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• Google Analytics 4 (GA4) vs Universal Analytics (UA)

  24 janvier 2022, par Erin — Analytics Tips

  March 2022 Update : It’s official ! Google announced that Universal Analytics will no longer process any new data as of 1 July 2023. Google is now pushing Universal Analytics users to switch to the latest version of GA – Google Analytics 4. 

  Currently, Google Analytics 4 is unable to accept historical data from Universal Analytics. Users need to take action before July 2022, to ensure they have 12 months of data built up before the sunset of Universal Analytics

  So how do Universal Analytics and Google Analytics 4 compare ? And what alternative options do you have ? Let’s dive in. 

  In this blog, we’ll cover :

  What is Google Analytics 4 ? 

  In October 2020, Google launched Google Analytics 4, a completely redesigned analytics platform. This follows on from the previous version known as Universal Analytics (or UA).

  Amongst its touted benefits, GA4 promises a completely new way to model data and even the ability to predict future revenue. 

  However, the reception of GA4 has been largely negative. In fact, some users from the digital marketing community have said that GA4 is awful, unusable and so bad it can bring you to tears.

  

  

  Google Analytics 4 vs Universal Analytics

  There are some pretty big differences between Google Analytics 4 and Universal Analytics but for this blog, we’ll cover the top three.

  1. Redesigned user interface (UI)

  GA4 features a completely redesigned UI to Universal Analytics’ popular interface. This dramatic change has left many users in confusion and fuelled some users to declare that “most of the time you are going round in circles to find what you’re looking for.”

  

  2. Event-based tracking

  Google Analytics 4 also brings with it a new data model which is purely event-based. This event-based model moves away from the typical “pageview” metric that underpins Universal Analytics.

  3. Machine learning insights

  Google Analytics 4 promises to “predict the future behavior of your users” with their machine-learning-powered predictive metrics. This feature can “use shared aggregated and anonymous data to improve model quality”. Sounds powerful, right ?

  Unfortunately, it only works if at least 1,000 returning users triggered the relevant predictive condition over a seven-day period. Also, if the model isn’t sustained over a “period of time” then it won’t work. And according to Google, if “the model quality for your property falls below the minimum threshold, then Analytics will stop updating the corresponding predictions”.

  This means GA4’s machine learning insights probably won’t work for the majority of analytics users.

  Ultimately, GA4 is just not ready to replace Google’s Universal Analytics for most users. There are too many missing features.

  What’s missing in Google Analytics 4 ?

  Quite a lot. Even though it offers a completely new approach to analytics, there are a lot of key features and functions missing in GA4.

  Behavior Flow

  The Behavior Flow report in Universal Analytics helps to visualise the path users take from one page or Event to the next. It’s extremely useful when you’re looking for quick and clear insight. But it no longer exists in Google Analytics 4, and instead, two new overcomplicated reports have been introduced to replace it – funnel exploration report and path exploration report.

  The decision to remove this critical report will leave many users feeling disappointed and frustrated. 

  Limitations on custom dimensions

  You can create custom dimensions in Google Analytics 4 to capture advanced information. For example, if a user reads a blog post you can supplement that data with custom dimensions like author name or blog post length. But, you can only use up to 50, and for some that will make functionality like this almost pointless.

  Machine learning (ML) limitations

  Google Analytics 4 promises powerful ML insights to predict the likelihood of users converting based on their behaviors. The problem ? You need 1,000 returning users in one week. For most small-medium businesses this just isn’t possible.

  And if you do get this level of traffic in a week, there’s another hurdle. According to Google, if “the model quality for your property falls below the minimum threshold, then GA will stop updating the corresponding predictions.” To add insult to injury Google suggests that this might make all ML insights unavailable. But they can’t say for certain… 

  Views

  One cornerstone of Universal Analytics is the ability to configure views. Views allow you to set certain analytics environments for testing or cleaning up data by filtering out internal traffic, for example. 

  Views are great for quickly and easily filtering data. Preset views that contain just the information you want to see are the ideal analytics setup for smaller businesses, casual users, and do-it-yourself marketing departments.

  

  There are a few workarounds but they’re “messy [,] annoying and clunky,” says a disenfranchised Redditor.

  Another helpful Reddit user stumbled upon an unhelpful statement from Google. Google says that they “do not offer [the views] feature in Google Analytics 4 but are planning similar functionality in the future.” There’s no specific date yet though.

  Bounce rate

  Those that rely on bounce rate to understand their site’s performance will be disappointed to find out that bounce rate is also not available in GA4. Instead, Google is pushing a new metric known as “Engagement Rate”. With this metric, Google now uses their own formula to establish if a visitor is engaged with a site.

  Lack of integration

  Currently, GA4 isn’t ready to integrate with many core digital marketing tools and doesn’t accept non-Google data imports. This makes it difficult for users to analyse ROI and ROAS for campaigns measured in other tools. 

  Content Grouping

  Yet another key feature that Google has done away with is Content Grouping. However, as with some of the other missing features in GA4, there is a workaround, but it’s not simple for casual users to implement. In order to keep using Content Grouping, you’ll need to create event-scoped custom dimensions.

  Annotations 

  A key feature of Universal Analytics is the ability to add custom Annotations in views. Annotations are useful for marking dates that site changes were made for analysis in the future. However, Google has removed the Annotations feature and offered no alternative or workaround.

  Historical data imports are not available

  The new approach to data modelling in GA4 adds new functionality that UA can’t match. However, it also means that you can’t import historical UA data into GA4. 

  Google’s suggestion for this one ? Keep running UA with GA4 and duplicate events for your GA4 property. Now you will have two different implementations running alongside each other and doing slightly different things. Which doesn’t sound like a particularly streamlined solution, and adds another level of complexity.

  Should you switch to Google Analytics 4 ?

  So the burning question is, should you switch from Universal Analytics to Google Analytics 4 ? It really depends on whether you have the available resources and if you believe this tool is still right for your organisation. At the time of writing, GA4 is not ready for day-to-day use in most organisations.

  If you’re a casual user or someone looking for quick, clear insights then you will likely struggle with the switch to GA4. It appears that the new Google Analytics 4 has been designed for enterprise-scale businesses with large internal teams of analysts.

  

  Unfortunately, for most casual users, business owners and do-it-yourself marketers there are complex workarounds and time-consuming implementations to handle. Ultimately, it’s up to you to decide if the effort to migrate and relearn GA is worth it.

  Right now is the best time to draw the line and make a decision to either switch to GA4 or look for a better alternative to Google Analytics.

  Google Analytics alternative

  Matomo is one of the best Google Analytics alternatives offering an easy to use design with enhanced insights on our Cloud, On-Premise and on Matomo for WordPress solutions. 

  

  Matomo is an open-source analytics solution that provides a comprehensive, user-friendly and compliance-focused alternative to both Google Analytics 4 and Universal Analytics.

  The key benefits of using Matomo include :

  • Easy to use – Matomo provides a simpler interface and understandable KPIs. See for yourself with our live demo. 
  • Compliance – Future-proof your tech stack for looming privacy regulations. Matomo covers all of your ePrivacy, GDPR, HIPAA, CCPA, and PECR data compliance requirements. 
  • Data privacy and ownership – Your analytics data is 100% yours to own, with no external parties looking in.
  • Flexible, all-in-one solution – Get features like A/B Testing, Heatmaps, Session Recordings, SEO Web Vitals, Tag Manager, Media Analytics, Search Engine Keyword Performance, custom reports and much more. 
  • Integrations galore – Expand your Matomo capabilities by adding integrations from over 100 leading technologies.

  Plus, unlike GA4, Matomo will accept your historical data from UA so you don’t have to start all over again. Check out our 7 step guide to migrating from Google Analytics to find out how.

  Getting started with Matomo is easy. Check out our live demo and start your free 21-day trial. No credit card required.

  In addition to the limitations and complexities of GA4, there are many other significant drawbacks to using Google Analytics.

  Google’s data ethics are a growing concern of many and it is often discussed in the mainstream media. In addition, GA is not GDPR compliant by default and has resulted in 200k+ data protection cases against websites using GA.

  What’s more, the data that Google Analytics actually provides its end-users is extrapolated from samples. GA’s data sampling model means that once you’ve collected a certain amount of data Google Analytics will make educated guesses rather than use up its server space collecting your actual data. 

  The reasons to switch from Google Analytics are rising each day. 

  Wrap up

  The now required update to GA4 will add new layers of complexity, which will leave many casual web analytics users and marketers wondering if there’s a better way. Luckily there is. Get clear insights quickly and easily with Matomo – start your 21-day free trial now.

• How to Use Analytics & Reports for Marketing, Sales & More

  28 septembre 2023, par Erin — Analytics Tips

  By now, most professionals know they should be using analytics and reports to make better business decisions. Blogs and thought leaders talk about it all the time. But most sources don’t tell you how to use analytics and reports. So marketers, salespeople and others either skim whatever reports they come across or give up on making data-driven decisions entirely. 

  But it doesn’t have to be this way.

  In this article, we’ll cover what analytics and reports are, how they differ and give you examples of each. Then, we’ll explain how clean data comes into play and how marketing, sales, and user experience teams can use reports and analytics to uncover actionable insights.

  What’s the difference between analytics & reports ? 

  Many people speak of reports and analytics as if the terms are interchangeable, but they have two distinct meanings.

  A report is a collection of data presented in one place. By tracking key metrics and providing numbers, reports tell you what is happening in your business. Analytics is the study of data and the process of generating insights from data. Both rely on data and are essential for understanding and improving your business results.

  

  A science experiment is a helpful analogy for how reporting and analytics work together. To conduct an experiment, scientists collect data and results and compile a report of what happened. But the process doesn’t stop there. After generating a data report, scientists analyse the data and try to understand the why behind the results.

  In a business context, you collect and organise data in reports. With analytics, you then use those reports and their data to draw conclusions about what works and what doesn’t.

  Reports examples 

  Reports are a valuable tool for just about any part of your business, from sales to finance to human resources. For example, your finance team might collect data about spending and use it to create a report. It might show how much you spend on employee compensation, real estate, raw materials and shipping.

  On the other hand, your marketing team might benefit from a report on lead sources. This would mean collecting data on where your sales leads come from (social media, email, organic search, etc.). You could collect and present lead source data over time for a more in-depth report. This shows which sources are becoming more effective over time. With advanced tools, you can create detailed, custom reports that include multiple factors, such as time, geographical location and device type.

  

  Analytics examples 

  Because analytics requires looking at and drawing insights from data and reports to collect and present data, analytics often begins by studying reports. 

  In our example of a report on lead sources, an analytics professional might study the report and notice that webinars are an important source of leads. To better understand this, they might look closely at the number of leads acquired compared to how often webinars occur. If they notice that the number of webinar leads has been growing, they might conclude that the business should invest in more webinars to generate more leads. This is just one kind of insight analytics can provide.

  For another example, your human resources team might study a report on employee retention. After analysing the data, they could discover valuable insights, such as which teams have the highest turnover rate. Further analysis might help them uncover why certain teams fail to keep employees and what they can do to solve the problem.

  The importance of clean data 

  Both analytics and reporting rely on data, so it’s essential your data is clean. Clean data means you’ve audited your data, removed inaccuracies and duplicate entries, and corrected mislabelled data or errors. Basically, you want to ensure that each piece of information you’re using for reports and analytics is accurate and organised correctly.

  If your data isn’t clean and accurate, neither will your reports be. And making business decisions based on bad data can come at a considerable cost. Inaccurate data might lead you to invest in a channel that appears more valuable than it actually is. Or it could cause you to overlook opportunities for growth. Moreover, poor data maintenance and the poor insight it provides will lead your team to have less trust in your reports and analytics team.

  The simplest way to maintain clean data is to be meticulous when inputting or transferring data. This can be as simple as ensuring that your sales team fills in every field of an account record. When you need to import or transfer data from other sources, you need to perform quality assurance (QA) checks to make sure data is appropriately labelled and organised. 

  Another way to maintain clean data is by avoiding cookies. Most web visitors reject cookie consent banners. When this happens, analysts and marketers don’t get data on these visitors and only see the percentage of users who accept tracking. This means they decide on a smaller sample size, leading to poor or inaccurate data. These banners also create a poor user experience and annoy web visitors.

  Matomo can be configured to run cookieless — which, in most countries, means you don’t need to have an annoying cookie consent screen on your site. This way, you can get more accurate data and create a better user experience.

  Marketing analytics and reports 

  Analytics and reporting help you measure and improve the effectiveness of your marketing efforts. They help you learn what’s working and what you should invest more time and money into. And bolstering the effectiveness of your marketing will create more opportunities for sales.

  

  One common area where marketing teams use analytics and reports is to understand and improve their keyword rankings and search engine optimization. They use web analytics platforms like Matomo to report on how their website performs for specific keywords. Insights from these reports are then used to inform changes to the website and the development of new content.

  As we mentioned above, marketing teams often use reports on lead sources to understand how their prospects and customers are learning about the brand. They might analyse their lead sources to better understand their audience. 

  For example, if your company finds that you receive a lot of leads from LinkedIn, you might decide to study the content you post there and how it differs from other platforms. You could apply a similar content approach to other channels to see if it increases lead generation. You can then study reporting on how lead source data changes after you change content strategies. This is one example of how analysing a report can lead to marketing experimentation. 

  Email and paid advertising are also marketing channels that can be optimised with reports and analysis. By studying the data around what emails and ads your audience clicks on, you can draw insights into what topics and messaging resonate with your customers.

  

  Marketing teams often use A/B testing to learn about audience preferences. In an A/B test, you can test two landing page versions, such as two different types of call-to-action (CTA) buttons. Matomo will generate a report showing how many people clicked each version. From those results, you may draw an insight into the design your audience prefers.

  Sales analytics and reports 

  Sales analytics and reports are used to help teams close more deals and sell more efficiently. They also help businesses understand their revenue, set goals, and optimise sales processes. And understanding your sales and revenue allows you to plan for the future.

  One of the keys to building a successful sales strategy and team is understanding your sales cycle. That’s why it’s so important for companies to analyse their lead and sales data. For business-to-business (B2B) companies in particular, the sales cycle can be a long process. But you can use reporting and analytics to learn about the stages of the buying cycle, including how long they take and how many leads proceed to the next step.

  Analysing lead and customer data also allows you to gain insights into who your customers are. With detailed account records, you can track where your customers are, what industries they come from, what their role is and how much they spend. While you can use reports to gather customer data, you also have to use analysis and qualitative information in order to build buyer personas. 

  

  Many sales teams use past individual and business performance to understand revenue trends. For instance, you might study historical data reports to learn how seasonality affects your revenue. If you dive deeper, you might find that seasonal trends may depend on the country where your customers live. 

  

  Conversely, it’s also important to analyse what internal variables are affecting revenue. You can use revenue reports to identify your top-performing sales associates. You can then try to expand and replicate that success. While sales is a field often driven by personal relationships and conversations, many types of reports allow you to learn about and improve the process.

  Website and user behaviour analytics and reports 

  More and more, businesses view their websites as an experience and user behaviour as an important part of their business. And just like sales and marketing, reporting and analytics help you better understand and optimise your web experience. 

  Many web and user behaviour metrics, like traffic source, have important implications for marketing. For example, page traffic and user flows can provide valuable insights into what your customers are interested in. This can then drive future content development and marketing campaigns.

  

  You can also learn about how your users navigate and use your website. A robust web analytics tool, like Matomo, can supply user session recordings and visitor tracking. For example, you could study which pages a particular user visits. But Matomo also has a feature called Transitions that provides visual reports showing where a particular page’s traffic comes from and where visitors tend to go afterward. 

  

  As you consider why people might be leaving your website, site performance is another important area for reporting. Most users are accustomed to near-instantaneous web experiences, so it’s worth monitoring your page load time and looking out for backend delays. In today’s world, your website experience is part of what you’re selling to customers. Don’t miss out on opportunities to impress and delight them.

  Dive into your data

  Reporting and analytics can seem like mysterious buzzwords we’re all supposed to understand already. But, like anything else, they require definitions and meaningful examples. When you dig into the topic, though, the applications for reporting and analytics are endless.

  Use these examples to identify how you can use analytics and reports in your role and department to achieve better results, whether that means higher quality leads, bigger deal size or a better user experience.

  To see how Matomo can collect accurate and reliable data and turn it into in-depth analytics and reports, start a free 21-day trial. No credit card required.

• Making Your First-Party Data Work for You and Your Customers

  11 mars, par Alex Carmona

  At last count, 162 countries had enacted data privacy policies of one kind or another. These laws or regulations, without exception, intend to eliminate the use of third-party data. That puts marketing under pressure because third-party data has been the foundation of online marketing efforts since the dawn of the Internet.

  Marketers need to future-proof their operations by switching to first-party data. This will require considerable adjustment to systems and processes, but the reward will be effective marketing campaigns that satisfy privacy compliance requirements and bring the business closer to its customers.

  To do that, you’ll need a coherent first-party data strategy. That’s what this article is all about. We’ll explain the different types of personal data and discuss how to use them in marketing without compromising or breaching data privacy regulations. We’ll also discuss how to build that strategy in your business. 

  So, let’s dive in.

  The different data types

  There are four distinct types of personal data used in marketing, each subject to different data privacy regulations.

  Before getting into the different types, it’s essential to understand that all four may comprise one or more of the following :

  Identifying data	Name, email address, phone number, etc.
  Behavioural data	Website activity, app usage, wishlist content, purchase history, etc.
  Transactional data	Orders, payments, subscription details, etc.
  Account data	Communication preferences, product interests, wish lists, etc.
  Demographic data	Age, gender, income level, education, etc.
  Geographic Data	Location-based information, such as zip codes or regional preferences.
  Psychographic Data	Interests, hobbies and lifestyle preferences.

  First-party data

  When businesses communicate directly with customers, any data they exchange is first-party. It doesn’t matter how the interaction occurs : on the telephone, a website, a chat session, or even in person.

  Of course, the parties involved aren’t necessarily individuals. They may be companies, but people within those businesses will probably share at least some of the data with colleagues. That’s fine, so long as the data : 

  • Remains confidential between the original two parties involved, and 
  • It is handled and stored following applicable data privacy regulations.

  The core characteristic of first-party data is that it’s collected directly from customer interactions. This makes it reliable, accurate and inherently compliant with privacy regulations — assuming the collecting party complies with data privacy laws.

  A great example of first-party data use is in banking. Data collected from customer interactions is used to provide personalised services, detect fraud, assess credit risk and improve customer retention.

  Zero-party data

  There’s also a subset of first-party data, sometimes called zero-party data. It’s what users intentionally and proactively share with a business. It can be preferences, intentions, personal information, survey responses, support tickets, etc.

  What makes it different is that the collection of this data depends heavily on the user’s trust. Transparency is a critical factor, too ; visitors expect to be informed about how you’ll use their data. Consumers also have the right to withdraw permission to use all or some of their information at any time.

  

  Second-party data

  This data is acquired from a separate organisation that collects it firsthand. Second-party data is someone else’s first-party data that’s later shared with or sold to other businesses. The key here is that whoever owns that data must give explicit consent and be informed of who businesses share their data with.

  A good example is the cooperation between hotel chains, car rental companies, and airlines. They share joint customers’ flight data, hotel reservations, and car rental bookings, much like travel agents did before the internet undermined that business model.

  Third-party data

  This type of data is the arch-enemy of lawmakers and regulators trying to protect the personal data of citizens and residents in their country. It’s information collected by entities that have no direct relationship with the individuals whose data it is.

  Third-party data is usually gathered, aggregated, and sold by data brokers or companies, often by using third-party cookies on popular websites. It’s an entire business model — these third-party brokers sell data for marketing, analytics, or research purposes. 

  Most of the time, third-party data subjects are unaware that their data has been gathered and sold. Hence the need for strong data privacy regulations.

  Benefits of a first-party data strategy

  First-party data is reliable, accurate, and ethically sourced. It’s an essential part of any modern digital marketing strategy.

  More personalised experiences

  The most important application of first-party data is customising and personalising customers’ interactions based on real behaviours and preferences. Personalised experiences aren’t restricted to websites and can extend to all customer communication.

  The result is company communications and marketing messages are far more relevant to customers. It allows businesses to engage more meaningfully with them, building trust and strengthening customer relationships. Inevitably, this also results in stronger customer loyalty and better customer retention.

  Greater understanding of customers

  Because first-party data is more accurate and reliable, it can be used to derive valuable insights into customer needs and wants. When all the disparate first-party data points are centralised and organised, it’s possible to uncover trends and patterns in customer behaviour that might not be apparent using other data.

  This helps businesses predict and respond to customer needs. It also allows marketing teams to be more deliberate when segmenting customers and prospects into like-minded groups. The data can also be used to create more precise personas for future campaigns or reveal how likely a customer would be to purchase in response to a campaign.

  Build trust with customers

  First-party data is unique to a business and originates from interactions with customers. It’s also data collected with consent and is “owned” by the company — if you can ever own someone else’s data. If treated like the precious resource, it can help businesses build trust with customers.

  However, developing that trust requires a transparent, step-by-step approach. This gradually strengthens relationships to the point where customers are more comfortable sharing the information they’re asked for.

  However, while building trust is a long and sometimes arduous process, it can be lost in an instant. That’s why first-party data must be protected like the Crown Jewels.

  

  Components of a first-party data strategy

  Security is essential to any first-party data strategy, and for good reason. As Gartner puts it, a business must find the optimal balance between business outcomes and data risk mitigation. Once security is baked in, attention can turn to the different aspects of the strategy.

  Data collection

  There are many ways to collect first-party data ethically, within the law and while complying with data privacy regulations, such as Europe’s General Data Protection Regulation (GDPR). Potential sources include :

  Website activity	forms and surveys, behavioural tracking, cookies, tracking pixels and chatbots
  Mobile app interactions	in-app analytics, push notifications and in-app forms
  Email marketing	newsletter sign-ups, email engagement tracking, promotions, polls and surveys
  Events	registrations, post-event surveys and virtual event analytics
  Social media interaction	polls and surveys, direct messages and social media analytics
  Previous transactions	purchase history, loyalty programmes and e-receipts
  Customer service	call centre data, live chat, chatbots and feedback forms
  In-person interactions	in-store purchases, customer feedback and Wi-Fi sign-ins
  Gated content	whitepapers, ebooks, podcasts, webinars and video downloads
  Interactive content	quizzes, assessments, calculators and free tools
  CRM platforms	customer profiles and sales data
  Consent management	privacy policies, consent forms, preference setting

  Consent management

  It may be the final item on the list above, but it’s also a key requirement of many data privacy laws and regulations. For example, the GDPR is very clear about consent : “Processing personal data is generally prohibited, unless it is expressly allowed by law, or the data subject has consented to the processing.”

  For that reason, your first-party data strategy must incorporate various transparent consent mechanisms, such as cookie banners and opt-in forms. Crucially, you must provide customers with a mechanism to manage their preferences and revoke that consent easily if they wish to.

  Data management

  Effective first-party data management, mainly its security and storage, is critical. Most data privacy regimes restrict the transfer of personal data to other jurisdictions and even prohibit it in some instances. Many even specify where residents’ data must be stored.

  Consider this cautionary tale : The single biggest fine levied for data privacy infringement so far was €1.2 billion. The Irish Data Protection Commission imposed a massive fine on Meta for transferring EU users’ data to the US without adequate data protection mechanisms.

  Data security is critical. If first-party data is compromised, it becomes third-party data, and any customer trust developed with the business will evaporate. To add insult to injury, data regulators could come knocking. That’s why the trend is to use encryption and anonymisation techniques alongside standard access controls.

  Once security is assured, the focus is on data management. Many businesses use a Customer Data Platform. This software gathers, combines and manages data from many sources to create a complete and central customer profile. Modern CRM systems can also do that job. AI tools could help find patterns and study them. But the most important thing is to keep databases clean and well-organised to make it easier to use and avoid data silos.

  Data activation

  Once first-party data has been collected and analysed, it needs to be activated, which means a business needs to use it for the intended purpose. This is the implementation phase where a well-constructed first-party strategy pays off. 

  The activation stage is where businesses use the intelligence they gather to :

  • Personalise website and app experiences
  • Adapt marketing campaigns
  • Improve conversion rates
  • Match stated preferences
  • Cater to observed behaviours
  • Customise recommendations based on purchase history
  • Create segmented email campaigns
  • Improve retargeting efforts
  • Develop more impactful content

  Measurement and optimisation

  Because first-party data is collected directly from customers or prospects, it’s far more relevant, reliable, and specific. Your analytics and campaign tracking will be more accurate. This gives you direct and actionable insights into your audience’s behaviour, empowering you to optimise your strategies and achieve better results.

  The same goes for your collection and activation efforts. An advanced web analytics platform like Matomo lets you identify key user behaviour and optimise your tracking. Heatmaps, marketing attribution tools, user behaviour analytics and custom reports allow you to segment audiences for better traction (and collect even more first-party data).

  

  How to build a first-party data strategy

  There are five important and sequential steps to building a first-party data strategy. But this isn’t a one-time process. It must be revisited regularly as operating and regulatory environments change. There are five steps : 

  1. Audit existing data

  Chances are that customers already freely provide a lot of first-party data in the normal course of business. The first step is to locate this data, and the easiest way to do that is by mapping the customer journey. This identifies all the touchpoints where first-party data might be found.

  2. Define objectives

  Then, it’s time to step back and figure out the goals of the first-party data strategy. Consider what you’re trying to achieve. For example :

  • Reduce churn 
  • Expand an existing loyalty programme
  • Unload excess inventory
  • Improve customer experiences

  Whatever the objectives are, they should be clear and measurable.

  3. Implement tools and technology

  The first two steps point to data gaps. Now, the focus turns to ethical web analytics with a tool like Matomo. 

  To further comply with data privacy regulations, it may also be appropriate to implement a Consent Management Platform (CMP) to help manage preferences and consent choices.

  4. Build trust with transparency

  With the tools in place, it’s time to engage customers. To build trust, keep them informed about how their data is used and remind them of their right to withdraw their consent. 

  Transparency is crucial in such engagement, as outlined in the 7 GDPR principles.

  5. Continuously improve

  Rinse and repeat. The one constant in business and life is change. As things change, they expose weaknesses or flaws in the logic behind systems and processes. That’s why a first-party data strategy needs to be continually reviewed, updated, and revised. It must adapt to changing trends, markets, regulations, etc. 

  Tools that can help

  Looking back at the different types of data, it’s clear that some are harder and more bothersome to get than others. But capturing behaviours and interactions can be easy — especially if you use tools that follow data privacy rules.

  But here’s a tip. Google Analytics 4 isn’t compliant by default, especially not with Europe’s GDPR. It may also struggle to comply with some of the newer data privacy regulations planned by different US states and other countries.

  Matomo Analytics is compliant with the GDPR and many other data privacy regulations worldwide. Because it’s open source, it can be integrated with any consent manager.

  Get started today by trying Matomo for free for 21 days,
  no credit card required.

  Try Matomo for free