Category : marketing attribution
What's the real ROI on your Facebook Ads? [webinar]
Is your FB/Insta ad spend leading to high LTV customers? What happens after a shopper clicks on a link? One thing is clear: you've got to get the tracking right before you can start making data-driven decisions. Join Littledata and Beacon on Thursday, March 4th for a free webinar where we will explore the details of marketing attribution and Facebook campaign ROI. Pretty much all ecommerce brands today are using Facebook and Instagram ads as part of their digital marketing mix. When it comes to Facebook Ads, marketers are drawn to messaging about a strong return on investment. But are you measuring that return correctly? In this free webinar, you'll learn: Common issues with marketing attribution How to track post-click shopping behavior (what happens after someone clicks an ad) The importance of using external platforms for an unbiased view of marketing channels How to calculate complete ROI for your Facebook and Instagram Ad spend, including repeat purchases, refunds, and customer lifetime value (LTV) How benchmarking your site against similar brands can help make sense of the data Signup for the free webinar >>> About Littledata Littledata automatically fixes tracking for Shopify stores, offering complete marketing attribution, accurate sales data, and custom dimensions for lifetime value reporting. Check out our Shopify app for Google Analytics Learn more about our Shopify source for Segment Try Littledata's Facebook Ads connection free for 30 days Signup for the free webinar >>> About Beacon Beacon is the digital marketing campaign intelligence platform that is easy-to-use and presents real-time information based on data you can trust. It empowers marketers to accurately measure campaign results, take back control of their digital spend, and get a better ROI on their campaigns. Signup for the free webinar >>>
How to track Klaviyo flows and email campaigns in Google Analytics
Klaviyo is one of the most popular email marketing platforms for Shopify stores, but the analytics setup is often overlooked. By following a few simples rules, you can ensure accurate Klaviyo data alongside other sales and marketing data in Google Analytics. In this article we cover how to set up Google Analytics tracking for Klaviyo, including best practices for UTM parameters and dynamic variables, and how this tracking works alongside Littledata's Shopify to Google Analytics connection. Why Klaviyo Klaviyo is a popular customer engagement platform used by over 50,000 Shopify merchants. Their focus is on email and SMS automation, and they have been one of the major success stories in the Shopify ecosystem, recently closing a $200 million funding round. Klaviyo's features for Shopify include: Codeless signup forms Pre-built flow templates for quick automation Email campaigns for customers and leads Advanced segmentation and personalization, including product recommendations Many of Littledata's Shopify customers use Klaviyo in one way or another, as do almost all of our Shopify Plus customers. But we've noticed a trend where even the biggest Klaviyo users aren't correctly tracking Klaviyo flows in GA, which ends up blocking data-driven decisions for growth. Read on to see how to fix this. Why Google Analytics The Klaviyo dashboard has useful built-in reporting, but for ecommerce managers focused on more than just email, there are some significant limitations compared with a dedicated analytics platform like Google Analytics (GA). One key limitation is for sales attribution (marketing attribution for online sales). In Klaviyo, any sale that happens after engagement with an email is attributed to that email. This overstates Klaviyo's contribution to sales. For example, if a user first comes from a Facebook Campaign, then clicks on an abandoned cart email from Klaviyo, then goes on to complete a purchase after being retargeted in Facebook, Klaviyo will claim this as owned revenue attributed to that email engagement and credit Facebook with nothing! Another limitation of reporting in Klaviyo's dashboard is that it's hard to see the contribution of an entire email flow to sales, as opposed to the impact of a particular email message in the flow. In Google Analytics (if set up correctly) you can see multi-channel contribution to sales, comparing apples with apples across different marketing channels. What is UTM tracking? UTM parameters are extra data in the link the user clicks to tell Google Analytics (and Shopify) where the click came from. These parameters are automatically added by Google Ads, but for other platforms (e.g. Facebook or Klaviyo) you will need to add them manually or via the software. Why does this matter? Because link clicks coming without a UTM tag will typically be treated by GA as "direct" traffic -- in other words, the source of those visits will be unknown. [note]Read Littledata's free guide to common reasons Shopify doesn't match Google Analytics[/note] Recommended settings To provide the most reporting flexibility we recommend having the same standard UTM parameters across all email flows and campaigns. Klaviyo allows dynamic variables to be used in your default UTM tracking settings. To get the most out of your Klaviyo reporting in GA, we recommend using static values for Source and Medium, and dynamic values for Campaign and Content. You can change these defaults in go to Account > Settings > UTM Tracking UTM Parameter Campaign Email Value Flow Email Value Source (utm_source) 'Klaviyo' 'Klaviyo' Medium (utm_medium) 'email' 'email' Campaign (utm_campaign) Campaign name (Campaign id) Flow email name (Flow email id) Content (utm_content) Link text or alt text Link text or alt text [tip]Content is not a default parameter in Klaviyo, so you will need to add that manually (enter `utm_content` as a new parameter).[/tip] With static values for Source and Medium (Klaviyo / email), you will be able to see Klaviyo compared against other marketing channels in GA, and in particular how Klaviyo campaigns contribute to customer lifetime value and other key metrics for Shopify sales and marketing. We do not recommend sticking with Klaviyo's default UTM settings, where Klaviyo flows, for example, are given a dynamic variable that pulls in the name of the flow. You can already see that type of data in the Klaviyo analytics dashboard -- better to use GA for complete marketing analysis. Whichever naming convention you choose, consistency is essential. Many Littledata customers create internal spreadsheets to manage UTM naming conventions and channel groupings in GA, and run regular QA checks to ensure consistency. Note that we have analytics audit checks within the Littledata app, and we now offer analytics training on Plus plans. Enabling UTM parameters In addition to setting up the UTM Parameter values in your Klaviyo account, you need to enable UTM tracking to ensure that those parameters are applied to all emails in flows and campaigns. The first step is to enable global UTM settings. Go to Account > Settings > UTM Tracking Switch Automatically add UTM parameters to links to ON. Then click Update UTM Tracking Settings. This will ensure that the UTM parameters are added automatically to all emails sent via Klaviyo. Now that you have enabled UTM tracking, you need to make sure that you are using 'account defaults' for UTM tracking in your flows and email campaigns (as opposed to custom tracking). This should already be the case, but it's good to double-check. Disable any custom UTM tracking for flows or campaigns Make sure that the UTM settings for individual flows are set to 'Yes, use account defaults' Make sure that overall email campaign settings are set to use default UTM tracking as well. In your overall campaign settings, select 'Yes, use account defaults' In addition, when creating/editing a campaign, go to Tracking and make sure that 'Include tracking parameters' is ON and 'Customize tracking parameters' is OFF Tracking across all marketing channels The UTM settings above only solve part of the marketing attribution problem: getting the campaign information to the landing page. Commonly this marketing attribution is lost between the landing page and the order completing. You can try to do this manually with an in-house dev team, but Littledata has built a complete ecommerce tracking solution for Shopify and Google Analytics that works automatically. Our connections use a combination of client-side and server-side tracking to make sure that all marketing channels -- including email, paid channels, organic search and referrals -- are linked to sales, along with all touch points in between. We also track returns/refunds, repeat purchases, and subscriptions, so you can understand customer lifetime value on a deeper level. Read about all of the the events Littledata sends automatically. You can use these events for reporting and analysis, and also to build audiences for your Klaviyo campaigns! Reporting on Klaviyo flows in Google Analytics Google Analytics is a powerful reporting tool once you get to know how channel groupings and custom dimensions work. Here's a quick look at how to analyze your Klaviyo data in GA. Looking at campaign conversions in Google Analytics After you have enabled our recommended settings for UTM tags, you will have access to Klaviyo flow and campaign data in GA. You can look at this on its own, but also compared against other channels for engagement and acquisition. To see revenue and orders attributed to these campaigns, drill into the Klaviyo source and add campaign as a secondary dimension. If you set up the Flow email name as the utm_campaign above, then you can look at the contribution of that whole flow to sales. For example, without caring if the user clicked on email 1 or 2 in a 4-email flow, did clicking on any of the emails in that flow -- for example, the 'Browse Abandonment' flow -- result in sales? Going further, you could create a segment of users who came via an Instagram campaign, and see to what degree they were influenced by the email sequence. Will Google Analytics match Klaviyo? How does the data you now have in Google Analytics compare with what you see in your Klaviyo dashboard? Under the Conversions > Multi-Channel Funnels > Model Comparison Tool in GA, you can compare the default email attribution in GA (last non-direct click), with other attribution models more similar to Klaviyo's dashboard. Keep in mind that there is no model for 'all click' attribution, so the numbers you'll see in GA will always be lower. You can also look at the Multi-Channel Funnels > Top Conversion Paths report to see where Klaviyo fits into the user journey on your ecommerce site. [note]Google Analytics data can also be used as a source for other reporting tools, such as Data Studio and Tableau.[/note] Using Klaviyo with Segment If you are looking to do more with your Shopify and Klaviyo data, consider Segment. Littledata's Shopify source for Segment automatically sends a rich data set for use with a range of Segment destinations. Not only does our Segment connection get all of the post-click events into Segment, but it also sends any event associated with an email address onto Klaviyo as well -- providing a richer set of events, without a developer, than Klaviyo's own Shopify event tracking. For example, you can retarget users in Segment who have purchased a certain value, or got certain products to a stage of the checkout -- all without writing a line of code. Read more about how Littledata's Segment connection works, and check out the latest updates to our Shopify source for Segment. The connection now supports analytics destinations such as Mixpanel, Vero and Kissmetrics, and email marketing destinations including Klaviyo, Hubspot and Iterable. [subscribe]
Why does shop.app appear as a referral source in Google Analytics?
You may have noticed a new referral source appearing in your Google Analytics, or an increase in sales from the 'Referral' channel. This is a change Shopify made with the launch of the new Shop app, and can be easily fixed. What is Shop.app? SHOP by Shopify is a consumer mobile app, aggregating products and experiences from many Shopify merchants. It is heavily integrated with ShopPay, and so Shopify is now directing one-click checkout traffic to the shop.app domain instead of pay.shopify.com. How would SHOP fit into the user journey? There are two scenarios: 1. Customer is using Shop.app for checkout and payment Example journey: User clicks on Facebook Ad Lands on myshop.myshopify.com?utm_source=facebook Selects a product Logged in, and directed to shop.app for checkout Returns to myshop.myshopify.com for order confirmation In this scenario we should exclude shop.app as a referrer, as the original source of the order is really Facebook 2. End customer is using Shop.app for browsing / product discovery Example journey: User discovers product on shop.app Clicks product link to myshop.myshopify.com?utm_source=shop_app Logged in, and directed to shop.app for checkout Returns to myshop.myshopify.com for order confirmation Here, shop.app is the referrer but it will show up with UTM source How do I see the true source of the referral in Google Analytics? Firstly, you need to exclude shop.app as a referral source. Only in scenario 2 is SHOP genuinely a source of customers, and there the UTM source tag will ensure it appears as a referrer. Littledata's latest tracking script sets this up automatically. The second fix is harder. Unfortunately, at the time of writing, Shopify only sets utm_source=shop_app in the URL query parameters in scenario 2, and Google Analytics won't consider this a referral unless utm_medium is also set. So it appears under the (not set) channel. I've written a patch for our tracking script so that we set utm_medium as referral if only the source is specified, but you can also edit the default channel grouping in GA so that shop_app is grouped as a referral. Thirdly, you want to differentiate orders going through shop.app from the normal Shopify checkout. Littledata's Shopify app does this by translating the order tag shop_app into the transaction affiliation in Google Analytics, so the affiliation is Shopify, Shop App. Conclusion So if you're a Littledata customer: our app has got you covered. And if not there's a few changes you'll need to make in Google Analytics settings to make sure shop.app traffic is treated correctly.
6 FAQs you may have asked during a Littledata demo
Like many SaaS companies (and Shopify app developers), we get a LOT of merchants writing in with questions. Big, small, new, old, Shopify Plus, Shopify basic, headless Shopify, platform migrations from Magento...you name it. But some questions stand out for every Shopify store. For those of you who've gone through a demo with our support or sales team, it is highly likely that you asked one of the following questions about Littledata, Shopify and Google Analytics (GA): When's the right time to install Littledata? Do you fix marketing attribution? Should we use Segment? Why doesn't my Shopify data match what I see in GA? How do you capture complete revenue data? What's included in enterprise plans? And there's a reason why — these are the questions we get the most from merchants like you. In this post, we'll break down the answers as clearly and directly as possible. Plus, we'll give you the resources you need for more detailed answers. (Rather talk directly to a human? Book a demo). [subscribe] 1) When's the right time to install Littledata? In short, it really depends on your internal process. What do we mean by process? Let's put like this: why do you need accurate data? What will you do with it? If you're still working on your checkout architecture, it's probably not the right time. If you generally don't trust data to help make decisions about CRO, marketing plans, online product merchandising, retargeting, etc., then it's definitely not the right time (nor a good fit in general). But if you just don't trust your Shopify data in Google Analytics and want to trust it, then it definitely IS time. And if you're still shopping around for Shopify Plus development agencies, it's probably not the right time (though we can help recommend one). But in most cases, the time is NOW! Every ecommerce site and DTC brand has their own internal process for moving toward data-driven decision making, and whether you're ju or already en route to scale insanely fast, we're here to help. But don't take it from us. Here are some of the cases where clients have said they were really glad they started a free trial of Littledata then and didn't wait to fix their tracking: Migrating from another ecommerce platform (most often Magento) to Shopify Ramping up paid spend and want to make sure the data is accurate (most often Facebook Ads and Instagram Ads) Recently redesigned the site or checkout -- or added products by subscription -- and want to ensure complete sales data and better segmentation in Google Analytics Recently launched multi-currency (multiple "stores" in Shopify-speak) and looking for a way to segment marketing campaigns and track sales in Google Analytics And one of my favorites: "We were actually already loving Littledata but upgraded for analytics training and extra support!" [tip]Testing your new setup in a dev store or production site before moving to a live site? Let us know and we'll set up a free test account[/tip] 2) Do you fix marketing attribution? Yes. Littledata is uniquely suited to stores that really care about getting their data right, and that's especially true if you want accurate marketing attribution. Our app fixes attribution for Shopify stores automatically with a combination of server-side and client-side tracking. We stitch sessions together to make sure nothing's lost, so you can rely on Google Analytics or Segment (our current data destinations) as the single source of truth for both pre-click and post-click data, as well as more complex stuff like segmented remarketing, comparative attribution models and LTV calculations for subscription ecommerce. Our script uses gtag and GTM data layer, and can easily supplement and improve your GTM setup (though many clients find that they no longer need GTM). So if you're asking questions like "Why is an absurd amount of my traffic showing as Direct?" or "Is it possible to see the LTV by channel for our Shopify store?", we've got you covered. As our CEO puts it, "What's the real ROI on your Facebook Ads?" [tip]Get accurate campaign tracking and know your true ROAS with our connections for Facebook Ads and Google Ads[/tip] As an added bonus, we have ecommerce benchmarks in the app. So once you have accurate data, you can see if your Facebook referrals are higher or lower than average, as well as if there are technical factors such as page load speed affecting conversions. 3) Should we use Segment? If you're considering different data pipeline and customer data solution, we highly recommend Segment. It's a powerful, clean way to track customer data alongside anonymous browsing behavior, ad performance and more. In fact, we love Segment so much that we built the only recommended Segment connection for Shopify stores. Here's what one customer has to say about it: "This app seamlessly integrated Shopify with Segment. All of our data is flowing seamlessly from Shopify into all of our destinations via Segment." If you're comparing Segment against other CDPs like mParticle and Stitch, we're happy to chat about the pros and cons and give you an honest opinion about what's best for your ecommerce business. One thing our larger Segment users find particularly useful about Segment is that once a source is set up, it tends to run really smoothly. So Segment becomes a single source of truth in a way that few other data platforms can offer, with literally hundreds of destinations for using, acting on and modeling that data. [note]Using a Headless Shopify setup? Littledata fixes tracking for headless Shopify in Segment or Google Analytics. See the headless tracking demo for more details.[/note] 4) Why doesn't my Shopify data match what I see in Google Analytics? [tip]There's a free resource for that! Learn how to fix Shopify <> GA data differences in our free ebook[/tip] The truth is that Google Analytics (GA) and Shopify need a little help to play nice. Most marketers use GA to track performance, but having a good data setup — even for bare essentials like transactions and revenue — is harder than it looks. In some cases, you may need the help of a Google Analytics consultant or GA expert. For other stores (especially teams well-versed in GA tracking) don't need the help of an expert. There are many reasons for differences in tracking results, but let’s take a look at the top 6 reasons. a) Orders are never recorded in Google Analytics Usually, this happens because your customer never sees the order confirmation page. More commonly, this is caused by payment gateways not sending users back to the order "thank you" page. b) The Analytics / Google Tag Manager integration contains errors Shopify's integration with Google Analytics is a pretty basic one, tracking just a few of all the possible ecommerce events and micro-moments required for a complete picture. Although Shopify’s integration is designed to work for most standard stores, there are those who build a more personalised theme. In this case, they would require a custom integration with Google Analytics. But with Littledata's Shopify app, here's what you can track. c) A script in the page prevents tracking to work on your order thank you page Many websites have various dynamics on the thank you page in order to improve user experience and increase retention. But these scripts can sometimes fail and create a domino effect, preventing other modules from executing. d) Too many products included in one transaction Every time a page on your website loads, Google Analytics sends a hit-payload to its servers which contains by default a lot of user data starting from source, path, keywords etc. combined with the data for viewed or purchased products (name, brand, category, etc). This data query can grow quite long if the user adds products with long names and descriptions. But there is a size limit for each hit-payload of 8kb, which can include information for about 20 products. When this limit is reached, GA will not send the payload to its servers, resulting in lost purchase data. e) Too many interactions have been tracked in one session This inconsistency is not encountered as often, but it needs to be taken into account when setting up Google Analytics tracking. One of GA's limitations for standard tracking is that a session can contain only 500 hits. This means that interactions taking place after the hit limit is reached will be missed by Google Analytics. 5) How do you capture complete revenue data? It's magic. Or at least it might feel that way. Once you put our tracking script in your theme and install the relevant connections, Littledata uses a savvy combination of client-side and server-side tracking to capture every shopper interaction with your online store. Because our server-side tracking sends revenue data with purchase and refund events directly to your chosen data destination (Google Analytics or Segment), it's much more reliable than waiting for an event to fire when a confirmation page loads completely, or trying to hack together a way to capture revenue data with GTM from third-party checkouts. Our app often fixes revenue variance of 20-30%, even for large retailers! Behind the scenes the setup looks something like this: Not only does Littledata capture complete sales data, including refunds, but our Shopify integration also sets up custom dimensions in your Google Analytics account for smarter segmentation and long-term tracking. After all, smart ecommerce businesses know that revenue isn't just about the first purchase numbers -- you need to track what types of customers purchase more over time. For example, do customers who come from a particular marketing channel tend to make a number of smaller purchases that actually add up to higher lifetime revenue than those one-off big spenders? So we add custom dimensions including: Lifetime value (LTV) Last order date Shopify customer ID If you're using ReCharge for subscriptions, note that we also track subscription lifecycle events such as payment method updates and subscription updates, so you can do deep dives into not just revenue changes but the reasons for those changes. [tip]Do you really know which marketing channels bring you profitable customers? Learn from our CEO how to accurately calculate lifetime value[/tip] 6) What's included in Enterprise plans? At Littledata, we've been lucky to have a chance to scale along with Shopify. Larger brands have been increasingly drawn to the platform's ease of use, and Shopify Plus merchants now include Leesa, Bulletproof Coffee, LeSportsac and Gymshark. But even with Shopify's growth, there's a consistent problem: questionable analytics. One thing I really love about working at Littledata is that we’ve managed to keep the core tracking tools extremely affordable, while also offering a wider range of enterprise plans at approximately 1/10 the cost of hiring outside consultants or someone in-house. We have a range of options for enterprise plans to fit your needs and budget, grouped around two enterprise "tiers": enterprise basic and enterprise plus. Basic enterprise Basic enterprise plans can be paid monthly or annually. They include: Dedicated account manager Shopify Plus support Unlimited connections Unlimited country stores Every account manager at Littledata is an analytics expert. They can help to ensure accurate setup of your Segment or Google Analytics tracking, and recommend proven implementation and optimization strategies for Shopify Plus. After all, once you know that you can trust your data, focusing on the right metrics can make a world of difference. Enterprise Plus Enterprise Plus plans include everything in basic Enterprise plans, such as support from an analytics expert, plus custom setup and training to fit your needs. Options include: Custom setup Analytics training Manual data audits Segment support, including solutions engineering Google Tag Manager support Analytics 360 Suite support And a whole lot more. See what’s included in our enterprise analytics plans. In short, we’re here to make sure that you can trust your data — and use that data for actionable results. If you’d like to get started with the app, you can try it free for 30 days. We're also happy to walk you through the app — just book a demo with us online!
How to fix marketing attribution for iOS 14
The latest version of Safari, and all browsers running on iOS for iPhones or iPads, limit the ability for Google Analytics (and any other marketing tags) to track users across domains, and between visits more than a day apart. Here’s how to get this fixed for your site. This article was updated January 2021 to include the changes for iOS 14 How does this affect my analytics? Safari's Intelligent Tracking Prevention (ITP) dramatically changes how you can attribute marketing on one of the web's most popular browsers, and ITP 2.3 makes this even more difficult. How will the changes affect your analytics? Currently your marketing attribution in Google Analytics (GA) relies on tracking users across different visits on the same browser with a first-party user cookie - set on your domain by the GA tracking code. GA assigns every visitor an anonymous ‘client ID’ so that the user browsing your website on Saturday can be linked to the same browser that comes back on Monday to purchase. In theory this user-tracking cookie can last up to 2 years from the date of the first visit (in practice, many users clear their cookies more frequently than that), but anything more than one month is good enough for most marketing attribution. ITP breaks that user tracking in major ways: Any cookie set by the browser, will be deleted after 7 days (ITP 2.1)Any cookie set by the browser, after the user has come from a cross-domain link, will be deleted after one day (ITP 2.2)Any local storage set when the user comes from a cross domain link is wiped after 7 days of inactivity (ITP 2.3)With Safari 14, any script known to send events about the user is blocked from accessing cookies or any way of identifying the userFrom iOS 14 onwards all browsers will implement these restrictions by default, unless the user opts in to 'allow cross-website tracking'. This will disrupt your marketing attribution. Let’s take two examples. Visitor A comes from an affiliate on Saturday, and then comes back the next Saturday to purchase: Before ITP: sale is attributed to AffiliateAfter ITP: sale is attributed to ‘Direct’Why: 2nd visit is more than one day after the 1st Visitor B comes from a Facebook Ad to your latest blog post on myblog.com, and goes on to purchase: Before ITP: sale is attribute to FacebookAfter ITP: sale is attributed to ‘Direct’Why: the visit to the blog is not linked to the visit on another domain The overall effect will be an apparent increase in users and sessions from Safari or iPhones, as the same number of user journeys are broken in down into more, shorter journeys. How big is the problem? This is a big problem! Depending on your traffic sources it is likely to affect half of all your visits. Apple released iOS 14 and Safari 14 on 16th September 2021, and at the time of writing around 20% of all web visits came from iOS 14, and another 20% of visits from Safari 12 or higher, on a sample of larger sites. The volume for your site may vary; you can apply this Google Analytics segment to see exactly how many iOS users you have. The affected traffic will be greater if you have high mobile use or more usage in the US (where iPhones are more popular). Why is Apple making these changes? Apple has made a strong point of user privacy over the last few years. Their billboard ad at the CES conference in Las Vegas earlier this year makes that point clearly! Although Google Chrome has overtaken Safari, Internet Explorer and Firefox in popularity on the desktop, Safari maintains a very dominant position in mobile browsing due to the ubiquitous iPhone. Apple develops Safari to provide a secure web interface for their users, and with Intelligent Tracking Prevention (ITP) they intended to reduce creepy retargeting ads following you around the web. Genuine web analytics has just been caught in the cross-fire. Unfortunately this is likely not to be the last attack on web analytics, and a permanent solution may not be around for some time. Our belief is that users expect companies to track them across their own branded websites and so the workarounds below are ethical and not violating the user privacy that Apple is trying to protect. How to fix this There is only one fix I would recommend. I’m grateful to Simo Ahava for his research on all the possible solutions. If you’re lucky enough to use Littledata's Shopify app then contact our support team if you'd like to test the private beta of our 'trusted cookie' solution. Server-side cookie service ITP limits the ability of scripts to set cookies lasting for longer than 7 days (or 24 hours in some cases). But this limit is removed if a web server securely sets the HTTPS cookie, rather than via a browser script. This also has the advantage of making sure any cross-domain links tracked using GA's linker plugin can last more than one day after the click-through with ITP 2.3. The downside is this requires either adapting your servers, proxy servers or CDN to serve a cookie for GA and adapt the GA client-side libraries to work on a web server. If your company uses Node.js servers or a CDN like Amazon CloudFront or Cloudflare this may be significantly easier to achieve. If you don’t have direct control of your server infrastructure it’s a non-starter. Also, a caveat is that Apple recommends settings cookies as HttpOnly to be fully future proof - but those would then be inaccessible by the GA client tracking. Full technical details. What about other marketing tags working on Safari? All other marketing tags which track users across more than one session or one subdomain are going to experience the same problem. With Google Ads the best solution is to link your Ad account to Google Analytics, since this enables Google to use the GA cookie to better attribute conversion in Google Ads reporting. Facebook will no doubt provide a solution of their own, but in the meantime you can also attribute Facebook spend in GA using Littledata’s connection for Facebook Ads. Are there any downsides of making these changes? As with any technical solution, there are upsides and downsides. The main downside here is again with user privacy. Legally, you might start over-tracking users. By resetting cookies from the local storage that the user previously requested to be deleted, this could be violating a user’s right to be forgotten under GDPR. The problem with ITP is it is actually overriding the user’s preference to keep the cookie in usual circumstances, so there is no way of knowing the cookie was deleted by the user … or by Safari supposed looking out for the user! Unfortunately as with any customisation to the tracking code it brings more complexity to maintain, but I feel this is well worth the effort to maintain marketing attribution on one of the world's most popular browsers.
Ecommerce trends at Paris Retail Week
Physical or digital? We found merchants doubling down on both at Paris Retail Week. At the big event in Paris last month, we found retailers intent on merging the online and offline shopping experience in exciting new ways. See who we met and what the future of digital might hold for global ecommerce. Representatives from our European team had a great time at the big ecommerce event, one of the 'sectors' at Paris Retail Week. Outside of the event, it was great to have a chance to catch up with Maukau, our newest Shopify agency partner in France. (Bonjour!) Among the huge amount of digital sales and marketing trends we observed throughout the week, a few emerged again and again: mobile-first, phygital experience, and always-on, multi-channel marketing. Getting phygital Phygital? Is that a typo? Hardly. It’s the latest trend in ecommerce, and it was prevalent everywhere at Paris Retail Week. Phygital combines “physical” and “digital” experiences in a new ecosystem. This offers the consumer a full acquisition experience across different channels. From payment providers to marketing agencies, everyone was talking about going phygital. One of our favourite presentations was by AB Tasty. They focused on how optimising client experience can boost sales and conversions in the long-term. It’s not enough to promote your products, nor to link to an influencer for social proof -- you need to create a full customer experience. Starbucks and Nespresso are good examples of how this works offline, assuring that a customer who comes in to drink a coffee will linger around for the next 20-30 minutes. By keeping the customers in the shop, they will eventually order more. The goal is to reproduce this immediately sticky experience online too, and focusing on web engagement benchmarks is the best way to track your progress here. Using the example of conversion rate optimisation (CRO) for mobile apps, AB Tasty's Alexis Dugard highlighted how doing data-driven analysis of UI performance, on a very detailed level, can help clarify how mobile shopping connects with a wider brand experience. In the end, customer experience means knowing the customer. 81% of consumers are willing to pay more for an optimal customer experience. Brands that are reluctant to invest in customer experience, either online or offline, will hurt their bottom line, even if this isn't immediately apparent. Those brands that do invest in multi-channel customer experience are investing in long-term growth fuelled by higher Average Order Value (AOV). 81% of consumers are willing to pay more for an optimal customer experience -- the statistic speaks for itself! Another great talk was from Guillaume Cavaroc, a Facebook Academie representative, who discussed how mobile shopping now overlaps with offline shopping. He looked at experiments with how to track customers across their journeys, with mobile login as a focal point. In the Google Retail Reboot presentation, Loïc De Saint Andrieu, Cyril Grira and Salime Nassur pointed out the importance of data in retail. For ecommerce sites using the full Google stack, Google data represents the DNA of the companies and Google Cloud Platform is the motor of all the services, making multi-channel data more useful than ever in assisting with smart targeting and customer acquisition. The Google team also stated that online shopping experiences that don’t have enough data will turn to dust, unable to scale, and that in the future every website will become, in one way or another, a mobile app. In some ways, "phygital" really means mobile-first. This message that rang out clearly in France, which is a mobile-first country where a customer's first encounter with your brand or product is inevitably via mobile -- whether through a browser, specific app or social media feed. [subscribe] Multi-channel experience (and the data you need to optimise it) Physical marketing is making a comeback. Boxed CEO Chieh Huang and PebblePost founder Lewis Gersh presented the success of using online data for offline engagement, which then converts directly back on the original ecommerce site. Experimenting heavily in this area, they've seen personalised notes on invoices and Programmatic Direct Mail (with the notes based on viewed content) generate an increase of 28% in online conversion rate. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard, to name just a bit of the physical collateral that's becoming popular again -- and being done at a higher quality than in the years of generic direct mail. Our real-world mailboxes have become an uncluttered space, and customers crave the feel of a paperback catalogue or simple postcard. However, data is still the backbone of retail. In 2017 Amazon spent approximately $16 billion (USD) on data analysis, and it was worth every penny, generating around $177 billion in revenue. Analysing declarative and customer behaviour data on the shopper’s path-to-purchase is a must for merchants to compete with Amazon. Creating an omni-channel experience for the user should be your goal. This means an integrated and cohesive customer shopping experience, no matter how or where a customer reaches out. Even if you can't yet support an omni-channel customer experience, you should double down on multi-channel ecommerce. When Littledata's customers have questions about the difference, we refer them to Aaron Orendorff's clear explanation of omni-channel versus multi-channel over on the Shopify Plus blog: Omni-channel ecommerce...unifies sales and marketing to create a single commerce experience across your brand. Multi-channel ecommerce...while less integrated, allows customers to purchase natively wherever they prefer to browse and shop. Definitions aside, the goal is to reduce friction in the shopping experience. In other words, you should use anonymous data to optimise ad spend and product marketing. For marketers, this means going beyond pretty dashboards to look at more sophisticated attribution models. We've definitely seen this trend with Littledata's most successful enterprise customers. Ecommerce directors are now using comparative attribution models more than ever before, along with AI-based tools for deeper marketing insights, like understanding the real ROI on their Facebook Ads. The new seasonality So where do we go from here? In the world of ecommerce, seasonality no longer means just the fashion trends of spring, summer, autumn and winter. Online events like Black Friday and Cyber Monday (#BFCM) define offline shopping trends as well, and your marketing must match. "Black Friday" saw 125% more searches in 2017, and "Back to School" searches were up 100%. And it isn't just about the short game. Our own research last year found that Black Friday discounting is actually linked to next-season purchasing. Phygital or otherwise, are you ready to optimise your multi-channel marketing? If not, you're missing out on a ton of potential revenue -- and shoppers will move on to the next best thing.
What's the real ROI on your Facebook Ads?
For the past decade Facebook’s revenue growth has been relentless, driven by a switch from TV advertising and online banners to a platform seen as more targetable and measurable. When it comes to Facebook Ads, marketers are drawn to messaging about a strong return on investment. But are you measuring that return correctly? Facebook has spent heavily on its own analytics over the last three years, with the aim of making you -- the marketer -- fully immersed in the Facebook platform…and perhaps also to gloss over one important fact about Facebook’s reporting on its own Ads: most companies spend money with Facebook 'acquiring' users who would have bought from them anyway. Could that be you? Here are a few ways to think about tracking Facebook Ads beyond simple clicks and impressions as reported by FB themselves. The scenario Imagine a shopper named Fiona, a customer for your online fashion retail store. Fiona has browsed through the newsfeed on her Facebook mobile app, and clicks on your ad. Let’s also imagine that your site -- like most -- spends only a portion of their budget with Facebook, and is using a mix of email, paid search, affiliates and social to promote the brand. The likelihood that Fiona has interacted with more than one campaign before she buys is high. Now Fiona buys a $100 shirt from your store, and in Facebook (assuming you have ecommerce tracking with Pixel set up) the sale is linked to the original ad spend. Facebook's view of ROI The return on investment in the above scenario, as calculated by Facebook, is deceptively simple: Right, brilliant! So clear and simple. Actually, not that brilliant. You see Fiona had previously clicked on a Google Shopping ad (which is itself powered by two platforms, Google AdWords and the Google Merchant Center) -- how she found your brand -- and after Facebook, she was influenced by a friend who mentioned the product on Twitter, then finally converted by an abandoned cart email. So in reality Fiona’s full list of interactions with your ecommerce site looks like this: Google Shopping ad > browsed products Facebook Ad > viewed product Twitter post > viewed same product Link in abandoned cart email > purchase So from a multi-channel perspective, how should we attribute the benefit from the Facebook Ad? How do we track the full customer journey and attribute it to sales in your store? With enough data you might look at the probability that a similar customer would have purchased without seeing that Facebook Ad in the mix. In fact, that’s what the data-driven model in Google Marketing Platform 360 does. But without that level of data crunching we can still agree that Facebook shouldn’t be credited with 100% of the sale. It wasn’t the way the customer found your brand, or the campaign which finally convinced them to buy. Under the most generous attribution model we would attribute a quarter of the sale. So now the calculation looks like this: It cost us $2 of ad spend to bring $1 of revenue -- we should kill the campaign. But there's a catch Hang on, says Facebook. You forgot about Mark. Mark also bought the same shirt at your store, and he viewed the same ad on his phone before going on to buy it on his work computer. You marked the source of that purchase as Direct -- but it was due to the same Facebook campaign. Well yes, Facebook does have an advantage there in using its wide net of signed-in customers to link ad engagement across multiple devices for the same user. But take a step back. Mark, like Fiona, might have interacted with other marketing channels on his phone. If we can’t track cross-device for these other channels (and with Google Marketing Platform we cannot), then we should not give Facebook an unfair advantage in the attribution. So, back to multi-channel attribution from a single device. This is the best you have to work with right now, so how do you get a simple view of the Return on Advertising Spend, the real ROI on your ads? Our solution At Littledata we believe that Google Analytics is the best multi-channel attribution tool out there. All it misses is an integration with Facebook Ads to pull the ad spend by campaign, and some help to set up the campaign tagging (UTM parameters) to see which campaign in Facebook brought the user to your site. And we believe in smart automation. Littledata's Facebook Ads connection audits your Facebook campaign tagging and pulls ad cost daily into Google Analytics. This automated Facebook-Ads-to-Google-Analytics integration is a seamless way to pull Facebook Ads data into your overall ecommerce tracking -- something that would otherwise be a headache for marketers and developers. The integration checks Facebook Ads for accurate tagging and automatically pulls ad cost data into GA. The new integration is included with all paid plans. You can activate the connection from the Connections tab in your Littledata dashboard. It's that easy! (Not a subscriber yet? Sign up for a free trial on any plan today.) We believe in a world of equal marketing attribution. Facebook may be big, but they’re not the only platform in town, and any traffic they're sending your way should be analysed in context. Connecting your Facebook Ads account takes just a few minutes, and once the data has collected you’ll be able to activate reports to show the same kind of ROI calculation we did above. Will you join us on the journey to better data?
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.
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