Category : Analytics Setup
The World Cup guide to marketing attribution
It’s World Cup fever here at Littledata. Although two of the nationalities in our global team didn’t get through the qualifiers (US & Romania) we still have England and Russia to support in the next round. And I think the World Cup is a perfect time to explain how marketing attribution works through the medium of football. In football (or what our NYC office calls 'soccer'), scoring a goal is a team effort. Strikers put the ball in the net, but you need an incisive midfield pass to cut through the opposition, and a good move starts from the back row. ‘Route one’ goals scored from a direct punt up the pitch are rare; usually teams hit the goal from a string of passes to open up the opportunity. So imagine each touch of the ball is a marketing campaign on your site, and the goal is a visitor purchasing. You have to string a series of marketing ‘touches’ together to get the visitor in the back of the net. For most ecommerce sites it is 3 to 6 touches, but it may be more for high value items. Now imagine that each player is a different channel. The move may start with a good distribution from the Display Ads defender, then a little cut back from nimble Instagram in the middle. Facebook Ads does the running up the wing, but passes it back to Instagram for another pass out to the other wing for Email. Email takes a couple of touches and then crosses the ball inside for AdWords to score a goal – which spins if off the opposing defender (Direct). GOAL!!! In this neat marketing-football move all the players contribute, but who gets credit for the goal? Well that depends on the attribution model you are using. Marketing attribution as a series of football passes Last interaction This is a simplest model, but less informative for the marketing team. In this model the opposing defender Direct gets all the credit – even though he knew nothing about the end goal! Last non-direct click This is the attribution model used by Google Analytics (and other tools) by default. In this model, we attribute all of the goal to the last campaign which wasn’t a Direct (or session with unknown source). In the move above this is AdWords, who was the last marketing player to touch the ball. But AdWords is a greedy little striker, so do we want him to take all the credit for this team goal? First interaction You may be most interested in the campaign that first brought visitors to your website. In this model, Display ads would take all the credit as the first touch. Display often performs best when measured as first interaction (or first click), but then as a ‘defender’ it is unlikely to put the ball in the net on its own – you need striker campaigns as well. Time decay This model shares the goal between the different marketing players. It may seem weird that a player can have a fraction of a goal, but it makes it easy to sum up performance across lots of goals. The player who was closest to the goal gets the highest share, and then it decays as we go back in time from the goal. So AdWords would get 0.4, Email 0.5 (for the 2 touches before) and Instagram gets 0.1. [subscribe] Data-driven attribution This is a model available to Google Analytics 360 customers only. What the Data-driven model does is run through thousands of different goals scored and look at the contribution of each player to the move. So if the team was equally likely to score a goal without Facebook Ads run down the wing it will give Facebook less credit for the goal. By contrast, if very few goals get scored without that pass from Instagram in the midfield then Instagram gets more credit for the goal. This should be the fairest way to attribute campaigns, but the limitation is it only considers the last 4 touches before the goal. You may have marketing moves which are longer than 4 touches. Position based Finally you can define your own attribution weighting in Position Based model, based on which position the campaign was in before the goal. For example, you may want to give some weight to the first interaction and some to the last, but little to the campaigns in between. Still confused? Maybe you need a Littledata analytics expert to help build a suitable model for you. Or the advice of our automated coach known as the analytics audit. After all, every strategy could use a good audit to make sure it's complete and up-to-date. So go enjoy the football, and every time someone talks of that ‘great assist’ from the winger, think of how you can better track all the uncredited marketing campaigns helping convert customers on your site.
Google Analytics Data Retention policy - which reports does it limit?
From 25th May 2018 Google allowed you to automatically wipe user-level data from the reporting from before a cut-off date, to better comply with GDPR. We made the change for Littledata's account to wipe user-level data after 26 months, and this is what we found when reporting before February 2016. Reports you can still view before the user data removal Audience metrics Pageviews ✓ Sessions ✓ Users X Bounce rate ✓ Audience dimensions Demographics X OS / Browser X Location X User Type X Behaviour Pageviews ✓ Custom events X
Troubleshooting your Google Analytics goals setup (VIDEO)
https://www.youtube.com/watch?v=SGY013J9QGg So you've got your new sales plan in action and you've set up unique goals in Google Analytics. Are they tracking what you think they're tracking? Are you sure they're giving you reliable data? If you've audited your analytics setup, you might have noticed any number of incorrect audit checks about how you've set up custom events for your Google Analytics (GA) goals. Goals are used to make important business decisions, such as where to focus your design or advertising spend, so it's essential to get accurate data about them. In this quick video we cover common issues with setting up Google Analytics goals, including: Tracking pageviews rather than completed actions Selecting the wrong match type Inconsistent naming when tagging marketing campaigns Filters in your GA view rewriting URLs (so what you see in the browser is different from what you see in GA) Issues with cross-domain tracking [subscribe] In GA, a goal is any type of completed activity on your site or app. GA is a remarkably flexible platform, so you can use it to measure many different types of user behaviour. This could be visitors clicking a subscribe button, completing a purchase, signing up for membership -- known as 'conversion goals' -- or other types of goals such as 'destination goals', when a specific page loads, and 'duration goals', when a user spends over a particular amount of time on a page or set of pages. That all sounds well and good, but trouble comes if you simply set up goals and then trust the data they give you in GA, without double-checking to make sure that data's consistent and reliable. We hope you find the video useful. And don't despair -- even a little extra time spent on your GA setup can yield awesome results. Sign up for the Littledata app to audit your site for free, and let us know if you've experienced other common issues with setting up goals in GA.
GDPR compliance for ecommerce businesses
Ecommerce companies typically store lots of personally identifiable information (PII), so how can you make compliance easier without compromising analysis? With GDPR compliance now a prerequisite for doing business with European customers online, I wanted to expand on my previous article on GDPR and Google Analytics to focus on ecommerce. Firstly, who does this apply to? GDPR is European Union legislation that applies to any company trading in Europe: so if you sell online and deliver to European Union member countries, the regulations apply to you. It's essential that you understand how your online business is collecting and storing PII. Splitting PII from anonymous data points Your goal should be to maintain two separate data stores: one that contains customer details, from where you can look up what a specific customer bought, and one that contains anonymous data points, from where you can see performance and trends. The data store for the customer details will typically be your ecommerce back-end and/or CRM (see below). This will include name, email, address, purchase history, etc. It will link those with a customer number and orders numbers. If a customer wants the right of access all the relevant details should be in this store. We use Google Analytics as the anonymous data store (although you may have a different ecommerce analytics platform). There you can store data which only refers to the customer record. These are called pseudo-anonymous data points under GDPR: they are only identifiable to a customer if you can link the customer number or order number back to your ecommerce back-end. Pseudo-anonymous data points you can safely send to Google Analytics include: Order number / transaction ID Order value / transaction amount Tax & shipping Product names and quantities Customer number Hashed email address (possibly a more flexible to link back to the customer record) If a customer exercises their right to removal, removing them from the ecommerce back-end will be sufficient. You do not also have to remove them from your Google Analytics, since the order number and customer number now have nothing to refer to. You do still need due process to ensure access to Google Analytics is limited, as in extreme circumstances a combination of dimensions such as products, country / city and browser, could identify the customer. [subscribe] Isn’t it simpler to just have one store? Every extra data store you maintain increases the risk of data breaches and complexity of compliance – so why not just analyse a single customer data store? I can think of three reasons not to do so: Marketing agencies (and other third parties) need access to the ecommerce conversion data, but not the underlying customer data Removing a customer’s order history on request would impact your historic revenue and purchase volumes – not desirable Your CRM / ecommerce platform is not built for large scale analysis: it may lack the tools, speed and integrations needed to get meaningful insights Beware of accidental transfers There are a few danger areas where you may inadvertently be sending PII data to Google Analytics: Customer emails captured in a signup event A customised product name – e.g. ‘engraving for Edward Upton’ Address or name captured in a custom dimension Our PII audit check is a quick, free way to make sure that’s not happening. Multiple stores of customer details GDPR compliance becomes difficult when your customer record is fragmented across multiple data stores. For example, you may have product and order information in your ecommerce database, with further customer contact details in a CRM. The simplest advice is to set up automatic two-way integrations between the data stores, so updating the CRM updates the ecommerce platform and visa-versa. Removing customer records from one system should remove them from the other. If that’s not possible, then you need clear processes to update both systems when customer details change, so you can comply with the right to rectification. Conclusion GDPR compliance need not require changing analytics tools or databases, just a clear process for separating out personally identifiable information – and training for the staff involved in handing that data. I hope this brief overview has been helpful. For further advice on how your ecommerce systems comply, please contact us for a free consultation. Littledata has experience with every major analytics platform and a wide range of custom setups. However, as a number of global companies are concurrently prepping for compliance, we highly recommend that you get in touch sooner rather than later!
How to set up demographics tracking in Google Analytics (VIDEO)
Could you be missing out on your best customers - those that are more likely to convert, and more likely to make big purchases when they do? Watch this quick video to find out how to to set up demographics tracking in Google Analytics. [embed]https://www.youtube.com/watch?time_continue=5&v=PAeCubNxoKI[/embed] Demographics and interests data provides information about the types of customers that are using your site, along with the interests they express through their online travel and purchasing activities. Once you set up this tracking, you'll be able to see your customer base broken down by age group, gender and interests. This data isn't just nice to have; it helps you market to the biggest potential spenders by discovering who's most interested in your products or services. Analytics and AdWords use the same age, gender, and interests categories, so this is particularly useful for improving your targeting on the Google Display Network. [subscribe] That said, connecting demographics data with shopping activity and revenue is a complicated art. Our popular Buyer Personas feature automates reporting and shows you how to improve that spend. And we don't just stop with paid ads. We include personas for every significant channel, including email marketing, organic search, affiliates/referrals and social media campaigns. Wherever you want to use demographics targeting to increase revenue, we've got you covered.
How to set up campaign tagging in Google Analytics (VIDEO)
https://www.youtube.com/watch?v=YVxi0sQmro0&t=5s Google Analytics is only as smart as your tagging. To lower average CPA and increase conversions in a sustainable way, you need an in-depth view of customer acquisition channels. Accurate campaign tagging makes it possible to get the data needed to understand acquisition costs based on particular source and medium. If you want to improve marketing ROI, it's essential to get campaign tagging right in Google Analytics. But how does it all work? Follow the simple rules in this quick how-to video to make sure you're getting accurate data about where your traffic is coming from. [subscribe] Questions addressed in the setup video: What is a campaign in Google Analytics (GA)? What is UTM Parameter and how do I use it? Is it possible that a large volume of my 'Direct' traffic in GA is actually coming from sources such as email or social, but just wasn't tagged correctly? How do I know? I want to see all email marketing campaign traffic as one line item in my GA reports. Do spellings matter? Are UTM parameters case-sensitive? What are the best practices for GTM tagging using the Google Analytics Link Builder? For more info on custom campaign tracking, check out this detailed post about campaign parameters and how to use them. Remember that when you set up new campaigns or marketing channels, things can change or get lost in the mix. It's important to keep an eye on your analytics setup. Even once you've successfully set up campaign tagging in GA, we recommend auditing your analytics on a regular basis. And don't stop there. Once you've established data accuracy, follow in the footsteps of the most successful ecommerce sites and use Buyer Personas to get a clear view of which types of customers are more likely to convert in each channel. Now that's smart growth, driven by data!
Is Google Analytics compliant with GDPR?
How to see shopping behaviour for each product you sell (VIDEO)
Product performance can seem confusing, but it doesn't have to be. In this quick video, we show you how to use Google Analytics to see shopping behaviour related to each product you're selling. All you'll need to see this report is a site connected to Google Analytics with the Enhanced Ecommerce plugin setup. [embed]https://www.youtube.com/watch?v=YVGAdHTkw3s[/embed] Using the Shopping Behavior report in Google Analytics Whether your ecommerce site is large or small, the Shopping Behavior report makes it easy to drill deep into user behaviour to understand why some products are converting better than others. If a particular product isn't selling well, the Shopping Behavior report will help you figure out why. It shows how far shoppers engage with your products, from initial list views through to shopping cart activities. [subscribe] Reasons a product might not be selling well It isn't at an optimal place in a product list or display The product details, such as images and description, aren't sending the right message Customers are abandoning their shopping carts completely, or removing that particular product (or group of products, such as multiple pairs of jeans) after adding it Who knows? You haven't audited your Google Analytics setup lately so your customer behaviour data can't be trusted to help you improve Each of those issues requires different actions, sometimes by entirely different departments (ie. marketing, pricing, ux)! That's what makes the Shopping Behavior report so important for improving ecommerce sales and conversions. We hope you enjoyed this latest video in our series of Google Analytics how-to guides. Need help setting up Enhanced Ecommerce in Google Analytics, or ensuring that your data is accurate? Contact a Littledata consultant today.
Subscribe to Littledata news
Insights from the experts in ecommerce analytics
Try the top-rated Google Analytics app for Shopify stores
Get a 30-day free trial of Littledata for Google Analytics or Segment