limit No 3M (for download) Data sources The 360 Suite makes it especially easy to pull in data from a wide range of advertising platforms and sources, including non-Google products like Salesforce. For some of our enterprise customers, especially large ecommerce sites with a focus on PPC lead gen and retargeting, the ability to seamlessly integrate with DoubleClick is itself enough to make their 360-buy worthwhile! Features Google Analytics (free) 360 Suite (paid) AdWords Yes Yes AdSense Yes Yes DoubleClick Campaign Manager No Yes DoubleClick Bid Manager No Yes DoubleClick For Publishers No Yes Custom Data Sources Yes Yes Query-Time Data Import No Yes Salesforce No Yes BigQuery No Yes Additional perks (GTM 360, beta testing) In addition to the above benefits, being able to connect Google Analytics to other Google 360 Solutions like Google Optimize 360 and Google Tag Manager 360 is a big plus. As an added perk, Analytics 360 clients often get early access to beta programs for testing and product feedback -- getting directly involved with product development to suit their needs -- plus first-hand support from Google. Google 360 can be purchased directly from Google or through a sales partner. We don't currently sell the 360 Suite ourselves, but we’ve been a certified Google Analytics Service Partner since 2015, including Google Tag Manager and Google Optimize certification, and have extensive experience with custom tagging and reporting. Plus, we built the Littledata app around those analytics best-practices. Our larger consulting clients get the most benefits out of our enterprise plans, which include automated analytics audits, unlimited access to app features, custom setup and reporting, and a dedicated account manager to help ensure deep, accurate tracking. Whether or not you've already upgraded to Google Analytics 360, we highly recommend getting in touch to make sure you're able to use this powerful tool to its full potential!
Using Google Analytics to refine merchandising and product promotions
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.
Troubleshooting your Google Analytics goals setup (VIDEO)
https://www.youtube.com/watch?v=SGY013J9QGg So you've got your new sales plan in action and you've set up unique goals in Google Analytics. Are they tracking what you think they're tracking? Are you sure they're giving you reliable data? If you've audited your analytics setup, you might have noticed any number of incorrect audit checks about how you've set up custom events for your Google Analytics (GA) goals. Goals are used to make important business decisions, such as where to focus your design or advertising spend, so it's essential to get accurate data about them. In this quick video we cover common issues with setting up Google Analytics goals, including: Tracking pageviews rather than completed actions Selecting the wrong match type Inconsistent naming when tagging marketing campaigns Filters in your GA view rewriting URLs (so what you see in the browser is different from what you see in GA) Issues with cross-domain tracking [subscribe] In GA, a goal is any type of completed activity on your site or app. GA is a remarkably flexible platform, so you can use it to measure many different types of user behaviour. This could be visitors clicking a subscribe button, completing a purchase, signing up for membership -- known as 'conversion goals' -- or other types of goals such as 'destination goals', when a specific page loads, and 'duration goals', when a user spends over a particular amount of time on a page or set of pages. That all sounds well and good, but trouble comes if you simply set up goals and then trust the data they give you in GA, without double-checking to make sure that data's consistent and reliable. We hope you find the video useful. And don't despair -- even a little extra time spent on your GA setup can yield awesome results. Sign up for the Littledata app to audit your site for free, and let us know if you've experienced other common issues with setting up goals in GA.
GDPR compliance for ecommerce businesses
Ecommerce companies typically store lots of personally identifiable information (PII), so how can you make compliance easier without compromising analysis? With the deadline for GDPR compliance looming, I wanted to expand on my previous article on GDPR and Google Analytics to focus on ecommerce. Firstly, who does this apply to? GDPR is European Union legislation that applies to any company trading in Europe: so if you sell online and deliver to European Union member countries, the regulations apply to you. It's essential that you understand how your online business is collecting and storing PII. Splitting PII from anonymous data points Your goal should be to maintain two separate data stores: one that contains customer details, from where you can look up what a specific customer bought, and one that contains anonymous data points, from where you can see performance and trends. The data store for the customer details will typically be your ecommerce back-end and/or CRM (see below). This will include name, email, address, purchase history, etc. It will link those with a customer number and orders numbers. If a customer wants the right of access all the relevant details should be in this store. We use Google Analytics as the anonymous data store (although you may have a different ecommerce analytics platform). There you can store data which only refers to the customer record. These are called pseudo-anonymous data points under GDPR: they are only identifiable to a customer if you can link the customer number or order number back to your ecommerce back-end. Pseudo-anonymous data points you can safely send to Google Analytics include: Order number / transaction ID Order value / transaction amount Tax & shipping Product names and quantities Customer number Hashed email address (possibly a more flexible to link back to the customer record) If a customer exercises their right to removal, removing them from the ecommerce back-end will be sufficient. You do not also have to remove them from your Google Analytics, since the order number and customer number now have nothing to refer to. You do still need due process to ensure access to Google Analytics is limited, as in extreme circumstances a combination of dimensions such as products, country / city and browser, could identify the customer. [subscribe] Isn’t it simpler to just have one store? Every extra data store you maintain increases the risk of data breaches and complexity of compliance – so why not just analyse a single customer data store? I can think of three reasons not to do so: Marketing agencies (and other third parties) need access to the ecommerce conversion data, but not the underlying customer data Removing a customer’s order history on request would impact your historic revenue and purchase volumes – not desirable Your CRM / ecommerce platform is not built for large scale analysis: it may lack the tools, speed and integrations needed to get meaningful insights Beware of accidental transfers There are a few danger areas where you may inadvertently be sending PII data to Google Analytics: Customer emails captured in a signup event A customised product name – e.g. ‘engraving for Edward Upton’ Address or name captured in a custom dimension Our PII audit check is a quick, free way to make sure that’s not happening. Multiple stores of customer details GDPR compliance becomes difficult when your customer record is fragmented across multiple data stores. For example, you may have product and order information in your ecommerce database, with further customer contact details in a CRM. The simplest advice is to set up automatic two-way integrations between the data stores, so updating the CRM updates the ecommerce platform and visa-versa. Removing customer records from one system should remove them from the other. If that’s not possible, then you need clear processes to update both systems when customer details change, so you can comply with the right to rectification. Conclusion GDPR compliance need not require changing analytics tools or databases, just a clear process for separating out personally identifiable information – and training for the staff involved in handing that data. I hope this brief overview has been helpful. For further advice on how your ecommerce systems comply, please contact us for a free consultation. Littledata has experience with every major analytics platform and a wide range of custom setups. However, as a number of global companies are concurrently prepping for compliance, we highly recommend that you get in touch sooner rather than later!
How to improve AdWords retargeting using ecommerce checkout steps
In the ecommerce world, one of the smartest ways to improve ROI for marketing campaigns is to retarget customers who visited your website in the first place. These visitors are already in the market for the types of products that you sell, but how do you pull them back if they've dropped out of the checkout process? The most effective way to grab these customers is to target them based on where they dropped off. Luckily, Google lets you do exactly that: with the right analytics, you can set up retargeting campaigns based on checkout behaviour. At Littledata we've helped online stores in over 50 countries to improve marketing ROI using ecommerce tracking. In this post I share three simple steps you can take to improve your AdWords retargeting based on ecommerce checkout behaviour. 1. Set up accurate product tracking for your store Enhance Ecommerce tracking has been available from Google Analytics for a couple of years now. If you're already using this Google Analytics feature, good for you! Having product data means you can take advantage of this and create Audiences that then can be shared with AdWords (and other platforms). In order to improve AdWords retargeting using checkout steps, you must have checkout tracking and Enhanced Ecommerce enabled in Google Analytics. Then you can follow this checklist to set up accurate product tracking that can be used for Audiences in AdWords. Check out this resource (or share it with your lead developer): Google's Guide to Measuring a Checkout Repeat after me: "The fields must by dynamically populated! This is important!" Clarify where the checkout process starts and ends on your website (and again, if your developer is handling the setup make sure they're clear about each stage in your checkout funnel, including where the process starts and stops) Set up checkout tracking based on that process Once this data is successfully coming into Google Analytics, you're ready to create Audiences and share them with AdWords At this point, it's important to mention that there are a lot of elements to Enhanced Ecommerce tracking and each part needs to be set up separately. For example, you will not automatically be tracking product categories, listings and details. If you're not sure how to implement the full extent of Enhanced Ecommerce, we're here to help. If you're using the Shopify platform, you're in luck, as our Shopify reporting app's audit feature checks for accurate product and checkout-step tracking, and automatically assists with setting these up for you. The app works directly with the Google Analytics setup for your Shopify store, so you don't have to deal with Shopify's native reporting, which doesn't let you see how users are progressing through the checkout process. 2. Analyse customer behaviour, including checkout steps Shopping cart abandonment is the most frequent complaint we hear from ecommerce marketers. Why does someone add products to their shopping cart and then just abandon it completely? This isn't common in brick-and-mortar stores, so why does it happen so often online? Remember that online shoppers don't want to leave those things behind. They were attracted to those products and have expressed the desire to buy. But with a bad checkout flow, too much information or too little, they'll fly away and leave behind only unloved products with high shipping costs or under-promoted benefits. One of the best Enhanced Ecommerce use cases is the Checkout Behaviour report. This is essentially a Shopping Cart Abandonment report, showing weaknesses in your checkout process and where to invest your time and money to convince users that have added-to-cart to go ahead and complete a purchase. Why is this important and relevant to AdWords? Well, everything in marketing is about perspective. The above report doesn't only show you where you could improve your checkout flow, but also where you've lost customers. 'Lost' is the key word here. If you're losing a significant percentage of customers at the shipping stage of your checkout process, this is an opportunity to improve - and to market those improvements using AdWords. For example, you might look at that report and ask yourself: Are you charging customers too much for shipping? You can't really change that cost for all carts (we know that shipping costs are significant) but you could, for example, offer free shipping to shoppers with items in their cart over some profitability margin. Retargeting those users in Google AdWords is an effective way to show them that you're ready to reward them for making large purchases from your online store. Are you limiting yourself to too few territories? Put your analysts to work to find out where customers that leave the purchase flow want their goods to be delivered. Can you extend your logistical capabilities, or do you have a brick-and-mortar store nearby where you can direct these shoppers? Use AdWords retargeting to let them know. Of course, Google Analytics' native reports aren't for everyone. If you find them confusing or haven't worked extensively with enhanced ecommerce data, check out Littledata's report packs. These automated reports are an easy but comprehensive way to read and interpret ecommerce data without any hassle. For the purposes of tracking checkout steps to improve retargeting, I'd recommend our Ecommerce behaviour pack, which includes reports on shopping behaviour by marketing channel and checkout steps. [subscribe] 3. Set up retargeting campaigns based on that data How do you retarget users in AdWords based on Google Analytics data? Fear not, my brave colleagues! If you've made it to this step, you shouldn't have any trouble creating powerful retargeting campaigns. First you'll need to create a new Audience. In your Google Analytics Admin, find Audience Definitions in the middle of the screen near the bottom. Click on New Audience. Click on Create New and on this screen go to Conditions and Filter Users to Include the steps you want to target with this Audience. Set the Shopping Stage to contain (equal) 'Checkout_Abandonment' or 'Checkout_1', 'Checkout_2', etc. - wherever your customers have been falling off and leaving a basket full of goodies without completing the purchase. (Note that this field is auto-completed, so give GA a second after you start typing to show the options here.) You'll then need to set a time period. Think about your specific business and how far back you want to go with the search. Once you're happy with your selection, pick which Google AdWords account you'll want to link to this new Audience. That's it! You're now ready to run PPC promotions to a buy-ready audience that would otherwise have disappeared. I hope you've enjoyed this quick guide. Please drop me a line below and let me know how you use checkout steps in relation to AdWords. I always love to hear how other specialists in the field combine platforms to create perfect marketing. PRO TIP: If you're in a country with Google Merchant available, you can benefit from dynamic remarketing. This does take some extra setup on the product level, so let us know if you have specific questions. (And stay tuned - we're planning some Google Merchant Center-related posts for the near future.)
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