How to calculate customer lifetime value (CLV) for subscription ecommerce in Google Analytics
Many of Littledata's subscription customers come to us with a similar problem: how to calculate return on advertising spend, considering the varying customer lifetime value (CLV) of subscription signups. Calculating marketing ROI for subscription ecommerce is a big problem with a number of potential solutions, but even the initial problem is often misunderstood. In this post I break down what the problem is, and walk through two proven solutions for getting consistent, reliable CLV reporting in Google Analytics. What is customer lifetime value? I work with all kinds of subscription ecommerce businesses: beauty boxes, nutritional supplements, training courses and even sunglasses-by-the-month. All of them want to optimise customer acquisition costs. The common factor is they are all willing to pay way MORE than the value of the customers' first subscription payment... because they expect the customer to subscribe for many months. But for how many months exactly? That's the big question. Paying for a marketing campaign which bring trial customers who cancel after one payment - or worse, before the first payment - is very different from paying to attract sticky subscribers. A marketing director of a subscription business should be willing to pay WAY more to attract customers than stay 12 months than customers who only stay one month. 12 times more, to be precise. So how do we measure the different contribution of marketing campaigns to lifetime customer value? In Google Analytics you may be using ecommerce tracking to measure the first order value, but this misses the crucial detail of how long those shoppers will remain subscribers. With lifetime customer value segments we can make more efficient use of media, tailor adverts to different segments, find new customers with lookalike audiences and target loyalty campaigns. There are two ways for a marketing manager to see this data in Google Analytics: one is a more difficult, manual solution; the other is an easier, automated solution that ties recurring payments back to the original campaigns. A manual solution: segment orders and assign a lifetime value to each channel It's possible to see the required data in GA by manually segmenting orders and assigning a lifetime value to each channel. For this solution you'll need to join together: (a) the source of a sample of first orders from more than a year ago, by customer number or transaction ID and (b) the CLV of these customers The accuracy of the data set for A is limited by how your Google Analytics is set up: if your ecommerce marketing attribution is not accurate (e.g. using Shopify's out-the-box GA scripts) then any analysis is flawed. You can get B from your subscription billing solution, exporting a list of customer payments (and anonymising the name or email before you share the file internally). To link B to A, you'll need either to have the customer number or transaction ID of the first payment (if this is stored in Google Analytics). [subscribe] Then you can join the two data sets in Excel (using VLOOKUP or similar function), and average out the lifetime value by channel. Even though it's only a sample, if you have more than 100 customers in each major channel it should give you enough data to extrapolate from. Now you've got that CLV by channel, and assuming that is steady over time, you could import that back into Google Analytics by sending a custom event when a new customer subscribes with the 'event value' set as the lifetime value. The caveat is that CLV by channel will likely change over time, so you'll need to repeat the analysis every month. If you're looking to get away from manual solutions and excessive spreadsheets, read on... A better solution: tie recurring payments back to the original campaign(s) What if you could import the recurring payments into Google Analytics directly, as they are paid, so the CLV is constantly updated and can be segmented by campaign, country, device or any other standard GA dimension? This is what our Google Analytics connection for ReCharge does. Available for any store using Shopify as their ecommerce platform and ReCharge for recurring billing, the smart connection (integration) ties every recurring payment back to the campaigns in GA. Here's how the connector works The only drawback is that you'll need to wait a few months for enough customer purchase history (which feeds into CLV) to be gathered. We think it's worth the wait, as you then have accurate data going forward without needing to do any manual imports or exports. Then, if you also import your campaign costs automatically, you can do the Return on Investment (ROI) calculations directly in Google Analytics, using GA's new ROI Analysis report (under Conversions > Attribution), or in your favourite reporting tool. Do you have a unique way of tracking your marketing to maximise CLV? Are there other metrics you think are more important for subscription retailers? Littledata's connections are growing. We'll be launching integrations for other payment solutions later this year, so let us know if there's a particular one you'd like to see next.
How does page load speed affect bounce rate?
I’ve read many articles stating a link between faster page loading and better user engagement, but with limited evidence. So I looked at hard data from 1,840 websites and found that there’s really no correlation between page load speed and bounce rate in Google Analytics. Read on to find out why. The oft quoted statistic on page load speed is from Amazon, where each 100ms of extra loading delay supposed to cost Amazon $160m. Except that the research is from 2006, when Amazon’s pages were very static, and users had different expectations from pages – plus the conclusions may not apply to different kinds of site. More recently in 2013, Intuit presented results at the Velocity conference of how reducing page load speed from 15 seconds to 2 seconds had increased customer conversion by: +3% conversions for every second reduced from 15 seconds to 7 seconds +2% conversions for every second reduced from seconds 7 to 5 +1% conversions for every second reduced from seconds 4 to 2 So reducing load speed from 15 seconds to 7 seconds was worth an extra 24% conversion, but only another 8% to bring 7 seconds down to 2 seconds. Does page speed affect bounce rate? We collected data from 1,840 Google Analytics web properties, where both the full page load time (the delay between the first request and all the items on the page are loaded) and the bounce rate were within normal range. We then applied a Spearman’s Rank Correlation test, to see if being a higher ranked site for speed (lower page load time) you were likely to be a higher ranked site for bounce rate (lower bounce rate). What we found is almost no correlation (0.18) between page load speed and bounce rate. This same result was found if we looked at the correlation (0.22) between bounce rate and the delay before page content starts appearing (time to DOM ready) So what explains the lack of a link? I have three theories 1. Users care more about content than speed Many of the smaller websites we sampled for this research operate in niche industries or locations, where they may be the only source of information on a given topic. As a user, if I already know the target site is my best source for a topic, then I’ll be very patient while the content loads. One situation where users are not patient is when arriving from Google Search, and they know they can go and find a similar source of information in two clicks (one back to Google, and then out to another site). So we see a very high correlation between bounce rate and the volume of traffic from Google Search. This also means that what should concern you is speed relative to your search competitors, so you could be benchmarking your site speed against a group of similar websites, to measure whether you are above or below average. [subscribe] 2. Bounce rate is most affected by first impressions of the page As a user landing on your site I am going to make some critical decisions within the first 3 seconds: would I trust this site, is this the product or content I was expecting, and is it going to be easy to find what I need. If your page can address these questions quickly – by good design and fast loading of the title, main image etc – then you buy some more time before my attention wanders to the other content. In 2009, Google tried an experiment to show 30 search results to users instead of 10, but found the users clicking on the results dropped by 20%. They attributed this to the half a second extra it took to load the pages. But the precise issue was likely that it took half a second to load the first search result. Since users of Google mainly click on the first 3 results, the important metric is how long it took to load those - not the full page load. 3. Full page load speed is increasingly hard to measure Many websites already use lazy loading of images and other non-blocking loading techniques to make sure the bare bones of a page is fast to load, especially on a mobile device, before the chunkier content (like images and videos) are loaded. This means the time when a page is ready for the user to interact with is not a hard line. SpeedCurve, a tool focussed entirely on web page speed performance, has a more accurate way of tracking when the page is ‘visually complete’ based on actual filmstrips on the page loading. But in their demo of The Guardian page speed, the page is not visually complete until a video advert has rendered in the bottom right of the screen – and personally I’d be happy to use the page before then. What you can do with Google Analytics is send custom timing events, maybe after the key product image on a page has loaded, so you can measure speed as relevant to your own site. But doesn’t speed still affect my Google rankings? A little bit yes, but when Google incorporated speed as a ranking signal in 2010, their head of SEO explained it was likely to penalise only 1% of websites which were really slow. And my guess is in 7 years Google has increase the sophistication with which it measures ‘speed’. So overall you shouldn’t worry about page load times on their own. A big increase may still signal a problem, but you should be focussing on conversion rates or page engagement as a safer metric. If you do want to measure speed, try to define a custom speed measurement for the content of your site – and Littledata’s experts can work with you to set that custom reporting up.
What is the bounce rate in Google Analytics
The bounce rate is the number of web sessions where the user left your site after viewing just one page. It is a key measure for landing page engagement. This is the second article in the Q&A series. As I previous mentioned, I am going to continue answering some of the most-asked questions about Google Analytics and how it works. If you want to get an idea of how this works, you can visit PART 1(Pros and cons of using Google Analytics) of the series and see what questions we answered there. Here are the questions we will be tackling in this second article of the series: 1) What is the bounce rate in Google Analytics? 2) How is the Bounce rate calculated? 3) What is an ideal bounce rate? 1) What is the bounce rate in Google Analytics? The definition of the Bounce Rate as shown in the Google Analytics Help Centre is “the percentage of single-page sessions. Those are sessions in which the person left your site from the entrance page without interacting with any other page”. Why is this metric important? A high bounce rate shows you may have some problems on your website. Remember that the bounce rate is correlated to the content of your website and should be considered in the context of the purpose of the website. If you have a content website, a services website or an ecommerce website you need to look at the bounce rate in the big picture and analyse it using Advanced Segments to look at a specific category of pages, and see how they’re performing vs other sections. Some reasons for a high bounce rate are: Single page website: where the user never leaves the first page through their whole visit. A high bounce rate, in this case, is actually irrelevant: you should focus on how many visitors. In order to find out how people interact with your website, you can track Custom Events on the page. To get an accurate bounce rate in this case you need to set up the events as "interaction hits". Incorrect implementation: for a multiple page website, in order to track all the pages, you need to add a specific tracking code on all of the pages for a correct read of the data. In case the bounce rate is high, that might show that the tracking code is not correctly applied to all pages of the website. User Behaviour: the people that arrived on your site and left without doing anything else, either because they found the information that they wanted on that page and there was no need to access other pages or they simply entered by accident and didn’t find what they needed. Also if a user has a page bookmarked, enters the page and then leaves, that’s also counted as a bounce. Site design: when the implementation is done properly then you really might have a problem with the way the content is displayed. In this case consider looking at the landing pages, as they might not do justice to the content. Also, the keywords or ads that you use, might not reflect the content of your website and because of that, you need to optimise either the content or the keywords and ads. 2) How is the Bounce rate calculated? In Google Analytics, there are two indicators for the Bounce Rate. There is the Bounce Rate of a Web Page and then there is the Bounce Rate of a Website. The Bounce Rate of a Website is the total number of bounces across all of the pages on the website over the total number of entrances across all the pages on the website (both over the same determined period of time). This is represented in Google Analytics as a percentage shown in the table of all the pages displayed. The Bounce Rate of a Web Page is the total number of bounces on a page over the total number of entrances on the page (Both over the same determined period of time). The image above shows the equation for calculating it. This is also represented as a percentage but it is shown in the table for each page separately. Here is an article from OptimizeSMART in which they show us how to improve our bounce rate. [subscribe] 3) What is an ideal bounce rate? As I previously explained, the bounce rate should be as low as possible. In one of his articles, Avinash Kaushik who is a guru of Analytics tells us what the ideal bounce rate should be: “As a benchmark from my own personal experience over the years, it is hard to get a bounce rate under 20%. Anything over 35% is a cause for concern and anything above 50% is worrying.” To recap, in this article we managed to see what the Bounce Rate is, how it’s calculated and what is the ideal bounce rate we should strive for with our website. Make sure to check part 3 out, in which we will answer more questions about Google Analytics. Happy Reporting. Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
How to track recurring billing & subscriptions
Recurring billing & subscriptions have proved to be, at least in the last few years, the most viable model of business. The return on investment, value per customer and frequency of buying are all higher for any business that adopted a recurring subscription model. This article is focused on Shopify stores that use apps like ReCharge solution and Fix Google Analytics - Littledata app. Nonetheless, everything in here can be applied to all recurring payments business models. Recharge is the most used Shopify recurring billing solution powering thousands of stores and processing tens of thousands of orders daily. Fix Google Analytics - Littledata app completes ReCharge app by providing accurate sales attribution through Google Analytics. If you don't know what the Fix Google Analytics - Littledata app does, here is a short description: We fix your data collection, offer marketing insights and suggest improvements all in one app. Say goodbye to inaccurate data and start getting the full Enhanced Ecommerce experience. [subscribe] Install this app to get: Proper marketing attribution in Google Analytics Product views and shopping behavior Checkout conversion funnels (including voucher usage) Understanding of repeat buyers The first steps to install the Google Analytics tracking for Shopify are illustrated here: How to install the “Fix Google Analytics” Shopify app. Besides this, if you want to go ahead and make an advanced analysis of your customers then you need to make the following setup also: Enable the feature in Google Analytics Firstly, go into Google Analytics (both your normal Google Analytics property and the property that has been created by Littledata) and enable the User-ID feature by going to Admin > Property > Tracking info > User-ID. Click On, next, On, next, give the new view a name and you're done. Attention: The new view will start to collect data from the point of creation so you will need to wait a bit to use this report. The sources of the purchases will be collected from the point of creation so most of the orders will be shown in the first month from direct / (none). Enable the Enhanced Ecommerce feature in Google Analytics Go in Google Analytics, Click Admin. In the right side under view choose the new Registered Users view, that you've created earlier and click Ecommerce Setings. Toggle to ON and then click Next step. Toggle ON for Enhanced Ecommerce, save and you're done. How to see what was the initial source for recurring subscriptions? Using the registered view go under ACQUISITION -> All traffic -> Source/Medium or Channels. This report will show both new customers and recurring ones. We need to apply a segment to this report in order to show only the recurring users. This is how you set up the segment to exclude first time buyers: Now, with the above segment applied, you can check what was the original source or the sale for all transactions from repeating buyers. The other cool and helpful report is the AUDIENCE->Cohort Analysis report. You can see what was the retention of these users in this report for each day, week or month. This report must be read from left to right for the bellow image: Users that bought in December continued to buy in January in a proportion of 38% and in February in a proportion of only 10%. Combining this report with an advanced segment that excludes the first-time buyers AND includes only buyers that had their first transaction in December will provide the number of users that started the subscription package in December and what was the retention of these people. We would love to hear how you use these reports and what you think of the new version of our Fix Google Analytics - Littledata app.
Pros and cons of using Google Analytics
We've decided to start a series of content to answer basic questions you may have regarding Google Analytics. These are readily available but sometimes over complicated and we're experts at both analytics and making things simple. Hopefully, these blog posts will help clarify any questions you may have. In Part 1, we will be discussing: When to use Analytics? Pros and cons of using Google Analytics. 1. When to use Analytics? These days, everything on the internet is connected. Have you ever questioned how websites know your location and redirect you to the page of that specific location? Or have you ever seen those ads that appear all the time after you visit a specific website? That’s due to cookies, which are a set of parameters that get collected and interpreted. They are part of Digital Analytics or Google Analytics, which is a set of measurements that helps you in understanding which people you reach with your website and how your website performs. By performance, we refer to who is visiting your website, how someone interacts with your website, the decisions they take following those interactions and much more. Ultimately, you want to use Google Analytics whether you own a website, you're an online shop or if you are a marketer, in order to increase the marketing and sales efforts of the platforms you are using. If you are still not convinced that Analytics is a must have for an online property, check this article from AnalyticsNinja and you'll get an even deeper dive into why you should you use Analytics. [subscribe heading="Get the Littledata app" button_text="Free Google Analytics Audit"] 2. Pros and cons of using Google Analytics: Google Analytics is one of the most known and used tools to track Digital Analytics. There are definitely a lot of pros to using it but there are also some drawbacks. Pros: It’s free of charge so everyone can use it. You can use it on different digital environments such as websites, mobile applications, kiosks, or anything that has an internet connection There's a Google Analytics Academy, where you can get in-depth information and get educated about how to use it. You can connect your Google Analytics account with your AdWords account. You can also collect data from different platforms and sources. You can create custom goals and you can also track your ecommerce platform. You can create custom reports based on your needs. This way you can track specific information depending on your industry. The cons: In order to understand all the intricacies, you need to learn. The issue with that is that the information is sometimes hard to find, may be confusing, and overwhelming. The academy is also quite time-consuming so if you're on a time frame, it my not be feasible. The overall feel of the platform might also be a little bit overwhelming. There are too many dashboards and too many things to look at. The free version of Google Analytics suit almost anyone, but if your traffic is high and you'd like to upgrade to Premium, the price is $150,000. Another great article on this matter is written by the guys at Eethuu. Hopefully, this has helped give you more insights into Google Analytics! If you'd like more information or have any questions, get in touch. Check out Part 2(What is the bounce rate in Google Analytics) of the series! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
How to improve your landing pages
With the How to improve your landing pages using Google Analytics blog post, I wanted to help answer the question, why users are clicking a call-to-action but are not converting? Now I will give you some basic tips on how to improve your landing pages, overall. Do what you say and say what you do There is nothing more frustrating for a client than to hope for honey and receive salt. Donʼt promise one thing and then deliver something else .. or even worse nothing at all (a 404 page). For example, if you are giving away an ebook, and your CTA says “Get your free ebook”, donʼt provide a PayPal form on the next screen asking for $2.95 for the product you said would be free, or merely say “thanks for registering” without a link to the product you are offering. Yes, you will have gained a lead, but the customer is now worthless and will tell others about your unfair tactics. In order to get the answer to "Why don't they convert" check this checklist: Do you respect the above? If not this is your biggest business issue. Do you track how many clicks your call-to-action have? If not, see the previous blog post about tracking CTAs What is your conversion rate? Depending on your business model, a conversion rate of 5% to 20% is be normal. (Calculate users that finished the call-to-action divided by users that clicked the call-to-action button.) With these answers, you can figure out what your problem is. This will either be that the users are not clicking or the users are not converting. If the users are not converting you can: A/B test the layout of next page after they click the call-to-action button A/B test the text of the next page after they click the call-to-action button Provide online support on that page offering customers the option to ask direct questions Create a survey for the segment of users that clicked the call-to-action but didn't convert to find out why they didn't Have any questions? Comment below or get in touch! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
How Google Analytics works
How to improve your landing pages with clear CTAs
In the previous blog post, how to improve your landing pages using Google Analytics, we started analysis what makes a good landing page. Some of the ideas were related to call to actions. Your landing page must have a call to action (CTA) correlated with the marketing campaign and the full content of the page. Clear and unambiguous CTA(s) If you are offering app access, go with "Get Started" or "Create account" and don't say “Get your free ebook” or “go” or “submit”. Say short and clear what you want them to do. Don't mislead the users and don't use fancy words. When you're choosing the CTA for your landing page you should consider these three: what you say how your customer will interact with it where to place it What to say is the wording. If you want the customer to subscribe to the newsletter say "subscribe to the newsletter", if you want them to buy say "buy", if you want them to call say "call". Keep it short and clear. If the customer needs to subscribe you need to provide them with the field were to add their email address; If you want them to call you then you should use a dial function for mobile users or show the number for the desktop users; If you want them to buy then the press of the button should redirect them to a page where they can choose the option for delivery and payment. Where to place the call to action in your landing page is simple - where the customers will see it first. I presume you already have event tracking, in place (if no, find out how to set up in this blog post: Set up event tracking in GTM ). Based on some numbers from Google Analytics, let's see how good and bad engagement looks like for a landing page. Find out the level of engagement with the page Bounce rate: This will show you the number of people that entered this page and left without taking any other action (like seeing the second page or clicking on the call to action). The bounce rate will tell you how your whole landing page is engaging with the audience. In the example above, the landing page, /find-more has a bounce rate of 98,8%. This is very bad! On the other side, we have the landing page apps.shopify.littledata with 0% bounce rate. This is the holy grail of landing pages. These means that from an engagement point of view your landing page is perfect. As a rule: You should aim for at least the same bounce rate as you have on the entire website as a medium. Find out if your call to action performed Method 1 - Deducting from landing page report Go into Google Analytics -> in the search bar search landing page -> Choose Site content - Landing pages. Click on your landing page name and now add a second dimension: Second page. Find the link where your call to action redirects and analyse all elements in this report. If you don't have events in place, you will still be able to see how your traffic is clicking through the links on your landing page. If your landing page has more than 1 action then you can add a second dimension on the landing page report and see what was the second page they visited. In the example above, the call-to-action redirected them to the apps.shopify.com/littledata. From the numbers of sessions, we can see that only 10% of the users clicked the call-to-action button. 89% of the people wanted to find more about the product before purchasing. This is the example of bad engagement. The fact that 89% of the people wanted to find more means that we need to provide more details on the landing page and maybe have a clearer call-to-action. Method 2 - Deducting from Top Events report For this, go to Google Analytics and search for Top Events and add a second dimension to the report "Page". You can also build a custom report so you see the number of people that saw the page and the number of people that took the call-to-action. Have any questions? Comment below or get in touch! Get Social! Follow us on LinkedIn, Twitter, and Facebook and keep up-to-date with our Google Analytics insights.
The Freemium business model revisited
After I concluded that freemium is not the best business model for all, the continued rise of ‘free’ software has led me to revisit the same question. In a fascinating piece of research by Price Intelligently, over 10,000 technology executives were surveyed over 5 years. Their willingness to pay for core features of B2B software has declined from 100% in 2013 to just over 50% today – as a whole wave of VC-funded SaaS companies has flooded the market with free product. For add-ons like analytics, this drops to less than 30% willing to pay. “The relative value of features is declining. All software is going to $0” – Patrick Campbell, Price Intelligently Patrick sees this as an extension of the trend in physical products, where offshoring, global scale and cheaper routes to market online have led to relentless price depreciation (in real terms). I’m not so sure. Software is not free to manufacture, although the marginal cost is close to zero – since cloud hosting costs are so cheap. The fixed cost is the people-time to design and build the components, and the opportunities for lowering that cost – through offshoring the work or more productive software frameworks - have already been exploited by most SaaS companies. To pile on the pain, a survey of software executives also found that the average number of competitors in any given niche has increased from 10 to 15 over those 3 years. Even if software build costs are falling, those costs are being spread over a small number of customers – making the chance of breaking even lower. And the other big cost – Customer Acquisition (CAC) – is actually rising with the volume of competition. To sum up the depressing news so far: 1. Buyers have been conditioned to expect free software, which means you’ll have to give major features away for free 2. But you’ll have to pay more to acquire these non-paying users 3. And next year another competitor will be offering even more for free What is the route of this economic hole? Focussing on monetising a few existing customers for one. Most SaaS executives were focussed on acquiring new customers (more logos), probably because with a free product they expected to sweep up the market and worry about monetization later. But this turns out to be the least effective route to building revenue. For every 1% increment, Price Intelligently calculated how much this would increase revenue. i.e. If I signed up 101 users over the year, rather than 100, that would increase revenue by 2.3%. Monetization – increasing the Average Revenue Per User (ARPU) – has by far the larger impact, mainly because many customers don’t pay anything currently. In contrast, the impact of customer acquisition has fallen over 3 years, since the average customer is less likely to pay. Monetization is not about increasing prices for everyone – or charging for previously free features – but rather finding the small number who are willing to pay, and charging them appropriately. My company, Littledata, has many parallels to Profit Well (launched by Price Intelligently). We both offer analytics and insights on top of existing customer data – Littledata for Google Analytics behavioural data, and Profit Well for recurring revenue data from billing systems. And we have both had similar customer feedback: that the perceived value of the reporting is low, but the perceived value of the changes which the reporting spurs (better customer acquisition, increased retention etc) is high. So the value of our software is that it creates a requirement – which can then be filled by consulting work or ‘actionable’ modules. For myself, I can say that while focusing on new customer acquisition has been depressing, we have grown revenues once a trusted relationship is in place – and the customer really believes in Littledata’s reporting. For Littledata, as with many B2B software companies, we are increasingly content that 80% of our revenue comes from a tiny handful of loyal and satisfied users. In conclusion, while the cover price of software subscriptions is going to zero, it is still possible to generate profits as a niche SaaS business – if you understand the necessity of charging more to a few customers if the many are unwilling to pay. Freemium may be here to stay, but if customers want the software companies they rely on to stay they need to pay for the benefits. Would you like to further discuss? Comment below or get in touch!
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