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• Benefits and Shortcomings of Multi-Touch Attribution

  13 mars 2023, par Erin — Analytics Tips

  Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer. 

  Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales. 

  Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates. 

  The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process. 

  If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it. 

  What Are the Benefits of Multi-Touch Attribution ?

  Remember an old parable of blind men and an elephant ?

  Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

  Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too. 

  Better Understanding of Customer Journeys 

  On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages : 

  • Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel). 
  • Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel). 
  • Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel). 

  You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel. 

  For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion. 

  This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that. 

  Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.

  

  Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion. 

  For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion. 

  A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines. 

  The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

  Improved Budget Allocation 

  Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

  First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions. 

  For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

  

  By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types). 

  Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

  “Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.

  More Accurate Measurements 

  The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance. 

  In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking. 

  Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

  • How many touchpoints are involved in the conversions ? 
  • How long does it take for a lead to convert on average ? 
  • When and where do different audience groups convert ? 
  • What is your average win rate for different types of campaigns ?

  Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect. 

  At the highest level, you need to collect two data points :

  • Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals. 
  • Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events. 

  Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them. 

  The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used. 

  Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo). 

  Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

  Learn more about selecting the optimal multi-channel attribution model for your business.

  What Are the Limitations of Multi-Touch Attribution ?

  Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry. 

  Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email. 

  In addition, you should keep in mind several other limitations of multi-touch attribution software.

  Limited Marketing Mix Analysis 

  Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

  Multi-touch attribution tools cannot evaluate the impact of :

  • Dark social channels 
  • Word-of-mouth 
  • Offline promotional events
  • TV or out-of-home ad campaigns 

  If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.

  Time-Based Constraints 

  Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles. 

  

  Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel. 

  At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc. 

  Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ? 

  The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time. 

  

  Limited Access to Raw Data 

  In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied. 

  Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues. 

  In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making. 

  With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data. 

  AI Application 

  On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies. 

  To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

  Difficult Technical Implementation 

  Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

  

  Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc. 

  Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams. 

  Conclusion 

  Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations. 

  That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool. 

  Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool ! 

  Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried. 

• ffmdec : limit the backward seek to the last resync position

  9 mars 2015, par Andreas Cadhalpun

  ffmdec : limit the backward seek to the last resync position
  If resyncing leads to the same position as previously, it will again
  
  lead to a resync attempt, resulting in an infinite loop.
  Thus don’t seek back beyond the last syncpoint.
  Signed-off-by : Andreas Cadhalpun <Andreas.Cadhalpun@googlemail.com>
  
  Signed-off-by : Michael Niedermayer <michaelni@gmx.at>
  

  • [DH] libavformat/ffmdec.c

• Introducing Crash Analytics for Matomo

  30 août 2023, par Erin — Community, Plugins

  Bugs and development go hand in hand. As code matures, it contends with new browser iterations, clashes with ad blockers and other software quirks, resulting in the inevitable emergence of bugs. In fact, a staggering 13% of all pageviews come with lurking JavaScript errors.

  Monitoring for crashes becomes an unrelenting task. Amidst this never-ending effort to remove bugs, a SurveyMonkey study unveils a shared reality : a resounding 66% of individuals have encountered bug-ridden websites.

  These bugs lead to problems like malfunctioning shopping carts, glitchy checkout procedures and contact forms that just won’t cooperate. But they’re not just minor annoyances – they pose a real danger to your conversion rates and revenue.

  According to a study, 58% of visitors are inclined to abandon purchases as a result of bugs, while an astonishing 75% are driven to completely abandon websites due to these frustrating experiences.

  Imagine a website earning approximately 25,000 EUR per month. Now, factor in errors occurring in 13% of all pageviews. The result ? A potential monthly loss of 1,885 EUR.

  Meet Crash Analytics

  Driven by our vision to create an empowering analytics product, we’re excited to introduce Crash Analytics, an innovative plugin for Matomo On-Premise that automatically tracks bugs on your website.

  

  By offering insights into the precise bug location and the user’s interactions that triggered it, along with details about their device type, browser and more, Crash Analytics empowers you to swiftly address crashes, leading to an improved user experience, higher conversion rates and revenue growth.

  Soon, Crash Analytics will become available to Matomo Cloud users as well, so stay tuned for further updates and announcements.

  Say goodbye to lost revenue – never miss a bug again

  Even if you put your website through the toughest tests, it’s hard to predict every little hiccup that can pop up across different browsers, setups and situations. Factors such as ad blockers, varying internet speeds for visitors and browser updates can add an extra layer of complexity.

  When these crashes happen, you want to know immediately. However, according to a study, only 29% of surveyed respondents would report the existence of the site bug to the website operator. These bugs that go unnoticed can really hurt your bottom line and conversion rates, causing you to lose out on revenue and leaving your users frustrated and disappointed.

  

  Crash Analytics is here to bridge this gap. Armed with scheduled reporting (via email or texts) and automated alert functionalities, you gain the power to instantly detect bugs as they occur on your site. This proactive approach ensures that even the subtlest of issues are brought to your attention promptly. 

  With automated reports and alerts, you can also opt to receive notifications when crashes increase or ignore specific crashes that you deem insignificant. This keeps you in the loop with only the issues that truly matter, helping you cut out the noise and take immediate action.

  

  Easily forward crash data to developers and synchronise the efforts of technical teams and marketing experts. Track emerging, disappearing and recurring errors, ensuring that crash data is efficiently relayed to developers to prioritise fixes that matter.

  

  Plus, your finger is always on the pulse with real-time reports that offer a live view of crashes happening at the moment, an especially helpful feature after deploying changes. Use annotations to mark deploys and correlate them with crash data, enabling you to quickly identify if a new bug is linked to recent updates or modifications.

  

  And with our mobile app, you can effortlessly stay connected to your website’s performance, conveniently accessing crash information anytime and anywhere. This ensures you’re in complete control of your site’s health, even when you’re on the move.

  Streamline bug resolution with combined web and crash analytics

  Crash Analytics for Matomo doesn’t just stop at pinpointing bug locations ; it goes a step further by providing you with a holistic perspective of user interactions. Seamlessly combining Matomo’s traditional and behavioural web analytics features—like segments, session recordings and visitor logs—with crash data, this integrated approach unveils a wealth of insights so you can quickly resolve bugs. 

  For instance, let’s say a user encounters a bug while attempting to complete a purchase on your e-commerce website. Crash Analytics reveals the exact point of failure, but to truly grasp the situation, you delve into the session recordings. These recordings offer a front-row seat to the user’s journey—every click and interaction that led to the bug. Session recordings are especially helpful when you are struggling to reproduce an issue.

  

  Additionally, the combination of visitor logs with crash data offers a comprehensive timeline of a user’s engagement. This helps you understand their activity leading up to the bug, such as pages visited, actions taken and devices used. Armed with these multifaceted insights, you can confidently pinpoint the root causes and address the crash immediately.

  With segments, you have the ability to dissect the data and compare experiences among distinct user groups. For example, you can compare mobile visitors to desktop visitors to determine if the issue is isolated or widespread and what impact the issue is having on the user experience of different user groups. 

  The combination of crash data with Matomo’s comprehensive web analytics equips you with the tools needed to elevate user experiences and ultimately drive revenue growth.

  Start in seconds, shape as needed : Your path to a 100% reliable website

  Crash Analytics makes the path to a reliable website simple. You don’t have to deal with intricate setups—crash detection starts without any configuration. 

  Plus, Crash Analytics excels in cross-stack proficiency, seamlessly extending its capabilities beyond automatically tracking JavaScript errors to covering server-side crashes as well, whether they occur in PHP, Android, iOS, Java or other frameworks. This versatile approach ensures that Crash Analytics comprehensively supports your website’s health and performance across various technological landscapes.

  Elevate your website with Crash Analytics

  Experience the seamless convergence of bug tracking and web analytics, allowing you to delve into user interactions, session recordings and visitor logs. With the flexibility of customising real-time alerts and scheduled reports, alongside cross-stack proficiency, Crash Analytics becomes your trusted ally in enhancing your website’s reliability and user satisfaction to increase conversions and drive revenue growth. Equip yourself to swiftly address issues and create a website where user experiences take precedence.

  Start your 30-day free trial of our Crash Analytics plugin today, and stay tuned for its availability on Matomo Cloud.