Recherche avancée

Sur d’autres sites (12784)

• 10 Matomo Features You Possibly Didn’t Know About

  28 octobre 2022, par Erin

  Most users know Matomo as the privacy-focussed web analytics tool with data accuracy, superior to Google Analytics. 

  And we’re thrilled to be that — and more ! 

  At Matomo, our underlying product vision is to provide a full stack of accurate, user-friendly and privacy-mindful online marketing tools. 

  Over the years, we’ve expanded beyond baseline website statistics. Matomo Cloud users also get to benefit from additional powerful tools for audience segmentation, conversion optimisation, advanced event tracking and more. 

  Here are the top 10 advanced Matomo features you wish you knew about earlier (but won’t stop using now !). 

  Funnels

  At first glance, most customer journeys look sporadic. But every marketer will tell you that there is a method to almost every users’ madness. Or more precisely — there’s a method you can use to guide users towards conversions. 

  That’s called a customer journey — a schematic set of steps and actions people complete from developing awareness and interest in your solution to consideration and finally conversion.

  On average, 8 touchpoints are required to turn a prospect into a customer. Though the number can be significantly bigger in B2B sales and smaller for B2C Ecommerce websites. 

  With the Funnels feature, you can first map all the on-site touchpoints (desired actions) for different types of customers. Then examine the results you’re getting as prospects move through these checkbox steps.

  Funnel reports provide :

  • High-level metrics such as “Funnel conversion rate”, “Number of funnel conversions”, “Number of funnel entries”. 
  • Drilled-down reports for each funnel and each tracked action within it. This way you can track the success rates of each step and estimate their contribution to the cumulative effect.

  Segmented funnel reports for specific user cohorts (with Matomo Segmentation enabled).

  

  What makes funnels so fun (pun intended) ? The variety of use cases and configurations ! 

  You can build funnels to track conversion rates for :

  • Newsletter subscriptions
  • Job board applications 
  • Checkout or payment 
  • Product landing pages
  • Seasonal promo campaigns

  …. And pretty much any other page where users must complete a meaningful action. So go test this out. 

  Form Analytics

  On-site forms are a 101 tactic for lead generation. For most service businesses, a “contact request” or a “booking inquiry” submission means a new lead in your pipeline. 

  That said : the average on-site form conversion rates across industries stand at below 50% : 

  • Property – 37% 
  • Telecoms – 40%
  • Software — 46.83%

  That’s not bad, but it could be better. If only you could figure out why people abandon your forms….

  Oh wait, Matomo Form Analytics can supply you with answers. Form Analytics provide real-time information on key form metrics — total views, starter rate, submitter rate, conversions and more.

  

  Separately the average form hesitation time is also provided (in other words, the time a user contemplates if filling in a form is worth the effort). Plus, Matomo also tracks the time spent on form submission.

  You can review : 

  • Top drop-off fields – to understand where you are losing prospects. These fields should either be removed or simplified (e.g., with a dropdown menu) to increase conversions.
  • Most corrected-field – this will provide a clear indication of where your prospects are struggling with a form. Providing help text can simplify the process and increase conversions. 
  • Unesserary fields – with this metric, you’ll know which optional fields your leads aren’t interested in filling in and can remove them to help drive conversions. 

  With Form Analytics, you’ll be able to boost conversions and create a better on-site experience with accurate user data. 

  A/B testing

  Marketing is both an art and a science. A/B testing (or split testing) helps you statistically verify which creative ideas perform better. 

  A good conversion rate optimisation (CRO) practice is to test different elements and to do so often to find your top contenders.

  What can you split test ? Loads of things :

  • Page slogans and call-to-actions 
  • Button or submission form placements
  • Different landing page designs and layouts
  • Seasonal promo offers and banners
  • Pricing information 
  • Customer testimonial placements 

  More times than not, those small changes in page design or copy can lead to a double-digit lift in conversion rates. Accounting software Sage saw a 30% traffic boost after changing the homepage layout, copy and CTAs based on split test data. Depositphotos, in turn, got a 9.32% increase in account registration rate (CR) after testing a timed pop-up registration form. 

  The wrinkle ? A/B testing software isn’t exactly affordable, with tools averaging $119 – $1,995 per month. Plus, you then have to integrate a third-party tool with your website analytics for proper attribution — and this can get messy.

  Matomo saves you the hassle in both cases. An A/B testing tool is part of your Cloud subscription and plays nicely with other features — goal tracking, heatmaps, historic visitor profiles and more. 

  You can run split tests with Matomo on your websites or mobile apps — and find out if version A, B, C or D is the top performer. 

  

  Advertising Conversion Exports

  A well-executed search marketing or banner remarketing campaign can drive heaps of traffic to your website. But the big question is : How much of it will convert ?

  The AdTech industry has a major problem with proper attribution and, because of it, with ad fraud. 

  Globally, digital ad fraud will cost advertisers a hefty $8 billion by the end of 2022. That’s when another $74 million in ad budgets get wasted per quarter. 

  The reasons for ad budget waste may vary, but they often have a common denominator : lack of reliable conversion tracking data.

  Matomo helps you get a better sense of how you spend your cents with Advertising Conversion Reports. Unlike other MarTech analytics tools, you don’t need to embed any third-party advertising network trackers into your website or compromise user privacy.

  Instead, you can easily export accurate conversion data from Matomo (either manually via a CSV file or automated with an HTTPS link) into your Google Ads, Microsoft Advertising or Yandex Ads for cross-validation. This way you can get an objective view of the performance of different campaigns and optimise your budget allocations accordingly. 

  Find out more about tracking ad campaigns with Matomo.

  Matomo Tag Manager

  The marketing technology landscape is close to crossing 10,000 different solutions. Cross-platform advertising trackers and all sorts of customer data management tools comprise the bulk of that growing stack. 

  Remember : Each new tool embed adds extra “weight” to your web page. More tracking scripts equal slower page loading speed — and more frustration for your users. Likewise, extra embeds often means dialling up the developer (which takes time). Or tinkering with the site code yourself (which can result in errors and still raise the need to call a developer). 

  With Tag Manager, you can easily generate tags for :

  • Custom analytics reports 
  • Newsletter signups
  • Affiliates 
  • Form submission tracking 
  • Exit popups and surveys
  • Ads and more

  With Matomo Tag Manager, you can monitor, update or delete everything from one convenient interface. Finally, you can programme custom triggers — conditions when the tag gets activated — and specify data points (variables) it should collect. The latter is a great choice for staying privacy-focused and excluding any sensitive user information from processing. 

  With our tag management system (TMS), no rogue tags will mess up your analytics or conversion tracking. 

  Session recordings

  User experience (UX) plays a pivotal role in your conversion rates. 

  A five-year McKinsey study of 300 publicly listed companies found that companies with strong design practices have 32 percentage points higher revenue growth than their peers. 

  But what makes up a great website design and browsing experience ? Veteran UX designers name seven qualities :

  

  To figure out if your website meets all these criteria, you can use Session Recording — a tool for recording how users interact with your website. 

  By observing clicks, mouse moves, scrolls and form interactions you can determine problematic website design areas such as poor header navigation, subpar button placements or “boring” blocks of text. 

  Such observational studies are a huge part of the UX research process because they provide unbiased data on interaction. Or as Nielsen Norman Group puts it :

  “The way to get user data boils down to the basic rules of usability :

  • Watch what people actually do.
  • Do not believe what people say they do.
  • Definitely don’t believe what people predict they may do in the future.” 

  Most user behaviour analytics tools sell such functionality for a fee. With Matomo Cloud, this feature is included in your subscription. 

  Heatmaps

  While Session Replays provide qualitative insights, Heatmaps supply you with first-hand qualitative insights. Instead of individual user browsing sessions, you get consolidated data on where they click and how they scroll through your website. 

  

  Heatmaps are another favourite among UX designers and their CRO peers because you can :

  • Validate earlier design decisions around information architecture, page layout, button placements and so on. 
  • Develop new design hypotheses based on stats and then translate them into website design improvements. 
  • Identify distractive no-click elements that confuse users and remove them to improve conversions. 
  • Locate problematic user interface (UI) areas on specific devices or operating systems and improve them for a seamless experience.

  To get even more granular results, you can apply up to 100 Matomo segments to drill down on specific user groups, geographies or devices. 

  This way you can make data-based decisions for A/B testing, updating or redesigning your website pages. 

  Custom Alerts

  When it comes to your website, you don’t want to miss anything big — be it your biggest sales day or a sudden nosedive in traffic. 

  That’s when Custom Alerts come in handy. 

  

  With a few clicks, you can set up email or text-based alerts about important website metrics. Once you hit that metric, Matomo will send a ping. 

  You can also set different types of Custom Alerts for your teams. For example, your website administrator can get alerted about critical technical performance issues such as a sudden spike in traffic. It can indicate a DDoS attack (in the worst case) — and timely resolution is crucial here. Or suggest that your website is going viral and you might need to provision extra computing resources to ensure optimal site performance.

  Your sales team, in turn, can get alerted about new form submissions, so that they can quickly move on to lead scoring and subsequent follow-ups. 

  Use cases are plentiful with this feature. 

  Custom Dashboards and Reports

  Did you know you can get a personalised view of the main Matomo dashboards ? 

  By design, we made different website stats available as separate widgets. Hence, you can cherry-pick which stats get a prominent spot. Moreover, you can create and embed custom widgets into your Matomo dashboard to display third-party insights (e.g., POS data).

  Set up custom dashboard views for different teams, business stakeholders or clients to keep them in the loop on relevant website metrics. 

  

  Custom Reports feature, in turn, lets you slice and dice your traffic analytics the way you please. You can combine up to three different data dimensions per report and then add any number of supported metrics to get a personalised analytics report.

  For example, to zoom in on your website performance in a specific target market you can apply “location” (e.g., Germany) and “action type” (e.g., app downloads) dimensions and then get segmented data on metrics such as total visits, conversion rates, revenue and more. 

  Get to know even more ways to customise Matomo deployment.

  Roll Up Report

  Need to get aggregated traffic analytics from multiple web properties, but not ready to pay $150K per year for Google Analytics 360 for that ?

  We’ve got you with Roll-Up Reporting. You can get a 360-degree view into important KPIs like global revenue, conversion rates or form performance across multiple websites, online stores, mobile apps and even Intranet properties.

  

  Setting up this feature takes minutes, but saves you hours on manually exporting and cross-mapping data from different web analytics tools. 

  Channel all those saved hours into more productive things like increasing your conversion rates or boosting user engagement. 

  Avoid Marketing Tool Sprawl with Matomo 

  With Matomo as your website analytics and conversion optimisation app, you don’t need to switch between different systems, interfaces or have multiple tracking codes embedded on your site.

  And you don’t need to cultivate a disparate (and expensive !) MarTech tool stack — and then figure out if each of your tools is compliant with global privacy laws.

  All the tools you need are conveniently housed under one roof. 

  Want to learn more about Matomo features ? Check out product training videos next ! 

  21 day free trial. No credit card required.

  Your email address
  

  Your website address
  

  Your Analytics subdomain will be .matomo.cloud (change)

  Choose your analytics subdomain
  

  .matomo.cloud

  I hereby accept the terms and conditions and the data processing agreement (DPA).

  
  Your information will be used to create an account on our cloud service. For more information please consult our privacy policy.
  
  

  » Start improving your websites now

  

• What's the most desireable way to capture system display and audio in the form of individual encoded audio and video packets in go (language) ? [closed]

  11 janvier 2023, par Tiger Yang

  Question (read the context below first) :

  


  For those of you familiar with the capabilities of go, Is there a better way to go about all this ? Since ffmpeg is so ubiquitous, I'm sure it's been optomized to perfection, but what's the best way to capture system display and audio in the form of individual encoded audio and video packets in go (language), so that they can be then sent via webtransport-go ? I wish for it to prioritize efficiency and low latency, and ideally capture and encode the framebuffer directly like ffmpeg does.

  


  Thanks ! I have many other questions about this, but I think it's best to ask as I go.

  


  Context and what I've done so far :

  


  I'm writing a remote desktop software for my personal use because of grievances with current solutions out there. At the moment, it consists of a web app that uses the webtransport API to send input datagrams and receive AV packets on two dedicated unidirectional streams, and the webcodecs API to decode these packets. On the serverside, I originally planned to use python with the aioquic library as a webtransport server. Upon connection and authentication, the server would start ffmpeg as a subprocess with this command :

  


  ffmpeg -init_hw_device d3d11va -filter_complex ddagrab=video_size=1920x1080:framerate=60 -vcodec hevc_nvenc -tune ll -preset p7 -spatial_aq 1 -temporal_aq 1 -forced-idr 1 -rc cbr -b:v 400K -no-scenecut 1 -g 216000 -f hevc -

  


  What I really appreciate about this is that it uses windows' desktop duplication API to copy the framebuffer of my GPU and hand that directly to the on-die hardware encoder with zero round trips to the CPU. I think it's about as efficient and elegant a solution as I can manage. It then outputs the encoded stream to the stdout, which python can read and send to the client.

  


  As for the audio, there is another ffmpeg instance :

  


  ffmpeg -f dshow -channels 2 -sample_rate 48000 -sample_size 16 -audio_buffer_size 15 -i audio="RD Audio (High Definition Audio Device)" -acodec libopus -vbr on -application audio -mapping_family 0 -apply_phase_inv true -b:a 25K -fec false -packet_loss 0 -map 0 -f data -

  


  which listens to a physical loopback interface, which is literally just a short wire bridging the front panel headphone and microphone jacks (I'm aware of the quality loss of converting to analog and back, but the audio is then crushed down to 25kbps so it's fine) ()

  


  Unfortunately, aioquic was not easy to work with IMO, and I found webtransport-go https://github.com/adriancable/webtransport-go, which was a hell of a lot better in both simplicity and documentation. However, now I'm dealing with a whole new language, and I wanna ask : (above)

  


  EDIT : Here's the code for my server so far :

  


  


  


  package main

import (
    "bytes"
    "context"
    "fmt"
    "log"
    "net/http"
    "os/exec"
    "time"

    "github.com/adriancable/webtransport-go"
)

func warn(str string) {
    fmt.Printf("\n===== WARNING ===================================================================================================\n   %s\n=================================================================================================================\n", str)
}

func main() {

    password := []byte("abc")

    videoString := []string{
        "ffmpeg",
        "-init_hw_device", "d3d11va",
        "-filter_complex", "ddagrab=video_size=1920x1080:framerate=60",
        "-vcodec", "hevc_nvenc",
        "-tune", "ll",
        "-preset", "p7",
        "-spatial_aq", "1",
        "-temporal_aq", "1",
        "-forced-idr", "1",
        "-rc", "cbr",
        "-b:v", "500K",
        "-no-scenecut", "1",
        "-g", "216000",
        "-f", "hevc", "-",
    }

    audioString := []string{
        "ffmpeg",
        "-f", "dshow",
        "-channels", "2",
        "-sample_rate", "48000",
        "-sample_size", "16",
        "-audio_buffer_size", "15",
        "-i", "audio=RD Audio (High Definition Audio Device)",
        "-acodec", "libopus",
        "-mapping_family", "0",
        "-b:a", "25K",
        "-map", "0",
        "-f", "data", "-",
    }

    connected := false

    http.HandleFunc("/", func(writer http.ResponseWriter, request *http.Request) {
        session := request.Body.(*webtransport.Session)

        session.AcceptSession()
        fmt.Println("\nAccepted incoming WebTransport connection.")
        fmt.Println("Awaiting authentication...")

        authData, err := session.ReceiveMessage(session.Context()) // Waits here till first datagram
        if err != nil {                                            // if client closes connection before sending anything
            fmt.Println("\nConnection closed:", err)
            return
        }

        if len(authData) >= 2 && bytes.Equal(authData[2:], password) {
            if connected {
                session.CloseSession()
                warn("Client has authenticated, but a session is already taking place! Connection closed.")
                return
            } else {
                connected = true
                fmt.Println("Client has authenticated!\n")
            }
        } else {
            session.CloseSession()
            warn("Client has failed authentication! Connection closed. (" + string(authData[2:]) + ")")
            return
        }

        videoStream, _ := session.OpenUniStreamSync(session.Context())

        videoCmd := exec.Command(videoString[0], videoString[1:]...)
        go func() {
            videoOut, _ := videoCmd.StdoutPipe()
            videoCmd.Start()

            buffer := make([]byte, 15000)
            for {
                len, err := videoOut.Read(buffer)
                if err != nil {
                    break
                }
                if len > 0 {
                    videoStream.Write(buffer[:len])
                }
            }
        }()

        time.Sleep(50 * time.Millisecond)

        audioStream, err := session.OpenUniStreamSync(session.Context())

        audioCmd := exec.Command(audioString[0], audioString[1:]...)
        go func() {
            audioOut, _ := audioCmd.StdoutPipe()
            audioCmd.Start()

            buffer := make([]byte, 15000)
            for {
                len, err := audioOut.Read(buffer)
                if err != nil {
                    break
                }
                if len > 0 {
                    audioStream.Write(buffer[:len])
                }
            }
        }()

        for {
            data, err := session.ReceiveMessage(session.Context())
            if err != nil {
                videoCmd.Process.Kill()
                audioCmd.Process.Kill()

                connected = false

                fmt.Println("\nConnection closed:", err)
                break
            }

            if len(data) == 0 {

            } else if data[0] == byte(0) {
                fmt.Printf("Received mouse datagram: %s\n", data)
            }
        }

    })

    server := &webtransport.Server{
        ListenAddr: ":1024",
        TLSCert:    webtransport.CertFile{Path: "SSL/fullchain.pem"},
        TLSKey:     webtransport.CertFile{Path: "SSL/privkey.pem"},
        QuicConfig: &webtransport.QuicConfig{
            KeepAlive:      false,
            MaxIdleTimeout: 3 * time.Second,
        },
    }

    fmt.Println("Launching WebTransport server at", server.ListenAddr)
    ctx, cancel := context.WithCancel(context.Background())
    if err := server.Run(ctx); err != nil {
        log.Fatal(err)
        cancel()
    }

}

  


  


  



• What is Multi-Touch Attribution ? (And How To Get Started)

  2 février 2023, par Erin — Analytics Tips

  Good marketing thrives on data. Or more precisely — its interpretation. Using modern analytics software, we can determine which marketing actions steer prospects towards the desired action (a conversion event). 

  An attribution model in marketing is a set of rules that determine how various marketing tactics and channels impact the visitor’s progress towards a conversion. 

  Yet, as customer journeys become more complicated and involve multiple “touches”, standard marketing reports no longer tell the full picture. 

  That’s when multi-touch attribution analysis comes to the fore. 

  What is Multi-Touch Attribution ?

  Multi-touch attribution (also known as multi-channel attribution or cross-channel attribution) measures the impact of all touchpoints on the consumer journey on conversion. 

  Unlike single-touch reporting, multi-touch attribution models give credit to each marketing element — a social media ad, an on-site banner, an email link click, etc. By seeing impacts from every touchpoint and channel, marketers can avoid false assumptions or subpar budget allocations.

  To better understand the concept, let’s interpret the same customer journey using a standard single-touch report vs a multi-touch attribution model. 

  Picture this : Jammie is shopping around for a privacy-centred web analytics solution. She saw a recommendation on Twitter and ended up on the Matomo website. After browsing a few product pages and checking comparisons with other web analytics tools, she signs up for a webinar. One week after attending, Jammie is convinced that Matomo is the right tool for her business and goes directly to the Matomo website a starts a free trial. 

  • A standard single-touch report would attribute 100% of the conversion to direct traffic, which doesn’t give an accurate view of the multiple touchpoints that led Jammie to start a free trial. 
  • A multi-channel attribution report would showcase all the channels involved in the free trial conversion — social media, website content, the webinar, and then the direct traffic source.

  In other words : Multi-touch attribution helps you understand how prospects move through the sales funnel and which elements tinder them towards the desired outcome. 

  Types of Attribution Models

  As marketers, we know that multiple factors play into a conversion — channel type, timing, user’s stage on the buyer journey and so on. Various attribution models exist to reflect this variability. 

  

  First Interaction attribution model (otherwise known as first touch) gives all credit for the conversion to the first channel (for example — a referral link) and doesn’t report on all the other interactions a user had with your company (e.g., clicked a newsletter link, engaged with a landing page, or browsed the blog campaign).

  First-touch helps optimise the top of your funnel and establish which channels bring the best leads. However, it doesn’t offer any insight into other factors that persuaded a user to convert. 

  Last Interaction attribution model (also known as last touch) allocates 100% credit to the last channel before conversion — be it direct traffic, paid ad, or an internal product page.

  The data is useful for optimising the bottom-of-the-funnel (BoFU) elements. But you have no visibility into assisted conversions — interactions a user had prior to conversion. 

  Last Non-Direct attribution model model excludes direct traffic and assigns 100% credit for a conversion to the last channel a user interacted with before converting. For instance, a social media post will receive 100% of credit if a shopper buys a product three days later. 

  This model is more telling about the other channels, involved in the sales process. Yet, you’re seeing only one step backwards, which may not be sufficient for companies with longer sales cycles.

  Linear attribution model distributes an equal credit for a conversion between all tracked touchpoints.

  For instance, with a four touchpoint conversion (e.g., an organic visit, then a direct visit, then a social visit, then a visit and conversion from an ad campaign) each touchpoint would receive 25% credit for that single conversion.

  This is the simplest multi-channel attribution modelling technique many tools support. The nuance is that linear models don’t reflect the true impact of various events. After all, a paid ad that introduced your brand to the shopper and a time-sensitive discount code at the checkout page probably did more than the blog content a shopper browsed in between. 

  Position Based attribution model allocates a 40% credit to the first and the last touchpoints and then spreads the remaining 20% across the touchpoints between the first and last. 

  This attribution model comes in handy for optimising conversions across the top and the bottom of the funnel. But it doesn’t provide much insight into the middle, which can skew your decision-making. For instance, you may overlook cases when a shopper landed via a social media post, then was re-engaged via email, and proceeded to checkout after an organic visit. Without email marketing, that sale may not have happened.

  Time decay attribution model adjusts the credit, based on the timing of the interactions. Touchpoints that preceded the conversion get the highest score, while the first ones get less weight (e.g., 5%-5%-10%-15%-25%-30%).

  This multi-channel attribution model works great for tracking the bottom of the funnel, but it underestimates the impact of brand awareness campaigns or assisted conversions at mid-stage. 

  Why Use Multi-Touch Attribution Modelling

  Multi-touch attribution provides you with the full picture of your funnel. With accurate data across all touchpoints, you can employ targeted conversion rate optimisation (CRO) strategies to maximise the impact of each campaign. 

  Most marketers and analysts prefer using multi-touch attribution modelling — and for some good reasons.

  Issues multi-touch attribution solves 

  • Funnel visibility. Understand which tactics play an important role at the top, middle and bottom of your funnel, instead of second-guessing what’s working or not. 
  • Budget allocations. Spend money on channels and tactics that bring a positive return on investment (ROI). 
  • Assisted conversions. Learn how different elements and touchpoints cumulatively contribute to the ultimate goal — a conversion event — to optimise accordingly. 
  • Channel segmentation. Determine which assets drive the most qualified and engaged leads to replicate them at scale.
  • Campaign benchmarking. Compare how different marketing activities from affiliate marketing to social media perform against the same metrics.

  How To Get Started With Multi-Touch Attribution 

  To make multi-touch attribution part of your analytics setup, follow the next steps :

  1. Define Your Marketing Objectives 

  Multi-touch attribution helps you better understand what led people to convert on your site. But to capture that, you need to first map the standard purchase journeys, which include a series of touchpoints — instances, when a prospect forms an opinion about your business.

  Touchpoints include :

  • On-site interactions (e.g., reading a blog post, browsing product pages, using an on-site calculator, etc.)
  • Off-site interactions (e.g., reading a review, clicking a social media link, interacting with an ad, etc.)

  Combined these interactions make up your sales funnel — a designated path you’ve set up to lead people toward the desired action (aka a conversion). 

  Depending on your business model, you can count any of the following as a conversion :

  • Purchase 
  • Account registration 
  • Free trial request 
  • Contact form submission 
  • Online reservation 
  • Demo call request 
  • Newsletter subscription

  So your first task is to create a set of conversion objectives for your business and add them as Goals or Conversions in your web analytics solution. Then brainstorm how various touchpoints contribute to these objectives. 

  Web analytics tools with multi-channel attribution, like Matomo, allow you to obtain an extra dimension of data on touchpoints via Tracked Events. Using Event Tracking, you can analyse how many people started doing a desired action (e.g., typing details into the form) but never completed the task. This way you can quickly identify “leaking” touchpoints in your funnel and fix them. 

  2. Select an Attribution Model 

  Multi-attribution models have inherent tradeoffs. Linear attribution model doesn’t always represent the role and importance of each channel. Position-based attribution model emphasises the role of the last and first channel while diminishing the importance of assisted conversions. Time-decay model, on the contrary, downplays the role awareness-related campaigns played.

  To select the right attribution model for your business consider your objectives. Is it more important for you to understand your best top of funnel channels to optimise customer acquisition costs (CAC) ? Or would you rather maximise your on-site conversion rates ? 

  Your industry and the average cycle length should also guide your choice. Position-based models can work best for eCommerce and SaaS businesses where both CAC and on-site conversion rates play an important role. Manufacturing companies or educational services providers, on the contrary, will benefit more from a time-decay model as it better represents the lengthy sales cycles. 

  3. Collect and Organise Data From All Touchpoints 

  Multi-touch attribution models are based on available funnel data. So to get started, you will need to determine which data sources you have and how to best leverage them for attribution modelling. 

  Types of data you should collect : 

  • General web analytics data : Insights on visitors’ on-site actions — visited pages, clicked links, form submissions and more.
  • Goals (Conversions) : Reports on successful conversions across different types of assets. 
  • Behavioural user data : Some tools also offer advanced features such as heatmaps, session recording and A/B tests. These too provide ample data into user behaviours, which you can use to map and optimise various touchpoints.

  You can also implement extra tracking, for instance for contact form submissions, live chat contacts or email marketing campaigns to identify repeat users in your system. Just remember to stay on the good side of data protection laws and respect your visitors’ privacy. 

  Separately, you can obtain top-of-the-funnel data by analysing referral traffic sources (channel, campaign type, used keyword, etc). A Tag Manager comes in handy as it allows you to zoom in on particular assets (e.g., a newsletter, an affiliate, a social campaign, etc). 

  Combined, these data points can be parsed by an app, supporting multi-touch attribution (or a custom algorithm) and reported back to you as specific findings. 

  Sounds easy, right ? Well, the devil is in the details. Getting ample, accurate data for multi-touch attribution modelling isn’t easy. 

  Marketing analytics has an accuracy problem, mainly for two reasons :

  • Cookie consent banner rejection 
  • Data sampling application

  Please note that we are not able to provide legal advice, so it’s important that you consult with your own DPO to ensure compliance with all relevant laws and regulations.

  If you’re collecting web analytics in the EU, you know that showing a cookie consent banner is a GDPR must-do. But many consumers don’t often rush to accept cookie consent banners. The average consent rate for cookies in 2021 stood at 54% in Italy, 45% in France, and 44% in Germany. The consent rates are likely lower in 2023, as Google was forced to roll out a “reject all” button for cookie tracking in Europe, while privacy organisations lodge complaints against individual businesses for deceptive banners. 

  For marketers, cookie rejection means substantial gaps in analytics data. The good news is that you can fill in those gaps by using a privacy-centred web analytics tool like Matomo. 

  Matomo takes extra safeguards to protect user privacy and supports fully cookieless tracking. Because of that, Matomo is legally exempt from tracking consent in France. Plus, you can configure to use our analytics tool without consent banners in other markets outside of Germany and the UK. This way you get to retain the data you need for audience modelling without breaching any privacy regulations. 

  Data sampling application partially stems from the above. When a web analytics or multi-channel attribution tool cannot secure first-hand data, the “guessing game” begins. Google Analytics, as well as other tools, often rely on synthetic AI-generated data to fill in the reporting gaps. Respectively, your multi-attribution model doesn’t depict the real state of affairs. Instead, it shows AI-produced guesstimates of what transpired whenever not enough real-world evidence is available.

  4. Evaluate and Select an Attribution Tool 

  Google Analytics (GA) offers several multi-touch attribution models for free (linear, time-decay and position-based). The disadvantage of GA multi-touch attribution is its lower accuracy due to cookie rejection and data sampling application.

  At the same time, you cannot create custom credit allocations for the proposed models, unless you have the paid version of GA, Google Analytics 360. This version of GA comes with a custom Attribution Modeling Tool (AMT). The price tag, however, starts at USD $50,000 per year. 

  Matomo Cloud offers multi-channel conversion attribution as a feature and it is available as a plug-in on the marketplace for Matomo On-Premise. We support linear, position-based, first-interaction, last-interaction, last non-direct and time-decay modelling, based fully on first-hand data. You also get more precise insights because cookie consent isn’t an issue with us. 

  Most multi-channel attribution tools, like Google Analytics and Matomo, provide out-of-the-box multi-touch attribution models. But other tools, like Matomo On-Premise, also provide full access to raw data so you can develop your own multi-touch attribution models and do custom attribution analysis. The ability to create custom attribution analysis is particularly beneficial for data analysts or organisations with complex and unique buyer journeys. 

  Conclusion

  Ultimately, multi-channel attribution gives marketers greater visibility into the customer journey. By analysing multiple touchpoints, you can establish how various marketing efforts contribute to conversions. Then use this information to inform your promotional strategy, budget allocations and CRO efforts. 

  The key to benefiting the most from multi-touch attribution is accurate data. If your analytics solution isn’t telling you the full story, your multi-touch model won’t either. 

  Collect accurate visitor data for multi-touch attribution modelling with Matomo. Start your free 21-day trial now.