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• What is Behavioural Segmentation and Why is it Important ?

  28 septembre 2023, par Erin — Analytics Tips

  Amidst the dynamic landscape of web analytics, understanding customers has grown increasingly vital for businesses to thrive. While traditional demographic-focused strategies possess merit, they need to uncover the nuanced intricacies of individual online behaviours and preferences. As customer expectations evolve in the digital realm, enterprises must recalibrate their approaches to remain relevant and cultivate enduring digital relationships.

  In this context, the surge of technology and advanced data analysis ushers in a marketing revolution : behavioural segmentation. Businesses can unearth invaluable insights by meticulously scrutinising user actions, preferences and online interactions. These insights lay the foundation for precisely honed, high-performing, personalised campaigns. The era dominated by blanket, catch-all marketing strategies is yielding to an era of surgical precision and tailored engagement. 

  While the insights from user behaviours empower businesses to optimise customer experiences, it’s essential to strike a delicate balance between personalisation and respecting user privacy. Ethical use of behavioural data ensures that the power of segmentation is wielded responsibly and in compliance, safeguarding user trust while enabling businesses to thrive in the digital age.

  What is behavioural segmentation ?

  Behavioural segmentation is a crucial concept in web analytics and marketing. It involves categorising individuals or groups of users based on their online behaviour, actions and interactions with a website. This segmentation method focuses on understanding how users engage with a website, their preferences and their responses to various stimuli. Behavioural segmentation classifies users into distinct segments based on their online activities, such as the pages they visit, the products they view, the actions they take and the time they spend on a site.

  Behavioural segmentation plays a pivotal role in web analytics for several reasons :

  1. Enhanced personalisation :

  Understanding user behaviour enables businesses to personalise online experiences. This aids with delivering tailored content and recommendations to boost conversion, customer loyalty and customer satisfaction.

  2. Improved user experience :

  Behavioural segmentation optimises user interfaces (UI) and navigation by identifying user paths and pain points, enhancing the level of engagement and retention.

  3. Targeted marketing :

  Behavioural segmentation enhances marketing efficiency by tailoring campaigns to user behaviour. This increases the likelihood of interest in specific products or services.

  4. Conversion rate optimisation :

  Analysing behavioural data reveals factors influencing user decisions, enabling website optimisation for a streamlined purchasing process and higher conversion rates.

  5. Data-driven decision-making :

  Behavioural segmentation empowers data-driven decisions. It identifies trends, behavioural patterns and emerging opportunities, facilitating adaptation to changing user preferences and market dynamics.

  6. Ethical considerations :

  Behavioural segmentation provides valuable insights but raises ethical concerns. User data collection and use must prioritise transparency, privacy and responsible handling to protect individuals’ rights.

  The significance of ethical behavioural segmentation will be explored more deeply in a later section, where we will delve into the ethical considerations and best practices for collecting, storing and utilising behavioural data in web analytics. It’s essential to strike a balance between harnessing the power of behavioural segmentation for business benefits and safeguarding user privacy and data rights in the digital age.

  

  Different types of behavioural segments with examples

  1. Visit-based segments : These segments hinge on users’ visit patterns. Analyse visit patterns, compare first-time visitors to returning ones, or compare users landing on specific pages to those landing on others.
     • Example : The real estate website Zillow can analyse how first-time visitors and returning users behave differently. By understanding these patterns, Zillow can customise its website for each group. For example, they can highlight featured listings and provide navigation tips for first-time visitors while offering personalised recommendations and saved search options for returning users. This could enhance user satisfaction and boost the chances of conversion.
  2. Interaction-based segments : Segments can be created based on user interactions like special events or goals completed on the site.
     • Example : Airbnb might use this to understand if users who successfully book accommodations exhibit different behaviours than those who don’t. This insight could guide refinements in the booking process for improved conversion rates.
  3. Campaign-based segments : Beyond tracking visit numbers, delve into usage differences of visitors from specific sources or ad campaigns for deeper insights.
     • Example : Nike might analyse user purchase behaviour from various traffic sources (referral websites, organic, direct, social media and ads). This informs marketing segmentation adjustments, focusing on high-performance channels. It also customises the website experience for different traffic sources, optimising content, promotions and navigation. This data-driven approach could boost user experiences and maximise marketing impact for improved brand engagement and sales conversions.
  4. Ecommerce segments : Separate users based on purchases, even examining the frequency of visits linked to specific products. Segment heavy users versus light users. This helps uncover diverse customer types and browsing behaviours.
     • Example : Amazon could create segments to differentiate between visitors who made purchases and those who didn’t. This segmentation could reveal distinct usage patterns and preferences, aiding Amazon in tailoring its recommendations and product offerings.
  5. Demographic segments : Build segments based on browser language or geographic location, for instance, to comprehend how user attributes influence site interactions.
     • Example : Netflix can create user segments based on demographic factors like geographic location to gain insight into how a visitor’s location can influence content preferences and viewing behaviour. This approach could allow for a more personalised experience.
  6. Technographic segments : Segment users by devices or browsers, revealing variations in site experience and potential platform-specific issues or user attitudes.
     • Example : Google could create segments based on users’ devices (e.g., mobile, desktop) to identify potential issues in rendering its search results. This information could be used to guide Google in providing consistent experiences regardless of device.

  

  The importance of ethical behavioural segmentation

  Respecting user privacy and data protection is crucial. Matomo offers features that align with ethical segmentation practices. These include :

  • Anonymization : Matomo allows for data anonymization, safeguarding individual identities while providing valuable insights.
  • GDPR compliance : Matomo is GDPR compliant, ensuring that user data is handled following European data protection regulations.
  • Data retention and deletion : Matomo enables businesses to set data retention policies and delete user data when it’s no longer needed, reducing the risk of data misuse.
  • Secured data handling : Matomo employs robust security measures to protect user data, reducing the risk of data breaches.

  Real-world examples of ethical behavioural segmentation :

  1. Content publishing : A leading news website could utilise data anonymization tools to ethically monitor user engagement. This approach allows them to optimise content delivery based on reader preferences while ensuring the anonymity and privacy of their target audience.
  2. Non-profit organisations : A charity organisation could embrace granular user control features. This could be used to empower its donors to manage their data preferences, building trust and loyalty among supporters by giving them control over their personal information.

  

  Examples of effective behavioural segmentation

  Companies are constantly using behavioural insights to engage their audiences effectively. In this section, we’ll delve into real-world examples showcasing how top companies use behavioural segmentation to enhance their marketing efforts.

  

  1. Coca-Cola’s behavioural insights for marketing strategy : Coca-Cola employs behavioural segmentation to evaluate its advertising campaigns. Through analysing user engagement across TV commercials, social media promotions and influencer partnerships, Coca-Cola’s marketing team can discover that video ads shared by influencers generate the highest ROI and web traffic.

     This insight guides the reallocation of resources, leading to increased sales and a more effective advertising strategy.

  2. eBay’s custom conversion approach : eBay excels in conversion optimisation through behavioural segmentation. When users abandon carts, eBay’s dynamic system sends personalised email reminders featuring abandoned items and related recommendations tailored to user interests and past purchase decisions.

     This strategy revives sales, elevates conversion rates and sparks engagement. eBay’s adeptness in leveraging behavioural insights transforms user experience, steering a customer journey toward conversion.

  3. Sephora’s data-driven conversion enhancement : Data analysts can use Sephora’s behavioural segmentation strategy to fuel revenue growth through meticulous data analysis. By identifying a dedicated subset of loyal customers who exhibit a consistent preference for premium skincare products, data analysts enable Sephora to customise loyalty programs.

     These personalised rewards programs provide exclusive discounts and early access to luxury skincare releases, resulting in heightened customer engagement and loyalty. The data-driven precision of this approach directly contributes to amplified revenue from this specific customer segment.

  Examples of the do’s and don’ts of behavioural segmentation 

  

  Behavioural segmentation is a powerful marketing and data analysis tool, but its success hinges on ethical and responsible practices. In this section, we will explore real-world examples of the do’s and don’ts of behavioural segmentation, highlighting companies that have excelled in their approach and those that have faced challenges due to lapses in ethical considerations.

  Do’s of behavioural segmentation :

  • Personalised messaging :
    • Example : Spotify
      • Spotify’s success lies in its ability to use behavioural data to curate personalised playlists and user recommendations, enhancing its music streaming experience.
  • Transparency :
    • Example : Basecamp
      • Basecamp’s transparency in sharing how user data is used fosters trust. They openly communicate data practices, ensuring users are informed and comfortable.
  • Anonymization
    • Example : Matomo’s anonymization features
      • Matomo employs anonymization features to protect user identities while providing valuable insights, setting a standard for responsible data handling.
  • Purpose limitation :
    • Example : Proton Mail
      • Proton Mail strictly limits the use of user data to email-related purposes, showcasing the importance of purpose-driven data practices.
  • Dynamic content delivery : 
    • Example : LinkedIn
      • LinkedIn uses behavioural segmentation to dynamically deliver job recommendations, showcasing the potential for relevant content delivery.
  • Data security :
    • Example : Apple
      • Apple’s stringent data security measures protect user information, setting a high bar for safeguarding sensitive data.
  • Adherence to regulatory compliance : 
    • Example : Matomo’s regulatory compliance features
      • Matomo’s regulatory compliance features ensure that businesses using the platform adhere to data protection regulations, further promoting responsible data usage.

  Don’ts of behavioural segmentation :

  • Ignoring changing regulations
    • Example : Equifax
      • Equifax faced major repercussions for neglecting evolving regulations, resulting in a data breach that exposed the sensitive information of millions.
  • Sensitive attributes
    • Example : Twitter
      • Twitter faced criticism for allowing advertisers to target users based on sensitive attributes, sparking concerns about user privacy and data ethics.
  • Data sharing without consent
    • Example : Meta & Cambridge Analytica
      • The Cambridge Analytica scandal involving Meta (formerly Facebook) revealed the consequences of sharing user data without clear consent, leading to a breach of trust.
  • Lack of control
    • Example : Uber
      • Uber faced backlash for its poor data security practices and a lack of control over user data, resulting in a data breach and compromised user information.
  • Don’t be creepy with invasive personalisation
    • Example : Offer Moment
      • Offer Moment’s overly invasive personalisation tactics crossed ethical boundaries, unsettling users and eroding trust.

  These examples are valuable lessons, emphasising the importance of ethical and responsible behavioural segmentation practices to maintain user trust and regulatory compliance in an increasingly data-driven world.

  Continue the conversation

  Diving into customer behaviours, preferences and interactions empowers businesses to forge meaningful connections with their target audience through targeted marketing segmentation strategies. This approach drives growth and fosters exceptional customer experiences, as evident from the various common examples spanning diverse industries.

  In the realm of ethical behavioural segmentation and regulatory compliance, Matomo is a trusted partner. Committed to safeguarding user privacy and data integrity, our advanced web analytics solution empowers your business to harness the power of behavioral segmentation, all while upholding the highest standards of compliance with stringent privacy regulations.

  To gain deeper insight into your visitors and execute impactful marketing campaigns, explore how Matomo can elevate your efforts. Try Matomo free for 21-days, no credit card required. 

• A pragmatic strategy to merge multiple video files

  19 juin 2021, par saurav

  I currently am working on recording a multiparty video conference which supports up to 6 participants. I am recording the conference using a media server and storing audio/video streams individually for every participant.

  


  Next, I need to merge those individual recordings into a single video file and upload it to a cloud storage like aws s3. For this I am considering 2 options, either Gstreamer or FFMPEG. I am leaning towards FFMPEG as I have used FFMPEG previously. I currently am playing with FFMPEG things like the hstack and vstack filters etc.

  


  Here is the FFMPEG command I recently used to join 2 webm videos of 2 mins 40sec and 1min 40sec to create a mp4 video file for upload. Both the videos are 1280x720 in this case but I have included the scale part because in real life scenario different participants joining with different cameras produces video files of different resolution which is a problem for the hstack/vstack filter. Therefore, to make the video resolutions of all participant consistent, I have included the scale property.

  


  ffmpeg -i 1.webm -i 2.webm -filter_complex "[0:v]scale=1280:720,setsar=1[l];[1:v]scale=1280:720,setsar=1[r];[l][r]hstack;[0][1]amix" output-1280x720.mp4


  


  Currently I am facing 2 issues with this command.

  


  


  1. The output mp4 file is very big, in this case 140Mb (approx) for a less than 3 minutes video.

     


  


  2. How do I add delay to any video before starting to merge ?
     
Currently the videos are going out of sync if all the participants don't join at the same time which is highly unlikely to happen in a real world scenario.

     


  


  


  Any pointer in the right direction will be highly appreciated.

  


  Here is a log sample from FFmpeg (or see the full log link) :

  


  ffmpeg version 4.2.4-1ubuntu0.1 Copyright (c) 2000-2020 the FFmpeg developers
  built with gcc 9 (Ubuntu 9.3.0-10ubuntu2)
  configuration: --prefix=/usr --extra-version=1ubuntu0.1 --toolchain=hardened --libdir=/usr/lib/x86_64-linux-gnu --incdir=/usr/include/x86_64-linux-gnu --arch=amd64 --enable-gpl --disable-stripping --enable-avresample --disable-filter=resample --enable-avisynth --enable-gnutls --enable-ladspa --enable-libaom --enable-libass --enable-libbluray --enable-libbs2b --enable-libcaca --enable-libcdio --enable-libcodec2 --enable-libflite --enable-libfontconfig --enable-libfreetype --enable-libfribidi --enable-libgme --enable-libgsm --enable-libjack --enable-libmp3lame --enable-libmysofa --enable-libopenjpeg --enable-libopenmpt --enable-libopus --enable-libpulse --enable-librsvg --enable-librubberband --enable-libshine --enable-libsnappy --enable-libsoxr --enable-libspeex --enable-libssh --enable-libtheora --enable-libtwolame --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwavpack --enable-libwebp --enable-libx265 --enable-libxml2 --enable-libxvid --enable-libzmq --enable-libzvbi --enable-lv2 --enable-omx --enable-openal --enable-opencl --enable-opengl --enable-sdl2 --enable-libdc1394 --enable-libdrm --enable-libiec61883 --enable-nvenc --enable-chromaprint --enable-frei0r --enable-libx264 --enable-shared
  libavutil      56. 31.100 / 56. 31.100
  libavcodec     58. 54.100 / 58. 54.100
  libavformat    58. 29.100 / 58. 29.100
  libavdevice    58.  8.100 / 58.  8.100
  libavfilter     7. 57.100 /  7. 57.100
  libavresample   4.  0.  0 /  4.  0.  0
  libswscale      5.  5.100 /  5.  5.100
  libswresample   3.  5.100 /  3.  5.100
  libpostproc    55.  5.100 / 55.  5.100
Input #0, matroska,webm, from '3.webm':
  Metadata:
    title           : FFmpeg
    ENCODER         : Lavf58.29.100
  Duration: 00:01:39.63, start: 0.000000, bitrate: 707 kb/s
    Stream #0:0: Video: vp8, yuv420p(tv, bt470bg/unknown/unknown, progressive), 1280x720, SAR 1:1 DAR 16:9, 1k tbr, 1k tbn, 1k tbc (default)
    Metadata:
      DURATION        : 00:01:39.618000000
    Stream #0:1: Audio: opus, 48000 Hz, stereo, fltp (default)
    Metadata:
      DURATION        : 00:01:39.629000000
Input #1, matroska,webm, from '4.webm':
  Metadata:
    title           : FFmpeg
    ENCODER         : Lavf58.29.100
  Duration: 00:02:39.07, start: 0.000000, bitrate: 708 kb/s
    Stream #1:0: Video: vp8, yuv420p(tv, bt470bg/unknown/unknown, progressive), 1280x720, SAR 1:1 DAR 16:9, 1k tbr, 1k tbn, 1k tbc (default)
    Metadata:
      DURATION        : 00:02:39.050000000
    Stream #1:1: Audio: opus, 48000 Hz, stereo, fltp (default)
    Metadata:
      DURATION        : 00:02:39.068000000
Stream mapping:
  Stream #0:0 (vp8) -> scale
  Stream #0:1 (opus) -> amix:input0
  Stream #1:0 (vp8) -> scale
  Stream #1:1 (opus) -> amix:input1
  hstack -> Stream #0:0 (libx264)
  amix -> Stream #0:1 (aac)
Press [q] to stop, [?] for help
[libx264 @ 0x562b4842a500] using SAR=1/1
[libx264 @ 0x562b4842a500] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2
[libx264 @ 0x562b4842a500] profile High, level 6.1
[libx264 @ 0x562b4842a500] 264 - core 155 r2917 0a84d98 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=18 lookahead_threads=3 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00
Output #0, mp4, to 'output-new.mp4':
  Metadata:
    title           : FFmpeg
    encoder         : Lavf58.29.100
    Stream #0:0: Video: h264 (libx264) (avc1 / 0x31637661), yuv420p(progressive), 2560x720 [SAR 1:1 DAR 32:9], q=-1--1, 1k fps, 16k tbn, 1k tbc (default)
    Metadata:
      encoder         : Lavc58.54.100 libx264
    Side data:
      cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: -1
    Stream #0:1: Audio: aac (LC) (mp4a / 0x6134706D), 48000 Hz, stereo, fltp, 128 kb/s (default)
    Metadata:
      encoder         : Lavc58.54.100 aac

frame=  129 fps=0.0 q=33.0 size=       0kB time=00:00:00.23 bitrate=   1.6kbits/s dup=123 drop=0 speed=0.44x    
frame=  257 fps=228 q=33.0 size=       0kB time=00:00:00.51 bitrate=   0.8kbits/s dup=243 drop=0 speed=0.455x    
frame=  379 fps=224 q=33.0 size=     256kB time=00:00:00.73 bitrate=2855.1kbits/s dup=358 drop=0 speed=0.434x    
frame=  497 fps=222 q=33.0 size=     256kB time=00:00:00.86 bitrate=2431.5kbits/s dup=469 drop=0 speed=0.386x    
 
...
More than 1000 frames duplicated
...
  
frame=158751 fps=196 q=33.0 size=  134656kB time=00:02:39.00 bitrate=6937.4kbits/s dup=151385 drop=0 speed=0.196x    
frame=158851 fps=196 q=33.0 size=  134912kB time=00:02:39.00 bitrate=6950.6kbits/s dup=151482 drop=0 speed=0.196x    
frame=158983 fps=196 q=33.0 size=  134912kB time=00:02:39.00 bitrate=6950.6kbits/s dup=151610 drop=0 speed=0.196x    
frame=159081 fps=196 q=-1.0 Lsize=  137197kB time=00:02:39.07 bitrate=7065.2kbits/s dup=151706 drop=0 speed=0.196x    

video:132693kB audio:2494kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 1.486001%

[libx264 @ 0x562b4842a500] frame I:637   Avg QP:17.73  size:123895
[libx264 @ 0x562b4842a500] frame P:40088 Avg QP:19.73  size:  1134
[libx264 @ 0x562b4842a500] frame B:118356 Avg QP:27.54  size:    97
[libx264 @ 0x562b4842a500] consecutive B-frames:  0.8%  0.0%  0.0% 99.2%
[libx264 @ 0x562b4842a500] mb I  I16..4: 11.1% 67.3% 21.6%
[libx264 @ 0x562b4842a500] mb P  I16..4:  0.1%  0.1%  0.0%  P16..4:  2.6%  0.4%  0.3%  0.0%  0.0%    skip:96.5%
[libx264 @ 0x562b4842a500] mb B  I16..4:  0.0%  0.0%  0.0%  B16..8:  0.7%  0.0%  0.0%  direct: 0.0%  skip:99.3%  L0:38.7% L1:61.3% BI: 0.0%
[libx264 @ 0x562b4842a500] 8x8 transform intra:66.8% inter:71.4%
[libx264 @ 0x562b4842a500] coded y,uvDC,uvAC intra: 81.8% 89.5% 72.3% inter: 0.2% 0.4% 0.0%
[libx264 @ 0x562b4842a500] i16 v,h,dc,p: 25% 21% 17% 37%
[libx264 @ 0x562b4842a500] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 33% 22% 12%  4%  5%  6%  6%  6%  6%
[libx264 @ 0x562b4842a500] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 42% 24%  6%  4%  5%  5%  6%  4%  4%
[libx264 @ 0x562b4842a500] i8c dc,h,v,p: 42% 24% 26%  9%
[libx264 @ 0x562b4842a500] Weighted P-Frames: Y:0.0% UV:0.0%
[libx264 @ 0x562b4842a500] ref P L0: 82.4% 11.5%  5.3%  0.8%
[libx264 @ 0x562b4842a500] ref B L0: 83.0% 16.9%  0.1%
[libx264 @ 0x562b4842a500] ref B L1: 94.9%  5.1%
[libx264 @ 0x562b4842a500] kb/s:6833.11
[aac @ 0x562b4842b540] Qavg: 239.393


  


• Long Overdue MediaWiki Upgrade

  5 février 2014, par Multimedia Mike — General

  What do I do ? What I do ? This library book is 42 years overdue !
  I admit that it’s mine, yet I can’t pay the fine,
  Should I turn it in or should I hide it again ?
  What do I do ? What do I do ?

  I internalized the forgoing paean to the perils of procrastination by Shel Silverstein in my formative years. It’s probably why I’ve never paid a single cent in late fees in my entire life.

  However, I have been woefully negligent as the steward of the MediaWiki software that drives the world famous MultimediaWiki, the internet’s central repository of obscure technical knowledge related to multimedia. It is currently running of version 1.6 software. The latest version is 1.22.

  The Story So Far
  According to my records, I first set up the wiki late in 2005. I don’t know which MediaWiki release I was using at the time. I probably conducted a few upgrades in the early days, but that went by the wayside perhaps in 2007. My web host stopped allowing shell access and the MediaWiki upgrade process pretty much requires running a PHP script from a command line. Upgrade time came around and I put off the project. Weeks turned into months turned into years until, according to some notes, the wiki abruptly stopped working in July, 2011. Suddenly, there were PHP errors about “Namespace” being a reserved word.

  While I finally laid out a plan to upgrade the wiki after all these years, I eventually found that the problem had been caused when my webhost upgraded from PHP 5.2 -> 5.3. I also learned of a small number of code changes that caused the problem to go away, thus kicking the can down the road once more.

  Then a new problem showed up last week. I think it might be related to a new version of PHP again. This time, a few other things on my site broke, and I learned that my webhost now allows me to select a PHP version to use (with the version then set to “auto”, which didn’t yield much information). Rolling back to an earlier version of PHP might have solved the problem easily.

  But NO ! I made the determination that this goes no further. I want this wiki upgraded.

  The Arduous Upgrade Path
  There are 2 general upgrade paths I can think of :
  

  1. Upgrade in place on the server
  2. Upgrade offline and put the site back on the server

  Approach #1 is problematic since I don’t have direct shell access, though I considered using something like PHP Shell. Approach #2 involves getting the entire set of wiki files and a backup of the MySQL tables. This is workable since I keep automated backups of these items anyway.

  In fairly short order, I was able to set up a working copy of the MultimediaWiki hosted on a local Linux machine. Now what’s the move ? The MediaWiki software I’m running is 1.6.10. The very latest, as of this upgrade project is 1.22.2. I suppose it’s way too much to hope that the software will upgrade cleanly from 1.6.x straight to 1.22.x, but I guess it’s worth a shot…

  HA ! No chance. Okay, next idea is to march through the various versions and upgrade each in turn. MediaWiki has all their historic releases online, all the way back to the 1.3 lineage. I decided that the latest of each lineage should upgrade cleanly from anything in the previous version of lineage. E.g., 1.6.10 should upgrade cleanly to 1.7.3 (last in the 1.7 series). This seemed to be a workable strategy. So I downloaded the latest of each series, unpacked, and copied all the wiki files over the working installation and ran ‘php update.php’ in the maintenance/ directory.

  The process is tedious and not without its obstacles. I consider this penance for my years of wiki neglect. First, I run into the “PHP Parse error : syntax error, unexpected T_NAMESPACE, expecting T_STRING” issue, the same that I saw years ago after the webhost transitioned from PHP 5.2 -> 5.3. I could solve this by editing assorted files and changing “Namespace” -> “MWNamespace” (which is what MediaWiki did by version 1.13). But I would prefer not to.

  Instead, I downloaded the source for PHP 5.2 and compiled it in a separate directory, then called ‘/path/to/php/5.2/bin/php update.php’. Problem solved.

  The next problem is that a bunch of the database update scripts are specifying “Type=InnoDB”. This isn’t supported by modern MySQL databases. Now, it’s “Engine=InnoDB”. A quick search & replace at the command line fixes this for 1.6.x… and 1.7.x… and 1.8 through 1.12. Finally, at 1.13, it was no longer necessary. As a bonus, at 1.13, I was able to test the installation since Namespace had been renamed to MWNamespace. I would later learn that the table type modifications probably could have been simplified in by changing “$wgDBmysql4 = true ;” to “$wgDBmysql5 = true ;” somewhere in LocalSettings.php.

  Command line upgrading worked smoothly up through 1.18 series when I got a new syntax error :

  <br />
PHP Fatal error:  Call to a member function addMessages() on a non-object in /mnt/sdb1/archive/wiki/extensions/Cite.php on line 68<br />

  Best I could do was comment out that line. I hope that doesn’t break anything important.

  In the home stretch, the very last transition (1.21 -> 1.22) failed :

  PHP Fatal error :  Cannot redeclare wfProfileIn() (previously declared in 
  /mnt/sdb1/archive/wiki/includes/profiler/Profiler.php:33) in 
  /mnt/sdb1/archive/wiki/includes/ProfilerStub.php on line 25
  

  Apparently, this problem arises occasionally since 1.18. I found a way around it thanks to this page : Deleted the file StartProfiler.php. Who am I to argue ?

  Upon completing the transition to 1.22, the wiki doesn’t look correct– the pictures aren’t showing up. The solution was to fix the temporary directory via LocalSettings.php.

  Back To Production
  Okay, it all works again ! Locally, that is. How to get it back to the server ? My first idea was that, knowing that this upgrade process can succeed, try stepping through the upgrade process again, but tell the update.php scripts to access the database tables on multimedia.cx. This seemed to be working for awhile, even though the database update phase often took 4-5 minutes. However, the transition from 1.8.5 -> 1.9.6 took 75 minutes and then timed out. According to my notes, “This isn’t going to work.”

  The new process :

  1. Dump the database tables from the local database.
  2. Create a new database remotely (melanson_wiki_ng).
  3. Dump the database table into melanson_wiki_ng.
  4. Move the index.php file out of the wiki files directory temporarily (or rename).
  5. Modify the LocalSettings.php to talk to the new database.
  6. Perform a lftp mirror operation in order to send all the files up to the server.
  7. Send the index.php file and hope beyond hope that everything magically works.

  And that’s the story of how the updated MultimediaWiki came back online. Despite the database dump file being over 110 MB, it only tool MySQL 1m45s to transmit it all to the remote server (let’s hear it for the ‘–compress’ option). For comparison, inserting the tables back into a fresh local database took 1m07s.

  When the MultimediaWiki was first live again, it loaded, but ever so slowly. This is when I finally looked into optimization and found that I was lacking any caching. So as a bonus, the MultimediaWiki should be much faster now.

  Going Forward
  For all I know, I did everything described here in the hardest way possible. But at least I got it done. Unless I learn of a better process, future upgrades will probably look similar to this.

  Additionally, I should probably take some time to figure out what new features are part of the standard MediaWiki distribution nowadays.