Are you looking at the wrong Black Friday metrics?

Paying attention to the right ecommerce metrics can help you establish the best customer base and shopping experience for long-term growth. But many retailers still focus only on the most popular metrics -- especially during the online shopping craze of Black Friday and Cyber Monday (#BFCM). Over the next few weeks ecommerce managers will be obsessing over data, but which stats are the most important? Two popular metrics -- ecommerce conversion rate and average time on site -- may be misleading, so I recommend looking instead at longer-term benchmarks. Here's how it all breaks down. Littledata's ecommerce benchmark data now contains indicators from over 12,000 sites, making it an ideal place to get a realistic view of Black Friday stats. Last year we found that the impact on Black Friday and Cyber Monday was larger in 2017 than in 2016. Using that same data set of 440 high-traffic sites, I dove into the numbers to see how this affected other metrics. Metrics to avoid I think that overall ecommerce conversion rate is a bad metric to track. From the leading ecommerce websites we surveyed, the median increase was 30% during the BFCM event last year...but nearly a third of the stores saw their conversion rate dip as the extra traffic didn’t purchase, with this group seeing a median 26% drop. Some stores do extremely well with deals: four sites from our survey had more than a 15-fold increase in ecommerce conversion rate during BFCM, and nearly a quarter saw more than double the conversion rate over the period. But the real question is: will tracking conversion rate hour-by-hour help you improve it? What could you possibly change within in day? Another misleading metric is average time on site. You may be looking for signs that the the extra traffic on the holiday weekend is engaging, but this is not the one to watch. The time on site for visitors who only see one page will be zero, which will mask any real increase from engaged visitors. Where to focus instead Now, do you know what good performance on funnel conversion metrics would look like for your sector? If not, have a look at Littledata’s industry benchmarks which now cover over 500 global sectors. Littledata’s benchmarks also include historic charts to show you how metrics such as add-to-cart rate vary for the average retailer in your sector month by month. Next try the ‘speed’ performance page to see how fast a user would expect a site in your sector to be. If you see site speed (as measured in Google Analytics) drop below average during Black Friday trading it’s time to pick up the phone to your web host or web operations team. Then, are you tracking return on adverting spend for extra Facebook Ads you're running during the quarter? Ad costs will spike during the peak trading period, and you make not be getting the same volume of traffic conversion into sales. Here are some quick pointers. Facebook Ads. Littledata’s Facebook Ads connection will ensure accurate data, with a dedicated Facebook report pack for automated insights. Shopify. If you're running your site on the Shopify platform, read up on which metrics are most important for Shopify stores and check out Shopify's BFCM Toolbox for seasonal online marketing. Missions. Use Missions in the Littledata app to make permanent improvements to your user experience. BFCM may be over before you can make the changes, but customers will keep buying the rest of the year. For example, can you increase add-to-cart rate with tips such as highlighting faster selling items or recommending an alternative to out-of-stock products? So focus on some clearer metrics and I hope Black Friday brings you every success!

2018-11-19

Should you outsource your ecommerce operations?

After you've created an ecommerce startup, the initial goals are all about recovering costs and expenses. As soon as the profit margins rise and you've broken even, you face some big decisions that will decide the growth of your online business. First of all, should you start outsourcing? Because many first-time entrepreneurs think it's more cost-effective to do everything on their own, it is a common mistake to pass on hiring freelancers. In this post I’ll highlight the core benefits of outsourcing your ecommerce operations. Focus & growth There are many aspects to promoting your product, and ecommerce operations is an integral component of your company's growth. By outsourcing your ecommerce operations, you have the time to focus on the goals and growth of your company. When hiring a freelancer from a reputable marketplace such as FreeeUp.com, your contract will protect both parties. The roles are clearly defined and you get expert advice in key areas. Your time is valuable, and when you free up your days to re-focus on growing sales, the sky is the limit. Short-term & long-term options First of all, this isn't an all-or-nothing decision. Hiring freelancers can be short-term or long-term depending on the needs of your business. By delegating specific tasks to various experts, your business has the opportunity to grow and flourish as you originally intended. You also have the unique opportunity to scale as needed without the commitments that traditional employment requires. And experts are exactly that - experts! Why reinvent the wheel? The need for a skillset As your company grows, your knowledge grows. Creating an ecommerce startup has a steep learning curve, however, and outsourcing for expert advice makes a lot of sense. Coaching a freelancer is not required as they are already specialized in their skillset. By hiring freelancers, your business can grow outside of your core expertise. For instance, why spend time learning about optimizing landing pages for conversions when you can just hire an Optimizely expert? Furthermore, professionalism is a must when running a business. Your company will gain a professional profile with experts at your side. Until you've gained the expertise, winging it is just bad business. If you've spent countless hours (or possibly weeks) researching ecommerce operating skills, it is time to consider hiring outside of your skillset. Freelancers are highly knowledgeable in their specific niches, and outsourcing your ecommerce operations (and other important roles such as social media and marketing), will benefit your business. Working at full capacity Being more efficient with your time is a smart business decision. When you're stretched too thin or feeling overwhelmed with all the tasks of the company, hiring a freelancer is a no-brainer. Avoiding business burnout is key. As the owner/founder/boss (and probably CMO/CEO to boot), your business needs you to be working at full capacity. Making a list of the tasks that need to be completed is a smart business move. The next step is to start outsourcing as needed. You can learn from these experts and expand your business while optimising your time in the areas you already know -- while maintaining a clear overview of your ecommerce site. Excellent customer service (doesn't necessarily start with you) There's no question that customer service is a key component for the success of your business. Platforms like Shopify have emphasized this to their merchants to help them grow. Today's consumers are demanding, and catering to your customers’ needs can quickly take all your time and energy. Remaining professional requires focus and support, which is why hiring freelancers to maintain exceptional customer service is a key component to the growth of your company. Upgrades & maintenance Ultimately, the goal is to keep everything running smoothly. When you regularly hit profit margins and your goals are being met, upgrades and maintenance will be an ongoing issue. You might want to expand your server capacity due to increased traffic, for instance, or revamp your blog. It's no surprise that the top benchmarks for growing a Shopify store include page load speeds and server response time. Even though upgrades and maintenance to support growth are positive issues, it can be time-consuming to keep everything afloat. Moreover, once you meet your goals, you’ll want to expand. Hiring freelancers allows you to make sure that everything runs smoothly as you venture out into new areas or even new businesses. The bottom line is that one person cannot do it all. Outsourcing for various skillsets will make a world of difference for your company -- and your peace of mind. Start outsourcing your ecommerce operations The benefits of outsourcing your ecommerce operations to freelancers are countless. By outsourcing your ecommerce operations, you free up valuable time to remain focused and goal-oriented. Your business started from passion -- it is important to maintain that vision and hire freelancers to help meet your targets and objectives.   This is a guest post by Connor Gillivan, CMO and co-owner of FreeeUp, a rapidly growing freelance marketplace making hiring online simpler (check out their info on hiring for ecommerce). He has sold over $30 million online and hired hundreds of freelancers himself to build his companies.

2018-08-30

How Littledata helps Shopify stores comply with GDPR

When the GDPR regulation comes into effect later this month, it will impact all websites trading with EU citizens. That means any ecommerce site with customers in Europe! Is your Shopify store ready to comply? We recently updated our Shopify app (since release 7.8) to help Shopify stores which use Google Analytics comply with GDPR. In addition to automatic fixes to help your store comply, we include recommendations for how to update your site content (such as Terms and Conditions), and how to deal with the new 'two year rule'. If you're running a Shopify store, the time to act is now. Automatic fixes with our Shopify app The first two steps are done automatically when you install our GDPR-ready Shopify app. If you're already using Littledata's Shopify app, these two fixes can be applied when you upgrade to our latest tracking script (version 3.2). Here's what they address. 1. Anonymise customer IP addresses The IP address of your website visitor is considered personal information under GDPR, and to remove any risk that this is sent to Google’s servers in the USA, our script scrambles the last few digits of the IP address. Google already promises not to store the IP address, so this step is an extra level of safety. This slightly reduces the accuracy of tracking which city your visitor came from -- but we believe that this is a small price to pay for ensuring anonymity. 2. Filter personal emails and ZIP/postcodes from pageviews Many sites accidentally send personal data in the page URLs or titles tracked by Google Analytics. For example, apps with their own checkout often send the user email as a URL parameter like ‘/url?email=myname@gmail.com’. Our script now filters that personal data out at source, so the page path you’ll see in Google Analytics is ‘/url?email=REMOVED’. Additional manual steps There are two additional manual steps to ensure that Google Analytics for your Shopify store is GDPR-compliant. 3. Update your terms and conditions You need to update your website T&Cs to ensure users are aware of the Google Analytics Advertising Features that our Shopify app activates and Google uses to identify user demographics, such as gender and interests. We are not lawyers, but we suggest using something similar to these sentences to describe what data is collected, how you (and we) use the data, and how how users can opt out: Our site uses Google Analytics Advertising Features to deduce your gender, age group and interests based on other types of websites you have visited. We use this in aggregate to understand which demographics engage with areas of our website. You can opt out with Google's browser add-on. 4. Remove user-specific information after 2 years You should also change the data retention period for your Google Analytics web property, so that Google removes all user-specific information from their database after 2 years. To make this change, logging to your GA account and go to the Settings cog, and then Property > Tracking info > Data Retention. Use the 'data retention' drop-down menu to select to keep user data for 26 months, and mark 'reset on new activity' to ON. This means that after 26 months, if the user has not come back to your website, any user cookie will be deleted. We think this sensible to comply with the Right to Erasure without making any practical limits to your analysis. Right to Erasure feature coming soon! We're also working on a feature to help websites comply with the Right to Erasure or Right to be Forgotten. Here's a summary of that aspect of the regulation, from the summary of key changes at EUGDPR.org. Right to be Forgotten Also known as Data Erasure, the right to be forgotten entitles the data subject to have the data controller erase his/her personal data, cease further dissemination of the data, and potentially have third parties halt processing of the data. The conditions for erasure, as outlined in article 17, include the data no longer being relevant to original purposes for processing, or a data subject's withdrawing consent. It should also be noted that this right requires controllers to compare the subjects' rights to "the public interest in the availability of the data" when considering such requests. Littledata's Right to Erasure feature will ensure that when you delete a customer from your Shopify admin interface, any references to that customer are deleted from Google Analytics. This won’t affect aggregate reporting, such as number of web sessions or transactions. When do GDPR regulations take effect? The official enforcement date for General Data Protection Regulation (GDPR) is 25 May 2018. At that time any organisations in non-compliance may face heavy fines. In short, we recommend implementing the fixes above ASAP for your Shopify store. All you need is Google Analytics account and our Shopify app. And do check our blog regularly for updates. This is the best place to hear about new Littledata features relating to GDPR, as well as news and analysis about how the regulations affect different types of online businesses, including ecommerce websites, subscription businesses, and membership-based sites such as large charities and nonprofits. Looking for additional support? Contact us about GDPR consulting for analytics setup.

2018-05-02

Tracking the online customer journey for luxury ecommerce

Today I'm excited to be participating in the Innovation Meets Fashion event in Lugano, Switzerland. As an increasing amount of luxury and fashion retail moves online, high-end brands are finding it complicated to track the complete customer journey. In many cases, difficulties in tracking customers through to eventual purchase are holding back investment in the digital experience and online marketing. But it doesn't have to be this way. We've found a straightforward correlation in ecommerce between the average ticket price of the item being purchased and the number of web pages or sessions before that purchase is made. Simply put, customers spend longer considering big ticket items than they do with smaller ticket items and impulse purchases. Luxury retail involves many touch points with the brand across your websites, social sites and physical stores. The problem is that the longer than online customer journey, the harder it is to get consistent data on which top-of-funnel experiences are leading to purchasing. So first the bad news: since many potential customers browse anonymously, perfect ecommerce tracking across a long online and offline journey is not possible. Tracking browsers based on first-party cookies (such as Google Analytics) will fail when customers use multiple devices, clear their cookies or browse in-app (such as from Facebook). Yet there are three ways we have seen retailers selling high value items increase the reliability of their online behavioural data. 1. Track online shopping behaviour in detail Understanding whether customers browse certain products, view the detail of product variants and even add-to-cart is a good proxy for seeing which campaigns eventually convert. Does your brand have a good understanding of how each marketing channel influences browsing behaviour, after the landing page but before the checkout? 2. Offer a good reason to get customers to login before buying VIP offers, registering for events and discounts all offer a good way of getting customers to login from different devices. With the correct analytics setup, this login information can be used (without infringing the users’ privacy) to link together different interactions they make across multiple devices 3. Make the most of your email list Even without having a login before purchase, customers clicking through links in a marketing email can allow the same stitching together of sessions. This means that if a customer visits a link from their mobile device, and on another week from their home laptop, these two devices can be linked as belonging to the same email – and therefore the same person. Luxury online retail involves a complex journey. Littledata is here to make your tracking and reporting both easy and accurate. Sign up today to get started with our complete analytics suite, and feel free to reach out to our Google Analytics consultants with questions about best practices for luxury ecommerce. Your success is our success!

2018-03-26

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. 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!

2018-02-13

How to drive more traffic to your ecommerce site

Are you following a strategy to increase ecommerce site traffic, or are you shooting in the dark? In this guest post, Courtney McGhee outlines proven ways to get more web visitors. So you’ve created your ecommerce site and you’ve set up your social media profiles. Why isn’t your audience flocking to your site, cash in hand? The truth is, creating your website and social presence is only the first step toward generating traffic. Your strategies on these platforms will ultimately determine the amount of traffic that lands on your pages. You need to invest time, create relationships and sometimes even invest some money if you want to boost your numbers. In this guide, I'll show you proven ways to drive ecommerce site traffic. Step 1: Decide how many daily visitors you need Setting a clear, attainable goal should be the first step if you want to increase your traffic. Marketing strategies can be overwhelming if you don’t first determine what your goal should be. First, decide how much annual revenue you are looking to earn. Let’s look at the example of $350,000. Next, divide your total annual sales by the value of your average order. Let’s say your average order costs $50. This calculation gives you the number of annual orders you will need to reach your sales goal. For our example, that number would be 7000, or about 19 orders each day. Let’s realistically assume that 19 orders per day come from a conversion rate of 2%. That means you will need around 960 daily visitors if you are going to have 19 orders each day. These numbers will show you how much time you need to spend on generating traffic and can help you set attainable and measurable goals. Once you've decided on the amount of traffic you're shooting for, make sure your Google Analytics setup is giving you accurate data about all of your websites (including microsites) and isn't duplicating visitors. You'll also want to set up goals for specific events, such as when a customer adds items to their cart, signs up for your email list or completes a checkout. It's better to set up this tracking early before launching your new strategy--otherwise you won't know whether or not your new strategy worked! Step 2: Start your search engine optimization (SEO) Search engines are (or should be) one of the biggest sources of your traffic. Now, it’s time to milk them for all they’re worth. Search Engine Optimization (SEO) should be a main focus to drive organic traffic to your site. Whether or not you have just launched your ecommerce store, you should make a habit of reviewing each page and product on your site. To do this, you need to start an SEO audit. Enter your URL on an SEO tool like WooRank, and start an Advanced Review. You can add up to three competitors here to take your SEO up a notch. Add keywords you want to track in the Keyword Tool, and choose the location where you want to focus on. In the keyword tool, you will be able to see the volume and rank for each keyword and how you are doing against your competition. There are plenty of free keyword research tools available if you aren’t sure which ones you should be targeting. Now that you have chosen your keywords to use for optimization efforts, you should make sure you are using them in a consistent and natural way. Using them in your title tags, meta descriptions and body content will help you become more visible to your target audience. To really optimize your keyword strategy, I recommend setting up site-search tracking to see what visitors are searching for on your site and also monitoring how keywords convert on your site by adding Search Console to your Google Analytics account before moving onto the next step. Step 3: Craft your content...carefully Even for an ecommerce site, it is essential to have useful, relevant and authoritative content. Of course, it is critical to have product images, but product descriptions will really help you boost your traffic. With product descriptions, you can weave in the keywords you can easily rank for that can also drive conversions. It’s actually easier to rank higher for long tail, localized keywords that will align with your visitors’ search queries. If you are selling garden supplies and you can rank highly for “planter for tomatoes”, the produce descriptions should use “planter for tomatoes”. Include that phrase in the title, as well. The product images need to be clear and representative of the actual product you are selling. Don’t forget to include the alt text with every image you use. This should go without saying, but don’t use images you downloaded from the internet that aren’t pictures of what you are actually selling. Also, you can create content like product reviews or comparisons of different brands and models that are optimized for “planter for tomatoes”. You can experiment with other types of content on social media, like videos, that can help you rank highly on search results. Videos related to the product that can also be embedded on your site is another easy way to incorporate your keywords in your content. Step 4: Tap into social media influencers In terms of brand engagement, Instagram is one of the best platforms. There is a whopping 25% more engagement on Instagram compared to other social media platforms. Also, studies show that nearly 25% of online shoppers are influenced by social media recommendations. In order to tap into the influencer market, you need to find the people who are willing to feature your products to their many followers. Finding those people, though, is easier said than done. A tool like WEBSTA can help you find the most popular Instagram hashtags and accounts. Once you find the influencer with a substantial amount of followers that aligns with your general category, you can contact him or her and ask for your product to be featured. Step 5: Entice visitors with contests Let’s be honest: everyone loves a good freebie. Does your site have a gift that your customers will find worthwhile? Use your social media profiles, your website and your influencers to get the word out that you are having a contest for free goodies. If your potential customers think your gift is valuable, they will share it with their friends and families. The only con to this strategy is attracting people who are only interested in free stuff. These users will likely never convert to customers, so use this option only when it makes sense for your brand. Step 6: Publish user reviews Search Engine Land noted that 88% of shoppers trust reviews they read online. You can encourage your users to leave reviews on your website and social media accounts. Reviews will help you rank higher in search results, and users are more likely to click on your site/social media pages. User reviews ensure fresh, relevant content - a big plus in Google’s eyes. Here are some more stats from Econsultancy on why user reviews are so valuable: Bad reviews improve conversions by 67% 63% of customers are more likely to make a purchase from a website with user reviews Reviews generate an average boost in sales of 18% Step 7: Pay-Per-Click (PPC) advertising At least 43% of ecommerce traffic, on average, comes from Google search (organic). But, more than a quarter of traffic is coming from Google AdWords, according to Wolfgang Digital. So, it’s important to have both your SEO and PPC set up correctly. As mentioned above, during your keyword research find the keyword your audience uses most, like “tomato planters”. This includes the long tail keywords, too, like “best planters for tomatoes”. Now, run a PPC campaign including both keywords. Primary keywords will generate more traffic, while long tail keywords will drive less traffic but higher conversion rates. To increase conversions even more, you can link your AdWords account to your Analytics account, then use Buyer Personas for specific marketing channels to target those users that are more likely to spend money on your site. So, are you ready for real growth? Bringing traffic to your ecommerce sites all starts with setting a clearly-defined goal. You need to know where your existing traffic is coming from, and optimize all of your platforms for your visitors and search engine bots. Incorporating other strategies, when done correctly, will help you bring more eyes to your site. Contests and PPC advertising are great ways to get your product in front of your target audience. I hope this guide helps take your online store to the next level! Courtney McGhee is on the Marketing Team at WooRank, an SEO audit tool that has helped millions of websites with their SEO efforts. A former journalist in North Carolina, Courtney shifted gears and entered the digital marketing world in Brussels, Belgium.

2018-02-08

Black Friday discounting increases next season’s purchasing

I knew Black Friday had reached ‘late adopter’ stage this week when a company I’d bought fencing panels from - fencing panels – emailed me their holiday season promotions. But the real question is whether all these promotions serve to drive customer loyalty or just attract bargain hunters? At Littledata we looked at aggregate data from 143 retailers who participated most in 2016 Black Friday, versus 143 retailers who did not. For the first 23 days of November 2017 – before Black Friday – the median year-on-year increase in sales was 13% for those pushing discounts the previous year, versus only 1% growth for those avoiding Black Friday discounting *. Our conclusion is that retailers who discounted most heavily on Black Friday 2016 saw a lasting benefit in extra sales a year after the sales period. However, we don’t know whether these extra sales were profitable enough to pay for the seasonal promotions. Another possible explanation is that higher-growth retailers are more active in marketing Black Friday, but in either event the discount season has done them no harm over the following year. In a follow up post next week we’ll compare the peak discount trading – and see if on average these same stores increased their participation this year or reigned it back. Looking at 2016, it seems Black Friday was bigger than the year before for our cohort of 270 UK retailers – but at the expense of sales later in the season. Yet in the UK we are not close to US-levels of hysteria yet, where a much greater proportion of the last quarter’s sales are done on that weekend. The other interesting question is what sectors does Black Friday affect? Reflecting back on my 2016 post, it may be a surprise that the biggest boost of over 100% average increase in sales comes for Health & Beauty stores; whereas technology and computer stores on average saw a boost of 40% for the week. (The graph shows the difference with the average sales volumes in November & December, by sector, for 3 selected weeks.) And perhaps I shouldn’t have been surprised by those fencing panels: business and industrial sites saw a big boost too! Interested in tracking online sales activity for your own site this holiday shopping season? Littledata's ecommerce analytics software provides accurate data and automated reporting to help you track promotions and drive conversions and customer loyalty. * The statistical detail I took a group of 573 retailers we have tracked for at least 2 years, and looked at the ratio of Black Friday weekend sales (Friday, Saturday, Sunday, Monday) to the 2 month average for November and December. Those in the top quartile (trading 2.6 times above average during the Black Friday season) were deemed to have participated; those in the bottom quartile, showing a dip in trading over that weekend were deemed not to have participated. I then looked at the year-on-year growth in revenue between November 2016 (first 23 days) and the same period in November 2017, for the discount versus non-discount group. A t-test between the groups found a 18% probability that the two groups had the same mean, not allowing us to dismiss the null hypothesis.  

2017-11-24

Is Google Analytics compliant with GDPR?

From May 2018 the new General Data Protection Regulations (GDPR) will come into force in the European Union, causing all marketers and data engineers to re-consider how they store, transmit and manage data – including Google Analytics. If your company uses Google Analytics, and you have customers in Europe, then this guide will help you check compliance. The rights enshrined by GDPR relate to any data your company holds which is personally identifiable: that is, can be tied back to a customer who contacts you. The simplest form of compliance, and what Google requires in the GA Terms of Use, is that you do not store any personally identifiable information. Imagine a customer calls your company and using the right of access asks what web analytics you hold on them. If it is impossible for anyone at your company (or from your agencies) to identify that customer in GA, then the other right of rectification and right of erasure cannot apply. Since it is not possible to selectively delete data in GA (without deleting the entire web property history) this is also the only practical way to comply. The tasks needed to meet depends on your meaning of ‘impossible to identify’! Basic Compliance Any customer data sent ‘in the clear’ to GA is a clear break of their terms, and can result in Google deleting all your analytics for that period. This would include: User names sent in page URLs Phone numbers captured during form completion events Email addresses used as customer identifiers in custom dimensions If you’re not sure, our analytics audit tool includes a check for all these types of personally identifiable information. You need to filter out the names and emails on the affected pages, in the browser; applying a filter within GA itself is not sufficient. But I prefer a belt-and-braces approach to compliance, so you should also look at who has access to the Google Analytics account, and ensure that all those with access are aware of the need not to capture personal data and GDPR more generally. You should check your company actually owns the Google Analytics account (not an agency), and if not transfer it back. At the web property level, you should check only a limited number of admins have permission to add and remove users, and that all the users only have permission to the websites they are directly involved in. Or you could talk to us about integrations with your internal systems to automatically add and remove users to GA based on roles in the company. Full Compliance Other areas which could possibly be personally identifiable and you may need to discuss are: IP addresses Postcodes/ZIP codes Long URLs with lots of user-specific attributes The customer’s IP address is not stored by Google in a database, or accessible to any client company, but it could potentially be accessed by a Google employee. If you’re concerned there is a plug-in to anonymise the last part of the IP address, which still allows Google to detect the user’s rough location. ZIP codes are unlikely to be linked to a user, but in the UK some postcodes could be linked to an individual household – and to a person, in combination with the web pages they visited. As with IPs, the best solution is to only send the first few digits (the ‘outcode’) to GA, which still allows segmenting by location. Long URLs are problematic in reporting (since GA does not allow more than 50,000 different URL variants in a report) but also because, as with postcodes, a combination of lots of marginally personal information could lead to a person. For example, if the URL was mysite.com/form?gender=female&birthdate=31-12-1980&companyName=Facebook&homeCity=Winchester This could allow anyone viewing those page paths in GA to identify the person. The solution is to replace long URLs with a shortened version like mysite.com/form And for bonus points... All European websites are required to get visitors to opt in to a cookie policy, which covers the use of the GA tracker cookie. But does your site log whether that cookie policy was accepted, by using a custom event? Doing so would protect you from a web-savvy user in the future who wanted to know what information has been stored against the client ID used in his Google cookie. I feel this client ID is outside the scope of GDPR, but guaranteeing that the user on GA can be linked to opt-in consent of the cookie will help protect against any future data litigation. The final area of contention is hashing emails. This is the process used to convert a plain email like ‘me@gmail.com’ into a unique string like ‘uDpWb89gxRkWmZLgD’. The theory is that hashing is a one-way process, so I can’t regenerate the original personal email from the hash, rendering it not personal. The problem is that some common hashing algorithms can be cracked, so actually the original email can be deduced from a seemingly-random string. The result is that under GDPR, such email hashes are considered 'pseudonymized' - the resulting data can be more widely shared for analysis, but still needs to be handled with care. For extra security, you could add a ‘salt’ to the hashing, but this might negate the whole reason why you want to store a user email in the first place – to link together different actions or campaigns from the same user, without actually naming the user. There are ways around that strike a compromise. Contact Littledata for a free initial consultation or a GDPR compliance audit.

2017-10-19

5 (bad) reasons not to do a Google Analytics audit

Does this sound familiar? 'We know our data's bad, but we don't have the time or resources to fix it'. Or, even worse: 'I checked a bunch of other metrics and they didn't justify our current ad spend, so I think I'll just present that same old report at the meeting today...again. Luckily we haven't fixed our Google Analytics setup to track too much relevant data about other marketing channels, or to connect those channels directly to revenue, because then we might need to change our whole strategy!' There's still a lot of confusion out there about the role and scope of an analytics audit. With a free audit tool directly in the  app, Littledata is on a mission to change this. Here are some (slightly exaggerated) versions of common objections to doing an analytics audit, and how to overcome them. 1. You don't know what a Google Analytics audit is Okay, not to start this somewhat ironic post with an entirely un-ironic objection, but not understanding the process is probably the only good reason not to audit your analytics setup. Luckily an analytics audit is actually very straightforward: it's simply a check of your analytics configuration and implementation. Some consultants and last-gen apps can make the audit process seem confusing and disorienting. If that's been your experience, we're here to help. Our free Google Analytics audit tool explains the process in real time. Not only that, but many tracking and reporting issues can be fixed automatically by the app (hello, intelligent algorithm!). 2. You don't believe in marketing ROI There are a lot of fluffy tools out there. Google Analytics isn't one of them. It's not that all digital marketers take action based on analytics, but a majority of the top ones do. That's what makes them the best. If you need convincing that accurate data is the secret sauce behind higher marketing ROI (return on investment), check out the recent Google Analytics research with Econsultancy, where they found that '60% of leading marketers routinely take action based on analytics, and are also 48% more likely than mainstream marketers to say their strategy is strongly data-driven'. 3. You trust everything you read online Failing to audit your analytics setup is basically the same as believing that everything you read online is true, no matter the source. Why? Because bad data produces bad reports. This is true no matter how fancy your reporting templates might be, or how much time you've spent making spreadsheets of Google Analytics data look accessible. Unless you regularly audit your analytics setup, how do you know if you're tracking the right things in the right manner? This is especially true if you're using an otherwise awesome ecommerce platform like Shopify, which has notoriously questionable tracking that also happens to be easy to fix with the right analytics app. 4. You think that the customer is always wrong Customer happiness isn't just a buzzword, it's increasingly what's driving the growth and expansion of online businesses, especially in the ecommerce space. Big players like Amazon learned this early on, and they built an effective - and addictive - customer experience around heaps of data on everything from affiliate ads to repeat buying activity. Think you don't have access to those same tools? Think again. If you want to build a better customer experience, it's essential to start with the correct Google Analytics setup and end the guessing games about where your leads and customers come from, and how they act. That's where the audit comes in. 5. You're betting on failure Are you betting that your own company will fail? Unless you secretly run an ecommerce hedge fund and have shorted your own startup, this is probably a bad idea. Auditing your data tracking across the customer life cycle is a sure way to see what's working, what's not, and what can be improved. Otherwise you're stuck with bad data and revenue tracking that might not have much to do with the reality - or the future - of your online business. Is there a better way? Look, we get it. Change can be scary, but choosing to stay stuck in the same data rut isn't the way forward. We've helped over a thousand online businesses fix their Google Analytics setup to capture accurate, relevant data. Littledata's industry-leading automated audit tool is free to run as often as you'd like. Sign up today and start trusting your data.

by Ari
2017-09-07

Custom reporting for marketing agencies

Are you a digital marketing agency looking for new reporting solutions? As our agency partnerships continue to grow, we thought it would be useful to outline how Littledata's custom reporting helps forward-thinking agencies cut down on reporting time, visualise data and improve performance for their clients. The marketing landscape is complex, but your reporting doesn't have to be overly complicated. With such a wide range of channels and sites to track, many agencies struggle to find the best analytics tools. To you we say: Welcome, you've finally found a solution that both simplifies and enhances the reporting process. Smarter reporting and accurate analytics Do you produce regular campaign performance reports in Excel or Google Sheets for your clients? Have you rejected other reporting solutions as being too rigid or complex for your needs? Then Littledata’s custom reports could work well for you and your clients. We automate the data fetching and calculations you currently run manually, and display the results to clients in a streamlined web app. We'll even show you the most important metrics, and report on key changes - automatically. One key advantage over tools such as Tableau, Data Studio or Chartio is that you can define a template report and then roll it out for many different web properties (or segments of websites) with the click of a button. Compared with other solutions you may have considered we also offer: Full support in data setup, report design and client onboarding Branded report packs for your clients and customers Complete life cycle data on your clients' customers, from marketing attribution to repeat purchases (including for subscription-based businesses) 1st line support to end users Flexibility to calculate any metrics (using Google Sheets in our processing pipeline) Comparison to industry benchmarks for sales, marketing and web performance - or create private benchmarks amongst your own client base Actionable insights for any online business to improve marketing ROI and increase conversions, whether one large ecommerce site or a series of micro-sites Integration of Google Analytics with Google Search Console data for powerful SEO reports We’re also open to discussions about white-labelling the Littledata app. This type of partnership works best for agencies with at least 20 clients ready to take advantage of our intelligent analytics tools. Please contact us if you’d like a demo, to see how this has worked for existing customers, or to discuss a particular client’s needs. Get ready to love your analytics :)

2017-08-09

What to test with Google Optimize

So you’ve got a brand new tool in your web performance kit – Google Optimize – and now you want to put it to good use. What can you test with Optimize and how does it work? Firstly, what are the different options for setting up an experiment? AB Test Using the in-page editor you can create an altered version of the page you wish to test. This could be a change of text copy, different styling, or swapping in a different image. You can also add new scripts or HTML if you’re familiar with coding. The way this works is Optimize adds a script after the page loads to manipulate the page text, images or styles. I recommend not switching header elements or large images using this method as, depending on your website setup, there may be a noticeable flicker– try a redirection test below. You can create many versions with subtly different changes (C, D and E versions if you want) – but remember you’ll need a large volume of traffic to spot significant differences between lots of variations. You can also limit the test to a certain segment of users – maybe only first time visitors, or those on mobile devices. Multivariate Test Similar to an AB test, a multivariate test is used when you have a few different aspects of the page to change (e.g. image and headline text) and you want to see which combination is most engaging. To get a significant result, you'll need a large volume of traffic - even more than testing many options in AB tests.   Redirection Test This is where you have two different versions of a page – or a different flow you want to start users on. Optimize will split your visitors, so some see the original page and some are redirected to the B version. A redirection test is best when the page content or functionality is very different – perhaps using a whole different layout. The disadvantage is you’ll need a developer to build the B version of the page, which may limit the speed of cycling tests.   Personalisation Personalisation is not officially supported by Optimize right now, but we’ve found it to be a useful tool. You can assign 99.9% of the visitors who match certain criteria to see the alternative version of the page. An example is where you have a special offer or local store in a particular city - see our step-by-step local personalisation example. You can ensure that all the visitors from that city see a different version of the page. Unfortunately on the free version of Google Optimize you are limited to 3 concurrent ‘experiments’ – so it won’t be a good solution if you want to run similar personalisation across lots of cities or groups of users. Next the question is where to start with tests...   Start with the landing pages Landing pages get the greater volume of traffic, and are where small visual changes (as opposed to new product features) make the biggest difference to user engagement. This greater volume allows you to get a significant result quicker, meaning you can move on to the next test quicker. And keep on improving!   So what exactly could you test using Google Optimize? Here are six ideas to get you going.   1. Could call-to-actions (CTA) be clearer? Changing the colour or contrast of a key button or link on the page (within your brand guidelines) usually results in more visitors clicking it. This might involve changing the style of the CTA itself, or removing elements close by on the page – to give the CTA more space to stand out.   2. Are you giving the user too many choices? In Steve Krug’s classic Don’t Make me Think he explains how any small confusion in the user’s mind can stop them making any choice. Every choice the user has to make is an opportunity for them to give up. Try hiding one of the options and seeing if more users overall choose any of the remaining options.   3. Is the mobile page too long? As many sites move to responsive designs that switch layout on smaller screens, this has led to mobile pages becoming very long. User may get ‘scroll fatigue’ before then get to critical elements on the page. Try cutting out non-essential sections for mobile users, or editing copy or images to make the page shorter. You could also try switching sections so that the call-to-action is higher up the page on mobile – although this is harder to achieve without a redirection test.   4. Is localisation important to your users? You may have discussed providing local language content for your users, and been unsure if it is worth the costs of translation and maintenance. Why not test the benefits for a single location? As with the personalisation tests, you can show a different local language (or local currency) version of the page to half the users in the single location (e.g. Spanish for visitors from Mexico) and see if they convert better.   5. Does the user need more reassurance before starting to buy? It easier to build experiments which remove elements to the page, but you should also consider adding extra explanation messages. A common problem on ecommerce stores is that visitors are unsure what the shipping charges or timing will be before adding to cart. Could you add a short sentence at the start of the journey (maybe on a product page) to give an outline of your shipping policy? Or maybe some logos of payment methods you accept?   6. Changing header navigation If your site has a complex mix of products that has evolved over time it may be time to try a radical new categorisation – maybe splitting products by gender or price point rather than by type. For this test, you’ll want to target only new visitors – so you don’t confuse regular visitors until you’re sure it’s permanent. You will also need to make the navigation changes on all pages across the site.   Good luck! Littledata also offering consulting and AB testing support, so please contact us for any further advice.

2017-05-30

Shopify Marketing Events vs Google Analytics

At the Shopify Unite conference today I heard plenty of great ideas such as ShopifyPay but the most interesting for me as a data specialist was the marketing events API. Since we launched our Fix Google Analytics Shopify app earlier this year we’ve known that reporting was a weak spot in Shopify’s platform offering, and they admit that ‘understanding marketing campaign performance’ is one of the biggest challenges of Shopify merchants right now. The ability for other Shopify apps to plug their campaign cost and attribution data into Shopify (via the marketing events API) is a logical step to building Shopify’s own analytics capability, but I don’t believe it will be a substitute for Google Analytics (GA) anytime soon. Here’s why: 1. Google Analytics is the industry standard Every online marketer has used Google Analytics, and many have favourite reports they’ve learned to interpret. Moving them to use a whole new analysis platform will take time– and it’s taken GA 10 years to achieve that dominance. 2. GA provides platform-agnostic data collection For a store using Shopify as their only source of insights, moving away from Shopify would mean losing all the historic marketing performance data – so it would be very hard to make like-for-like comparisons between the old platform and the new. Many of our customers have used GA during and after a platform shift to get continuous historical data. Which ties into my first point that over 85% of businesses have a history of data in GA. 3. Incomplete marketing tagging will still cause issues Making valid analysis on multi-channel marketing performance relies on having ALL the campaigns captured - which is why our GA audit tool checks for completeness of campaign tagging. Shopify’s tracking relies on the same ‘utm_campaign’ parameters as GA, and campaigns that are not properly tagged at the time cannot be altered retrospectively. 4. Google is rapidly developing Google Analytics I’d like to see the Shopify marketing event collection evolve from its launch yesterday, but Google already has a team of hundreds working on Google Analytics, and it seems unlikely that Shopify will be able to dedicate resources to keep up with the functionality that power users need. 5. More integrations are needed for full campaign coverage Shopify’s marketing analysis will only be available for apps that upgrade to using the new API.  Marketing Events has launched with integrations for Mailchimp and Facebook (via Kit) but it won’t cover many of the major channels (other emails, AdWords, DoubleClick for Publishers) that stores use. Those integrations will get built in time, but until then any attribution will be skewed. 6. GA has many third-party integrations Our experience is that any store interested in their campaign attribution quickly wants more custom analysis or cuts of the data. Being able to export the data into Littledata’s custom reports (or Google Sheets or Excel) is a popular feature – and right now Shopify lacks a reporting API to provide the same customisations. You can only pull raw event data back out. That said, there are flaws with how GA attribution works. Importing campaign cost data is difficult and time consuming in GA – apart from the seamless integration with AdWords – and as a result hardly any of the stores we monitor do so. If Shopify can encourage those costs to be imported along with the campaign dates, then the return on investment calculations will be much easier for merchants. I also think Shopify has taken the right pragmatic approach to attribution windows. It counts a campaign as ‘assisting’ the sale if it happens within 30 days of the campaign, and also whether it was ‘last click’ or ‘first click’. I’ve never seen a good reason to get more complicated than that with multi-channel reports in GA, and it’s unlikely that many customers remember a campaign longer than 30 days ago. In conclusion, we love that Shopify is starting to take marketing attribution seriously, and we look forward to helping improve the marketing events feature from its launch yesterday, but we recommend anyone with a serious interest in their marketing performance sticks to Google Analytics in the meantime (and use our Shopify app to do so).

2017-04-21

6 reasons Facebook ads don’t match the data you see in Google Analytics

If you run Facebook Ads and want to see how they perform in Google Analytics, you may have noticed some big discrepancies between the data available in Facebook Ad Manager and GA. Both systems use different ways to track clicks and visitors, so let’s unpick where the differences are. There are two kinds of metrics you’ll be interested in: ‘website clicks’ = the number of Facebook users who clicked on an advert on your own site, and (if you do ecommerce) the transaction value which was attributed to that advert. Website Clicks vs Sessions from Facebook 1. GA isn’t picking up Facebook as the referrer If users click on a link in Facebook’s mobile app and your website opens in an in-app browser, the browser may not log that ‘facebook.com’ was the referrer. You can override this (and any other link) by setting the medium, source, campaign and content attributes in the link directly. e.g. www.mysite.com?utm_medium=social&utm_source=facebook.com&utm_campaign=ad Pro Tip: you can use GA’s URL builder to set the UTM tags on every Facebook campaign link for GA. In GA, under the Admin tag and then ‘Property settings’ you should also tick the box saying ‘Allow manual tagging (UTM values) to override auto-tagging (GCLID values)’ to make this work more reliably. 2. The user leaves the page before the GA tag fires There’s a time delay between a user clicking on the advert in Facebook and being directed to your site. On a mobile, this delay may be several seconds long, and during the delay, the user will think about going back to safety (Facebook’s app) or just closing the app entirely. This will happen more often if the visitor is not familiar with your brand, and also when the page contents are slow to load. By Facebook’s estimation the GA tracking won’t fire anywhere between 10% and 80% of clicks on a mobile, but fewer than 5% of clicks on a desktop. It depends on what stage in the page load the GA pixel is requested. If you use a tag manager, you can control this firing order – so try firing the tag as a top priority and when the tag container is first loaded. Pro Tip: you can also use Google's mobile site speed suggestions to improve mobile load speed, and reduce this post-click drop-off. 3. A Javascript bug is preventing GA receiving data from in-app browsers It’s possible your page has a specific problem that prevents the GA tag firing only for mobile Safari (or Android equivalent). You’ll need to get your developers to test out the landing pages specifically from Facebook’s app. Luckily Facebook Ad Manager has a good way to preview the adverts on your mobile. Facebook Revenue vs GA Ecommerce revenue 4. Attribution: post-click vs last non-direct click Currently, Facebook has two types of attribution: post-view and post-click. This means any sale the user makes after viewing the advert or clicking on the advert, within the attribution window (typically 28 days after clicking and 1 day after viewing), is attributed to that advert. GA, by contrast, can use a variety of attribution models, the default being last non-direct click. This means that if the user clicks on an advert and on the same device buys something within the attribution window (typically 30 days), it will be attributed to Facebook.  GA doesn't know about views of the advert. If another campaign brings the same user to your site between the Facebook ad engagement and the purchase, this other campaign takes the credit as the ‘last non-direct click’. So to match as closely as possible we recommend setting the attribution window to be '28 days after clicking the ad' and no 'after view' attribution in Facebook (see screenshot above) and then creating a custom attribution model in GA, with the lookback window at 28 days, and the attribution 'linear' The differences typically come when: a user engages with more than one Facebook campaign (e.g. a brand campaign and a re-targeting one) where the revenue will only be counted against the last campaign (with a priority for ads clicked vs viewed) a user clicks on a Facebook ad, but then clicks on another advert (maybe Adwords) before buying. Facebook doesn’t know about this 2nd advert, so will attribute all the revenue to the Facebook ad. GA knows better, and will attribute all (or part) of it to Adwords. 5. Facebook cross-device tracking The main advantage Facebook has over GA is that users log in to its platform across all of their devices, so it can stitch together the view of a mobile advert on day 1 with a purchase made from the user’s desktop computer on day 2. Here’s a fuller explanation. By contrast, unless that user logs into your website on both devices, and you have cross-device tracking setup, GA won’t attribute the sale to Facebook. 6. Date of click vs date of purchase In Facebook, revenue is attributed to the date the user saw the advert; in GA it is to the date of purchase. So if a user clicks on the advert on 1st September, and then buys on the 3rd September, this will appear on the 1st on Facebook – and on the 3rd in GA. 7. The sampling problem Finally, did you check if the GA report is sampled? In the top right of the screen, in the grey bar, you'll see that the report is based on a sample.  If that sample is less than 100% it means the numbers you see are estimates.  The smaller the sample size used, the larger the possibility of error.  So in this example, a 45% sample of 270,000 sessions could skew our results plus or minus 0.2% in the best case. As a rule of thumb, Google applies sampling when looking over more than 500,000 sessions (even if you select the 'greater precision' option from the drop-down menu). You can check your own sample using this confidence interval calculator. Conclusion Altogether, there’s a formidable list of reasons why the data will never be an exact match, but I hope it gives you a way to optimise the tracking. Please let us know if you’ve seen other tracking issues aside from these.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-08

WWI Codebreaking and Interpretation

Reading Max Hasting’s excellent book on The Secret War, 1939-1945, I was struck by the parallel between the rise of radio communications in the 1930s and the more recent rise in internet data. The transmission of military and diplomatic messages by radio in the 1930s and 1940s provided intelligence agencies with a new gold mine. Never before had so much potential intelligence been floating in the ether, and yet it threatened to flood their limited manpower with a tide of trivia. The bottleneck was rarely in the interception (trivial with a radio set) or even decryption (made routine by Bletchley Park with the Enigma codes), but rather in filtering down to the tiny number of messages that contained important facts – and getting that information in real time to the commanders in the field. The Ultra programme (Britain’s decryption of German radio intercepts) was perennially understaffed due to the fact that other civil servants couldn’t be told how important it was. At Ultra’s peak in 1943, only around 50% of the 1,500 Luftwaffe messages a day were being processed – and it is unknown how many of those were in time to avert bombing raids. The new age of technology provided an almost infinitely wide field for exploration, as well as the means of addressing this: the trick was to focus attention where it mattered. The Secret War, page 203 The ‘new age of technology’ in the last two decades poses much the same problem. Data on internet behaviour is abundant: there are countless signals to listen to about your website performance, and the technology to monitor users is commonplace. And the bottleneck is still the same: the filtering of useful signals, and getting those insights to the ‘commanders’ who need them in real time. I started Littledata to solve this modern problem in interpreting website analytics for managers of online businesses. There is no decryption involved, but there is a lot of statistics and data visualisation know-how in making billions of data points appreciable by a company manager. Perhaps the most important aspect of our service is to provide insights in answer to a specific question: Group-Captain Peter Stewart, who ran the Royal Air Force’s photo-reconnaissance operations, was exasperated by a senior offer who asked for ‘all available information’ on one European country. Stewart responded that he could only provide useful information if he knew roughly what intelligence the suppliant wanted – ‘naval, military, air or ecclesiastical’. The Secret War, page 203 In the world of online commerce, the question is something like whether the client needs insights into the checkout conversion rate of all customers (to improve site design) or for a specific marketing campaign (to improve campaign targeting). So by focusing on insights which are relevant to the scale, stage or sector of the client company, and making these accessible in a real-time dashboard, Littledata can feed into decision making in a way that raw data can never do. Want to discuss this further? Get in touch or comment below!   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2017-02-01

Enhanced ecommerce tracking for travel booking sites

Every online business presence has a goal. These goals (bookings, donations, subscribers, events, or purchases) are the reason for our efforts. But how many of us really track how our goals really perform? In this article, you will find out how to take these business goals and track them on Google Analytics with an ecommerce approach. This article is not about how to set up goals in Google Analytics, but if you are interested in finding out more about the setup or what there are, then read: Setting up a destination goal funnel in Google Analytics. The advantage of using an ecommerce approach for non-ecommerce websites is that after the setup is done, you have a basis to develop correct marketing strategies. You will know what channels brings you money, you will know what channels interact with each other and you can adjust your budget to maximise the ROI. If you're in the business of selling tickets (planes, concerts, conferences), book medical exams or collect donations, this article concerns you! I will show you a step-by-step guide on where to implement the Enhanced Ecommerce features and I will provide links for each to find out how to implement them. Let's say you are Wizz Air. You sell flight tickets and book cars and so on. Promotion impressions and promotion clicks Each time Wizz Air displays a banner with some kind of marketing communication that banner can be tracked as a "promotion" in Google Analytics. In Google Analytics, you can see the performance of each banner and make decisions to replace them, change the order or even make them bigger based on the tracking you implement. The technicalities: implementing via Google Tag Manager or implementing via Google Analytics. After you implement the tracking and create the tags (for GTM) you will be able to see the data in Google Analytics under Ecommerce > Marketing > Internal Promotions Based on the position, click-thru-rate, and revenue gained for each, Wizz Air can then rearrange banners, eliminate some of them or boost their visibility. Ecommerce activities (catalogue views, service page views, click on call to actions) Wizz Air provides multiple sections on the website where you can search for flights. These sections can be mapped as product lists. For WizzAir, the product lists are in the homepage section, timetable section, and maps section. Typically, Google Analytics and Google Tag Manager requests the fields below when sending a product list view (product impressions). I will provide you with a schema that will capture the flight booking particularities but you can use your own business specific examples. When you click on a red point on the map, the customer can see the flights from a particular city. We will send all the flight information from that city as product impressions. 'id': 'LTN - PRG',                          // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',             // City name of departure - City name for arrival 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                      // If the page has the option to add the date we will add the date as a MMDDYY When the search button is present, you send the action "click". ga('ec:setAction', 'click', {                                    // click action. 'list': 'Maps'                                                          // Product list (string). }); After searching, the client can see the selection page from the product list. For Wizz Air customers, they can search the best price and see the package options. In the case of Wizz Air, these pages can be considered the product pages. The usual structure that needs to be sent to Google Analytics and Google Tag Manager is: 'id': 'LTN - PRG',                                    // The departure airport code - The arrival airport code 'name': 'London Luton - Prague',          // City name of departure - City name for arrival 'category': 'Flight',                                 // WizzAir offers flight booking along with car booking, and hotel booking 'brand': 'WizzAir',                               // If this would be a tourism agency instead of WizzAir will be other company. 'variant': '010117',                             // If the page has the option to add the date we will add the date as a MMDDYY Each time the client changes the day a new detail view should be sent. Clicking on the price box will trigger an Add to cart action. The usual content of an Add To cart activity is: 'name': 'London Luton - Prague',    // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                               // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                          // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                        // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                         //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Flight number 'dimenstion2': 'WizzGO'              // Package option (Basic, Wizz Go, Wizz Plus) Check out steps and booking In the case of Wizz Air, each "continue" button will send a checkout step to Google Analytics. Sending the checkout steps will provide insights about where the customers drop off and what process steps can be improved. Wizz Air has a 4-steps checkout (choose flight, choose passengers, services, and payment). The final thing to send is the transaction (the booking). The structure and implementation details for Google Analytics and Google Tag Manager are in the links and the fields, in this case, will be: 'ecommerce': { 'purchase': { 'actionField': { 'id': 'T12345',                                           // Transaction ID. Required for purchases and refunds. 'affiliation': 'booking.com'                    // Affiliation agent, 'revenue': '35.43',                                 // Total booking value (incl. tax, airport fees etc) 'tax':'4.90', 'shipping': '5.99',                                 //can use this field to capture airport fees or thir party operators fees 'coupon': 'SUMMER_SALE'              //if a discount cupon was used }, 'products': [{                                      //if the flight has a return flight then two products will be sent 'name': 'London Luton - Prague',     // The departure airport code - The arrival airport code 'id': 'LTN - PRG',                                // City name of departure - City name for arrival 'price': '61.99',                                  // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '010117',                          //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN13432',           // Fligh number 'dimenstion2': 'WizzGO'               // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'         // Optional fields may be omitted or set to empty string. }, { 'name': 'Prague -London Luton',    // The departure airport code - The arrival airport code 'id': 'PRG -LTN',                               // City name of departure - City name for arrival 'price': '61.99',                                 // Selected price for the flight 'brand': 'WizzAir',                           // If this would be a tourism agency instead of WizzAir will be other company. 'category': 'Flight',                         // WizzAir offers flight booking along with car booking, and hotel booking 'variant': '150117',                        //If the page has the option to add the date we will add the date as a MMDDYY 'quantity': 1'                                   // Person number 'dimenstion1': 'LTN2143432',        // Flight number 'dimenstion2': 'WizzGO'             // Package option (Basic, Wizz Go, Wizz Plus) 'coupon': 'SUMMER_SALE'        // Optional fields may be omitted or set to empty string. }] } } Sending all these steps to Google Analytics about the customer activity, on any kind of website, will provide you with information about return on marketing spends, improve page layout performance, improve conversion rate, find out insights about customer needs and a lot more. Having the full enhanced ecommerce setup is very powerful and can bring many advantages. You can test the full setup on the Google Analytics demo account. Have any questions or need some help? Please get in touch or comment below!  

2017-01-24

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

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

Android users buy 4x more than Apple users. Why?

Looking at a sample of 400 ecommerce websites using Littledata, we found mobile ecommerce conversion rates vary hugely between operating systems. For Apple devices, it is only 1% (and 0.6% for the iPhone 6), whereas for Android devices the conversion rate is nearly 4% (better than desktop). It’s become accepted wisdom that a great ‘mobile experience’ is essential for serious online retailers. As 60% of all Google searches now happen on mobile, and over 80% of Facebook ad clicks come from mobile, it’s highly likely the first experience new customers have of your store is on their phone. So is it because most websites look worse on an iPhone, or iPhone users are pickier?! There’s something else going on: conversion rate on mobile actually dropped for these same sites from July to October (1.25% to 1.26%) this year, even as the share of mobile traffic increased. Whereas on desktop, from July (low-season) to October (mid-season for most retailers), the average ecommerce conversion rate jumped from 2% to 2.5%. It seems during holiday-time, consumers are more willing to use their phones to purchase (perhaps because they are away from their desks). So the difference between Android and iOS is likely to do with cross-device attribution. The enduring problem of ecommerce attribution is that it’s less likely that customers complete the purchase journey on their phone. And on an ecommerce store you usually can’t attribute the purchase to the initial visit on their phone, meaning you are seriously underestimating the value of your mobile traffic. I think iPhone users are more likely to own a second device (and a third if you count the iPad), and so can more easily switch from small screen browsing to purchase on a large screen. Whereas Android users are less likely to own a second device, and so purchase on one device. That means iPhone users do purchase – but you just can’t track them as well. What’s the solution? The only way to link the visits on a phone with the subsequent purchases on another device is to have some login functionality. You can do that by getting users to subscribe to an email list, and then linking that email to their Google Analytics sessions. Or offering special discounts for users that create an account. But next time your data tells you it’s not worth marketing to iPhone users, think again. Need help with your Google Analytics set up? 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.  

2016-11-02

Making the detection of significant trends in your traffic easier to see

Our core belief at Littledata is that machines are better at spotting significant changes in your website’s performance than a human analyst. We’ve now made it easier for you to get specific alerts, reducing the time spent wading through data. This is the story of how we produced the new trend detection algorithm. Enjoy! Back in 2014, we developed the first version of an algorithm to detect if today or this week’s traffic was significantly different from previous periods. This allows managers to focus in on the aspects of the traffic or particular marketing campaigns which are really worthy of their attention. Although the first version was very sensitive, it also picked up too many changes for a single person to investigate. In technical language, it was not specific in enough. In June and July, Littledata collaborated with a working group of mathematicians from around Europe to find a better algorithm. The European Study Group with Industry (ESGI) originated in the University of Limerick’s mathematics department in Ireland and has helped hundreds of businesses link up with prominent mathematicians in the field to solve real-world problems. Littledata joined the latest study group in University College, Dublin in July, and was selected by a dozen mathematicians as the focus for their investigation. Andrew Parnell from the statistics department at University College, Dublin helped judge the output from the four teams that we split the group into. The approach was to use an algorithm to test the algorithms; in other words, we pitted a group of statistical strategies against each other, from clustering techniques to linear regression, through to Twitter’s own trend detection package, and compared their total performance across a range of training data sets. Initially, the Twitter package looked to be doing well, but in fact, it had been developed specifically to analyse huge volumes of tweets and perform badly when given low volumes of web traffic. In between our host’s generous hospitality, with Guinness, Irish folk music, and quite a lot of scribbling of formulas on beer mats, myself and our engineer (Gabriel) worked with the statisticians to tweak the algorithms. Eventually, a winner emerged, being sensitive enough to pick up small changes in low traffic websites, but also specific enough to ignore the random noise of daily traffic. The new trend detection algorithm has been live since the start of August and we hope you enjoy the benefits. Our web app allows for fewer distractions and more significant alerts tailored to your company’s goals, which takes you back to our core belief that machines are able to spot major changes in website performances better than a human analyst. If you’re interested in finding out how our web app can help you streamline your Google Analytics’ data, please get in touch! Further reading: 7 quick wins to speed up your site analysis techniques Online reporting turning information into knowledge Will a computer put you out of a job?

2016-09-08

How to use Enhanced Ecommerce in Google Analytics to optimise product listings

Ecommerce reporting in Google Analytics is typically used to measure checkout performance or product revenue.  However, by analysing events at the top of the funnel, we can see which products need better images, descriptions or pricing to improve conversion. Space on product listing pages is a valuable commodity, and products which get users to click on them – but don’t then result in conversion – need to be removed or amended.  Equally, products that never get clicked within the list may need tweaking. Littledata ran this analysis for a UK retailer with Google Analytics Enhanced Ecommerce installed.  The result was a scatter plot of product list click-through-rate (CTR) – in this case, based on the ratio of product detail views to product listing views – versus product add-to-cart rate.  For this retailer, it was only possible to buy a product from the detail page. We identified three problem categories of product, away from the main cluster: Quick sellers: these had an excellent add-to-cart rate, but did not get enough list clicks.  Many of them were upsell items, and should be promoted as ‘you may also like this’. Poor converters: these had high click-through rates, but did not get added to cart. Either the product imaging, description or features need adjusting. Non-starters: never get clicked on within the list. Either there are incorrectly categorised, or the thumbnail/title doesn’t appeal to the audience.  They need to be amended or removed. How we did it Step 1 - Build a custom report in GA We need three metrics for each product name (or SKU) - product list views, product detail views and product add to carts - and then add 'product' as a dimension. Step 2 - Export the data into Excel Google Analytics can't do the statistical functional we need, so Excel is our favoured tool.  Pick a decent time series (we chose the last three months) and export. Step 3 - Calculate List > Detail click through This website is not capturing Product List CTR as a separate metric in GA, so we need to calculate as Product Detail Views divided by Product List Views.  However, our function will ignore products where there were less than 300 list views, where the rate is too subject to chance. Step 4 - Calculate Detail > Add to Cart rate Here we need to calculate Product Adds to Cart divided by Product Detail Views.  Again, our function will ignore products where there were less than 200 detail views. Step 5 - Exclude outliers We will use an upper and lower bound of the median +/- three standard deviations to remove improbable outliers (most likely from tracking glitches). First we calculate the median ( =MEDIAN(range) ) and the standard deviation for the population ( =STDEV.P(range) ).  Then we can write a formula to filter out all those outside of the range. Step 6 - Plot the data Using the scatter plot type, we specify List > Detail rate as the X axis and Detail > Add to Cart as the Y axis. The next step would be to weight this performance by margin contribution: some poor converters may be worth keeping because the few sales they generate are high margin. If you are interested in setting up Enhanced Ecommerce to get this kind of data or need help with marketing analytics then please get in contact.   Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.

2016-03-31

5 myths of Google Analytics Spam

Google Analytics referral spam is a growing problem, and since Littledata has launched a feature to set up spam filters for you with one click, we’d like to correct a few myths circulating. 1. Google has got spam all under control Our research shows the problem exploded in May – and is likely to get worse as the tactics get copied. From January to April this year, there were only a handful of spammers, generally sending one or two hits to each web property, just to get on their reports. In May, this stepped up over one thousand-fold, and over a sample of 700 websites, we counted 430,000 spam referrals – an average of 620 sessions per web property, and enough to skew even a higher traffic website. The number of spammers using this tactic has also multiplied, with sites such as ‘4webmasters.org’ and ‘best-seo-offer.com’ especially prolific. Unfortunately, due to the inherently open nature of Google Analytics, where anyone can start sending tracking events without authentication, this is really hard for Google to fix. 2. Blocking the spam domains from your server will remove them from your reports A few articles have suggested changing your server settings to exclude certain referral sources or IP addresses will help clear us the problem. But this misunderstands how many of these ‘ghost referrals’ work: they are not actual hits on your website, but rather tracking events sent directly to Google’s servers via the Measurement Protocol. In this case, blocking the referrer from your own servers won’t do a thing – since the spammers can just go directly to Google Analytics.  It's also dangerous to amend the htaccess file (or equivalent on other servers), as it could prevent a whole lot of genuine visitors seeing your site. 3. Adding a filter will remove all historic spam Filters in Google Analytics are applied at the point that the data is first received, so they only apply to hits received AFTER the filter is added. They are the right solution to preventing future spam, but won’t clean up your historic reports. To do that you also need to set up a custom segment, with the same source exclusions are the filter. You can set up an exclusion segment by clicking 'Add Segment' and then red 'New Segment' button on the reporting pages and setting up a list of filters similar to this screenshot. 4. Adding the spammers to the referral exclusion list will remove them from reports This is especially dangerous, as it will hide the problem, without actually removing the spam from your reports. The referral exclusion list was set up to prevent visitors who went to a different domain as part of a normal journey on your website being counted as a new session when they returned. e.g. If the visitor is directed to PayPal to pay, and then returns to your site for confirmation, then adding 'paypal.com' to the referral exclusion list would be correct. However, if you add a spam domain to that list then the visit will disappear from your referral reports... but  still, be included under Direct traffic. 5. Selecting the exclude known bots and spiders in the view setting will fix it Google released a feature in 2014 to exclude known bots and spiders from reports. Unfortunately, this is mainly based on an IP address - and the spammers, in this case, are not using consistent IP addresses, because they don't want to be excluded. So we do recommend opting into the bot exclusion, but you shouldn't rely on it to fix your issue Need more help? Comment below or get in touch!

2015-05-28

How to audit your Web Analytics Ecommerce tracking

Most companies will see a discrepancy between the transaction volumes recorded via web analytics and those recorded via internal sales or financial database. This article focuses on how to find and reduce that discrepancy, to give greater credibility to your web analytics data. Following on from our article on common Google Analytics setup problems, we are often asked why Google Analytics ecommerce tracking is not a 100% match with other records, and what is an acceptable level of difference. Inspired by a talk from Richard Pickett at Ensighten, here is a checklist to run through to reduce the sources of mismatch. The focus here is Google Analytics Ecommerce tracking, but it could apply to other systems. In summary, you wouldn’t ever expect there to be a 1:1 match, due to the different paths the two events take over the internet. The general consensus is that anything less than 4% of difference in transaction volumes is good, but could sometimes persist up to 10%. Factors that affect this target rate include how many users have got ad blockers or disable Google Analytics (popular in Germany, for example), what proportion are on mobile devices (which suffer from more network interruptions) and how the purchase thank you / confirmation page is built. So on to the list. 1. Are other Javascript errors on the page blocking the ecommerce event in certain situations? The most common reason for the tracking script not executing in the browser is that another bug on your page has blocked it (see GDS research). The bug may only be affecting certain older browsers (like Internet Explorer 7), and have missed your own QA process, so the best approach is to use Google Tag Manager to listen for any Javascript error events on the confirmation page and send these to Google Analytics as custom events. That way your users do the testing for you, and you can drill into exactly which browsers and versions the bugs are affecting. 2. Is the tracking code as far up the page as it could be? If the user drops their internet connection before the whole page loads then the ecommerce event data won’t get a chance to fire. The best approach is to load the script at the bottom of the <head> element or top of the <body>.  The Google Analytics script itself won't block the page load, and arguably in this one purchase confirmation page, the tracking is more important than the user experience. 3. Is the tracking code firing before all the page data has loaded? The inverse of the previous problem: you may need to delay firing the tracking code until the data is ready. This is particularly an issue if your ecommerce transaction data is ‘scraped’ from the HTML elements via Google Tag Manager. If the page elements in question have not loaded before the ecommerce tracking script runs, then the product names, SKUs and prices will be empty – or returning an error. 4. Is the problem only your ecommerce tracking script or just page tracking is general? It could be that the way you are sending the transaction data (e.g. product name, price, quantity) is the problem, or that the page tracking overall is failing in some cases. You can pinpoint where the problem lies by comparing the pageviews of the confirmation page, with the number of ecommerce events tracked. Caveat: on many sites, there’s another route to seeing the purchase confirmation page, which doesn’t involve purchasing (for example as a receipt of a historic purchase). In that case, you may need to capture a unique purchase event, which only fires when a new purchase is confirmed – but without any information on the transaction or products. 5. Are events from your test site excluded? Most companies will have a development, staging or user acceptance testing server to where the website is tested, and test users can purchase.  Are you blocking the tracking from these test sites? Some possible ways to block the test site(s) would be: Set up sub-domain specific blocking rules in Google Tag Manager (or better) Divert the tracking from your test subdomains to a test Google Analytics account, using a lookup macro/variable Set up filters in the Google Analytics view to exclude 6. Is your tag set with a high priority? Tag manager only. If you use Google Tag Manager and have multiple tags firing on the tracking page it’s possible that other tags are blocking your ecommerce data tag from firing. Under ‘Advanced settings’ in the tag editor, you can set a higher priority number for tag firing; I assume the ecommerce data to Google Analytics is always the first priority. 7. Are any strings in the product name properly escaped? A common problem is apostrophes: if your product name contains a quote mark character, then it will break the following Javascript. See Pete’s bunnies – the strings in yellow are valid, and everything after the stray apostrophe will be misinterpreted. The solution is to run a script across any text field to either strip out the quotation marks or replace any quotes with their HTML equivalent (eg &quot;). 8. Are your quantities all integers? One of our clients was selling time slots, and so had the ‘quantity’ of the ecommerce tracking data equivalent to a number of hours. Timeslots sold in half-hours (e.g. 1.5 hours) were not tracking… because Google Analytics only recognises a quantity which is a whole number, so sending ‘1.05’ will not be recognised as 1. 9. Are any possible ‘undefined’ values handled? It may be that the data on your products is incomplete, and some products that people buy do not have a name, price or SKU. The safest approach is to have some fall-back values in your Javascript tracking code to look for undefined or non-text variables and post a default value to Google Analytics. E.g. If ‘product name’ is undefined then post ‘No product name’, or for price, the default should be ‘0.00’. These will then clearly show up in your Ecommerce Product performance reports and the data can be cleaned up. 10. Are users reloading the page and firing duplicate tracking events? Check whether this is a problem for your site by using our duplicate transactions custom report to see multiple events with the same transaction ID. A solution is to set a ‘has tracked’ cookie after the ecommerce tracking has been sent the first time, and then check whether the cookie is set before sending again. 11. Are users going back to the page and firing the tracking at a later date? The sessions column in the transactionID report in step 9 should give you an idea of whether the problem is repeat page loads in one session, or users revisiting the page in another session. If you see duplicate transaction IDs appearing in other sessions there are a couple of possibilities to investigate: Could users be seeing the page again by clicking on a link to an email, or from a list of historic orders? Are there any back-end admin pages that might link to the confirmation page as a receipt? In both cases, the solution is to have a different URL for the receipt that the one where the ecommerce tracking is fired. If there are any other troubleshooting steps you have found helpful, please let us know in the comments or get in touch!  

2015-03-17

Complete picture of your ecommerce business

From marketing channels to buying behaviour, Littledata is the ultimate Google Analytics toolbox.

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