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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. 

• 7 Ecommerce Metrics to Track and Improve in 2024

  12 avril 2024, par Erin

  You can invest hours into market research, create the best ads you’ve ever seen and fine-tune your budgets. But the only way to really know if your digital marketing campaigns move the needle is to track ecommerce metrics.

  It’s time to put your hopes and gut feelings aside and focus on the data. Ecommerce metrics are key performance indicators that can tell you a lot about the performance of a single campaign, a traffic source or your entire marketing efforts. 

  That’s why it’s essential to understand what ecommerce metrics are, key metrics to track and how to improve them. 

  Ready to do all of the above ? Then, let’s get started.

  What are ecommerce metrics ? 

  An ecommerce metric is any metric that helps you understand the effectiveness of your digital marketing efforts and the extent to which users are taking a desired action. Most ecommerce metrics focus on conversions, which could be anything from making a purchase to subscribing to your email list.

  You need to track ecommerce metrics to understand how well your marketing efforts are working. They are essential to helping you run a cost-effective marketing campaign that delivers a return on investment. 

  For example, tracking ecommerce metrics will help you identify whether your digital marketing campaigns are generating a return on investment or whether they are actually losing money. They also help you identify your most effective campaigns and traffic sources. 

  Ecommerce metrics also help you spot opportunities for improvement both in terms of your marketing campaigns and your site’s UX. 

  For instance, you can use ecommerce metrics to track the impact on revenue of A/B tests on your marketing campaigns. Or you can use them to understand how users interact with your website and what, if anything, you can do to make it more engaging.

  What’s the difference between conversion rate and conversion value ?

  The difference between a conversion rate and a conversion value is that the former is a percentage while the latter is a monetary value. 

  There can be confusion between the terms conversion rate and conversion value. Since conversions are core metrics in ecommerce, it’s worth taking a minute to clarify. 

  Conversion rates measure the percentage of people who take a desired action on your website compared to the total number of visitors. If you have 100 visitors and one of them converts, then your conversion rate is 1%. 

  Here’s the formula for calculating your conversion rate :

  Conversion Rate (%) = (Number of conversions / Total number of visitors) × 100

  

  Using the example above :

  Conversion Rate = (1 / 100) × 100 = 1%

  Conversion value is a monetary amount you assign to each conversion. In some cases, this is the price of the product a user purchases. In other conversion events, such as signing up for a free trial, you may wish to assign a hypothetical conversion value. 

  To calculate a hypothetical conversion value, let’s consider that you have estimated the average revenue generated from a paying customer is $300. If the conversion rate from free trial to paying customer is 20%, then the hypothetical conversion value for each free trial signup would be $300 multiplied by 20%, which equals $60. This takes into account the number of free trial users who eventually become paying customers.

  So the formula for hypothetical conversion value looks like this :

  

  Hypothetical conversion value = (Average revenue per paying customer) × (Conversion rate)

  Using the values from our example :

  Hypothetical conversion value = $300 × 20% = $60

  The most important ecommerce metrics and how to track them

  There are dozens of ecommerce metrics you could track, but here are seven of the most important. 

  Conversion rate

  Conversion rate is the percentage of visitors who take a desired action. It is arguably one of the most important ecommerce metrics and a great top-level indicator of the success of your marketing efforts. 

  You can measure the conversion rate of anything, including newsletter signups, ebook downloads, and product purchases, using the following formula :

  

  Conversion rate = (Number of people who took action / Total number of visitors) × 100

  You usually won’t have to manually calculate your conversion rate, though. Almost every web analytics or ad platform will track the conversion rate automatically.

  Matomo, for instance, automatically tracks any conversion you set in the Goals report.

  

  As you can see in the screenshot, your site’s conversions are plotted over a period of time and the conversion rate is tracked below the graph. You can change the time period to see how your conversion rate fluctuates.

  If you want to go even further, track your new visitor conversion rate to see how engaging your site is to first-time visitors. 

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  Cost per acquisition

  Cost per acquisition (CPA) is the average cost of acquiring a new user. You can calculate your overall CPA or you can break CPA down by email campaign, traffic source, or any other criteria. 

  Calculate CPA by dividing your total marketing cost by the number of new users you acquire.

  

  CPA = Total marketing cost​ / Number of new users acquired 

  So if your Google Ads campaign costs €1,000 and you acquire 100 new users, your CPA is €10 (1000/100=10).

  It’s important to note that CPA is not the same as customer acquisition cost. Customer acquisition cost considers the number of paying customers. CPA looks at the number of users taking a certain action, like subscribing to a newsletter, making a purchase, or signing up for a free trial.

  Cost per acquisition is a direct measure of your marketing efforts’ effectiveness, especially when comparing CPA to average customer spend and return on ad spend. 

  If your CPA is higher than the average customer spend, your marketing campaign is profitable. If not, then you can look at ways to either increase customer spend or decrease your cost per acquisition.

  Customer lifetime value

  Customer lifetime value (CLV) is the average amount of money a customer will spend with your ecommerce brand over their lifetime. 

  Customer value is the total worth of a customer to your brand based on their purchasing behaviour. To calculate it, multiply the average purchase value by the average number of purchases. For instance, if the average purchase value is €50 and customers make 5 purchases on average, the customer value would be €250.

  Use this formula to calculate customer value :

  

  Customer value = Average purchase value × Average number of purchases

  Then you can calculate customer lifetime value using the following formula :

  

  CLV = Customer value × Average customer lifespan

  In another example, let’s say you have a software company and customers pay you €500 per year for an annual subscription. If the average customer lifespan is 5 years, then the Customer Lifetime Value (CLV) would be €2,500.

  Customer lifetime value = €500 × 5 = €2,500

  Knowing how much potential customers are likely to spend helps you set accurate marketing budgets and optimise the price of your products. 

  Return on investment

  Return on investment (ROI) is the amount of revenue your marketing efforts generate compared to total spend. 

  It’s usually calculated as a percentage using the following formula :

  

  ROI = (Revenue / Total spend) × 100

  If you spend €1,000 on a paid ad campaign and your efforts bring in €5,000, then your ROI is 500% (5,000/1,000 × 100).

  With a web analytics tool like Matomo, you can quickly see the revenue generated from each traffic source and you can drill down further to compare different social media channels, search engines, referral websites and campaigns to get more granular view. 

  

  In the example above in Matomo’s Marketing Attribution feature, we can see that social networks are generating the highest amount of revenue in the year. To calculate ROI, we would need to compare the amount of investment to each channel. 

  Let’s say we invested $1,000 per year in search engine optimisation and content marketing, the return on investment (ROI) stands at approximately 2576%, based on a revenue of $26,763.48 per year. 

  Conversely, for organic social media campaigns, where $5,000 was invested and revenue amounted to $71,180.22 per year, the ROI is approximately 1323%. 

  Despite differences in revenue generation, both channels exhibit significant returns on investment, with SEO and content marketing demonstrating a much higher ROI compared to organic social media campaigns. 

  With that in mind, we might want to consider shifting our marketing budget to focus more on search engine optimisation and content marketing as it’s a greater return on investment.

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  Return on ad spend

  Return on ad spend (ROAS) is similar to return on investment, but it measures the profitability of a specific ad or campaign.

  Calculate ROAS using the following formula :

  

  ROAS = Revenue / Ad cost 

  A positive ROAS means you are making money. If you generate €3 for every €1 you spend on advertising, for example, there’s no reason to turn off that campaign. If you only make €1 for every €2 you spend, however, then you need to shut down the campaign or optimise it. 

  Bounce rate

  Bounce rate is the percentage of visitors who leave your site without taking another action. Calculate it using the following formula :

  

  Bounce rate = (Number of visitors who bounce / Total number of visitors) × 100

  Some portion of users will always leave your site immediately, but you should aim to make your bounce rate as low as possible. After all, every customer that bounces is a missed opportunity that you may never get again. 

  You can check the bounce rate for each one of your site’s pages using Matomo’s page analytics report. Web analytics tools like Google Analytics can track bounce rates for online stores also. 

  

  Bounce rate is calculated automatically. You can sort the list of pages by bounce rate allowing you to prioritise your optimisation efforts. 

  Don’t stop there, though. Explore bounce rate further by comparing your mobile bounce rate vs. desktop bounce rate by segmenting your traffic. This will highlight whether your mobile site needs improving. 

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  Click-through rate

  Your clickthrough rate (CTR) tells you the number of people who click on your ads as a percentage of total impressions. You can calculate it by dividing the number of clicks your ad gets by the total number of times people see it. 

  So the formula looks like this :

  

  CTR (%) = (Number of clicks / Total impressions​) × 100

  If an ad gets 1,000 impressions and 10 people click on it, then the CTR will be 10/1,000 × 100 = 1%

  You don’t usually need to calculate your clickthrough rate manually, however. Most ad platforms like Google Ads will automatically calculate CTR.

  What is considered a good ecommerce sales conversion rate ?

  This question is so broad it’s almost impossible to answer. The thing is, sales conversion rates vary massively depending on the conversion event and the industry. A good conversion rate in one industry might be terrible in another. 

  That being said, research shows that the average website conversion rate across all industries is 2.35%. Of course, some websites convert much better than this. The same study found that the top 25% of websites across all industries have a conversion rate of 5.31% or higher. 

  How can you improve your conversion rate ?

  Ecommerce metrics don’t just let you track your campaign’s ROI, they help you identify ways to improve your campaign. 

  Use these five tips to start improving your marketing campaign’s conversion rates today :

  Run A/B tests

  The most effective way to improve almost all of the ecommerce metrics you track is to test, test, and test again.

  A/B testing or multivariate testing compares two different versions of the same content, such as a landing page or blog post. Seeing which version performs better can help you squeeze as many conversions as possible from your website and ad campaigns. But only if you test as many things as possible. This should include :

  • Ad placement
  • Ad copy
  • CTAs
  • Headlines
  • Straplines
  • Colours
  • Design

  To create and analyse tests and their results effectively, you’ll need either an A/B testing platform or a web analytics solution like Matomo, which offers one out of the box.

  

  Matomo’s A/B Testing feature makes it easy to create and track tests over time, breaking down each test’s variations by the metrics that matter. It automatically calculates statistical significance, too, meaning you can be sure you’re making a change for the better. 

  Try Matomo for Free

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

• Revision 32594 : plugins en minuscules, et alias pour les noms de sites

  1er novembre 2009, par fil@… — Log

  plugins en minuscules, et alias pour les noms de sites