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B2B Customer Journey Map : A Quickfire Guide for Growth
20 mai, par ErinWhat is a company’s biggest asset?
Its product? Its employees? Its unique selling proposition?
More and more people are recognising it’s something else entirely: your customers.
Without your customers, your business can’t exist.
Nearly 77% of B2B buyers found the buying process too complicated.
With more competition than ever, it’s crucial you provide the best possible experience for them.
That’s where your customer journey comes in.
If you’re in the B2B space, you need to know how to map out the journey.
By building a B2B customer journey map, you’ll be able to analyse the weak spots in the customer journey so you can improve the experience (and generate more revenue).
In this article, we break down the B2B customer journey stages, how to build a customer journey map and how Matomo can help you track your customer journey automatically.
What is a B2B customer journey?
Every customer goes through a specific path within your business.
At some point in time, they found out about you and eventually bought your products.
A B2B customer journey is the collection of touchpoints your customer has with your business from start to finish.
From discovery to purchase (and more), your customers go through a specific set of touches you can track. By analysing this journey, you can get a snapshot of your user experience.
One way to track the customer journey is with a B2B customer journey map.
It helps you to quickly see the different steps your customers take in their path with your business.
With it, you can quickly identify weak spots and successes to improve the customer journey.
5 stages of the B2B customer journey
Every one of your customers is unique. Their specific needs and their journey.
It’s all different.
But, there are crucial steps they take through their journey as your customer.
It’s the same path your entire customer base takes.
Here are the five stages of the B2B customer journey (and why you should track them):
1. Awareness
Awareness is the first stage that every B2B buyer goes through when they start their journey in B2B companies as a customer.
At this stage, your target buyer understands they have a problem they need solving. They’re out, actively trying to solve this problem.
This is where you can stand out from the competition and give them a good first impression.
Some helpful content you could create to do this is:
- Blog posts
- Social media posts
- Ebooks
- Whitepapers
2. Consideration
Next up, your buyer persona has an awareness of your company. But, now they’ve started narrowing down their options for potential businesses they’re interested in.
They’ve selected yours as a potential business to hand their hard-earned cash over to, but they’re still making up their mind.
At this point, you need to do what you can to clear up any objections and doubts in their mind and make them trust you.
Some helpful content you could create here include:
- Product demos by your sales team
- Webinars
- Case studies
3. Conversion
Next up, your target buyer has compared all their options and decided on you as the chosen product/company.
This is where the purchase decision is made — when the B2B buyer actually signs or clicks “buy.”
Here, you’ll want to provide more:
- Case studies
- Live demos
- Customer service
- Customer reviews/testimonials
4. Loyalty
Your B2B buyer is now a customer. But, not all customers return. The majority will slip away after the first purchase. If you want them to return, you need to fuel the relationship and nurture them even more.
You’ll want to shift your efforts to nurturing the relationship with a post-purchase strategy where you build on that trust, seek customer feedback to prove high customer satisfaction and reward their loyalty.
Some content you may want to create here includes:
- Thank you emails
- Follow-up emails
- Follow-up calls
- Product how-tos
- Reward program
- Surveys
5. Advocacy
The final stage of the B2B customer journey map is advocacy.
This is the stage beyond loyalty where your customers aren’t just coming back for more; they’re actively telling others about you.
This is the cream of the crop when it comes to the B2B buyer stages, and it happens when you exceed customer expectations repeatedly.
Your goal should be to eventually get all of your customers to this stage. Because then, they’re doing free marketing for you.
This is only possible when a customer receives enough positive B2B customer experiences with your company where the value they’ve received far exceeds what they perceived they have given.
Here are a few pieces of content you can create to fuel advocacy:
- Surveys
- Testimonial requests
- Referral program
Difference between B2C and B2B customer journeys
Every person on earth who buys something enters the customer journey.
But, not all customer journeys are created equal.
This is especially true when you compare the B2C and B2B customer journeys.
While there are similarities, the business-to-consumer (B2C) journey has clear differences compared to the business-to-business (B2B) journey.
The most obvious difference between the two journeys is that B2B customer journeys are far more complex.
Not only are these two companies selling to different audiences, but they also have to deploy a completely different set of strategies to lead their customers down the path as far as they can go.
While the journey structures are similar (from awareness to advocacy), there are differing motivating behaviours.
Here’s a table showing the difference between B2C and B2B in the customer journey:
Different Factors B2B B2C Target audience Smaller, industry more important Larger, general consumer Buyer Multiple decision-makers One decision-maker Buying decision Based on needs of the organisation with multiple stakeholders Based on an individual’s pain points Buying process Multiple steps Single step Customer retention Organisational needs and ROI-based Individual emotional factors Repeat sales driver Deep relationship Repetition, attention-based Step-by-step guide to building a B2B customer journey map
Now that you’ve got a basic understanding of the typical B2B customer journey, it’s time to build out your map so you can create a visual representation of the journey.
Here are six steps you need to take to craft an effective B2B customer journey map in your business:
1. Identify your target audience (and different segments)
The first step in customer journey mapping is to look at your target audience.
You need to understand who they are and what different segments make up your audience.
You need to look at the different roles each person plays within the journey.
Unlike B2C, you’re not usually dealing with a single person. You likely have a few decision-makers you need to interact with to close a deal.
The average B2B deal involves 6 to 10 people.
Analyse the different roles and responsibilities of your audience.
Figure out what requirements they need to onboard you. Understand each person’s level of influence in the buying decision.
2. Determine your customers’ goals
Now that you have a clear understanding of each person involved in the buying process, it’s time to analyse their unique needs and goals.
Unlike B2C, which will include a single person with a single set of needs and goals, you have to look at several people through the decision-making process.
What is every decision-maker’s goal?
An entry-level admin will have much different goals than a CEO.
Understand each of their needs as it will be key to selling them and taking you to the next person in the chain of command.
3. Lean on data and analytics
Now it’s time to analyse your data.
You don’t want to guess what will work on your B2B buyers. Instead, leverage data that proves what’s working (and what’s not).
Analytics software like Matomo are crucial tools in your B2B customer journey toolkit.
Matomo can help you make data-driven decisions to fuel customer acquisition and loyalty to help get more customers all the way to the advocacy stage.
Using Matomo (which analyses and interprets different data sources) can give you a holistic view of what’s going on at each stage of the journey so you can reach your goals.
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4. Draw out customer journey stages
Now that you have your data-backed plan, it’s time for some customer journey mapping.
You can do this on paper or use a diagram tool to create a visual B2B customer journey map.
Here, you’ll draw out every single stage in your customer journey, including every single touchpoint from different decision-makers.
5. Determine each customer touchpoint
Once you’ve drawn up the customer journey stages, you’ll have a key list of B2B customer journey touchpoints to implement.
Write down every single customer interaction possible on the journey through.
This could be reading an email, a blog post or watching a video on your home page.
It could be an advertisement, a phone call or a follow-up email.
It could even be a live demo or video sales call (meeting).
6. Identify your own goals
Now that you’ve got your visual B2B customer journey mapping done, it’s time to go back to you and your company.
What are your goals?
What are the end results you’re looking for here?
You’ve got your current map in place. Now, how would you like customers to go through this journey?
Where would you like them to end up?
Look back at your company’s primary objectives if you’re stuck here.
If your company is looking to increase profit margins, then maybe you want to focus more on retention, so you’re spending less on acquisition (and leaning more on recurring revenue from existing customers).
How to create a Matomo funnel to track your B2B customer journey
If you want to start tracking and optimising your B2B customer journey, you need to have a good grasp on your funnel.
The reality is that your customer journey is your funnel.
They’re one and the same.
Your customer journeys through your sales funnel.
So, if you want to optimise it, then you need to see what’s going on at each stage of your funnel.
With Matomo, you can map out your entire funnel and track key events like conversions.
This allows you to identify where your site visitors are having problems, where they’re exiting and other obstacles they’re facing on their journey through.
To start, you first define what events or touchpoints you want included. This could mean:
- Landing on your website
- Visiting a product page
- Adding something to cart
- Going to checkout
- Clicking “buy”
Then, at each stage, you’ll see conversion rates.
For example, if only 3% of your visitors go from landing on your website to the product page, you likely have an issue between your homepage (and other pages) and your product pages.
Or, if you can get people to add to cart, but you rarely get people going to checkout, there’s likely a problem to fix on your add-to-cart page.
By leveraging Matomo’s funnels feature, you get to see your entire customer journey (and where people are falling off) so you understand what you need to optimise to grow your business.
If you’re ready to start building and optimising your customer journey today, then try Matomo for free for 21 days.
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21 day free trial. No credit card required.
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GA360 vs GA4 : Key Differences and Challenges
20 mai, par ErinWhile the standard Universal Analytics (UA) was sunset for free users in July 2023, Google Analytics 360 (GA360) users could postpone the switch to GA4 for another 12 months. But time is running out. As July is rapidly approaching, GA360 customers need to prepare for the switch to Google Analytics 4 (GA4) or another solution.
This comparison post will help you understand the differences between GA360 vs. GA4. We’ll dive beneath the surface, examining each solution’s privacy implications and their usability, features, new metrics and measurement methods.
What is Google Analytics 4 (Standard)?
GA4 is the latest version of Google Analytics, succeeding Universal Analytics. It was designed to address privacy issues with Universal Analytics, which made compliance with privacy regulations like GDPR difficult.
It completely replaced Universal Analytics for free users in July 2023. GA4 Standard features many differences from the original UA, including:
- Tracking and analysis are now events-based.
- Insights are primarily powered by machine learning. (There are fewer reports and manual analysis tools).
- Many users find the user interface to be too complex compared to Universal Analytics.
The new tracking, reports and metrics already make GA4 feel like a completely different web analytics platform. The user interface itself also includes notable changes in navigation and implementation. These changes make the transition hard for experienced analysts and digital marketers alike.
For a more in-depth look at the differences, read our comparison of Google Analytics 4 and Universal Analytics.
What is Google Analytics 360
Google Analytics 360 is a paid version of Google Analytics, mostly aimed at enterprises that need to analyse a large amount of data.
It significantly increases standard limits on data collection, sampling and processing. It also improves data granularity with more custom events and dimensions.
Transitioning from Universal Analytics 360 to GA4 360
You may still use the Universal Analytics tag and interface if you’ve been a Google Analytics 360 customer for multiple years. However, access to Universal Analytics 360 will be discontinued on July 1, 2024. Unlike the initial UA sunset (free version), you won’t be able to access the interface or your data after that, so it will be deleted.
That means you will have to adapt to the new GA4 user interface, reports and metrics before the sunset or find an alternative solution.
What is the difference between GA4 360 and free GA4?
The key differences between GA4 360 and free GA4 are higher data limits, enterprise support, uptime guarantees and more robust administrative controls.
GA4 offers most of the same features across the paid and free versions, but there are certain limits on data sampling, data processing and integrations. With the free version, you also can’t define as detailed events using event parameters as you can with GA4 360.
Higher data collection, accuracy, storage and processing limits
The biggest difference that GA4 360 brings to the table is more oomph in data collection, accuracy and analysis.
You can collect more specific data (with 100 event parameters instead of 25 for custom metrics). GA4 360 lets you divide users using more custom dimensions based on events or user characteristics. Instead of 50 per property, you get up to 125 per property.
And with up to 400 custom audiences, 360 is better for companies that heavily segment their users. More audiences, events and metrics per property mean more detailed insights.
Sampling limits are also of a completely different scale. The max sample size in GA4 360 is 100x the free version of GA4, with up to 1 billion events per query. This makes analysis a lot more accurate for high-volume users. A slice of 10 million events is hardly representative if you have 200 million monthly events.
Finally, GA4 360 lets you store all of that data for longer (up to 50 months vs up to 14 months). While new privacy regulations demand that you store user data only for the shortest time possible, website analytics data is often used for year-over-year analysis.
Enterprise-grade support and uptime guarantees
Because GA360 users are generally enterprises, Google offers service-level agreements for uptime and technical support response times.
- Tracking: 99.9% uptime guarantee
- Reporting: 99% uptime guarantee
- Data processing: within 4 hours at a 98% uptime guarantee
The free version of GA4 includes no such guarantees and limited access to professional support in the first place.
Integrations
GA4 360 increases limits for BigQuery and Google Ads Manager exports.
The standard limits in the free version are 1 million events per day to BigQuery. In GA4 360, this is increased to billions of events per day. You also get up to 400 audiences for Search Ads 360 instead of the 100 limit in standard GA4.
Roll-up analytics for agencies and enterprises
If you manage a wide range of digital properties, checking each one separately isn’t very effective. You can export the data into a tool like Looker Studio (formerly Google Data Studio), but this requires extra work.
With GA360, you can create “roll-up properties” to analyse data from multiple properties in the same space. It’s the best way to analyse larger trends and patterns across sites and apps.
Administration and user access controls
Beyond roll-up reporting, the other unique “advanced features” found in GA360 are related to administration and user access controls.
First, GA360 lets you create custom user roles, giving different access levels to different properties. Sub-properties and roll-up properties are also useful tools for data governance purposes. They make it easier to limit access for specific analysts to the area they’re directly working on.
You can also design custom reports for specific roles and employees based on their access levels.
Pricing
While GA4 is free, Google Analytics 360 is priced based on your traffic volume.
With the introduction of GA4, Google implemented a revised pricing model. For GA4 360, pricing typically begins at USD $50,000/year which covers up to 25 million events per month. Beyond this limit, costs increase based on data usage, scaling accordingly.
What’s not different: the interface, metrics, reports and basic features
GA4 360 is the same analytics tool as the free version of GA4, with higher usage limits and a few enterprise features. You get more advanced tracking capabilities and more accurate analysis in the same GA4 packaging.
If you already use and love GA4 but need to process more data, that’s great news. But if you’re using UA 360 and are hesitant to switch to the new interface, not so much.
Making the transition from UA to GA4 isn’t easy. Transferring the data means you need to figure out how to work with the API or use Google BigQuery.
Plus, you have to deal with new metrics, reports and a new interface. For example, you don’t get to keep your custom funnel reports. You need to use “funnel explorations.”
Going from UA to GA4 can feel like starting from scratch in a completely new web analytics tool.
Which version of Google Analytics 4 is right for you?
Standard GA4 is a cost-effective web analytics option, but it’s not without its problems:
- If you’re used to the UA interface, it feels clunky and difficult to analyse.
- Data sampling is prevalent in the free version, leading to inaccuracies that can negatively affect decision-making and performance.
And that’s just scratching the surface of common GA4 issues.
Google Analytics 4 360 is a more reliable web analytics solution for enterprises. However, it suffers from many issues that made the GA4 transition painful for many free UA users last year.
- You need to rebuild reports and adjust to the new complex interface.
- To transfer historical data, you must use spreadsheets, the API, or BigQuery.
You will still lose some of the data due to changes to the metrics and reporting.
What if neither option is right for you? Key considerations for choosing a Google Analytics alternative
Despite what Google would like you to think, GA4 isn’t the only option for website analytics in 2024 — far from it. For companies that are used to UA 360, the right alternative can offer unique benefits to your company.
Privacy regulations and future-proofing your analytics and marketing
Although less flagrant than UA, GA4 is still in murky waters regarding compliance with GDPR and other privacy regulations.
And the issue isn’t just that you can get fined (which is bad enough). As part of a ruling, you may be ordered to change your analytics platform and protocol, which can completely disrupt your marketing workflow.
When most marketing teams rely on web analytics to judge the ROI of their campaigns, this can be catastrophic. You may even have to pause campaigns as your team makes the adjustments.
Avoid this risk completely by going with a privacy-friendly alternative.
Features beyond basic web analytics
To understand your users, you need to look at more than just events and conversions.
That’s why some web analytics solutions have built-in behavioural analytics tools. Features like heatmaps (a visual pattern of popular clicks, scrolling and cursor movement) can help you understand how users interact with specific pages.
Matomo allows you to consolidate behavioural analytics and regular web analytics into a single platform. You don’t need separate tools and subscriptions for heatmaps, session recordings, from analytics, media analytics and A/B testing. You can do all of this with Matomo.
With insights about visits, sales, conversions, and usability in the same place, it’s a lot easier to improve your website.
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Usability and familiar metrics
The move to event tracking means new metrics, reports and tools. So, if you’re used to Universal Analytics, it can be tricky to transition to GA4.
But there’s no need to start from zero, learning to work with a brand-new interface. Many competing web analytics platforms offer familiar reports and metrics — ones your team has gotten used to. This will help you speed up the time to value with a shorter learning curve.
Why Matomo is a better option than GA4 360 for UA 360 users
Matomo offers privacy-friendly tracking, built from the ground up to comply with regulations — including IP anonymisation and DoNotTrack settings. You also get 100% ownership of the data, which means we will never use your data for our own profit (unlike Google and other data giants).
This is a big deal, as breaking GDPR rules can lead to fines of up to 4% of your annual revenue. At the same time, you’ll also future-proof your marketing workflow by choosing a web analytics provider built with privacy regulations in mind.
Plus, for legacy UA 360 users, the Matomo interface will also feel a lot more intuitive and familiar. Matomo also provides marketing attribution models you know, like first click, which GA4 has removed.
Finally, you can access various behavioural analytics tools in a single platform — heatmaps, session recordings, form analytics, A/B testing and more. That means you don’t need to pay for separate solutions for conversion rate optimisation efforts.
And the transition is smooth. Matomo lets you import Universal Analytics data and offers ready-made Google Ads integration and Looker Studio Connector.
Join over 1 million websites that choose Matomo as their web analytics solution. Try it free for a 21-days. No credit card required.
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GA360 Sunset : Is Now the Time to Switch ?
20 mai, par ErinGoogle 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.
Try Matomo for Free
Get the web insights you need, without compromising data accuracy.
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.
Try Matomo for Free
21 day free trial. No credit card required.
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A Guide to GDPR Sensitive Personal Data
13 mai, par ErinThe General Data Protection Regulation (GDPR) is one of the world’s most stringent data protection laws. It provides a legal framework for collection and processing of the personal data of EU individuals.
The GDPR distinguishes between “special categories of personal data” (also referred to as “sensitive”) and other personal data and imposes stricter requirements on collection and processing of sensitive data. Understanding these differences will help your company comply with the requirements and avoid heavy penalties.
In this article, we’ll explain what personal data is considered “sensitive” according to the GDPR. We’ll also examine how a web analytics solution like Matomo can help you maintain compliance.
What is sensitive personal data?
The following categories of data are treated as sensitive:
- Personal data revealing:
- Racial or ethnic origin;
- Political opinions;
- Religious or philosophical beliefs;
- Trade union membership;
- Genetic and biometric data;
- Data concerning a person’s:
- Health; or
- Sex life or sexual orientation.
- Personal data revealing:
Sensitive vs. non-sensitive personal data: What’s the difference?
While both categories include information about an individual, sensitive data is seen as more private, or requiring a greater protection.
Sensitive data often carries a higher degree of risk and harm to the data subject, if the data is exposed. For example, a data breach exposing health records could lead to discrimination for the individuals involved. An insurance company could use the information to increase premiums or deny coverage.
In contrast, personal data like name or gender is considered less sensitive because it doesn’t carry the same degree of harm as sensitive data.
Unauthorised access to someone’s name alone is less likely to harm them or infringe on their fundamental rights and freedoms than an unauthorised access to their health records or biometric data. Note that financial information (e.g. credit card details) does not fall into the special categories of data.
Legality of processing
Under the GDPR, both sensitive and nonsensitive personal data are protected. However, the rules and conditions for processing sensitive data are more stringent.
Article 6 deals with processing of non-sensitive data and it states that processing is lawful if one of the six lawful bases for processing applies.
In contrast, Art. 9 of the GDPR states that processing of sensitive data is prohibited as a rule, but provides ten exceptions.
It is important to note that the lawful bases in Art. 6 are not the same as exceptions in Art. 9. For example, while performance of a contract or legitimate interest of the controller are a lawful basis for processing non-sensitive personal data, they are not included as an exception in Art. 9. What follows is that controllers are not permitted to process sensitive data on the basis of contract or legitimate interest.
The exceptions where processing of sensitive personal data is permitted (subject to additional requirements) are:
- Explicit consent: The individual has given explicit consent to processing their sensitive personal data for specified purpose(s), except where an EU member state prohibits such consent. See below for more information about explicit consent.
- Employment, social security or social protection: Processing sensitive data is necessary to perform tasks under employment, social security or social protection law.
- Vital interests: Processing sensitive data is necessary to protect the interests of a data subject or if the individual is physically or legally incapable of consenting.
- Non-for-profit bodies: Foundations, associations or nonprofits with a political, philosophical, religious or trade union aim may process the sensitive data of their members or those they are in regular contact with, in connection with their purposes (and no disclosure of the data is permitted outside the organisation, without the data subject’s consent).
- Made public: In some cases, it may be permissible to process the sensitive data of a data subject if the individual has already made it public and accessible.
- Legal claims: Processing sensitive data is necessary to establish, exercise or defend legal claims, including legal or in court proceedings.
- Public interest: Processing is necessary for reasons of substantial public interest, like preventing unlawful acts or protecting the public.
- Health or social care: Processing special category data is necessary for: preventative or occupational medicine, providing health and social care, medical diagnosis or managing healthcare systems.
- Public health: It is permissible to process sensitive data for public health reasons, like protecting against cross-border threats to health or ensuring the safety of medicinal products or medical devices.
- Archiving, research and statistics: You may process sensitive data if it’s done for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes.
In addition, you must adhere to all data handling requirements set by the GDPR.
Important: Note that for any data sent that you are processing, you always need to identify a lawful basis under Art. 6. In addition, if the data sent contains sensitive data, you must comply with Art. 9.
Explicit consent
While consent is a valid lawful basis for processing non-sensitive personal data, controllers are permitted to process sensitive data only with an “explicit consent” of the data subject.
The GDPR does not define “explicit” consent, but it is accepted that it must meet all Art. 7 conditions for consent, at a higher threshold. To be “explicit” a consent requires a clear statement (oral or written) of the data subject. Consent inferred from the data subject’s actions does not meet the threshold.
The controller must retain records of the explicit consent and provide appropriate consent withdrawal method to allow the data subject to exercise their rights.
Examples of compliant and non-compliant sensitive data processing
Here are examples of when you can and can’t process sensitive data:
- When you can process sensitive data: A doctor logs sensitive data about a patient, including their name, symptoms and medicine prescribed. The hospital can process this data to provide appropriate medical care to their patients. An IoT device and software manufacturer processes their customers’ health data based on explicit consent of each customer.
- When you can’t process sensitive data: One example is when you don’t have explicit consent from a data subject. Another is when there’s no lawful basis for processing it or you are collecting personal data you simply do not need. For example, you don’t need your customer’s ethnic origin to fulfil an online order.
Other implications of processing sensitive data
If you process sensitive data, especially on a large scale, GDPR imposes additional requirements, such as having Data Privacy Impact Assessments, appointing Data Protection Officers and EU Representatives, if you are a controller based outside the EU.
Penalties for GDPR non-compliance
Mishandling sensitive data (or processing it when you’re not allowed to) can result in huge penalties. There are two tiers of GDPR fines:
- €10 million or 2% of a company’s annual revenue for less severe infringements
- €20 million or 4% of a company’s annual revenue for more severe infringements
In the first half of 2023 alone, fines imposed in the EU due to GDPR violations exceeded €1.6 billion, up from €73 million in 2019.
Examples of high-profile violations in the last few years include:
- Amazon: The Luxembourg National Commission fined the retail giant with a massive $887 million fine in 2021 for not processing personal data per the GDPR.
- Google: The National Data Protection Commission (CNIL) fined Google €50 million for not getting proper consent to display personalised ads.
- H&M: The Hamburg Commissioner for Data Protection and Freedom of Information hit the multinational clothing company with a €35.3 million fine in 2020 for unlawfully gathering and storing employees’ data in its service centre.
One of the criteria that affects the severity of a fine is “data category” — the type of personal data being processed. Companies need to take extra precautions with sensitive data, or they risk receiving more severe penalties.
What’s more, GDPR violations can negatively affect your brand’s reputation and cause you to lose business opportunities from consumers concerned about your data practices. 76% of consumers indicated they wouldn’t buy from companies they don’t trust with their personal data.
Organisations should lay out their data practices in simple terms and make this information easily accessible so customers know how their data is being handled.
Get started with GDPR-compliant web analytics
The GDPR offers a framework for securing and protecting personal data. But it also distinguishes between sensitive and non-sensitive data. Understanding these differences and applying the lawful basis for processing this data type will help ensure compliance.
Looking for a GDPR-compliant web analytics solution?
At Matomo, we take data privacy seriously.
Our platform ensures 100% data ownership, putting you in complete control of your data. Unlike other web analytics solutions, your data remains solely yours and isn’t sold or auctioned off to advertisers.
Additionally, with Matomo, you can be confident in the accuracy of the insights you receive, as we provide reliable, unsampled data.
Matomo also fully complies with GDPR and other data privacy laws like CCPA, LGPD and more.
Start your 21-day free trial today; no credit card required.
Disclaimer
We are not lawyers and don’t claim to be. The information provided here is to help give an introduction to GDPR. We encourage every business and website to take data privacy seriously and discuss these issues with your lawyer if you have any concerns.
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What Is Data Misuse & How to Prevent It ? (With Examples)
13 mai, par ErinYour data is everywhere. Every time you sign up for an email list, log in to Facebook or download a free app onto your smartphone, your data is being taken.
This can scare customers and users who fear their data will be misused.
While data can be a powerful asset for your business, it’s important you manage it well, or you could be in over your head.
In this guide, we break down what data misuse is, what the different types are, some examples of major data misuse and how you can prevent it so you can grow your brand sustainably.
What is data misuse?
Data is a good thing.
It helps analysts and marketers understand their customers better so they can serve them relevant information, products and services to improve their lives.
But it can quickly become a bad thing for both the customers and business owners when it’s mishandled and misused.
Data misuse is when a business uses data outside of the agreed-upon terms. When companies collect data, they need to legally communicate how that data is being used.
Who or what determines when data is being misused?
Several bodies:
- User agreements
- Data privacy laws
- Corporate policies
- Industry regulations
There are certain laws and regulations around how you can collect and use data. Failure to comply with these guidelines and rules can result in several consequences, including legal action.
Keep reading to discover the different types of data misuse and how to prevent it.
3 types of data misuse
There are a few different types of data misuse.
If you fail to understand them, you could face penalties, legal trouble and a poor brand reputation.
1. Commingling
When you collect data, you need to ensure you’re using it for the right purpose. Commingling is when an organisation collects data from a specific audience for a specific reason but then uses the data for another purpose.
One example of commingling is if a company shares sensitive customer data with another company. In many cases, sister companies will share data even if the terms of the data collection didn’t include that clause.
Another example is if someone collects data for academic purposes like research but then uses the data later on for marketing purposes to drive business growth in a for-profit company.
In either case, the company went wrong by not being clear on what the data would be used for. You must communicate with your audience exactly how the data will be used.
2. Personal benefit
The second common way data is misused in the workplace is through “personal benefit.” This is when someone with access to data abuses it for their own gain.
The most common example of personal benefit data muse is when an employee misuses internal data.
While this may sound like each instance of data misuse is caused by malicious intent, that’s not always the case. Data misuse can still exist even if an employee didn’t have any harmful intent behind their actions.
One of the most common examples is when an employee mistakenly moves data from a company device to personal devices for easier access.
3. Ambiguity
As mentioned above, when discussing commingling, a company must only use data how they say they will use it when they collect it.
A company can misuse data when they’re unclear on how the data is used. Ambiguity is when a company fails to disclose how user data is being collected and used.
This means communicating poorly on how the data will be used can be wrong and lead to misuse.
One of the most common ways this happens is when a company doesn’t know how to use the data, so they can’t give a specific reason. However, this is still considered misuse, as companies need to disclose exactly how they will use the data they collect from their customers.
Laws on data misuse you need to follow
Data misuse can lead to poor reputations and penalties from big tech companies. For example, if you step outside social media platforms’ guidelines, you could be suspended, banned or shadowbanned.
But what’s even more important is certain types of data misuse could mean you’re breaking laws worldwide. Here are some laws on data misuse you need to follow to avoid legal trouble:
General Data Protection Regulation (GDPR)
The GDPR, or General Data Protection Regulation, is a law within the European Union (EU) that went into effect in 2018.
The GDPR was implemented to set a standard and improve data protection in Europe. It was also established to increase accountability and transparency for data breaches within businesses and organisations.
The purpose of the GDPR is to protect residents within the European Union.
The penalties for breaking GDPR laws are fines up to 20 million Euros or 4% of global revenues (whatever the higher amount is).
The GDPR doesn’t just affect companies in Europe. You can break the GDPR’s laws regardless of where your organisation is located worldwide. As long as your company collects, processes or uses the personal data of any EU resident, you’re subject to the GDPR’s rules.
If you want to track user data to grow your business, you need to ensure you’re following international data laws. Tools like Matomo—the world’s leading privacy-friendly web analytics solution—can help you achieve GDPR compliance and maintain it.
With Matomo, you can confidently enhance your website’s performance, knowing that you’re adhering to data protection laws.
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California Consumer Privacy Act (CCPA)
The California Consumer Privacy Act (CCPA) is another important data law companies worldwide must follow.
Like GDPR, the CCPA is a data privacy law established to protect residents of a certain region — in this case, residents of California in the United States.
The CCPA was implemented in 2020, and businesses worldwide can be penalised for breaking the regulations. For example, if you’re found violating the CCPA, you could be fined $7,500 for each intentional violation.
If you have unintentional violations, you could still be fined, but at a lesser fee of $2,500.
The Gramm-Leach-Bliley Act (GLBA)
If your business is located within the United States, then you’re subject to a federal law implemented in 1999 called The Gramm-Leach-Bliley Act (GLB Act or GLBA).
The GLBA is also known as the Financial Modernization Act of 1999. Its purpose is to control the way American financial institutions handle consumer data.
In the GLBA, there are three sections:
- The Financial Privacy Rule: regulates the collection and disclosure of private financial data.
- Safeguards Rule: Financial institutions must establish security programs to protect financial data.
- Pretexting Provisions: Prohibits accessing private data using false pretences.
The GLBA also requires financial institutions in the U.S. to give their customers written privacy policy communications that explain their data-sharing practices.
4 examples of data misuse in real life
If you want to see what data misuse looks like in real life, look no further.
Big tech is central to some of the biggest data misuses and scandals.
Here are a few examples of data misuse in real life you should take note of to avoid a similar scenario:
1. Facebook election interference
One of history’s most famous examples of data misuse is the Facebook and Cambridge Analytica scandal in 2018.
During the 2018 U.S. midterm elections, Cambridge Analytica, a political consulting firm, acquired personal data from Facebook users that was said to have been collected for academic research.
Instead, Cambridge Analytica used data from roughly 87 million Facebook users.
This is a prime example of commingling.
The result? Cambridge Analytica was left bankrupt and dissolved, and Facebook was fined $5 billion by the Federal Trade Commission (FTC).
2. Uber “God View” tracking
Another big tech company, Uber, was caught misusing data a decade ago.
Why?
Uber implemented a new feature for its employees in 2014 called “God View.”
The tool enabled Uber employees to track riders using their app. The problem was that they were watching them without the users’ permission. “God View” lets Uber spy on their riders to see their movements and locations.
The FTC ended up slapping them with a major lawsuit, and as part of their settlement agreement, Uber agreed to have an outside firm audit their privacy practices between 2014 and 2034.
3. Twitter targeted ads overstep
In 2019, Twitter was found guilty of allowing advertisers to access its users’ personal data to improve advertisement targeting.
Advertisers were given access to user email addresses and phone numbers without explicit permission from the users. The result was that Twitter ad buyers could use this contact information to cross-reference with Twitter’s data to serve ads to them.
Twitter stated that the data leak was an internal error.
4. Google location tracking
In 2020, Google was found guilty of not explicitly disclosing how it’s using its users’ personal data, which is an example of ambiguity.
The result?
The French data protection authority fined Google $57 million.
8 ways to prevent data misuse in your company
Now that you know the dangers of data misuse and its associated penalties, it’s time to understand how you can prevent it in your company.
Here are eight ways you can prevent data misuse:
1. Track data with an ethical web analytics solution
You can’t get by in today’s business world without tracking data. The question is whether you’re tracking it safely or not.
If you want to ensure you aren’t getting into legal trouble with data misuse, then you need to use an ethical web analytics solution like Matomo.
With it, you can track and improve your website performance while remaining GDPR-compliant and respecting user privacy. Unlike other web analytics solutions that monetise your data and auction it off to advertisers, with Matomo, you own your data.
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2. Don’t share data with big tech
As the data misuse examples above show, big tech companies often violate data privacy laws.
And while most of these companies, like Google, appear to be convenient, they’re often inconvenient (and much worse), especially regarding data leaks, privacy breaches and the sale of your data to advertisers.
Have you ever heard the phrase: “You are the product?” When it comes to big tech, chances are if you’re getting it for free, you (and your data) are the products they’re selling.
The best way to stop sharing data with big tech is to stop using platforms like Google. For more ideas on different Google product alternatives, check out this list of Google alternatives.
3. Identity verification
Data misuse typically isn’t a company-wide ploy. Often, it’s the lack of security structure and systems within your company.
An important place to start is to ensure proper identity verification for anyone with access to your data.
4. Access management
After establishing identity verification, you should ensure you have proper access management set up. For example, you should only give specific access to specific roles in your company to prevent data misuse.
5. Activity logs and monitoring
One way to track data misuse or breaches is by setting up activity logs to ensure you can see who is accessing certain types of data and when they’re accessing it.
You should ensure you have a team dedicated to continuously monitoring these logs to catch anything quickly.
6. Behaviour alerts
While manually monitoring data is important, it’s also good to set up automatic alerts if there is unusual activity around your data centres. You should set up behaviour alerts and notifications in case threats or compromising events occur.
7. Onboarding, training, education
One way to ensure quality data management is to keep your employees up to speed on data security. You should ensure data security is a part of your employee onboarding. Also, you should have regular training and education to keep people informed on protecting company and customer data.
8. Create data protocols and processes
To ensure long-term data security, you should establish data protocols and processes.
To protect your user data, set up rules and systems within your organisation that people can reference and follow continuously to prevent data misuse.
Leverage data ethically with Matomo
Data is everything in business.
But it’s not something to be taken lightly. Mishandling user data can break customer trust, lead to penalties from organisations and even create legal trouble and massive fines.
You should only use privacy-first tools to ensure you’re handling data responsibly.
Matomo is a privacy-friendly web analytics tool that collects, stores and tracks data across your website without breaking privacy laws.
With over 1 million websites using Matomo, you can track and improve website performance with:
- Accurate data (no data sampling)
- Privacy-friendly and compliant with privacy regulations like GDPR, CCPA and more
- Advanced features like heatmaps, session recordings, A/B testing and more
Try Matomo free for 21-days. No credit card required.
Try Matomo for Free
21 day free trial. No credit card required.
