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• Alias Artifacts

  26 avril 2013, par Multimedia Mike — General

  Throughout my own life, I have often observed that my own sense of nostalgia has a window that stretches about 10-15 years past from the current moment. Earlier this year, I discovered the show “Alias” and watched through the entire series thanks to Amazon Prime Instant Video (to be fair, I sort of skimmed the fifth and final season which I found to be horribly dull, or maybe franchise fatigue had set in). The show originally aired from 2001-2006 so I found that it fit well within the aforementioned nostalgia window.

  
  
  

  But what was it, exactly, about the show that triggered nostalgia ? The computers, of course ! The show revolved around spies and espionage and cutting-edge technology necessarily played a role. The production designer for the series must have decided that Unix/Linux == awesome hacking and so many screenshots featured Linux.

  Since this is still nominally a multimedia blog, I’ll start of the screenshot recon with an old multimedia player. Here is a vintage Mac OS desktop running an ancient web browser (probably Netscape) that’s playing a full-window video (probably QuickTime embedded directly into the browser).

  
  

  

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  Let’s jump right into the Linux side of things. This screenshot makes me particularly sentimental since this is exactly what a stock Linux/KDE desktop looked like circa 2001-2003 and is more or less what I would have worked with on my home computer at the time :

  
  

  

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  Studying that screenshot, we see that the user logs in as root, even to the desktop environment. Poor security practice ; I would expect better from a bunch of spooks.

  Echelon
  Look at the terminal output in the above screenshot– it’s building a program named Echelon, an omniscient spy tool inspired by a real-world surveillance network of the same name. In the show, Echelon is used to supply plot-convenient intelligence. At one point, some antagonists get their hands on the Echelon source code and seek to compile it. When they do, they will have access to the vast surveillance network. If you know anything about how computers work, don’t think about that too hard.

  Anyway, it’s interesting to note that Echelon is a properly autotool’d program– when the bad guys finally got Echelon, installation was just a ‘make install’ command away. The compilation was very user-friendly, though, as it would pop up a nice dialog box showing build progress :

  
  

  

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  Examining the build lines in both that screenshot and the following lines, we can see that Echelon cares about files such as common/db_err.c and bt_curadj.c :

  
  

  

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  A little googling reveals that these files both belong to the Berkeley DB library. That works ; I can imagine a program like this leveraging various database packages.

  Computer Languages
  The Echelon source code stuff comes from episode 2.11 : “A Higher Echelon”. While one faction had gotten a hold of the actual Echelon source code, a rival faction had abducted the show’s resident uber-nerd and, learning that they didn’t actually receive the Echelon code, force the nerd to re-write Echelon from scratch. Which he then proceeds to do…

  
  

  

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  The code he’s examining there appears to be C code that has something to do with joystick programming (JS_X_0, JS_Y_1, etc.). An eagle-eyed IMDb user contributed the trivia that he is looking at the file /usr/include/Linux/joystick.h.

  Getting back to the plot, how could the bad buys possibly expect him to re-write a hugely complex piece of software from scratch ? You might think this is the height of absurdity for a computer-oriented story. You’ll be pleased to know that the writers agreed with that assessment since, when the program was actually executed, it claimed to be Echelon, but that broke into a game of Pong (or some simple game). Suddenly, it makes perfect sense why the guy was looking at the joystick header file.

  This is the first bit of computer-oriented fun that I captured when I was watching the series :

  
  

  

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  This printout purports to be a “mainframe log summary”. After some plot-advancing text about a security issue, it proceeds to dump out some Java source code.

  SSH
  Secure Shell (SSH) frequently showed up. Here’s a screenshot in which a verbose ‘ssh -v’ connection has just been closed, while a telnet command has apparently just been launched (evidenced by “Escape character is ‘^]’.”) :

  
  

  

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  This is followed by some good old Hollywood Hacking in which a free-form database command is entered through any available command line interface :

  
  

  

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  I don’t remember the episode details, but I’m pretty sure the output made perfect sense to the character typing the command. Here’s another screenshot where the SSH client pops up an extra-large GUI dialog element to notify the user that it’s currently negotiating with the host :

  
  

  

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  Now that I look at that screenshot a little more closely, it appears to be a Win95/98 program. I wonder if there was an SSH client that actually popped up that gaudy dialog.

  There’s a lot of gibberish in this screenshot and I wish I had written down some details about what it represented according to the episode’s plot :

  
  

  

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  It almost sounds like they were trying to break into a network computer. Analyzing MD5 structure… public key synthesized. To me, the funniest feature is the 7-digit public key. I’m a bit rusty on the math of the RSA cryptosystem, but intuitively, it seems that the public and private keys need to be of roughly equal lengths. I.e., the private key in this scenario would also be 7 digits long.

  Gadgets
  Various devices and gadgets were seen at various junctures in the show. Here’s a tablet computer from back when tablet computers seemed like fantastical (albeit stylus-requiring) devices– the Fujitsu Stylistic 2300 :

  
  

  

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  Here’s a videophone from an episode that aired in 2005. The specific model is the Packet8 DV326 (MSRP of US$500). As you can see from the screenshot, it can do 384 kbps both down and up.

  
  

  

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  I really regret not writing down the episode details surrounding this gadget. I just know that it was critical that the good guys get it and keep from falling into the hands of the bad guys.

  
  

  

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  As you can see, the (presumably) deadly device contains a Samsung chip and a Lexar chip. I have to wonder what device the production crew salvaged this from (probably just an old cell phone).

  Other Programs

  The GIMP photo editor makes an appearance while scrubbing security camera footage, and serves as the magical Enhance Button (at least they slung around the term “gamma”) :

  
  

  

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  I have no idea what MacOS-based audio editing program this is. Any ideas ?

  
  

  

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  FTP shows up in episode 2.12, “The Getaway”. It’s described as a “secure channel” for communication, which is quite humorous to anyone versed in internet technology.

  
  

  

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• Attribution Tracking (What It Is and How It Works)

  23 février 2024, par Erin

  Facebook, TikTok, Google, email, display ads — which one is best to grow your business ? There’s one proven way to figure it out : attribution tracking.

  Marketing attribution allows you to see which channels are producing the best results for your marketing campaigns.

  In this guide, we’ll show you what attribution tracking is, why it’s important and how you can leverage it to accelerate your marketing success.

  What is attribution tracking ?

  By 2026, the global digital marketing industry is projected to reach $786.2 billion.

  With nearly three-quarters of a trillion U.S. dollars being poured into digital marketing every year, there’s no doubt it dominates traditional marketing.

  The question is, though, how do you know which digital channels to use ?

  By measuring your marketing efforts with attribution tracking.

  

  So, what is attribution tracking ?

  Attribution tracking is where you use software to keep track of different channels and campaign efforts to determine which channel you should attribute conversion to.

  In other words, you can (and should) use attribution tracking to analyse which channels are pushing the needle and which ones aren’t.

  By tracking your marketing efforts, you’ll be able to accurately measure the scale of impact each of your channels, campaigns and touchpoints have on a customer’s purchasing decision.

  If you don’t track your attribution, you’ll end up blindly pouring time, money, and effort into activities that may or may not be helpful.

  Attribution tracking simply gives you insight into what you’re doing right as a marketer — and what you’re doing wrong.

  By understanding which efforts and channels are driving conversions and revenue, you’ll be able to properly allocate resources toward winning channels to double down on growth.

  Matomo lets you track attribution across various channels. Whether you’re looking to track your conversions through organic, referral websites, campaigns, direct traffic, or social media, you can see all your conversions in one place.

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  Why attribution tracking is important

  Attribution tracking is crucial to succeed with your marketing since it shows you your most valuable channels.

  It takes the guesswork out of your efforts.

  You don’t need to scratch your head wondering what made your campaigns a success (or a failure).

  While most tools show you last click attribution by default, using attribution tracking, or marketing attribution, you can track revenue and conversions for each touchpoint.

  For example, a Facebook ad might have no led to a conversion immediately. But, maybe the visitor returned to your website two weeks later through your email campaign. Attribution tracking will give credit over longer periods of time to see the bigger picture of how your marketing channels are impacting your overall performance.

  Here are five reasons you need to be using attribution tracking in your business today :

  

  1. Measure channel performance

  The most obvious way attribution tracking helps is to show you how well each channel performs.

  When you’re using a variety of marketing channels to reach your audience, you have to know what’s actually doing well (and what’s not).

  This means having clarity on the performance of your :

  • Emails
  • Google Ads
  • Facebook Ads
  • Social media marketing
  • Search engine optimisation (SEO)
  • And more

  Attribution tracking allows you to measure each channel’s ROI and identify how much each channel impacted your campaigns.

  It gives you a more accurate picture of the performance of each channel and each campaign.

  With it, you can easily break down your channels by how much they drove sales, conversions, signups, or other actions.

  With this information, you can then understand where to further allocate your resources to fuel growth.

  2. See campaign performance over longer periods of time

  When you start tracking your channel performance with attribution tracking, you’ll gain new insights into how well your channels and campaigns are performing.

  The best part — you don’t just get to see recent performance.

  You get to track your campaign results over weeks or months.

  For example, if someone found you through Google by searching a question that your blog had an answer to, but they didn’t convert, your traditional tracking strategy would discount SEO.

  But, if that same person clicked a TikTok ad you placed three weeks later, came back, and converted — SEO would receive some attribution on the conversion.

  Using an attribution tracking tool like Matomo can help paint a holistic view of how your marketing is really doing from channel to channel over the long run.

  Try Matomo for Free

  Get the web insights you need, without compromising data accuracy.

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  3. Increase revenue

  Attribution tracking has one incredible benefit for marketers : optimised marketing spend.

  When you begin looking at how well your campaigns and your channels are performing, you’ll start to see what’s working.

  Attribution tracking gives you clarity into the performance of campaigns since it’s not just looking at the first time someone clicks through to your site. It’s looking at every touchpoint a customer made along the way to a conversion.

  By understanding what channels are most effective, you can pour more resources like time, money and labour into those effective channels.

  By doubling down on the winning channels, you’ll be able to grow like never before.

  Rather than trying to “diversify” your marketing efforts, lean into what’s working.

  This is one of the key strategies of an effective marketer to maximise your campaign returns and experience long-term success in terms of revenue.

  4. Improve profit margins

  The final benefit to attribution tracking is simple : you’ll earn more profit.

  Think about it this way : let’s say you’re putting 50% of your marketing spend into Facebook ads and 50% of your spend into email marketing.

  You do this for one year, allocating $500,000 to Facebook and $500,000 to email.

  Then, you start tracking attribution.

  You find that your Facebook ads are generating $900,000 in revenue. 

  That’s a 1,800% return on your investment.

  Not bad, right ?

  Well, after tracking your attribution, you see what your email revenue is.

  In the past year, you generated $1.7 million in email revenue.

  That’s a 3,400% return on your investment (close to the average return of email marketing across all industries).

  In this scenario, you can see that you’re getting nearly twice as much of a return on your marketing spend with email.

  So, the following year, you decide to go for a 75/25 split.

  Instead of putting $500,000 into both email and Facebook ads and email, you put $750,000 into email and $250,000 into Facebook ads.

  You’re still diversifying, but you’re doubling down on what’s working best.

  The result is that you’ll be able to get more revenue by investing the same amount of money, leaving you with higher profit margins.

  Different types of marketing attribution tracking

  There are several types of attribution tracking models in marketing.

  Depending on your goals, your business and your preferred method, there are a variety of types of attribution tracking you can use.

  Here are the six main types of attribution tracking :

  

  1. Last interaction

  Last interaction attribution model is also called “last touch.”

  It’s one of the most common types of attribution. The way it works is to give 100% of the credit to the final channel a customer interacted with before they converted into a customer.

  This could be through a paid ad, direct traffic, or organic search.

  One potential drawback of last interaction is that it doesn’t factor in other channels that may have assisted in the conversion. However, this model can work really well depending on the business.

  2. First interaction

  This is the opposite of the previous model.

  First interaction, or “first touch,” is all about the first interaction a customer has with your brand.

  It gives 100% of the credit to the channel (i.e. a link clicked from a social media post). And it doesn’t report or attribute anything else to another channel that someone may have interacted with in your marketing mix.

  For example, it won’t attribute the conversion or revenue if the visitor then clicked on an Instagram ad and converted. All credit would be given to the first touch which in this case would be the social media post. 

  The first interaction is a good model to use at the top of your funnel to help establish which channels are bringing leads in from outside your audience.

  3. Last non-direct

  Another model is called the last non-direct attribution model. 

  This model seeks to exclude direct traffic and assigns 100% credit for a conversion to the final channel a customer interacted with before becoming a customer, excluding clicks from direct traffic.

  For instance, if someone first comes to your website from an emai campaignl, and then, a week later, directly visits and buys a product, the email campaign gets all the credit for the sale.

  This attribution model tells a bit more about the whole sales process, shedding some more light on what other channels may have influenced the purchase decision.

  4. Linear

  Another common attribution model is linear.

  This model distributes completely equal credit across every single touchpoint (that’s tracked). 

  Imagine someone comes to your website in different ways : first, they find it through a Google search, then they click a link in an email from your campaign the next day, followed by visiting from a Facebook post a few days later, and finally, a week later, they come from a TikTok ad. 

  Here’s how the attribution is divided among these sources :

  • 25% Organic
  • 25% Email
  • 25% Facebook
  • 25% TikTok ad

  This attirubtion model provides a balanced perspective on the contribution of various sources to a user’s journey on your website.

  5. Position-based

  Position-based attribution is when you give 40% credit to both the first and last touchpoints and 20% credit is spread between the touchpoints in between.

  This model is preferred if you want to identify the initial touchpoint that kickstarted a conversion journey and the final touchpoint that sealed the deal.

  The downside is that you don’t gain much insight into the middle of the customer journey, which can make it hard to make effective decisions.

  For example, someone may have been interacting with your email newsletter for seven weeks, which allowed them to be nurtured and build a relationship with you.

  But that relationship and trust-building effort will be overlooked by the blog post that brought them in and the social media ad that eventually converted them.

  6. Time decay

  The final attribution model is called time decay attribution.

  This is all about giving credit based on the timing of the interactions someone had with your brand.

  For example, the touchpoints that just preceded the sale get the highest score, while the first touchpoints get the lowest score.

  For example, let’s use that scenario from above with the linear model :

  • 25% SEO
  • 25% Email
  • 25% Facebook ad
  • 25% Organic TikTok

  But, instead of splitting credit by 25% to each channel, you weigh the ones closer to the sale with more credit.

  Instead, time decay may look at these same channels like this :

  • 5% SEO (6 weeks ago)
  • 20% Email (3 weeks ago)
  • 30% Facebook ad (1 week ago)
  • 45% Organic TikTok (2 days ago)

  One downside is that it underestimates brand awareness campaigns. And, if you have longer sales cycles, it also isn’t the most accurate, as mid-stage nurturing and relationship building are underlooked. 

  Leverage Matomo : A marketing attribution tool

  Attribution tracking is a crucial part of leading an effective marketing strategy.

  But it’s impossible to do this without the right tools.

  A marketing attribution tool can give you insights into your best-performing channels automatically. 

  

  One of the best marketing attribution tools available is Matomo, a web analytics tool that helps you understand what’s going on with your website and different channels in one easy-to-use dashboard.

  With Matomo, you get marketing attribution as a plug-in or within Matomo On-Premise or for free in Matomo Cloud.

  The best part is it’s all done with crystal-clear data. Matomo gives you 100% accurate data since it doesn’t use data sampling on any plans like Google Analytics.

  To start tracking attribution today, try Matomo’s 21-day free trial. No credit card required.

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• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.