Recherche avancée

Sur d’autres sites (4007)

• Output file does not show up after executing ffmpeg command [closed]

  19 février 2024, par davai

  I'm using ffmpeg to combine an MP3 + G file and produce an MP4 file. I've placed the source code / .exe file for 'ffmpeg' in the project folder, and the MP3 + G files are also in the project folder. I also set the MP4 output to show up in the project folder as well. The weird thing is that, initially, I was producing output files, and while trying to tweak the constant rate factor, the MP4 output just stopped showing up entirely. I'm also not receiving any errors while running the code, and it does print out that the file has been successfully created, despite nothing showing up in the project folder.

  


  
        String mp3FilePath = "C:/Users/exampleuser/pfolder/example.mp3";
        String gFilePath = "C:/Users/exampleuser/pfolder/example.cdg";
        String mp4OutputPath = "C:/Users/exampleuser/pfolder/example.mp4";

        try
        {
            String[] command = {
                    "C:/Users/tonih/IdeaProjects/MP3GtoMP4Conversion/ffmpeg/ffmpeg-2024-02-19-git-0c8e64e268-full_build/bin/ffmpeg.exe",
                    "-i", mp3FilePath,       // Input MP3 file
                    "-r", "25",              // Frame rate
                    "-loop", "1",            // Loop input video
                    "-i", gFilePath,         // Input G file
                    "-c:v", "libx264",       // Video codec
                    "-preset", "slow",       // Encoding preset for quality (choose according to your requirement)
                    "-crf", "18",            // Constant Rate Factor (lower is higher quality, typical range 18-28)
                    "-c:a", "aac",           // Audio codec
                    "-b:a", "320k",          // Audio bitrate
                    "-shortest",             // Stop when the shortest stream ends
                    mp4OutputPath            // Output MP4 file
            };

            Process process = Runtime.getRuntime().exec(command);
            process.waitFor();
            System.out.println("MP4 file created successfully: " + mp4OutputPath);
        }
        catch (IOException | InterruptedException e)
        {
            e.printStackTrace();
        }


  


• Enterprise web analytics : Quick start guide (and top tools)

  10 juillet, par Joe — Analytics Tips

  Without data, you’ll get lost in the sea of competition.

  This is even more important for large organisations.

  Data helps you :

  • Optimise customer experiences
  • Navigate complex business decisions
  • Create a roadmap to sustainable brand growth
  • Data can power differentiation, especially within fiercely competitive sectors.

  How do you get the benefits of data in a large organisation ?

  Enterprise web analytics.

  In this guide, we’ll cover everything you need to know about enterprise web analytics to enhance website performance, improve customer experiences and increase conversions.

  What is enterprise web analytics ?

  Enterprise web analytics help large organisations capture, analyse, and act on website data to optimise customer experiences and make informed decisions. By providing insight into customer interactions, user behaviour and preferences, they’re vital in helping big businesses improve their websites.

  

  Enterprise web analytics can extract data from web pages and reveal a range of performance metrics, including :

  • Pageviews
  • Average time on page
  • Actions per visit
  • Bounce rate
  • Conversions
  • Traffic sources
  • Device type
  • Event tracking
  • And more

  You can track this data daily or access monthly reports, which will give you valuable insights into optimising user engagement, improving your website’s search engine traffic, and meeting business goals like increased conversion rates.

  For large organisations, web analytics isn’t just about measuring traffic. Instead, it’s an asset you can use to identify issues in your web strategy so you can gain insights that will fuel sustainable business growth.

  An advanced analytics strategy goes beyond the digital channels, page views and bounce rates of traditional analytics.

  Instead, modern web analytics incorporates behavioural analytics for deeper analysis and insight into user experiences. These advanced features include :

  • Heatmaps (or scroll maps) to track scroll behaviour on each page
  • User flow reports to see the pages your users visit in the customer journey
  • Session recordings to analyse user interactions (step-by-step)

  Taking a two-pronged approach to web analytics that includes both traditional and behavioural metrics, organisations get a clearer picture of users and their brand interactions.

  Different needs of enterprise companies

  Let’s dive deeper into the different needs of enterprise companies and how enterprise web analytics can help solve them :

  Access more storage

  Let’s face it. Large organisations have complex IT infrastructures and vast amounts of data.

  The amount of data to capture, analyse and store isn’t slowing down anytime soon.

  Enterprise web analytics can help handle and store large amounts of data in ways that serve the entire organisation.

  Enable cross-organisational data consumption

  It’s one thing to access data in a small company. You’ve got yourself and a few employees. That’s easy.

  But, it’s another thing to enable an organisation with thousands of employees with different roles to access complex data structures and large amounts of data.

  Enterprise web analytics allows big companies to enable their entire workforce to gain access to the data they need when they need it.

  Increase security

  As mentioned above, large organisations can use enterprise web analytics to help hundreds or even thousands of employees access their web data.

  However, some data shouldn’t be accessed by every type of employee. For example, some organisations may only want certain data accessed by executives, and some employees may not need to access certain types of data that may confuse or overwhelm them.

  Enterprise web analytics can help you grant access to certain types of data based on your role in the company, ensuring the security of sensitive data in your organisation.

  Improve privacy

  You can keep your data secure from internal breaches with enterprise web analytics. But, how do you protect customer data ?

  With all-inclusive privacy measures.

  To ensure that your customers’ privacy and data are protected, choose a web analytics solution that’s compliant with the latest and most important privacy measures, such as GDPR, LGPD and CCPA.

  Taking a privacy-first approach to data helps ensure your protection from potential legal action or fines.

  Enterprise web analytics best practices

  Want to make sure you get the most out of your web analytics strategy ?

  

  Be clear on what metrics you want to track

  You can track a ton of data in your organisation, but you may not need to. To ensure you’re not wasting time and resources tracking irrelevant numbers, you should make sure you’re clear from day one on the metrics you want to track.

  Start by making a list of key data points relevant to your business.

  For example, if you have an online marketplace, you’ll want to track specific ecommerce metrics like conversion rate, total visits, bounce rates, traffic source, etc.

  Don’t take data at face value

  Numbers alone can’t tell you the whole story of what’s happening in your organisation. It’s crucial you add context to your data, no matter what.

  Dozens of factors could impact your data and visitors’ interactions with your site, so you should always try to look beyond the numbers to see if there are other factors at play.

  For example, you might see that your site traffic is down and think your search engine optimisation (SEO) efforts aren’t working. Meanwhile, there could have been a major Google algorithm update or some sort of seasonality in a key market.

  On the other hand, you might see some positive signals that things are going well with your organic social media strategy because you saw a large influx of traffic from Instagram. But, there could be more to the story.

  For example, an Instagram influencer with five million followers may have just posted a reel reviewing your product or service without you knowing it, leading to a major traffic spike for your website.

  Remember to add notes to your web analytics data if necessary to ensure you can reference any insights from your data to maintain that point of context.

  Ensure your data is accurate

  With web analytics, data is everything. It will help you see where your traffic is coming from, how your users are behaving, and gain actionable insights into how you can improve your website and user experience.

  But if your data isn’t accurate, your efforts will be futile.

  Accurate data is crucial for launching an effective web analytics strategy. Data sampling and simple tracking errors can lead to inaccurate numbers and misleading conclusions. 

  If a tool relies on cookies to collect data, then it’s relying on a faulty data collection system. Cookies give users the option to opt out of tracking, making it challenging to get a clear picture of every user interaction.

  For example, some platforms like Google Analytics use data sampling to make predictions about traffic rather than relying on accurate data collection, leading to inaccurate numbers and conclusions.

  To ensure you’re making decisions based on accurate data, find a solution that doesn’t rely on inaccurate data collection methods like data sampling or cookies.

  Lean on visual data tools to improve analysis

  Enterprise organisations deal with a ton of data. There are endless data points to track, and it can be easy to lose track of what’s going on with the bigger picture.

  One of the best ways to interpret your data is to use a data visualisation tool to integrate with your web analytics solution, like Looker or PowerBI.

  Make sure your chosen platform lets you export your data easily so you can link it with a visual support tool.

  With Matomo, you can easily export your data into Google BigQuery to warehouse your customer data and visualise it through other tools (without the need for APIs, scripts or additional tools).

  Use advanced web analytics

  Web analytics is quite broad, and different tools will offer various features you can access in your analytics dashboard.

  Take advantage of advanced features that utilise both traditional and behavioural data for deeper insights.

  • Use heatmaps to better understand what parts of your web pages your visitors are focusing on to improve conversion rates.
  • Review session recordings to see the exact steps your customers take as they interact with your website.
  • Conduct A/B tests to see which call to action, headline, or image provides the optimal user experience.

  There are dozens of advanced features available, so take the time to make sure your chosen tool has everything you need.

  Choose a privacy-focused tool

  Obviously, not every tool is created equal, and most of the software on the market isn’t suitable for enterprise businesses.

  As a large organisation, the most important step is to choose a trusted enterprise web analytics tool to ensure it’s capable of fitting within a company of your size.

  It needs to have great infrastructure and be able to handle large amounts of data.

  Another crucial factor is to check that the tool is compatible with your website or app. Does it integrate easily with it ? What about your other software ? Will it integrate with those as well and fit into your current tech stack ?

  Most importantly, you need a platform that can provide the data and insights your organisation needs.

  Make sure the tool you choose is GDPR-compliant and privacy-friendly. The last thing you want is to be sued or fined because you chose the wrong software. 

  Consumers are growing more cautious about privacy and data risks, so picking a privacy-focused tool will help build trust with customers.

  Top 5 enterprise web analytics tools

  Now that you understand enterprise web analytics and how to get the most out of it, it’s time to talk about tools.

  You need to make sure you’re using the right web analytics software to improve productivity, optimise website performance and grow your brand without compromising on the infrastructure required for large organisations to thrive.

  Here are five of the best enterprise solutions available :

  Features and pricing comparison

  	GDPR compliant	On-premise option	100% data ownership	Traditional analytics	Behavioural analytics	Awarded best enterprise software
  Matomo	✔️	✔️	✔️	✔️	✔️	✔️
  Amplitude	✔️	✔️			✔️
  Adobe	✔️			✔️	✔️
  GA360				✔️
  Contentsquare	✔️	✔️			✔️	✔️

  Use Matomo to power your website analytics

  Web analytics help enterprise organisations reach new users, improve engagement with current users or grow their web presence.

  These advanced solutions support cross-organisational data consumption, enhance data privacy and security and allow brands to create the web experiences they know customers will love.

  

  Matomo can help you unlock the potential of your website strategy with traditional and behavioural analytics and accurate data. Trusted by over 1 million websites, Matomo’s open-source software is an ethical web solution that helps organisations of all sizes improve decision-making and customer experiences without compromising on privacy or security.

  Start your free 21-day trial now. 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.

  

  (Image Source)

  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. 

  

  (Image Source) 

  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.