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• How to create a spectrogram image from an audio file in Python just like how FFMPEG does ?

  2 mai 2020, par hamandishe Mk

  My code :

  



  import matplotlib.pyplot as plt
from matplotlib.pyplot import specgram
import librosa
import librosa.display
import numpy as np
import io
from PIL import Image

samples, sample_rate = librosa.load('thabo.wav')
fig = plt.figure(figsize=[4, 4])
ax = fig.add_subplot(111)
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
ax.set_frame_on(False)
S = librosa.feature.melspectrogram(y=samples, sr=sample_rate)
librosa.display.specshow(librosa.power_to_db(S, ref=np.max))
buf = io.BytesIO()
plt.savefig(buf,  bbox_inches='tight',pad_inches=0)

# plt.close('all')
buf.seek(0)
im = Image.open(buf)
# im = Image.open(buf).convert('L')
im.show()
buf.close()


  



  Spectrogram produced

  



  

  



  Using FFMPEG

  



  ffmpeg -i thabo.wav -lavfi showspectrumpic=s=224x224:mode=separate:legend=disabled spectrogram.png

  



  Spectrogram produced

  



  

  



  Please help, i want a spectrogram that is exactly the same as the one produced by FFMPEG, for use with a speech recognition model exported from google's teachable machine.
Offline recognition

  


• What is Audience Segmentation ? The 5 Main Types & Examples

  16 novembre 2023, par Erin — Analytics Tips

  The days of mass marketing with the same message for millions are long gone. Today, savvy marketers instead focus on delivering the most relevant message to the right person at the right time.

  They do this at scale by segmenting their audiences based on various data points. This isn’t an easy process because there are many types of audience segmentation. If you take the wrong approach, you risk delivering irrelevant messages to your audience — or breaking their trust with poor data management.

  In this article, we’ll break down the most common types of audience segmentation, share examples highlighting their usefulness and cover how you can segment campaigns without breaking data regulations.

  What is audience segmentation ?

  Audience segmentation is when you divide your audience into multiple smaller specific audiences based on various factors. 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.

  

  Audience segmentation inherently makes a lot of sense. Consider this : an urban office worker and a rural farmer have vastly different needs. By targeting your marketing efforts towards agriculture workers in rural areas, you’re honing in on a group more likely to be interested 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. They would run a golf club ad in a golf magazine, not in the national newspaper.

  How narrow you can make your audience segments by leveraging multiple data points has changed.

  Why audience segmentation matters

  In a survey by McKinsey, 71% of consumers said they expected personalisation, and 76% get frustrated when a vendor doesn’t deliver.

  

  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 you to take that data and give them relevant product recommendations — like a shoe polishing kit if you bought nice leather loafers.

  If you don’t do any sort of audience segmentation, you’re likely to frustrate your customers with post-sale campaigns. If, for example, you just send the same follow-up email to all customers, you’d damage many relationships. Some might ask : “What ? Why would you think I need that ?” Then they’d promptly opt out of your email marketing campaigns.

  To avoid that, you need to segment your audience so you can deliver relevant content at all stages of the customer journey.

  5 key types of audience segmentation

  To help you deliver the right content to the right person or identify crucial insights in analytics, you can use five types of audience segmentation : demographic, behavioural, psychographic, technographic and transactional.

  

  Demographic segmentation 

  Demographic segmentation is when you segment a larger audience based on demographic data points like location, age or other factors.

  The most basic demographic segmentation factor is location, which is easy to leverage in marketing efforts. For example, geographic segmentation can use IP addresses and separate marketing efforts by country. 

  But more advanced demographic data points are becoming increasingly sensitive to handle. Especially in Europe, GDPR makes advanced demographics a more tentative subject. Using age, education level and employment to target marketing campaigns is possible. But you need to navigate this terrain thoughtfully and responsibly, ensuring meticulous adherence to privacy regulations.

  Potential data points :

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

  Example of effective demographic segmentation :

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

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

  

  Each collection is featured 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 — you can just single out the factors when making your campaign. But if you don’t want to rely on these data-mining companies, that doesn’t mean you have no options for segmentation.

  Consider allowing people to self-select their interests or preferences by incorporating a short survey within your email sign-up form. This simple addition can enhance engagement, decrease bounce rates, and ultimately improve conversion rates, offering valuable insights into audience preferences.

  This is a great way to segment ethically and without the need of data-mining companies.

  Behavioural segmentation

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

  You use various data points to segment your target audience based on their actions.

  Potential data points :

  • Page visits
  • Referral source
  • Clicks
  • Downloads
  • Video plays
  • Goal completion (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 such as Matomo to uncover patterns. By segmenting actions like specific clicks and downloads, pinpoint valuable trends—identifying actions that significantly enhance visitor conversions. 

  

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

  Then, you can set up a conditional CTA within the video player. Make it pop up after the user has watched the entire video. Use a specific form and sign them up to a specific segment for each case study. This way, you know 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 is when you segment audiences based on your interpretation 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. But to rugby athletes, the ad is very relevant.

  

  If you want to avoid social platforms, you can use surveys about hobbies and interests to segment your target audience in an ethical way.

  Technographic segmentation

  Technographic segmentation is when you single out specific parts of your audience based on which 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 :

  Upon noticing a considerable influx of tablet users accessing their platform, a leading news outlet decided to optimise their tablet browsing experience. They overhauled the website interface, focusing on smoother navigation and better readability for tablet users. These changes offered tablet users a seamless and enjoyable reading experience tailored precisely to their device.

  Transactional segmentation

  Transactional segmentation is when you use your customers’ purchase history to better target your marketing message to their needs.

  When consumers prefer personalisation, they typically mean based on their actual transactions, not their social media profiles.

  Potential data points :

  • Average order value
  • Product categories purchased within X months
  • X days since the last purchase of a consumable product

  Example of effective transactional segmentation :

  A pet supply store identifies a segment of customers consistently purchasing cat food but not other pet products. They create targeted email campaigns offering discounts or loyalty rewards specifically for cat-related items to encourage repeat purchases within this segment.

  If you want to improve customer loyalty and increase revenue, the last thing you should do is send generic marketing emails. Relevant product recommendations or coupons are the best way to use transactional segmentation.

  B2B-specific : Firmographic segmentation

  Beyond the five 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 :

  • Company size
  • Number of employees
  • Company industry
  • Geographic location (office)

  Example of effective firmographic segmentation :

  Companies of different sizes won’t need the same solution — so segmenting leads by company size is one of the most common and effective examples of B2B audience segmentation.

  The difference here is that B2B campaigns are often segmented through manual research. With an account-based marketing approach, you start by researching your potential customers. You then separate the target audience into smaller segments (or even a one-to-one campaign).

  Start segmenting and analysing your audience more deeply with Matomo

  Segmentation is a great place to start if you want to level up your marketing efforts. Modern consumers expect to get relevant content, and you must give it to them.

  But doing so in a privacy-sensitive way is not always easy. You need the right approach to segment your customer base without alienating them or breaking regulations.

  That’s where Matomo comes in. Matomo champions privacy compliance while offering comprehensive insights and segmentation capabilities. With robust privacy controls and cookieless configuration, it ensures GDPR and other regulations are met, empowering data-driven decisions without compromising user privacy.

  Take advantage of our 21-day free trial to get insights that can help you improve your marketing strategy and better reach your target audience. No credit card required.

• A Comprehensive Guide to Robust Digital Marketing Analytics

  15 novembre 2023, par Erin — Analytics Tips

  First impressions are everything. This is not only true for dating and job interviews but also for your digital marketing strategy. Like a poorly planned job application getting tossed in the “no thank you” pile, 38% of visitors to your website will stop engaging with your content if they find the layout unpleasant. Thankfully, digital marketers can access data that can be harnessed to optimise websites and turn those “no thank you’s” into “absolutely’s.”

  So, how can we transform raw data into valuable insights that pay off ? The key is web analytics tools that can help you make sense of it all while collecting data ethically. In this article, we’ll equip you with ways to take your digital marketing strategy to the next level with the power of web analytics.

  What are the different types of digital marketing analytics ?

  Digital marketing analytics are like a cipher into the complex behaviour of your buyers. Digital marketing analytics help collect, analyse and interpret data from any touchpoint you interact with your buyers online. Whether you’re trying to gauge the effectiveness of a new email marketing campaign or improve your mobile app layout, there’s a way for you to make use of the insights you gain.

  

  As we go through the eight commonly known types of digital marketing analytics, please note we’ll primarily focus on what falls under the umbrella of web analytics. 

  1. Web analytics help you better understand how users interact with your website. Good web analytics tools will help you understand user behaviour while securely handling user data. 
  2. Learn more about the effectiveness of your organisation’s social media platforms with social media analytics. Social media analytics include user engagement, post reach and audience demographics. 
  3. Email marketing analytics help you see how email campaigns are being engaged with.
  4. Search engine optimisation (SEO) analytics help you understand your website’s visibility in search engine results pages (SERPs). 
  5. Pay-per-click (PPC) or campaign analytics measure the performance of paid advertising campaigns.
  6. Content marketing analytics focus on how your content is performing with your audience. 
  7. Customer analytics helps organisations identify and examine buyer behaviour to retain the biggest spenders. 
  8. Mobile app analytics track user interactions within mobile applications. 

  Choosing which digital marketing analytics tools are the best fit for your organisation is not an easy task. When making these decisions, it’s critical to remember the ethical implications of data collection. Although data insights can be invaluable to your organisation, they won’t be of much use if you lose the trust of your users. 

  Tips and best practices for developing robust digital marketing analytics 

  So, what separates top-notch, robust digital marketing analytics from the rest ? We’ve already touched on it, but a big part involves respecting user privacy and ethically handling data. Data security should be on your list of priorities, alongside conversion rate optimisation when developing a digital marketing strategy. In this section, we will examine best practices for using digital marketing analytics while retaining user trust.

  

  Clear objectives

  Before comparing digital marketing analytics tools, you should define clear and measurable goals. Try asking yourself what you need your digital marketing analytics strategy to accomplish. Do you want to improve conversion rates while remaining data compliant ? Maybe you’ve noticed users are not engaging with your platform and want to fix that. Save yourself time and energy by focusing on the most relevant pain points and areas of improvement.

  Choose the right tools for the job

  Don’t just base your decision on what other people tell you. Take the tool for a test drive — free trials allow you to test features and user interfaces and learn more about the platform before committing. When choosing digital marketing analytics tools, look for ones that ensure data accuracy as well as compliance with privacy laws like GDPR.

  Don’t overlook data compliance

  GDPR ensures organisations prioritise data protection and privacy. You could be fined up to €20 million, or 4% of the previous year’s revenue for violations. Without data compliance practices, you can say goodbye to the time and money spent on digital marketing strategies. 

  Don’t sacrifice data quality and accuracy

  Inaccurate and low-quality data can taint your analysis, making it hard to glean valuable insights from your digital marketing analytics efforts. Many analytics tools only show sampled data or use AI and ML to fill data gaps, potentially compromising the accuracy and completeness of your analytics. 

  When your analytics are based on incomplete or inaccurate data, it’s like trying to assemble a puzzle with missing pieces—you might get a glimpse of the whole picture, but it’s never quite clear. Accurate data isn’t just helpful—it’s the backbone of smart marketing strategies. It lets you make confident decisions and enables precise targeting for greater impact.

  Communicate your findings

  Having insights is one thing ; effectively communicating complex data findings is just as important. Customise dashboards to display key metrics aligned with your objectives. Make sure to automate reports, allowing stakeholders to stay updated without manual intervention. 

  Understand the user journey

  To optimise your conversion rates, you need to understand the user journey. Start by analysing visitors interactions with your website — this will help you identify conversion bottlenecks in your sales or lead generation processes. Implement A/B testing for landing page optimisation, refining elements like call-to-action buttons or copy, and leverage Form Analytics to make informed, data-driven improvements to your forms.

  Continuous improvement

  Learn from the data insights you gain, and iterate your marketing strategies based on the findings. Stay updated with evolving web analytics trends and technologies to leverage new growth opportunities. 

  Why you need web analytics to support your digital marketing analytics toolbox

  You wouldn’t set out on a roadtrip without a map, right ? Digital marketing analytics without insights into how users interact with your website are just as useless. Used ethically, web analytics tools can be an invaluable addition to your digital marketing analytics toolbox. 

  The data collected via web analytics reveals user interactions with your website. These could include anything from how long visitors stay on your page to their actions while browsing your website. Web analytics tools help you gather and understand this data so you can better understand buyer preferences. It’s like a domino effect : the more you understand your buyers and user behaviour, the better you can assess the effectiveness of your digital content and campaigns. 

  Web analytics reveal user behaviour, highlighting navigation patterns and drop-off points. Understanding these patterns helps you refine website layout and content, improving engagement and conversions for a seamless user experience.

  

  Concrete CMS harnessed the power of web analytics, specifically Matomo’s Form Analytics, to uncover crucial insights within their user onboarding process. Their data revealed a significant issue : the “address” input field was causing visitors to drop off and not complete the form, severely impacting the overall onboarding experience and conversion rate.

  Armed with these insights, Concrete CMS made targeted optimisations to the form, resulting in a substantial transformation. By addressing the specific issue identified through Form Analytics, they achieved an impressive outcome – a threefold increase in lead generation.

  This case is a great example of how web analytics can uncover customer needs and preferences and positively impact conversion rates. 

  Ethical implications of digital marketing analytics

  As we’ve touched on, digital marketing analytics are a powerful tool to help better understand online user behaviour. With great power comes great responsibility, however, and it’s a legal and ethical obligation for organisations to protect individual privacy rights. Let’s get into the benefits of practising ethical digital marketing analytics and the potential risks of not respecting user privacy : 

  • If someone uses your digital platform and then opens their email one day to find it filled with random targeted ad campaigns, they won’t be happy. Avoid losing user trust — and facing a potential lawsuit — by informing users what their data will be used for. Give them the option to consent to opt-in or opt-out of letting you use their personal information. If users are also assured you’ll safeguard personal information against unauthorised access, they’ll be more likely to trust you to handle their data securely.
  • Protecting data against breaches means investing in technology that will let you end-to-end encrypt and securely store data. Other important data-security best practices include access control, backing up data regularly and network and physical security of assets.

  A fine line separates digital marketing analytics and misusing user data — many companies have gotten into big trouble for crossing it. (By big trouble, we mean millions of dollars in fines.) When it comes to digital marketing analytics, you should never cut corners when it comes to user privacy and data security. This balance involves understanding what data can be collected and what should be collected and respecting user boundaries and preferences.

  

  Learn more 

  We discussed a lot of facets of digital marketing analytics, namely how to develop a robust digital marketing strategy while prioritising data compliance. With Matomo, you can protect user data and respect user privacy while gaining invaluable insights into user behaviour with 100% accurate data. Save your organisation time and money by investing in a web analytics solution that gives you the best of both worlds. 

  If you’re ready to begin using ethical and robust digital marketing analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.