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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. 

• OCPA, FDBR and TDPSA – What you need to know about the US’s new privacy laws

  22 juillet 2024, par Daniel Crough

  On July 1, 2024, new privacy laws took effect in Florida, Oregon, and Texas. People in these states now have more control over their personal data, signaling a shift in privacy policy in the United States. Here’s what you need to know about these laws and how privacy-focused analytics can help your business stay compliant.

  Consumer rights are front and centre across all three laws

  The Florida Digital Bill of Rights (FDBR), Oregon Consumer Privacy Act (OCPA), and Texas Data Privacy and Security Act (TDPSA) grant consumers similar rights.

  Access : Consumers can access their personal data held by businesses.

  Correction : Consumers can correct inaccurate data.

  Deletion : Consumers may request data deletion.

  Opt-Out : Consumers can opt-out of the sale of their personal data and targeted advertising.

  Oregon Consumer Privacy Act (OCPA)

  The Oregon Consumer Privacy Act (OCPA), signed into law on June 23, 2023, and effective as of July 1, 2024, grants Oregonians new rights regarding their personal data and imposes obligations on businesses. Starting July 1, 2025, authorities will enforce provisions that require data protection assessments, and businesses must recognize universal opt-out mechanisms by January 1, 2026. In Oregon, the OCPA applies to business that :

  • Either conduct business in Oregon or offer products and services to Oregon residents

  • Control or process the personal data of 100,000 consumers or more, or

  • Control or process the data of 25,000 or more consumers while receiving over 25% of their gross revenues from selling personal data.

  Exemptions include public bodies like state and local governments, financial institutions, and insurers that operate under specific financial regulations. The law also excludes protected health information covered by HIPAA and other specific federal regulations.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for high-risk processing activities, such as those involving sensitive data or targeting children.

  Consent for Sensitive Data : Businesses must secure explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Universal Opt-out : Starting January 1, 2025, businesses must acknowledge universal opt-out mechanisms, like the Global Privacy Control, that allow consumers to opt out of data collection and processing activities.

  Enforcement

  The Oregon Attorney General can issue fines up to $7,500 per violation. There is no private right of action.

  Unique characteristics of the OCPA

  The OCPA differs from other state privacy laws by requiring affirmative opt-in consent for processing sensitive and children’s data, and by including nonprofit organisations under its scope. It also requires global browser opt-out mechanisms starting in 2026.

  Florida Digital Bill of Rights (FDBR)

  The Florida Digital Bill of Rights (FDBR) became law on June 6, 2023, and it came into effect on July 1, 2024. This law targets businesses with substantial operations or revenues tied to digital activities and seeks to protect the personal data of Florida residents by granting them greater control over their information and imposing stricter obligations on businesses. It applies to entities that :

  • Conduct business in Florida or provide products or services targeting Florida residents,

  • Have annual global gross revenues exceeding $1 billion,

  • Receive 50% or more of their revenues from digital advertising or operate significant digital platforms such as app stores or smart speakers with virtual assistants.

  Exemptions include governmental entities, nonprofits, financial institutions covered by the Gramm-Leach-Bliley Act, and entities covered by HIPAA.

  Business obligations

  Data Security Measures : Companies are required to implement reasonable data security measures to protect personal data from unauthorised access and breaches.

  Handling Sensitive Data : Explicit consent is required for processing sensitive data, which includes information like racial or ethnic origin, religious beliefs, and biometric data.

  Non-Discrimination : Entities must ensure they do not discriminate against consumers who exercise their privacy rights.

  Data Minimisation : Businesses must collect only necessary data.

  Vendor Management : Businesses must ensure that their processors and vendors also comply with the FDBR, regarding the secure handling and processing of personal data.

  Enforcement

  The Florida Attorney General can impose fines of up to $50,000 per violation, with higher penalties for intentional breaches.

  Unique characteristics of the FDBR

  Unlike broader privacy laws such as the California Consumer Privacy Act (CCPA), which apply to a wider range of businesses based on lower revenue thresholds and the volume of data processed, the FDBR distinguishes itself by targeting large-scale businesses with substantial revenues from digital advertising. The FDBR also emphasises specific consumer rights related to modern digital interactions, reflecting the evolving landscape of online privacy concerns.

  Texas Data Privacy and Security Act (TDPSA)

  The Texas Data Privacy and Security Act (TDPSA), signed into law on June 16, 2023, and effective as of July 1, 2024, enhances data protection for Texas residents. The TDPSA applies to entities that :

  • Conduct business in Texas or offer products or services to Texas residents.

  • Engage in processing or selling personal data.

  • Do not fall under the classification of small businesses according to the U.S. Small Business Administration’s criteria, which usually involve employee numbers or average annual receipts. 

  The law excludes state agencies, political subdivisions, financial institutions compliant with the Gramm-Leach-Bliley Act, and entities compliant with HIPAA.

  Business obligations

  Data Protection Assessments : Businesses must conduct data protection assessments for processing activities that pose a heightened risk of harm to consumers, such as processing for targeted advertising, selling personal data, or profiling.

  Consent for Sensitive Data : Businesses must get explicit consent before collecting, processing, or selling sensitive personal data, such as racial or ethnic origin, religious beliefs, health information, biometric data, and geolocation.

  Companies must have adequate data security practices based on the personal information they handle.

  Data Subject Access Requests (DSARs) : Businesses must respond to consumer requests regarding their personal data (e.g., access, correction, deletion) without undue delay, but no later than 45 days after receipt of the request.

  Sale of Data : If businesses sell personal data, they must disclose these practices to consumers and provide them with an option to opt out.

  Universal Opt-Out Compliance : Starting January 1, 2025, businesses must recognise universal opt-out mechanisms like the Global Privacy Control, enabling consumers to opt out of data collection and processing activities.

  Enforcement

  The Texas Attorney General can impose fines up to $25,000 per violation. There is no private right of action.

  Unique characteristics of the TDPSA

  The TDPSA stands out for its small business carve-out, lack of specific thresholds based on revenue or data volume, and requirements for recognising universal opt-out mechanisms starting in 2025. It also mandates consent for processing sensitive data and includes specific measures for data protection assessments and privacy notices.

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  Privacy notices across Florida, Oregon, and Texas

  All three laws include a mandate for privacy notices, though there are subtle variations in their specific requirements. Here’s a breakdown of these differences :

  FDBR privacy notice requirements

  Clarity : Privacy notices must clearly explain the collection and use of personal data.

  Disclosure : Notices must inform consumers about their rights, including the right to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  Specificity : Businesses must disclose if they sell personal data or use it for targeted advertising.

  Security Practices : The notice should describe the data security measures in place.

  OCPA privacy notice requirements

  Comprehensive Information : Notices must provide information about the personal data collected, the purposes for processing, and any third parties that can access it.

  Consumer Rights : Must plainly outline consumers’ rights to access, correct, delete their data, and opt-out of data sales, targeted advertising, and profiling.

  Sensitive Data : To process sensitive data, businesses or entities must get explicit consent and communicate it.

  Universal Opt-Out : Starting January 1, 2026, businesses must recognise and honour universal opt-out mechanisms.

  TDPSA privacy notice requirements

  Detailed Notices : Must provide clear and detailed information about data collection practices, including the data collected and the purposes for its use.

  Consumer Rights : Must inform consumers of their rights to access, correct, delete their data, and opt-out of data sales and targeted advertising.

  High-Risk Processing : Notices should include information about any high-risk processing activities and the safeguards in place.

  Sensitive Data : To process sensitive data, entities and businesses must get explicit consent.

  What these laws mean for your businesses

  Businesses operating in Florida, Oregon, and Texas must now comply with these new data privacy laws. Here’s what you can do to avoid fines :

  1. Understand the Laws : Familiarise yourself with the specific requirements of the FDBR, OCPA, and TDPSA, including consumer rights and business obligations.

  2. Implement Data Protection Measures : Ensure you have robust data security measures in place. This includes conducting regular data protection assessments, especially for high-risk processing activities.

  3. Update Privacy Policies : Provide clear and comprehensive privacy notices that inform consumers about their rights and how their data is processed.

  4. Obtain Explicit Consent : For sensitive data, make sure you get explicit consent from consumers. This includes information like health, race, sexual orientation, and more.

  5. Manage Requests Efficiently : Be prepared to handle requests from consumers to access, correct, delete their data, and opt-out of data sales and targeted advertising within the stipulated timeframes.

  6. Recognise Opt-Out Mechanisms : For Oregon, businesses must be ready to implement and recognise universal opt-out mechanisms by January 1, 2026. In Texas, opt-out enforcement begins in 2026. In Florida, the specific opt-out provisions began on July 1, 2024.

  7. Stay Updated : Keep abreast of any changes or updates to these laws to ensure ongoing compliance. Keep an eye on the Matomo blog or sign up for our newsletter to stay in the know.

  Are we headed towards a more privacy-focused future in the United States ?

  Florida, Oregon, and Texas are joining states like California, Virginia, Colorado, Connecticut, Utah, Iowa, Indiana, Tennessee, and Montana in strengthening consumer privacy protections. This trend could signify a shift in US policy towards a more privacy-focused internet, underlining the importance of consumer data rights and transparent business practices. Even if these laws do not apply to your business, considering updates to your data and privacy policies is wise. Fortunately, there are tools and solutions designed for privacy and compliance to help you navigate these changes.

  Avoid fines and get better data with Matomo

  Most analytics tools don’t prioritize safeguarding user data. At Matomo, we believe everyone has the right to data sovereignty, privacy and amazing analytics. Matomo offers a solution that meets privacy regulations while delivering incredible insights. With Matomo, you get :

  100% Data Ownership : Keep full control over your data, ensuring it is used according to your privacy policies.

  Privacy Protection : Built with privacy in mind, Matomo helps businesses comply with privacy laws.

  Powerful Features : Gain insights with tools like heatmaps, session recordings, and A/B testing.

  Open Source : Matomo’s is open-source and committed to transparency and customisation.

  Flexibility : Choose to host Matomo on your servers or in the cloud for added security.

  No Data Sampling : Ensure accurate and complete insights without data sampling.

  Privacy Compliance : Easily meet GDPR and other requirements, with data stored securely and never sold or shared.

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  Disclaimer : This content is provided for informational purposes only and is not intended as legal advice. While we strive to ensure the accuracy and timeliness of the information provided, the laws and regulations surrounding privacy are complex and subject to change. We recommend consulting with a qualified legal professional to address specific legal issues related to your circumstances. 

• B2B Marketing Attribution Guide : How to Master It in 2024

  21 mai 2024, par Erin

  The last thing you want is to invest your advertising dollars in channels, campaigns and ads that don’t work. But B2B marketing attribution — figuring out which marketing efforts drive revenue — is far from easy.

  With longer sales funnels and multiple people from the same company involved in the same sales process, B2B (business-to-business) is a different ballgame from B2C (business-to-consumer) marketing.

  In this guide, we break down what B2B marketing attribution is, how it’s different, which tools you can use to set it up and the best practices.

  What is B2B marketing attribution ?

  Marketing attribution in B2B companies is about figuring out where your high-value leads come from — nailing down long customer journeys across many different touchpoints.

  

  The goal is to determine which campaigns and content contributed to various parts of the customer journey. It’s a complex process that needs a reliable, privacy-focused web analytics tool and a CRM that integrates with it.

  This process significantly differs from traditional marketing attribution, where you focus more on short sales cycles from individual customers. With multiple contributing decision makers, B2B attribution requires more robust systems.

  What makes marketing attribution different for B2B ?

  The key differences between B2B and B2C marketing attribution are a longer sales funnel and more people involved in the sales process.

  The B2B sales funnel is significantly longer and more complex

  The typical B2C sales funnel is often broken down into four simple stages :

  1. Awareness : when a prospect first finds out about your product or brand
  2. Interest : where a prospect starts to learn about the benefits of your product
  3. Desire : when a prospect understands that they need your product
  4. Action : the actual process of closing the sale

  Even the most simplified B2B sales funnel includes several key stages.

  

  Here’s a brief overview of each :

  1. Awareness : Buyers recognise they have a problem and start looking for solutions. Stand out with blog posts, social media updates, ebooks and whitepapers.
  2. Consideration : Buyers are aware of your company and are comparing options. Provide product demos, webinars and case studies to address their concerns and build trust.
  3. Conversion : Buyers have chosen your product or company. Offer live demos, customer service, case studies and testimonials to finalise the purchase.
  4. Loyalty : Buyers have made a purchase and are now customers. Nurture relationships with thank you emails, follow-ups, how-tos, reward programs and surveys to encourage repeat business.
  5. Advocacy : Loyal customers become advocates, promoting your brand to others. Encourage this with surveys, testimonial requests and a referral program.

  A longer sales cycle typically involves not only more touchpoints but also extended decision-making processes.

  More teams are involved in the marketing and sales process

  The last differentiation in B2B attribution is the number of people involved. Instead of clear-cut sales and marketing teams, revenue teams are becoming more common.

  They include all go-to-market teams like sales, marketing, customer success and customer support. In B2B sales, long-term customer relationships can be incredibly valuable. As such, the focus shifts away from new customer acquisition alone.

  For example, you can also track and optimise your onboarding process. Marketing gets involved in post-sale efforts to boost loyalty. Sales reps follow up with customer success to get new sales angles and insights. Customer support insights drive future product development.

  Everyone works together to meet high-level company goals.

  The next section will explore how to set up an attribution system.

  How to find the right mix of B2B marketing attribution tools

  For most B2B marketing teams, the main struggle with attribution is not with the strategy but with creating a reliable system that gives them the data points they need to implement that strategy.

  We’ll outline one approach you can take to achieve this without a million-dollar budget or internal data science team.

  Use website analytics to track touchpoints

  The first thing you want to do is install a reliable website analytics solution on your website. 

  Once you’ve got your analytics in place, use campaign tracking parameters to track touchpoints from external campaigns like email newsletters, social media ads, review sites (like Capterra) and third-party partner campaigns.

  This way, you get a clear picture of which sources are driving traffic and conversions, helping you improve your marketing strategies.

  With analytics installed, you can track the referring sources of visits, engagement and conversion events. A robust solution like Matomo tracks everything from traffic sources, marketing attribution and visitor counts to behavioural analytics, like clicks, scrolling patterns and form interactions on your site.

  Marketing attribution will give you a cohesive view of which traffic sources and campaigns drive conversions and revenue over long periods. With Matomo’s marketing attribution feature, you can even use different marketing attribution models to compare results :

  

  For example, in a single report, you can compare the last interaction, first interaction and linear (three common marketing attribution models). 

  In total, Matomo has 6 available attribution models to choose from :

  1. First interaction
  2. Last interaction
  3. Last non-direct 
  4. Linear
  5. Position based
  6. Time decay 

  These additional attribution models are crucial for B2B sites. While other web analytics solutions often limit to last-click attribution, this model isn’t optimal for B2B with extended sales cycles.

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  Use a CRM to integrate customer data from multiple sources

  Use your CRM software to integrate customer data from multiple sources. This will give you the ability to get meaningful B2B marketing insights. For example, you can get company-level insights so you can view conversion information by company, not just by person.

  Done effectively, you can close the loop back to analytics data by integrating data from multiple teams and platforms. 

  Implement self-reported attribution

  To further enhance the data, add qualifying questions in the lead signup process to create a hybrid attribution model. This is also known as self-reported attribution.

  

  Your web analytics platform won’t always be able to track the source of certain visits — for instance, “dark social” or peer-to-peer sharing, where links are shared privately and are not easily traceable by analytics tools.

  Doing self-reported attribution is crucial for getting a holistic image of your customer journey. 

  However, self-reported attribution isn’t foolproof ; users may click randomly or inaccurately recall where they first heard about you. So it’s essential to blend this data with your analytics to gain a more accurate understanding.

  Best practices for handling B2B prospect data in a privacy-sensitive world 

  Lastly, it’s important to respect your prospects’ privacy and comply with privacy regulations when conducting B2B marketing attribution.

  Privacy regulations and their enforcement are rapidly gaining momentum around the globe. Meta recently received a record GDPR fine of €1.2 billion for insufficient privacy measures when handling user data by the Irish Data Protection Agency.

  If you don’t want to risk major fines (or customers feeling betrayed), you shouldn’t follow in the same footsteps.

  Switch to a privacy-friendly web analytics

  Instead of using a controversial solution like Google Analytics, use a privacy-friendly web analytics solution like Matomo, Fathom or Plausible. 

  These alternatives not only ensure compliance with regulations like GDPR but also provide peace of mind amid the uncertain relationship between Google and GDPR. Google Analytics has faced bans in recent years, raising concerns about the future of the solution.

  While organisations governed by GDPR can currently use Google Analytics, there’s no guarantee of its continued availability.

  Make the switch to privacy-friendly web analytics to avoid potential fines and disruptive rulings that could force you to change platforms urgently. Such disruptions can be catastrophic for marketing teams heavily reliant on web analytics for tracking campaigns, business goals and marketing efforts.

  Improve your B2B marketing attribution with Matomo

  Matomo’s privacy-by-design architecture makes it the perfect analytics platform for the modern B2B marketer. Matomo enables you to meet even the strictest privacy regulations.

  At the same time, through campaign tracking URLs, marketing attribution, integrations and our API, you can track the results of various marketing channels and campaigns effectively. We help you understand the impact of each dollar of your marketing budget. 

  If you want a competitive edge over other B2B companies, try Matomo for free for 21 days. No credit card required.

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