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• First-party data explained : Benefits, use cases and best practices

  25 juillet, par Joe

  Third-party cookies are being phased out, and marketers who still depend on them for user insights need to find alternatives.

  Google delayed the complete deprecation of third-party cookies until early 2025, but many other browsers, such as Mozilla, Brave, and Safari, have already put a stop to them. Plus, looking at the number of data leak incidents, like the one where Twitter leaked 200 million user emails, collecting and using first-party data is a great alternative. 

  In this post, we explore the ins and outs of first-party data and examine how to collect it. We’ll also look at various use cases and best practices to implement first-party data collection.

  What is first-party data ?

  First-party data is information organisations collect directly from customers through their owned channels. 

  Organisations can capture data without intermediaries when people interact with their website, mobile app, social media accounts or other customer-facing systems.

  For example, businesses can track visitor behaviour, such as bounce rates and time spent browsing particular pages. This activity is considered first-party data when it occurs on the brand’s digital property.

  Some examples include :

  • Demographics : Age, gender, location, income level
  • Contact information : Email addresses, phone numbers
  • Behavioural insights : Topics of interest, content engagement, browsing history
  • Transactional data : Purchase history, shopping preferences

  A defining characteristic is that this information comes straight from the source, with the customer’s willingness and consent. This direct collection method is why first-party data is widely regarded as more reliable and accurate than second or third-party data. With browsers like Chrome fully phasing out third-party cookies by the end of 2025, the urgency for adopting more first-party data strategies is accelerating across industries.

  How to collect first-party data 

  Organisations can collect first-party data in various ways. 

  Website pixels

  In this method, organisations place small pieces of code that track visitor actions like page views, clicks and conversions. When visitors land on the page, the pixel activates and collects data about their behaviour without interrupting the user experience. 

  Website analytics tools

  With major browsers like Safari and Firefox already blocking third-party cookies (and Chrome is phasing them out soon, there’s even more pressure on organisations to adopt first-party data strategies.

  Website analytics tools like Matomo help organisations collect first-party data with features like visitor tracking and acquisition analysis to analyse the best channels to attract more users. 

  Multi-attribution modelling that helps businesses understand how different touchpoints (social media channels or landing pages) persuade visitors to take a desired action (like making a purchase). 

  

  (Image Source)

  Other activities include :

  • Cohort analysis 
  • Heatmaps and session recordings 
  • SEO keyword tracking
  • A/B testing 
  • Paid ads performance tracking

  

  Heatmap feature in Matomo

  Account creation on websites

  When visitors register on websites, they provide information like names, email addresses and often demographic details or preferences.

  Newsletters and subscriptions 

  With email subscriptions and membership programs, businesses can collect explicit data (preferences selected during signup) and implicit data (engagement metrics like open rates and click patterns).

  Gated content

  Whitepapers, webinars or exclusive articles often ask for contact information when users want access. This approach targets specific audience segments interested in particular topics.

  Customer Relationship Management (CRM) systems

  CRM platforms collect information from various touchpoints and centralise it to create unified customer profiles. These profiles include detailed user information, like interaction history, purchase records, service inquiries and communication preferences.

  Mobile app activity

  Mobile in-app behaviours can assist businesses in gathering data such as :

  • Precise location information (indicating where customers interact with the app)
  • Which features they use most often
  • How long they stay on different screens
  • Navigation patterns

  This mobile-specific data helps organisations understand how their customers behave on smaller screens and while on the move, insights that website data alone cannot provide.

  Point of Sale (PoS) systems

  Modern checkout systems don’t just process payments. PepsiCo proved this by growing its first-party data stores by more than 50% through integrated PoS systems. 

  Today’s PoS technology captures detailed information about each transaction :

  • Item(s) sold
  • Price (discounts, taxes, tip)
  • Payment type (card, cash, digital wallet)
  • Time and date
  • Loyalty/rewards number
  • Store/location

  Plus, when connected with loyalty programs where customers identify themselves (by scanning a card or entering a phone number), these systems link purchase information to individuals. 

  This creates valuable historical records showing how customer preferences evolve and offering insight into :

  • Which products are frequently purchased together
  • The time of the day, week, month, or year when items sell best
  • Which promotions or special offers are most effective

  Server-side tracking 

  Most websites track user behaviour through code that runs in the visitor’s web browser (client-side tracking). 

  Server-side tracking takes a different approach by collecting data directly on the company’s own servers. 

  Because the tracking happens on company servers rather than browsers, ad-blocking software doesn’t block it. 

  Organisations gain more consistent data collection and greater control over their customer information. This privacy-friendly approach lets companies get the data they need without relying on third-party tracking scripts.

  Now that we understand how organisations can gather first-party data, let us explore its use cases. 

  Use cases of first-party data 

  Businesses can use first-party data in many ways, from creating customer profiles to personalising user experiences.

  Developing comprehensive customer profiles

  First-party data can help create detailed customer profiles. 

  Here are some examples :

  • Demographic profiles : Age, gender, location, job role and other personal characteristics.
  • Behavioural profiles : Website activity, purchase history and engagement with marketing campaigns that focus on how users interact with businesses and their offerings. 
  • Psychographic profiles : Customer’s interests, values and lifestyle preferences.
  • Transactional profiles : Purchase patterns, including the types of products they buy, how often they purchase and their total spending.

  The benefit of developing these profiles is that businesses can then create specific campaigns for each profile, instead of running random campaigns. 

  For example, a subscription service business may have a behavioural profile of ‘inactive users’. To reignite interest, they can offer discounts or limited-time freebies to these users.

  Crafting relevant content

  First-party data shows what types of content customers engage with most. 

  If customers love watching videos, businesses can create more video content. If a blog gets more readership for its tech articles, it can focus on tech-related content to adjust to readers’ preferences. 

  Uncovering new marketing opportunities

  First-party data lets businesses analyse customer interactions in a way that can reveal untapped markets. 

  For example, if a company sees that many website visitors are from a particular region, it might consider launching campaigns in that area to boost sales. 

  Personalising experiences

  89% of decision-makers believe personalisation is key to business success in the next three years. 

  First-party data helps organisations to tailor experiences based on individual preferences. 

  

  For example, an e-commerce site can recommend products based on previous purchases or browsing history. Shoppers with abandoned carts can get reminders. 

  It’s also helpful to see how customers respond to different types of communication. Certain groups may prefer emails, and some may prefer text messages. Similarly, some users spend more time on quizzes and interactive content like wizards or calculators. 

  By analysing this, businesses can adjust their strategies so that users get a personal experience when they visit a website.

  Optimising operations

  The use cases of first-party data don’t just apply to the marketing domain. They’re also valuable for operations. When businesses analyse customer order patterns, they can spot the best locations for fulfilment centres that reduce shipping time and costs.

  For example, an online retailer might discover that most customers are concentrated in urban areas and decide to open fulfilment centres closer to those locations.

  Or, in the public sector, transport companies can use first-party data to optimise routes and fine-tune fare simulation tools. By analysing rider queries, travel preferences and interaction data, they can :

  • Prioritise high-demand routes during peak hours 
  • Adjust fare structures to reflect common trip or rider patterns
  • Make personalised travel suggestions based on individual user history.

  Benefits of first-party data 

  First-party data offers two significant benefits : accuracy and compliance. It comes directly from the customers and can be considered more accurate and reliable. But that’s not it. 

  First-party data aligns with many data privacy regulations, like the GDPR and CCPA. That’s because first-party data collection requires explicit consent, which means the data remains confidential. This builds compliance, and customers develop more trust in the business.

  Best practices to collect and manage first-party data 

  Though first-party data comes with many benefits, how should organisations collect and manage it ? What are the best practices ? Let’s take a look. 

  Define clear goals

  Though defining clear goals seems like overused advice, it’s one of the most important. If a business doesn’t know why it’s collecting first-party data, all the information gathering becomes purposeless. 

  Businesses can think of different goals to achieve from first-party data collection : improving customer relationships, enhancing personalisation or increasing ROI. 

  Once these goals are concrete, they can guide data collection strategies and help understand whether they’re working.

  Establish a privacy policy

  A privacy policy is a document that explains why a business is collecting a user’s data and what it will do with it. By being open and honest, this policy builds trust with customers, so customers feel safe sharing their information. 

  For example, an e-commerce privacy policy may read like : 

  “At (Business name), your privacy is important to us. We collect your information when you create an account or buy something. This information includes your name, email and purchase history. We use this data to give you a better shopping experience and suggest products that you’ll find useful. We follow all data privacy laws like GDPR to keep your personal information safe.” 

  For organisations that use Matomo, we suggest updating the privacy policy to explain how Matomo is used and what data it collects. Here’s a privacy policy template for Matomo users that can be easily copied and pasted. 

  For a GDPR compatible privacy policy, read How to complete your privacy policy with Matomo analytics under GDPR.

  Simplify consent processes

  Businesses should obtain explicit user consent before collecting their data, as shown in the image below. 

  

  (Image Source) 

  To do this, integrate user-friendly consent management platforms that let customers easily access, view, opt out of, or delete their information.

  To ensure consent practices align with GDPR standards, follow these key steps :
  

  GDPR-compliant consent checklist
  ✅ State the purpose clearly	Describe data usage in plain terms.
  ✅ Use granular opt-ins	Separate consents by purpose.
  ✅ Avoid pre-ticked boxes	Active choices only.
  ✅ Enable easy opt-out	Simple and accessible withdrawal.
  ✅ Log consent	Timestamp and record every opt-in.
  ✅ Review periodically	Audit for accuracy and relevance.

  Comply with platform-specific restrictions

  In addition to general consent practices, businesses must comply with platform-specific restrictions. This includes obtaining explicit permissions for :

  • Location services : Users must consent to sharing their location data.
  • Contact lists : Businesses need permission to access and use contact information.
  • Camera and microphone Use : Users must consent to using the camera and microphone 
  • Advertising IDs : On platforms like iOS, businesses must obtain consent to use advertising IDs. 

  For example, Zoom asks the user if it can access the camera and the microphone by default.

  Utilise multiple data collection channels

  Instead of relying on just one source to collect first-party data, it is better to use multiple channels. Gather first-party data from diverse sources such as websites, mobile apps, CRM systems, email campaigns, and in-store interactions (for richer datasets). This way, businesses get a more complete picture of their customers.

  Implementing a strong data governance framework with proper tooling, taxonomy, and maintenance practices is also vital for better data usability.

  Use privacy-focused analytics tools 

  Focus on not just collecting data but also doing it in a way that’s secure and ethical. 

  Use tools like Matomo to track user interactions and gather meaningful analytics. For example, Matomo heatmaps can give you a visual insight into where users click and scroll, all while following all the data privacy laws.

  

  (Image Source) 

  What is second-party data ? 

  Second-party data is information that one company collects from its customers and shares with another company. It’s like “second-hand” first-party data because it’s collected directly from customers but used by a different business.

  Companies purchase second-party data from trusted partners instead of getting it directly from the customer. For example, hotel chains can use customer insights from online travel agencies, like popular destinations and average stay lengths, to refine their pricing strategies and offer more relevant perks.

  When using second-party data, it’s essential to :

  • Be transparent : Share with customers that their data is being shared with partners. 
  • Conduct regular audits : Ensure the data is accurate and handled properly to maintain strong privacy standards. If their data standards don’t seem that great, consider looking elsewhere.

  What is third-party data ? 

  Third-party data is collected from various sources, such as public records, social media or other online platforms. It’s then aggregated and sold to businesses. Organisations get third-party data from data brokers, aggregators and data exchanges or marketplaces. 

  Some examples of third-party data include life events from user social media profiles, like graduation or facts about different organisations, like the number of employees and revenue.

  For example, a data broker might collect information about people’s interests from social media and sell it to a company that wants to target ads based on those interests.

  Third-party data often raises privacy concerns due to its collection methods. One major issue is the lack of transparency in how this data is obtained. 

  Consumers often don’t know that their information is being collected and sold by third-party brokers, leading to feelings of mistrust and violation of privacy. This is why data privacy guidelines have evolved. 

  What is zero-party data ? 

  Zero-party data is the information that customers intentionally share with a business. Some examples include surveys, product ratings and reviews, social media polls and giveaways.

  Organisations collect first-party data by observing user behaviours, but zero-party data is the information that customers voluntarily provide. 

  

  Zero-party data can provide helpful insights, but self-reported information isn’t always accurate. People don’t always do what they say. 

  For example, customers in a survey may share that they consider quality above all else when purchasing. Still, looking at their actual behaviour, businesses can see that they make a purchase only when there’s a clearance or a sale.

  First-party data can give a broader view of customer behaviours over time, which zero-party data may not always be able to capture. 

  Therefore, while zero-party data offers insights into what customers say they want, first-party data helps understand how they behave in real-world scenarios. Balancing both data types can lead to a deeper understanding of customer needs.

  Getting valuable customer insights without compromising privacy 

  Matomo is a powerful tool for organisations that want to collect first-party data. We’re a full-featured web analytics tool that offers features that allow businesses to track user interactions without compromising the user’s personal information. Below, we share how.

  Data ownership

  Matomo allows organisations to own their analytics data, whether on-premise or in their chosen cloud. This means we don’t share your data with anyone else. This aligns with GDPR’s requirement for data sovereignty and minimises third-party risks.

  Pseudonymisation of user IDs

  Matomo allows organisations to pseudonymise user IDs, replacing them with a salted hash function. 

  

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  Since the user IDs have different names, no one can trace them back to a specific person.

  IP address anonymisation

  Data anonymisation refers to removing personally identifiable information (PII) from datasets so individuals can’t be readily identified.

  Matomo automatically anonymises visitor IP addresses, which helps respect user privacy. For example, if the visitor’s IP address is 199.513.1001.123, Matomo can mask it to 199.0.0.0. 

  It can also anonymise geo-location information, such as country, region and city, ensuring this data doesn’t directly identify users.

  

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  Consent management

  Matomo offers an opt-out option that organisations can add to their website, privacy policy or legal page. 

  Matomo tracks everyone by default, but visitors can opt out by clicking the opt-out checkbox. 

  Our DoNotTrack technology helps businesses respect user choices to opt out of tracking from specific websites, such as social media or advertising platforms. They can simply select the “Support Do Not Track preference.”

  These help create consent workflows and support audit trails for regulators. 

  Data storage and deletion

  Keeping visitor data only as long as necessary is a good practice by default. 

  To adhere to this principle, organisations can configure Matomo to automatically delete old raw data and old aggregated report data. 

  Here’s a quick case study summarising how Matomo features can help organisations collect first-party data. CRO:NYX found that Google Analytics struggled to capture accurate data from their campaigns, especially when running ads on the Brave browser, which blocks third-party cookies.

  They then switched to Matomo, which uses first-party cookies by default. This approach allowed them to capture accurate data from Brave users without putting user privacy at stake. 

  The value of Matomo in first-party data strategies 

  First-party data gives businesses a reliable way to connect with audiences and to improve marketing strategies. 

  Matomo’s ethical web analytics lets organisations collect and analyse this data while prioritising user privacy. 

  With over 1 million websites using Matomo, it’s a trusted choice for organisations of all sizes. As a cloud-hosted service and a fully self-hosted solution, Matomo supports organisations with strong data sovereignty needs, allowing them to maintain full control over their analytics infrastructure.

  Ready to collect first-party data while securing user information ? Start your free 21-day trial, no credit card required.

• What is audience segmentation ? The 8 main types and examples

  8 juillet, par Joe

  Marketers must reach the right person at the right time with the most relevant messaging. Customers now expect personalised experiences, which means generic campaigns won’t work. Audience segmentation is the key to doing this. 

  This isn’t an easy process because there are many types of audience segmentation. The wrong approach or poor data management can lead to irrelevant messaging or lost customer trust.

  This article breaks down the most common types of audience segmentation with examples highlighting their usefulness and information on segmenting campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation involves dividing a customer base into distinct, smaller groups with similar traits or common characteristics. The goal is to deliver a more targeted marketing message or to glean unique insights from analytics.

  It can be as broad as dividing a marketing campaign by location or as specific as separating audiences by their interests, hobbies and behaviour.

  Consider this : an urban office worker and a rural farmer have vastly different needs. Targeted marketing efforts aimed at agriculture workers in rural areas can stir up interest in farm equipment. 

  Audience segmentation has existed since the beginning of marketing. Advertisers used to select magazines and placements based on who typically read them. For example, they would run a golf club ad in a golf magazine, not the national newspaper.

  Now that businesses have more customer data, audience segments can be narrower and more specific.

  Why audience segmentation matters

  Hyken’s latest Customer Service and CX Research Study revealed that 81% of customers expect a personalised experience.

  These numbers reflect expectations from consumers who have actively engaged with a brand — created an account, signed up for an email list or purchased a product.

  They expect relevant product recommendations — like a shoe polishing kit after buying nice leather loafers.

  Without audience segmentation, customers can get frustrated with post-sale activities. For example, the same follow-up email won’t make sense for all customers because each is at a different stage of the user journey. 

  Some more benefits that audience segmentation offers : 

  • Personalised targeting is a major advantage. Tailored messaging makes customers feel valued and understood, enhancing their loyalty to the brand. 
  • Businesses can understand users’ unique needs, which helps in better product development. For example, a fitness brand might develop separate offerings for casual exercisers and professional athletes.
  • Marketers can allocate more resources to the most promising segments. For example, a luxury skincare brand might target affluent customers with premium ads and use broader campaigns for entry-level products.

  8 types of audience segmentation

  There are eight types of audience segmentation : demographic, behavioural, psychographic, technographic, transactional, contextual, lifecycle and predictive segmentation.

  

  Let’s take an in-depth look at each of them.

  Demographic segmentation 

  Demographic segmentation divides a larger audience based on data points like location, age or other factors.

  The most basic segmentation factor is location, which is critical in marketing campaigns. Geographic segmentation can use IP addresses to separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle, especially in Europe, where the GDPR makes advanced demographics a more tentative subject. 

  It’s also possible to use age, education level, and occupation to target marketing campaigns. It’s essential to navigate this terrain thoughtfully, responsibly, and strictly adhere to privacy regulations.

  Potential data points :

  • Location
  • Age
  • Marital status
  • Income
  • Employment 
  • Education

  Example of effective demographic segmentation :

  A clothing brand targeting diverse locations must account for the varying weather conditions. In colder regions, showcasing winter collections or insulated clothing might resonate more with the audience. Conversely, promoting lightweight or summer attire would be more effective in warmer climates. 

  Here are two ads run by North Face on Facebook and Instagram to different audiences to highlight different collections :

  

  (Image Source)

  Each collection features differently and uses a different approach with its copy and even the media. With social media ads, targeting people based on advanced demographics is simple enough — just single out the factors when building a campaign. And it’s unnecessary to rely on data mining to get information for segmentation. 

  Consider incorporating a short survey into email sign-up forms so people can self-select their interests and preferences. This is a great way to segment ethically and without the need for data-mining companies. Responses can offer valuable insights into audience preferences while enhancing engagement, decreasing bounce rates, and improving conversion rates.

  Behavioural segmentation

  Behavioural segmentation segments audiences based on their interaction with a website or an app.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Conversions (e.g., signing up for a newsletter or purchasing a product)

  Example of using behavioural segmentation to improve campaign efficiency :

  One effective method involves using a web analytics tool like Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, identify what can significantly enhance visitor conversions. 

  

  For example, if a case study video substantially boosts conversion rates, elevate its prominence to capitalise on this success.

  Then, set up a conditional CTA within the video player. Make it pop up after the user finishes watching the video. Use a specific form and assign it to a particular segment for each case study. This way, you can get the prospect’s ideal use case without surveying them.

  This is an example of behavioural segmentation that doesn’t rely on third-party cookies.

  Psychographic segmentation

  Psychographic segmentation involves segmenting audiences based on interpretations of their personality or preferences.

  Potential data points :

  • Social media patterns
  • Follows
  • Hobbies
  • Interests

  Example of effective psychographic segmentation :

  Here, Adidas segments its audience based on whether they like cycling or rugby. It makes no sense to show a rugby ad to someone who’s into cycling and vice versa. However, for rugby athletes, the ad is very relevant.

  

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  Brands that want to avoid social platforms can use surveys about hobbies and interests to segment their target audience ethically.

  Technographic segmentation

  Technographic segmentation separates customers based on the hardware or software they use. 

  Potential data points :

  • Type of device used
  • Device model or brand
  • Browser used

  Example of segmenting by device type to improve user experience :

  After noticing a serious influx of tablet users accessing their platform, a leading news outlet optimised their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better tablet-readability. These changes gave users a more enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation uses customers’ past purchases to match marketing messages with user needs.

  Consumers often relate personalisation with their actual transactions rather than their social media profiles. 

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • Most recent purchase date

  Example of effective transactional segmentation :

  Relevant product recommendations and coupons are among the best uses of transactional segmentation. These individualised marketing emails can strengthen brand loyalty and increase revenue.

  A pet supply store identifies a segment of customers who consistently purchase cat food but not other pet products. To encourage repeat purchases within this segment, the store creates targeted email campaigns offering discounts or loyalty rewards for cat-related items.

  Contextual segmentation 

  Contextual segmentation helps marketers connect with audiences based on real-time factors like time of day, weather or location. It’s like offering someone exactly what they need when they need it the most.

  Potential data points :

  • GPS location
  • Browsing activity
  • Device type

  Examples of contextual segmentation :

  A ride-hailing app might promote discounted rides during rush hour in busy cities or suggest carpooling options on rainy days. Similarly, an outdoor gear retailer could target users in snowy regions with ads for winter jackets or snow boots.

  The key is relevance. Messages that align with what someone needs at that moment feel helpful rather than intrusive. Businesses need tools like geolocation tracking and real-time analytics to make this work. 

  Also, keep it subtle and respectful. While personalisation is powerful, being overly intrusive can backfire. For example, instead of bombarding someone with notifications every time they pass a store, focus on moments when an offer truly adds value — like during bad weather or peak commute times.

  Lifecycle segmentation 

  Lifecycle segmentation is about crafting interactions based on where customers are in their journey with a brand.

  

  Lifecycle segmentation isn’t just about selling ; it’s about building relationships. After a big purchase like furniture, sending care tips instead of another sales pitch shows customers that the brand cares about their experience beyond just the sale.

  This approach helps brands avoid generic messaging that might alienate customers. By understanding the customer’s lifecycle stage, businesses can tailor their communications to meet specific needs, whether nurturing new relationships or rewarding long-term loyalty.

  Potential data points :

  • Purchase history
  • Sign-up dates

  Examples of effective lifecycle segmentation :

  An online clothing store might send first-time buyers a discount code to encourage repeat purchases. On the other hand, if someone hasn’t shopped in months, they might get an email with “We miss you” messaging and a special deal to bring them back.

  Predictive segmentation 

  Predictive segmentation uses past behaviour and preferences to understand or predict what customers might want next. Its real power lies in its ability to make customers feel understood without them having to ask for anything.

  Potential data points :

  • Purchase patterns
  • Browsing history
  • Interaction frequency

  Examples of effective predictive segmentation :

  Streaming platforms are great examples — they analyse what shows and genres users watch to recommend related content they might enjoy. Similarly, grocery delivery apps can analyse past orders to suggest when to reorder essentials like milk or bread.

  B2B-specific : Firmographic segmentation

  Beyond the eight main segmentation types, B2B marketers often use firmographic factors when segmenting their campaigns. It’s a way to segment campaigns that go beyond the considerations of the individual.

  Potential data points :

  • Annual revenue
  • Number of employees
  • Industry
  • Geographic location (main office)

  Example of effective firmographic segmentation :

  Startups and well-established companies will not need the same solution, so segmenting leads by size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns involve more manual research. With an account-based marketing approach, you start by researching potential customers. Then, you separate the target audience into smaller segments (or even a one-to-one campaign).

  Audience segmentation challenges (+ how to overcome them) 

  Below, we explore audience segmentation challenges organisations can face and practical ways to overcome them.

  Data privacy 

  Regulations like GDPR and CCPA require businesses to handle customer data responsibly. Ignoring these rules can lead to hefty fines and harm a brand’s reputation. Customers are also more aware of and sensitive to how their data is used, making transparency essential.

  Businesses should adopt clear data policies and provide opt-out options to build trust and demonstrate respect for user preferences. 

  

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  Privacy-focused analytics tools can help businesses handle these requirements effectively. For example, Matomo allows businesses to anonymise user data and offers features that give users control over their tracking preferences.

  Data quality

  Inconsistent, outdated or duplicate data can result in irrelevant messaging that frustrates customers instead of engaging them.

  This is why businesses should regularly audit their data sources for accuracy and completeness.

  Integrate multiple data sources into a unified platform for a more in-depth customer view. Implement data cleansing processes to remove duplicates, outdated records, and errors. 

  Segment management 

  Managing too many segments can become overwhelming, especially for businesses with limited resources. Creating and maintaining numerous audience groups requires significant time and effort, which may not always be feasible.

  Automated tools and analytics platforms can help. Matomo Segments can analyse reports on specific audience groups based on criteria such as visit patterns, interactions, campaign sources, ecommerce behaviour, demographics and technology usage for more targeted analysis.

  Detailed reporting of each segment’s characteristics can further simplify the process. By prioritising high-impact segments — those that offer the best potential return on investment — businesses can focus their efforts where they matter most.

  Behaviour shifts 

  Customer behaviour constantly evolves due to changing trends, new technology and shifting social and economic conditions. 

  Segmentation strategies that worked in the past can quickly become outdated. 

  Businesses need to monitor market trends and adjust their strategies accordingly. Flexibility is key here — segmentation should never be static.

  For example, if a sudden spike in mobile traffic is detected, campaigns can be optimised for mobile-first users.

  Tools and technologies that help 

  Here are some key segmentation tools to support your efforts : 

  • Analytics platforms : Get insights into audience behaviour with Matomo. Track user interactions, such as website visits, clicks and time spent on pages, to identify patterns and segment users based on their online activity.
  • CRM systems : Utilize customer records to create meaningful segments based on characteristics like purchase history or engagement levels.
  • Marketing automation platforms : Streamline personalised messages by automating emails, social media posts or SMS campaigns for specific audience segments.
  • Consent management tools : Collect and manage user consent, implement transparent data tracking and provide users with opt-out options. 
  • Survey tools : Gather first-party data directly from customers. 
  • Social listening solutions : Monitor conversations and brand mentions across social media to gauge audience sentiment.

  Start segmenting and analysing audiences more deeply with Matomo

  Modern consumers expect to get relevant content, and segmentation can make this possible.

  But doing so in a privacy-sensitive way is not always easy. Organisations need to adopt an approach that doesn’t break regulations while still allowing them to segment their audiences. 

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. It provides features for privacy control, enables cookieless configurations, and supports compliance with GDPR and other regulations — all without compromising user privacy. 

  Take advantage of Matomo’s 21-day free trial to explore its capabilities firsthand — no credit card required.

• Python librosa NoBackendError even though ffmpeg is installed

  11 avril 2020, par Slavko Kovačević

  I recently installed librosa (package I've been using for a while on different PC) on my new PC with Windows 10 running. After that I've downloaded latest static version of ffmpeg and copied it to C: and added it to the Path. Tested ffmpeg and it works like a charm ! For python I am using Anaconda environment and after starting Jupyter Notebook and running librosa.load(path, sr = None) I've got

  



   in <module>&#xA;----> 1 audio = librosa.load(pathToJson)&#xA;&#xA;~\anaconda3\envs\tf_gpu\lib\site-packages\librosa\core\audio.py in load(path, sr, mono, offset, duration, dtype, res_type)&#xA;    117 &#xA;    118     y = []&#xA;--> 119     with audioread.audio_open(os.path.realpath(path)) as input_file:&#xA;    120         sr_native = input_file.samplerate&#xA;    121         n_channels = input_file.channels&#xA;&#xA;~\anaconda3\envs\tf_gpu\lib\site-packages\audioread\__init__.py in audio_open(path, backends)&#xA;    114 &#xA;    115     # All backends failed!&#xA;--> 116     raise NoBackendError()&#xA;&#xA;NoBackendError:&#xA;</module>

  



  strange isn't it ? Then I went all over the internet, doing whatnot trying to fix it and then I've got an idea to run my line of code inside anaconda command interface and it WORKS ?? How is this possible ? It is the same environment.

  



  python
Python 3.7.7 (default, Mar 23 2020, 23:19:08) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import librosa
>>> librosa.load('test.wav')
(array([-0.00027   , -0.00039953, -0.0003659 , ..., -0.16393574,
       -0.17814247,  0.        ], dtype=float32), 22050)


  



  I did a lot of testing and I really prefer my Jupyter so any help would be appreciated. I tried the following : I've added C:\ffmpeg\bin and C:\ffmpeg to my Path for both User and System Variables. After that I've made specific variables for ffmpeg and ffmpeg_bin for both User and System Variables. No luck. After that I've tried installing ffmpeg using conda, without success. The last thing I've tested is this :

  



  import audioread
audioread.ffdec.FFmpegAudioFile('test.wav')


  



  and that works. Thanks