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• Benefits and Shortcomings of Multi-Touch Attribution

  13 mars 2023, par Erin — Analytics Tips

  Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer. 

  Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales. 

  Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates. 

  The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process. 

  If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it. 

  What Are the Benefits of Multi-Touch Attribution ?

  Remember an old parable of blind men and an elephant ?

  Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

  Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too. 

  Better Understanding of Customer Journeys 

  On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages : 

  • Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel). 
  • Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel). 
  • Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel). 

  You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel. 

  For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion. 

  This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that. 

  Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.

  

  Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion. 

  For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion. 

  A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines. 

  The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

  Improved Budget Allocation 

  Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

  First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions. 

  For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

  

  By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types). 

  Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

  “Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.

  More Accurate Measurements 

  The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance. 

  In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking. 

  Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

  • How many touchpoints are involved in the conversions ? 
  • How long does it take for a lead to convert on average ? 
  • When and where do different audience groups convert ? 
  • What is your average win rate for different types of campaigns ?

  Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect. 

  At the highest level, you need to collect two data points :

  • Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals. 
  • Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events. 

  Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them. 

  The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used. 

  Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo). 

  Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

  Learn more about selecting the optimal multi-channel attribution model for your business.

  What Are the Limitations of Multi-Touch Attribution ?

  Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry. 

  Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email. 

  In addition, you should keep in mind several other limitations of multi-touch attribution software.

  Limited Marketing Mix Analysis 

  Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

  Multi-touch attribution tools cannot evaluate the impact of :

  • Dark social channels 
  • Word-of-mouth 
  • Offline promotional events
  • TV or out-of-home ad campaigns 

  If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.

  Time-Based Constraints 

  Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles. 

  

  Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel. 

  At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc. 

  Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ? 

  The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time. 

  

  Limited Access to Raw Data 

  In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied. 

  Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues. 

  In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making. 

  With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data. 

  AI Application 

  On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies. 

  To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

  Difficult Technical Implementation 

  Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

  

  Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc. 

  Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams. 

  Conclusion 

  Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations. 

  That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool. 

  Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool ! 

  Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried. 

• What Are Website KPIs (10 KPIs and Best Ways to Track Them)

  3 mai 2024, par Erin

  Trying to improve your website’s performance ?

  Have you ever heard the phrase, “What gets measured gets managed ?”

  To improve, you need to start crunching your numbers.

  The question is, what numbers are you supposed to track ?

  If you want to improve your conversions, then you need to track your website KPIs.

  In this guide, we’ll break down the top website KPIs you need to be tracking and how you can track them so you can double down on what’s working with your website (and ditch what’s not).

  Let’s begin.

  What are website KPIs ?

  Before we dive into website KPIs, let’s define “KPI.”

  A KPI is a key performance indicator.

  You can use this measurable metric to track progress toward a specific objective.

  A website KPI is a metric to track progress towards a specific website performance objective.

  

  Website KPIs help your business identify strengths and weaknesses on your website, activities you’re doing well (and those you’re struggling with).

  Web KPIs can give you and your team a target to reach with simple checkpoints to show you whether you’re on the right track toward your goals.

  By tracking website KPIs regularly, you can ensure your organisation performs consistently at a high level.

  Whether you’re looking to improve your traffic, leads or revenue, keeping a close eye on your website KPIs can help you reach your goals.

  10 Website KPIs to track

  If you want to improve your site’s performance, you need to track the right KPIs.

  While there are plenty of web analytics solutions on the market today, below we’ll cover KPIs that are automatically tracked in Matomo (and don’t require any configuration).

  Here are the top 10 website KPIs you need to track to improve site performance and grow your brand :

  1. Pageviews

  Website pageviews are one of the most important KPIs to track.

  What is it exactly ?

  It’s simply the number of times a specific web page has been viewed on your site in a specific time period.

  For example, your homepage might have had 327 pageviews last month, and only 252 this month. 

  This is a drop of 23%. 

  A drop in pageviews could mean your search engine optimisation or traffic campaigns are weakening. Alternatively, if you see pageviews rise, it could mean your marketing initiatives are performing well.

  High or low pageviews could also indicate potential issues on specific pages. For example, your visitors might have trouble finding specific pages if you have poor website structure.

  

  2. Average time on page

  Now that you understand pageviews, let’s talk about average time on page.

  This is simple : it’s the average amount of time your visitors spend on a particular web page on your site.

  This isn’t the average time they spend on your website but on a specific page.

  If you’re finding that you’re getting steady traffic to a specific web page, but the average time on the page is low, it may mean the content on the page needs to be updated or optimised.

  Tracking your average time on page is important, as the longer someone stays on a page, the better the experience.

  This isn’t a hard and fast rule, though. For specific types of content like knowledge base articles, you may want a shorter period of time on page to ensure someone gets their answer quickly.

  3. Bounce rate

  Bounce rate sounds fun, right ?

  Well, it’s not usually a good thing for your website.

  A bounce rate is how many users entered your website but “bounced” away without clicking through to another page.

  Your bounce rate is a key KPI that helps you determine the quality of your content and the user experience on individual pages.

  You could be getting plenty of traffic to your site, but if the majority are bouncing out before heading to new pages, it could mean that your content isn’t engaging enough for your visitors.

  Remember, like average time on page, your bounce rate isn’t a black-and-white KPI.

  A higher bounce rate may mean your site visitors got exactly what they needed and are pleased.

  But, if you have a high bounce rate on a product page or a landing page, that is a sign you need to optimise the page.

  4. Exit rate

  Bounce rate is the percentage of people who left the website after visiting one page.

  Exit rate, on the other hand, is the percentage of website visits that ended on a specific page.

  For example, you may find that a blog post you wrote has a 19% exit rate and received 1,000 visits that month. This means out of the 1,000 people who viewed this page, 190 exited after visiting it.

  On the other hand, you may find that a second blog post has 1,000 pageviews, but a 10% exit rate, with only 100 people leaving the site after visiting this page.

  What could this mean ?

  This means the second page did a better job keeping the person on your website longer. This could be because :

  • It had more engaging content, keeping the visitors’ interest high
  • It had better internal links to other relevant pieces of content
  • It had a better call to action, taking someone to another web page

  If you’re an e-commerce store and notice that your exit rate is higher on your product, cart or checkout pages, you may need to adjust those pages for better conversions.

  

  5. Average page load time

  Want to know another reason you may have a high exit rate or bounce rate on a page ?

  Your page load time.

  The average page load time is the average time it takes (in seconds) from the moment you click through to a page until it has fully rendered within your browser.

  In other words, it’s the time it takes after you click on a page for it to be fully functional.

  Your average load time is a crucial website KPI because it significantly impacts page performance and the user experience.

  How important is your page load time ?

  Nearly 53% of website visitors expect e-commerce pages to load in 3 seconds or less.

  You will likely lose visitors if your pages take too long to load.

  You could have the best content on a web page, but if it takes too long to load, your visitors will bounce, exit, or simply be frustrated.

  6. Conversions

  

  Conversions.

  It’s one of the most popular words in digital marketing circles.

  But what does it mean ?

  A conversion is simply the number of times someone takes a specific action on your website.

  For example, it could be wanting someone to :

  • Read a blog post
  • Click an external link
  • Download a PDF guide
  • Sign up to your email list
  • Comment on your blog post
  • Watch a new video you uploaded
  • Purchase a limited-edition product
  • Sign up for a free trial of your software

  To start tracking conversions, you need to first decide what your business goals are for your website.

  With Matomo, you can set up conversions easily through the Goals feature. Simply set up your website goals, and Matomo will automatically track the conversions towards that objective (as a goal completion).

  Simply choose what conversion you want to track, and you can analyse when conversions occur through the Matomo platform.

  7. Conversion rate

  

  Now that you know what a conversion is, it’s time to talk about conversion rate.

  This key website KPI will help you analyse your performance towards your goals.

  Conversion rate is simply the percentage of visitors who take a desired action, like completing a purchase, signing up for a newsletter, or filling out a form, out of the total number of visitors to your website or landing page.

  Understanding this percentage can help you plan your marketing strategy to improve your website and business performance.

  For instance, let’s say that 2% of your website visitors purchase a product on your digital storefront.

  Knowing this, you could tweak different levers to increase your sales.

  If your average order value is $50 and you get 100,000 visits monthly, you make about $100,000.

  Let’s say you want to increase your revenue.

  One option is to increase your traffic by implementing campaigns to increase different traffic sources, such as social media ads, search ads, organic social traffic, and SEO.

  If you can get your traffic to 120,000 visitors monthly, you can increase your revenue to $120,000 — an additional $20,000 monthly for the extra 20,000 visits.

  Or, if you wanted to increase revenue, you could ignore traffic growth and simply improve your website with conversion rate optimisation (CRO).

  CRO is the practice of making changes to your website or landing page to encourage more visitors to take the desired action.

  If you can get your conversion rate up to 2.5%, the calculation looks like this :

  100,000 visits x $50 average order value x 2.5% = $125,000/month.

  8. Average time spent on forms

  If you want more conversions, you need to analyse forms.

  Why ?

  Form analysis is crucial because it helps you pinpoint where users might be facing obstacles. 

  By identifying these pain points, you can refine the form’s layout and fields to enhance the user experience, leading to higher conversion rates.

  In particular, you should track the average time spent on your forms to understand which ones might be causing frustration or confusion. 

  The average time a visitor spends on a form is calculated by measuring the duration between their first interaction with a form field (such as when they focus on it) and their final interaction.

  Find out how Concrete CMS tripled their leads using Form Analytics.

  9. Play rate

  One often overlooked website KPI you need to be tracking is play rate.

  What is it exactly ?

  The percentage of visitors who click “play” on a video or audio media format on a specific web page.

  For example, if you have a video on your homepage, and 50 people watched it out of the 1,000 people who visited your website today, you have a play rate of 5%.

  Play rate lets you track whenever someone consumes a particular piece of audio or video content on your website, like a video, podcast, or audiobook.

  Not all web analytics solutions offer media analytics. However, Matomo lets you track your media like audio and video without the need for configuration, saving you time and upkeep.

  10. Actions per visit

  Another crucial website KPI is actions per visit.

  This is the average number of interactions a visitor has with your website during a single visit.

  For example, someone may visit your website, resulting in a variety of actions :

  • Downloading content
  • Clicking external links
  • Visiting a number of pages
  • Conducting specific site searches

  Actions per visit is a core KPI that indicates how engaging your website and content are.

  The higher the actions per visit, the more engaged your visitors typically are, which can help them stay longer and eventually convert to paying customers.

  Track your website KPIs with Matomo today

  Running a website is no easy task.

  There are dozens of factors to consider and manage :

  • Copy
  • Design
  • Performance
  • Tech integrations
  • And more

  But, to improve your website and grow your business, you must also dive into your web analytics by tracking key website KPIs.

  Managing these metrics can be challenging, but Matomo simplifies the process by consolidating all your core KPIs into one easy-to-use platform.

  As a privacy-friendly and GDPR-compliant web analytics solution, Matomo tracks 20-40% more data than other solutions. So you gain access to 100% accurate, unsampled insights, enabling confident decision-making.

  Join over 1 million websites that trust Matomo as their web analytics solution. Try it free for 21 days — no credit card required.

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• GA360 Sunset : Is Now the Time to Switch ?

  20 mai 2024, par Erin

  Google pushed the sunset date of Universal Analytics 360 to July 2024, giving enterprise users more time to transition to Google Analytics 4. This extension is also seen by some as time to find a suitable alternative. 

  While Google positions GA4 as an upgrade to Universal Analytics, the new platform has faced its fair share of backlash. 

  So before you rush to meet the new sunset deadline, ask yourself this question : Is now the time to switch to a Google Analytics alternative ?

  In this article, we’ll explain what the new GA360 sunset date means and show you what you could gain by choosing a privacy-friendly alternative. 

  What’s happening with the final GA360 sunset ?

  Google has given Universal Analytics 360 properties with a current 360 licence a one-time extension, which will end on 1 July 2024.

  Why did Google extend the sunset ?

  In a blog post on Google, Russell Ketchum, Director of Product Management at Google Analytics, provided more details about the final GA360 sunset. 

  In short, the tech giant realised it would take large enterprise accounts (which typically have complex analytics setups) much longer to transition smoothly. The extension gives them time to migrate to GA4 and check everything is tracking correctly. 

  What’s more, Google is also focused on improving the GA4 experience before more GA360 users migrate :

  “We’re focusing our efforts and investments on Google Analytics 4 to deliver a solution built to adapt to a changing ecosystem. Because of this, throughout 2023 we’ll be shifting support away from Universal Analytics 360 and will move our full focus to Google Analytics 4 in 2024. As a result, performance will likely degrade in Universal Analytics 360 until the new sunset date.”

  Despite the extension, the July sunset is definitive. 

  Starting the week of 1 July 2024, you won’t be able to access any Universal Analytics properties or the API (not even with read-only access), and all data will be deleted.

  In other words, it’s not just data collection that will cease at the start of July. You won’t be able to access the platform, and all your data will be deleted. 

  What GA360 features is Google deprecating, and when ?

  If you’re wondering which GA360 features are being deprecated and when, here is the timeline for Google’s final GA360 sunset :

  • 1 January 2024 : From the beginning of the year, Google doesn’t guarantee all features and functionalities in UA 360 will continue to work as expected. 
  • 29 January 2024 : Google began deprecating a string of advertising and measurement features as it shifts resources to focus on GA4. These features include :
    • Realtime reports
    • Lifetime Value report
    • Model Explorer
    • Cohort Analysis
    • Conversion Probability report
    • GDN Impression Beta
  • Early March 2024 : Google began deprecating more advertising and measurement features. Deprecated advertising features include Demographic and Interest reports, Publisher reporting, Phone Analytics, Event and Salesforce Data Import, and Realtime BigQuery Export. Deprecated measurement features include Universal Analytics property creation, App Views, Unsampled reports, Custom Tables and annotations.
  • Late March 2024 : This is the last recommended date for migration to GA4 to give users three months to validate data and settings. By this date, Google recommends that you migrate your UA’s Google Ads links to GA4, create new Google Ad conversions based on GA4 events, and add GA4 audiences to campaigns and ad groups for retargeting. 
  • 1 July 2024 : From 1 July 2024, you won’t be able to access any UA properties, and all data will be deleted.

  What’s different about GA4 360 ? 

  GA4 comes with a new set of metrics, setups and reports that change how you analyse your data. We highlight the key differences between Universal Analytics and GA4 below. 

  

  New dashboard

  The layout of GA4 is completely different from Universal Analytics, so much so that the UX can be very complex for first-time and experienced GA users alike. Reports or metrics that used to be available in a couple of clicks in UA now take five or more to find. While you can do more in theory with GA4, it takes much more work. 

  New measurements

  The biggest difference between GA4 and UA is how Google measures data. GA4 tracks events — and everything counts as an event. That includes pageviews, scrolls, clicks, file downloads and contact form submissions. 

  The idea is to anonymise data while letting you track complex buyer journeys across multiple devices. However, it can be very confusing, even for experienced marketers and analysts. 

  New metrics

  You won’t be able to track the same metrics in GA4 as in Universal Analytics. Rather than bounce rate, for example, you are forced to track engagement rate, which is the percentage of engaged sessions. These sessions last at least ten seconds, at least two pageviews or at least one conversion event. 

  Confused ? You’re not alone. 

  New reports

  Most reports you’ll be familiar with in Universal Analytics have been replaced in GA4. The new platform also has a completely different reporting interface, with every report grouped under the following five headings : realtime, audience, acquisition, behaviour and conversions. It can be hard for experienced marketers, let alone beginners, to find their way around these new reports. 

  AI insights

  GA4 has machine learning (ML) capabilities that allow you to generate AI insights from your data. Specifically, GA4 has predictive analytics features that let you track three trends : 

  • Purchase probability : the likelihood that a consumer will make a purchase in a given timeframe.
  • Churn probability : the likelihood a customer will churn in a given period.
  • Predictive revenue : the amount of revenue a user is likely to generate over a given period. 

  Google generates these insights using historical data and machine learning algorithms. 

  Cross-platform capabilities

  GA4 also offers cross-platform capabilities, meaning it can track user interactions across websites and mobile apps, giving businesses a holistic view of customer behaviour. This allows for better decision-making throughout the customer journey.

  Does GA4 360 come with other risks ?

  Aside from the poor usability, complexity and steep learning curve, upgrading your GA360 property to GA4 comes with several other risks.

  GA4 has a rocky relationship with privacy regulations, and while you can use it in a GDPR-compliant way at the moment, there’s no guarantee you’ll be able to do so in the future. 

  This presents the prospect of fines for non-compliance. A worse risk, however, is regulators forcing you to change web analytics platforms in the future—something that’s already happened in the EU. Migrating to a new application can be incredibly painful and time-consuming, especially when you can choose a privacy-friendly alternative that avoids the possibility of this scenario. 

  If all this wasn’t bad enough, switching to GA4 risks your historical Universal Analytics data. That’s because you can’t import Universal Analytics data into GA4, even if you migrate ahead of the sunset deadline.

  Why you should consider a GA4 360 alternative instead

  With the GA360 sunset on the horizon, what are your options if you don’t want to deal with GA4’s problems ? 

  The easiest solution is to migrate to a GA4 360 alternative instead. And there are plenty of reasons to migrate from Google Analytics to a privacy-friendly alternative like Matomo. 

  Keep historical data

  As we’ve explained, Google isn’t letting users import their Universal Analytics data from GA360 to GA4. The easiest way to keep it is by switching to a Google Analytics alternative like Matomo that lets you import your historical data. 

  Any business using Google Analytics, whether a GA360 user or otherwise, can import data into Matomo using our Google Analytics Importer plugin. It’s the best way to avoid disruption or losing data when moving on from Universal Analytics.

  Collect 100% accurate data

  Google Analytics implements data sampling and machine learning to fill gaps in your data and generate the kind of predictive insights we mentioned earlier. For standard GA4 users, data sampling starts at 10 million events. For GA4 360 users, data sampling starts at one billion events. Nevertheless, Google Analytics data may not accurately reflect your web traffic. 

  You can fix this using a Google Analytics alternative like Matomo that doesn’t use data sampling. That way, you can be confident that your data-driven decisions are being made with 100% accurate user data. 

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  Get the web insights you need, without compromising data accuracy.

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  Guarantee user privacy first

  Google has a stormy relationship with the EU-US Data Privacy Framework—being banned and added back to the framework in recent years.

  Currently, organisations governed by GDPR can use Google Analytics to collect data about EU residents, but there’s no guarantee of their ability to do so in the future. Nor does the Framework prevent Google from using EU customer data for ulterior purposes such as marketing and training large language models. 

  By switching to a privacy-focused alternative like Matomo, you don’t have to worry about your user’s data ending up in the wrong hands.

  Upgrade to an all-in-one analytics tool

  Switching from Google Analytics can actually give organisations access to more features. That’s because some GA4 alternatives, like Matomo, offer advanced conversion optimisation features like heatmaps, session recordings, A/B testing, form analytics and more right out of the box. 

  

  This makes Matomo a great choice for marketing teams that want to minimise their tech stack and use one tool for both web and behavioural analytics. 

  Get real-time reports

  GA4 isn’t the best tool for analysing website visitors in real time. That’s because it can take up to 4 hours to process new reports in GA360.

  However, Google Analytics alternatives like Matomo have a range of real-time reports you can leverage.

  

  In Matomo, the Real Time Visitor World Map and other reports are processed every 15 minutes. There is also a Visits in Real-time report, which refreshes every five seconds and shows a wealth of data for each visitor. 

  Matomo makes migration easy

  Whether it’s the poor usability, steep learning curve, inaccurate data or privacy issues, there’s every reason to think twice about migrating your UA360 account to GA4. 

  So why not migrate to a Google Analytics alternative like Matomo instead ? One that doesn’t sample data, guarantees your customers’ privacy, offers all the features GA4 doesn’t and is already used by over 1 million sites worldwide.

  Making the switch is easy. Matomo is one of the few web analytics tools that lets you import historical Google Analytics data. In doing so, you can continue to access your historical data and develop more meaningful insights by not having to start from scratch.

  If you’re ready to start a Google Analytics migration, you can try Matomo free for 21 days — no credit card required. 

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