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• How to use web analytics to acquire new customers

  2 septembre 2020, par Joselyn Khor — Analytics Tips, Marketing
  Are you investing time and money into marketing your business and unsure if it’s paying off ? Web analytics provides the tools and insights to help you know which marketing channels to target and focus on. Without it you might be going in blind and missing opportunities that might’ve been easily found in your metrics.

  Attracting the right visitors to your website

  To help acquire new customer on your website, you firstly need to attract the right visitors to your website. Then capturing their attention and engaging them in your content. Finally you’ll want to convert by driving them through a streamlined funnel/buyer’s journey on your website all backed up by data.

  So, how do you attract audiences to your site with a web analytics tool like Matomo ?

  1. Figure out who your audience is through the Visitor Profiles feature. 
  2. Calculate the Cost of Customer Acquisition (CAC) to plan for growth. To grow and make your business/website sustainable, you’ll need to earn more money from a customer than you spend on acquiring them. How to calculate : Divide marketing spend by the number of customers acquired. 
  3. Figure out which marketing channels e.g., social media, PPC, SEO, content marketing, etc., you should invest more in and which of those you should focus less on.

  How Matomo helps businesses acquire new customers 

  1. Use the Acquisitions feature
  2. Use funnels
  3. Study Visitor Profiles
  4. Focus on SEO efforts
  5. Look at the Multi Attribution feature
  6. Set goals
  7. Set Advanced eCommerce reporting

  1. Use the Acquisitions feature

  Matomo Analytics has a dedicated Acquisition feature to help with some of the heavy-lifting, making it easy for you to formulate targeted acquisition plans.

  

  This feature helps you learn who your potential customers are and figure out what marketing channels are converting the best for these visitors.

  • Learn what traffic you get from external websites : Knowing who’s helping you succeed from external websites is a crucial step to be able to focus your attention. Paid sponsorships, guest blog posts or even spending more on advertising on the particular website could result in greater traffic.

  • Social Networks : See which social media channels are connecting with the audiences you want. Take the guesswork out by using only the ones you need. By finding out which social channels your ideal audience prefers, you can generate shareable, convincing and engaging content to drive shares and traffic through to your site.

  • Campaigns : Your marketing team may have spent precious time and resource coming up with campaigns that are designed to succeed, but how can you be so sure ? With Campaigns you can understand what marketing campaigns are working, what aren’t, and shift your marketing efforts accordingly to gain more visitors, more effectively, with less costs. Keep track of every ad and content piece you display across internal and external channels to see which is having the biggest impact on your business objectives. Learn more

  

  Watch this video to learn about the Acquisitions feature

  2. Use funnels

  Creating conversion funnels gives you the big picture on whether your acquisition plans are paying off and where they may be falling short.

  

  If the ultimate goal of your site is to drive conversions, then each funnel can tell you how effectively you’re driving traffic through to your desired outcome.

  By integrating this with Visitor Profiles, you can view historic visitor profiles of any individual user at any stage of the conversion funnel. You see the full user journey at an individual level, including where they entered the funnel from and where they exited. Learn more

  How to amplify acquisition strategies with Funnels : Use conversion funnels to guide acquisition as you can tell which entry point is bringing the most success and which one needs more attention. Tailor your strategies to zone in on areas that have the most potential. You can identify where your visitors are encountering obstacles from the start, that are stopping them from progressing through their journey on your site.

  3. Study Visitor Profiles

  Visitor Profiles helps you understand visitors on a user-by-user basis, detailing each visitors’ history into a profile which summarises every visit, action and purchase made.

  

  Better understand :

  • Why your visitors viewed your website.

  • Why your returning visitors continue to view your website.

  • What specifically your visitors are looking for and whether they found it on your website.

  The benefit is being able to see how a combination of acquisition channels play a part in a single buyer’s journey.

  How Visitor Profiles helps with acquisition : By understanding the full behavioural patterns of any individual user coming through from external channels, you’ll see the path that led them to take action, where they may have gotten lost, and how engaged they are with your business over time. This gives you an indication of what kinds of visitors you’re attracting and helps you craft a buyer persona that more accurately reflects the audience most interested in you.

  TRY IT FOR FREE

  4. Focus on SEO efforts

  Every acquisition plan needs a focus on maximising your Search Engine Optimization (SEO) efforts. When it comes to getting conclusive search engine referrer metrics, you need to be sure you’re getting ALL the insights to drive your SEO strategy.

  Integrate Google, Bing and Yahoo search consoles directly into your Matomo Analytics. This helps kickstart your acquisition goals as you rank highly for keywords that get the most traffic to your website.

  As another major SEO benefit, you can see how the most important search keywords to your business increased and decreased in ranking over time. 

  How to amplify acquisitions strategies with search engines and keywords : By staying on top of your competitors across ALL search engines, you may uncover traffic converting highly from one search engine, or find you could be losing traffic and business opportunities to your competitors across others.

  5. Look at the Multi Attribution feature

  Multi Attribution lets you measure the success of every touchpoint in the customer journey.

  

  Accurately measure (and assign value to) channels where visitors first engaged with your business, where they came from after that, as well as the final channel they came from before purchasing your product or service.

  No longer falsely over-estimate any marketing channel and make smarter decisions when determining acquisition spend to accurately calculate the Customer Acquisition Cost (CAC). Learn more

  6. Set your Goals

  What are the acquisition goals you want to achieve the most ? The Goals feature lets you measure the most important metrics you need to grow your business.

  

  Goals are crucial for building your marketing strategy and acquiring new customers. The more goals you track, the more you can learn about behavioural changes as you implement and modify paths that impact acquisition and conversions over time. You’ll understand which channels are converting the best for your business, which cities/countries are most popular, what devices will attract the most visitors and how engaged your visitors are before converting.

  This way you can see if your campaigns (SEO, PPC, signups, blogs etc.) or optimising efforts (A/B Testing, Funnels) have made an impact with the time and investment you have put in. Learn more

  7. Set Advanced Ecommence reporting

  If your website’s overall purpose is to generate revenue whether it be from an online store, asking for donations or from an online paid membership site ; the Ecommerce feature gives you comprehensive insights into your customers’ purchasing behaviours.

  

  When you use Ecommerce analytics, you heavily reduce risk when marketing your products to potential customers because you will understand who to target, what to target them with and where further opportunities exist to have the greatest impact for your business. Learn more

  Key takeaway

  Having the tools to ensure you’re creating a well planned acquisition strategy is key to attracting and capturing the attention of potential visitors/leads, and then driving them through a funnel/buyer’s journey on your website. Because of Matomo’s reputation as a trusted analytics platform, the features above can be used to assist you in making smarter data-driven decisions. You can pursue different acquisition avenues with confidence and create a strategy that’s agile and ready for success, all while respecting user privacy.

  TRY IT FOR FREE

  Want to learn how to increase engagement with Matomo ? Click here. We’ll go through how you can boost engagement on your website via web analytics.

• avformat/id3v2 : Don't reverse the order of id3v2 APICs

  13 avril 2021, par Andreas Rheinhardt

  avformat/id3v2 : Don't reverse the order of id3v2 APICs
  When parsing ID3v2 tags, special (non-text) metadata is not applied
  
  directly and unconditionally ; instead it is stored in a linked list
  
  in which elements are prepended. When traversing the list to add APICs
  
  (or private tags) at the end, the order is reversed. The same also
  
  happens for chapters and therefore the chapter parsing code already
  
  reverses the chapters.
  This commit changes this : By keeping pointers to both head and tail
  
  of the linked list one can preserve the order of the entries and
  
  remove the reordering code for chapters. Only the pointer to head
  
  will be exported : No current caller uses a nonempty list, so exporting
  
  both head and tail is unnecessary. This removes the functionality
  
  to combine the lists of special metadata read from different ID3v2 tags,
  
  but that doesn't make really much sense anyway (and would be trivial
  
  to implement if desired) and allows to remove the now unnecessary
  
  initializations performed by the callers.
  The FATE-reference for the id3v2-priv test had to be updated
  
  because the order of the tags read into the dict is reversed ;
  
  for id3v2-priv-remux only the md5 and not the ffprobe output
  
  of the remuxed file changes because the order of the private tags
  
  has up until now been reversed twice.
  The references for the aiff/mp3 cover-art tests needed to be updated,
  
  because the order of the attached pics is reversed upon reading.
  
  It is still not correct, because the muxers write the pics in the order
  
  in which they arrive at the muxer instead of the order given by
  
  pkt->stream_index.
  Signed-off-by : Andreas Rheinhardt <andreas.rheinhardt@outlook.com>
  

  • [DH] libavformat/aacdec.c
  • [DH] libavformat/aiffdec.c
  • [DH] libavformat/asf.c
  • [DH] libavformat/dsfdec.c
  • [DH] libavformat/id3v2.c
  • [DH] libavformat/id3v2.h
  • [DH] libavformat/iff.c
  • [DH] libavformat/omadec.c
  • [DH] libavformat/wavdec.c
  • [DH] tests/ref/fate/cover-art-aiff-id3v2-remux
  • [DH] tests/ref/fate/cover-art-mp3-id3v2-remux
  • [DH] tests/ref/fate/id3v2-priv
  • [DH] tests/ref/fate/id3v2-priv-remux

• Developing MobyCAIRO

  26 mai 2021, par Multimedia Mike — General

  I recently published a tool called MobyCAIRO. The ‘CAIRO’ part stands for Computer-Assisted Image ROtation, while the ‘Moby’ prefix refers to its role in helping process artifact image scans to submit to the MobyGames database. The tool is meant to provide an accelerated workflow for rotating and cropping image scans. It works on both Windows and Linux. Hopefully, it can solve similar workflow problems for other people.

  As of this writing, MobyCAIRO has not been tested on Mac OS X yet– I expect some issues there that should be easily solvable if someone cares to test it.

  The rest of this post describes my motivations and how I arrived at the solution.

  Background
  I have scanned well in excess of 2100 images for MobyGames and other purposes in the past 16 years or so. The workflow looks like this :

  
  

  

  Image workflow

  
  

  It should be noted that my original workflow featured me manually rotating the artifact on the scanner bed in order to ensure straightness, because I guess I thought that rotate functions in image editing programs constituted dark, unholy magic or something. So my workflow used to be even more arduous :

  
  

  

  I can’t believe I had the patience to do this for hundreds of scans

  
  

  Sometime last year, I was sitting down to perform some more scanning and found myself dreading the oncoming tedium of straightening and cropping the images. This prompted a pivotal question :

  
  Why can’t a computer do this for me ?
  

  After all, I have always been a huge proponent of making computers handle the most tedious, repetitive, mind-numbing, and error-prone tasks. So I did some web searching to find if there were any solutions that dealt with this. I also consulted with some like-minded folks who have to cope with the same tedious workflow.

  I came up empty-handed. So I endeavored to develop my own solution.

  Problem Statement and Prior Work
  
  I want to develop a workflow that can automatically rotate an image so that it is straight, and also find the most likely crop rectangle, uniformly whitening the area outside of the crop area (in the case of circles).

  As mentioned, I checked to see if any other programs can handle this, starting with my usual workhorse, Photoshop Elements. But I can’t expect the trimmed down version to do everything. I tried to find out if its big brother could handle the task, but couldn’t find a definitive answer on that. Nor could I find any other tools that seem to take an interest in optimizing this particular workflow.

  When I brought this up to some peers, I received some suggestions, including an idea that the venerable GIMP had a feature like this, but I could not find any evidence. Further, I would get responses of “Program XYZ can do image rotation and cropping.” I had to tamp down on the snark to avoid saying “Wow ! An image editor that can perform rotation AND cropping ? What a game-changer !” Rotation and cropping features are table stakes for any halfway competent image editor for the last 25 or so years at least. I am hoping to find or create a program which can lend a bit of programmatic assistance to the task.

  Why can’t other programs handle this ? The answer seems fairly obvious : Image editing tools are general tools and I want a highly customized workflow. It’s not reasonable to expect a turnkey solution to do this.

  Brainstorming An Approach
  I started with the happiest of happy cases— A disc that needed archiving (a marketing/press assets CD-ROM from a video game company, contents described here) which appeared to have some pretty clear straight lines :

  
  
  

  My idea was to try to find straight lines in the image and then rotate the image so that the image is parallel to the horizontal based on the longest single straight line detected.

  I just needed to figure out how to find a straight line inside of an image. Fortunately, I quickly learned that this is very much a solved problem thanks to something called the Hough transform. As a bonus, I read that this is also the tool I would want to use for finding circles, when I got to that part. The nice thing about knowing the formal algorithm to use is being able to find efficient, optimized libraries which already implement it.

  Early Prototype
  A little searching for how to perform a Hough transform in Python led me first to scikit. I was able to rapidly produce a prototype that did some basic image processing. However, running the Hough transform directly on the image and rotating according to the longest line segment discovered turned out not to yield expected results.

  
  
  

  It also took a very long time to chew on the 3300×3300 raw image– certainly longer than I care to wait for an accelerated workflow concept. The key, however, is that you are apparently not supposed to run the Hough transform on a raw image– you need to compute the edges first, and then attempt to determine which edges are ‘straight’. The recommended algorithm for this step is the Canny edge detector. After applying this, I get the expected rotation :

  
  
  

  The algorithm also completes in a few seconds. So this is a good early result and I was feeling pretty confident. But, again– happiest of happy cases. I should also mention at this point that I had originally envisioned a tool that I would simply run against a scanned image and it would automatically/magically make the image straight, followed by a perfect crop.

  Along came my MobyGames comrade Foxhack to disabuse me of the hope of ever developing a fully automated tool. Just try and find a usefully long straight line in this :

  
  

  Darn it, Foxhack…
  

  There are straight edges, to be sure. But my initial brainstorm of rotating according to the longest straight edge looks infeasible. Further, it’s at this point that we start brainstorming that perhaps we could match on ratings badges such as the standard ESRB badges omnipresent on U.S. video games. This gets into feature detection and complicates things.

  This Needs To Be Interactive
  At this point in the effort, I came to terms with the fact that the solution will need to have some element of interactivity. I will also need to get out of my safe Linux haven and figure out how to develop this on a Windows desktop, something I am not experienced with.

  I initially dreamed up an impressive beast of a program written in C++ that leverages Windows desktop GUI frameworks, OpenGL for display and real-time rotation, GPU acceleration for image analysis and processing tricks, and some novel input concepts. I thought GPU acceleration would be crucial since I have a fairly good GPU on my main Windows desktop and I hear that these things are pretty good at image processing.

  I created a list of prototyping tasks on a Trello board and made a decent amount of headway on prototyping all the various pieces that I would need to tie together in order to make this a reality. But it was ultimately slowgoing when you can only grab an hour or 2 here and there to try to get anything done.

  Settling On A Solution
  Recently, I was determined to get a set of old shareware discs archived. I ripped the data a year ago but I was blocked on the scanning task because I knew that would also involve tedious straightening and cropping. So I finally got all the scans done, which was reasonably quick. But I was determined to not manually post-process them.

  This was fairly recent, but I can’t quite recall how I managed to come across the OpenCV library and its Python bindings. OpenCV is an amazing library that provides a significant toolbox for performing image processing tasks. Not only that, it provides “just enough” UI primitives to be able to quickly create a basic GUI for your program, including image display via multiple windows, buttons, and keyboard/mouse input. Furthermore, OpenCV seems to be plenty fast enough to do everything I need in real time, just with (accelerated where appropriate) CPU processing.

  So I went to work porting the ideas from the simple standalone Python/scikit tool. I thought of a refinement to the straight line detector– instead of just finding the longest straight edge, it creates a histogram of 360 rotation angles, and builds a list of lines corresponding to each angle. Then it sorts the angles by cumulative line length and allows the user to iterate through this list, which will hopefully provide the most likely straightened angle up front. Further, the tool allows making fine adjustments by 1/10 of an angle via the keyboard, not the mouse. It does all this while highlighting in red the straight line segments that are parallel to the horizontal axis, per the current candidate angle.

  
  
  

  The tool draws a light-colored grid over the frame to aid the user in visually verifying the straightness of the image. Further, the program has a mode that allows the user to see the algorithm’s detected edges :

  
  
  

  For the cropping phase, the program uses the Hough circle transform in a similar manner, finding the most likely circles (if the image to be processed is supposed to be a circle) and allowing the user to cycle among them while making precise adjustments via the keyboard, again, rather than the mouse.

  
  
  

  Running the Hough circle transform is a significantly more intensive operation than the line transform. When I ran it on a full 3300×3300 image, it ran for a long time. I didn’t let it run longer than a minute before forcibly ending the program. Is this approach unworkable ? Not quite– It turns out that the transform is just as effective when shrinking the image to 400×400, and completes in under 2 seconds on my Core i5 CPU.

  For rectangular cropping, I just settled on using OpenCV’s built-in region-of-interest (ROI) facility. I tried to intelligently find the best candidate rectangle and allow fine adjustments via the keyboard, but I wasn’t having much success, so I took a path of lesser resistance.

  Packaging and Residual Weirdness
  I realized that this tool would be more useful to a broader Windows-using base of digital preservationists if they didn’t have to install Python, establish a virtual environment, and install the prerequisite dependencies. Thus, I made the effort to figure out how to wrap the entire thing up into a monolithic Windows EXE binary. It is available from the project’s Github release page (another thing I figured out for the sake of this project !).

  The binary is pretty heavy, weighing in at a bit over 50 megabytes. You might advise using compression– it IS compressed ! Before I figured out the --onefile command for pyinstaller.exe, the generated dist/ subdirectory was 150 MB. Among other things, there’s a 30 MB FORTRAN BLAS library packaged in !

  Conclusion and Future Directions
  Once I got it all working with a simple tkinter UI up front in order to select between circle and rectangle crop modes, I unleashed the tool on 60 or so scans in bulk, using the Windows forfiles command (another learning experience). I didn’t put a clock on the effort, but it felt faster. Of course, I was livid with proudness the whole time because I was using my own tool. I just wish I had thought of it sooner. But, really, with 2100+ scans under my belt, I’m just getting started– I literally have thousands more artifacts to scan for preservation.

  The tool isn’t perfect, of course. Just tonight, I threw another scan at MobyCAIRO. Just go ahead and try to find straight lines in this specimen :

  
  
  

  I eventually had to use the text left and right of center to line up against the grid with the manual keyboard adjustments. Still, I’m impressed by how these computer vision algorithms can see patterns I can’t, highlighting lines I never would have guessed at.

  I’m eager to play with OpenCV some more, particularly the video processing functions, perhaps even some GPU-accelerated versions.

  The post Developing MobyCAIRO first appeared on Breaking Eggs And Making Omelettes.