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• 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. 

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

• Google Optimize vs Matomo A/B Testing : Everything You Need to Know

  17 mars 2023, par Erin — Analytics Tips

  Google Optimize is a popular A/B testing tool marketers use to validate the performance of different marketing assets, website design elements and promotional offers. 

  But by September 2023, Google will sunset both free and paid versions of the Optimize product. 

  If you’re searching for an equally robust, but GDPR compliant, privacy-friendly alternative to Google Optimize, have a look at Matomo A/B Testing. 

  Integrated with our analytics platform and conversion rate optimisation (CRO) tools, Matomo allows you to run A/B and A/B/n tests without any usage caps or compromises in user privacy.

  Disclaimer : Please note that the information provided in this blog post is for general informational purposes only and is not intended to provide legal advice. Every situation is unique and requires a specific legal analysis. If you have any questions regarding the legal implications of any matter, please consult with your legal team or seek advice from a qualified legal professional.

  Google Optimize vs Matomo : Key Capabilities Compared 

  This guide shows how Matomo A/B testing stacks against Google Optimize in terms of features, reporting, integrations and pricing.

  Supported Platforms 

  Google Optimize supports experiments for dynamic websites and single-page mobile apps only. 

  If you want to run split tests in mobile apps, you’ll have to do so via Firebase — Google’s app development platform. It also has a free tier but paid usage-based subscription kicks in after your product(s) reaches a certain usage threshold. 

  Google Optimize also doesn’t support CRO experiments for web or desktop applications, email campaigns or paid ad campaigns.Matomo A/B Testing, in contrast, allows you to run experiments in virtually every channel. We have three installation options — using JavaScript, server-side technology, or our mobile tracking SDK. These allow you to run split tests in any type of web or mobile app (including games), a desktop product, or on your website. Also, you can do different email marketing tests (e.g., compare subject line variants).

  A/B Testing 

  A/B testing (split testing) is the core feature of both products. Marketers use A/B testing to determine which creative elements such as website microcopy, button placements and banner versions, resonate better with target audiences. 

  You can benchmark different versions against one another to determine which variation resonates more with users. Or you can test an A version against B, C, D and beyond. This is called A/B/n testing. 

  Both Matomo A/B testing and Google Optimize let you test either separate page elements or two completely different landing page designs, using redirect tests. You can show different variants to different user groups (aka apply targeting criteria). For example, activate tests only for certain device types, locations or types of on-site behaviour. 

  The advantage of Matomo is that we don’t limit the number of concurrent experiments you can run. With Google Optimize, you’re limited to 5 simultaneous experiments. Likewise, 

  Matomo lets you select an unlimited number of experiment objectives, whereas Google caps the maximum choice to 3 predefined options per experiment. 

  Objectives are criteria the underlying statistical model will use to determine the best-performing version. Typically, marketers use metrics such as page views, session duration, bounce rate or generated revenue as conversion goals. 

  

  Multivariate testing (MVT)

  Multivariate testing (MVT) allows you to “pack” several A/B tests into one active experiment. In other words : You create a stack of variants to determine which combination drives the best marketing outcomes. 

  For example, an MVT experiment can include five versions of a web page, where each has a different slogan, product image, call-to-action, etc. Visitors are then served with a different variation. The tracking code collects data on their behaviours and desired outcomes (objectives) and reports the results.

  MVT saves marketers time as it’s a great alternative to doing separate A/B tests for each variable. Both Matomo and Google Optimize support this feature. However, Google Optimize caps the number of possible combinations at 16, whereas Matomo has no limits. 

  Redirect Tests

  Redirect tests, also known as split URL tests, allow you to serve two entirely different web page versions to users and compare their performance. This option comes in handy when you’re redesigning your website or want to test a localised page version in a new market. 

  Also, redirect tests are a great way to validate the performance of bottom-of-the-funnel (BoFU) pages as a checkout page (for eCommerce websites), a pricing page (for SaaS apps) or a contact/booking form (for a B2B service businesses). 

  You can do split URL tests with Google Optimize and Matomo A/B Testing. 

  Experiment Design 

  Google Optimize provides a visual editor for making simple page changes to your website (e.g., changing button colour or adding several headline variations). You can then preview the changes before publishing an experiment. For more complex experiments (e.g., testing different page block sequences), you’ll have to codify experiments using custom JavaScript, HTML and CSS.

  In Matomo, all A/B tests are configured on the server-side (i.e., by editing your website’s raw HTML) or client-side via JavaScript. Afterwards, you use the Matomo interface to start or schedule an experiment, set objectives and view reports. 

  Experiment Configuration 

  Marketers know how complex customer journeys can be. Multiple factors — from location and device to time of the day and discount size — can impact your conversion rates. That’s why a great CRO app allows you to configure multiple tracking conditions. 

  Matomo A/B testing comes with granular controls. First of all, you can decide which percentage of total web visitors participate in any given experiment. By default, the number is set to 100%, but you can change it to any other option. 

  Likewise, you can change which percentage of traffic each variant gets in an experiment. For example, your original version can get 30% of traffic, while options A and B receive 40% each. We also allow users to specify custom parameters for experiment participation. You can only show your variants to people in specific geo-location or returning visitors only. 

  Finally, you can select any type of meaningful objective to evaluate each variant’s performance. With Matomo, you can either use standard website analytics metrics (e.g., total page views, bounce rate, CTR, visit direction, etc) or custom goals (e.g., form click, asset download, eCommerce order, etc). 

  In other words : You’re in charge of deciding on your campaign targeting criteria, duration and evaluation objectives.

  A free Google Optimize account comes with three main types of user targeting options : 

  • Geo-targeting at city, region, metro and country levels. 

  • Technology targeting  by browser, OS or device type, first-party cookie, etc. 
  • Behavioural targeting based on metrics like “time since first arrival” and “page referrer” (referral traffic source). 

  Users can also configure other types of tracking scenarios (for example to only serve tests to signed-in users), using condition-based rules. 

  Reporting 

  Both Matomo and Google Optimize use different statistical models to evaluate which variation performs best. 

  Matomo relies on statistical hypothesis testing, which we use to count unique visitors and report on conversion rates. We analyse all user data (with no data sampling applied), meaning you get accurate reporting, based on first-hand data, rather than deductions. For that reason, we ask users to avoid drawing conclusions before their experiment participation numbers reach a statistically significant result. Typically, we recommend running an experiment for at least several business cycles to get a comprehensive report. 

  Google Optimize, in turn, uses Bayesian inference — a statistical method, which relies on a random sample of users to compare the performance rates of each creative against one another. While a Bayesian model generates CRO reports faster and at a bigger scale, it’s based on inferences.

  Model developers need to have the necessary skills to translate subjective prior beliefs about the probability of a certain event into a mathematical formula. Since Google Optimize is a proprietary tool, you cannot audit the underlying model design and verify its accuracy. In other words, you trust that it was created with the right judgement. 

  In comparison, Matomo started as an open-source project, and our source code can be audited independently by anyone at any time. 

  Another reporting difference to mind is the reporting delays. Matomo Cloud generates A/B reports within 6 hours and in only 1 hour for Matomo On-Premise. Google Optimize, in turn, requires 12 hours from the first experiment setup to start reporting on results. 

  When you configure a test experiment and want to quickly verify that everything is set up correctly, this can be an inconvenience.

  User Privacy & GDPR Compliance 

  Google Optimize works in conjunction with Google Analytics, which isn’t GDPR compliant. 

  For all website traffic from the EU, you’re therefore obliged to show a cookie consent banner. The kicker, however, is that you can only show an Optimize experiment after the user gives consent to tracking. If the user doesn’t, they will only see an original page version. Considering that almost 40% of global consumers reject cookie consent banners, this can significantly affect your results.

  This renders Google Optimize mostly useless in the EU since it would only allow you to run tests with a fraction ( 60%) of EU traffic — and even less if you apply any extra targeting criteria. 

  In comparison, Matomo is fully GDPR compliant. Therefore, our users are legally exempt from displaying cookie-consent banners in most EU markets (with Germany and the UK being an exception). Since Matomo A/B testing is part of Matomo web analytics, you don’t have to worry about GDPR compliance or breaches in user privacy. 

  Digital Experience Intelligence 

  You can get comprehensive statistical data on variants’ performance with Google Optimize. But you don’t get further insights on why some tests are more successful than others. 

  Matomo enables you to collect more insights with two extra features :

  • User session recordings : Monitor how users behave on different page versions. Observe clicks, mouse movements, scrolls, page changes, and form interactions to better understand the users’ cumulative digital experience. 
  • Heatmaps : Determine which elements attract the most users’ attention to fine-tune your split tests. With a standard CRO tool, you only assume that a certain page element does matter for most users. A heatmap can help you determine for sure. 

  Both of these features are bundled into your Matomo Cloud subscription. 

  Integrations 

  Both Matomo and Google Optimize integrate with multiple other tools. 

  Google Optimize has native integrations with other products in the marketing family — GA, Google Ads, Google Tag Manager, Google BigQuery, Accelerated Mobile Pages (AMP), and Firebase. Separately, other popular marketing apps have created custom connectors for integrating Google Optimize data. 

  Matomo A/B Testing, in turn, can be combined with other web analytics and CRO features such as Funnels, Multi-Channel Attribution, Tag Manager, Form Analytics, Heatmaps, Session Recording, and more ! 

  You can also conveniently export your website analytics or CRO data using Matomo Analytics API to analyse it in another app. 

  Pricing 

  Google Optimize is a free tool but has usage caps. If you want to schedule more than 5 concurrent experiments or test more than 16 variants at once, you’ll have to upgrade to Optimize 360. Optimize 360 prices aren’t listed publicly but are said to be closer to six figures per year. 

  Matomo A/B Testing is available with every Cloud subscription (starting from €19) and Matomo On-Premise users can also get A/B Testing as a plugin (starting from €199/year). In each case, there are no caps or data limits. 

  Google Optimize vs Matomo A/B Testing : Comparison Table

  Features/capabilities	Google Optimize	Matomo A/B test
  Supported channels	Web	Web, mobile, email, digital campaigns
  A/B testing
  Multivariate testing (MVT)
  Split URL tests
  Web analytics integration	Native with UA/GA4	Native with Matomo You can also migrate historical UA (GA3) data to Matomo
  Audience segmentation	Basic	Advanced
  Geo-targeting
  Technology targeting
  Behavioural targeting	Basic	Advanced
  Reporting model	Bayesian analysis	Statistical hypothesis testing
  Report availability	Within 12 hours after setup	6 hours for Matomo Cloud 1 hour for Matomo On-Premise
  Heatmaps		Included with Matomo Cloud
  Session recordings		Included with Matomo Cloud
  GDPR compliance
  Support	Self-help desk on a free tier	Self-help guides, user forum, email
  Price	Free limited tier	From €19 for Cloud subscription From €199/year as plugin for On-Premise

  Final Thoughts : Who Benefits the Most From an A/B Testing Tool ?

  Split testing is an excellent method for validating various assumptions about your target customers. 

  With A/B testing tools you get a data-backed answer to research hypotheses such as “How different pricing affects purchases ?”, “What contact button placement generates more clicks ?”, “Which registration form performs best with new app subscribers ?” and more. 

  Such insights can be game-changing when you’re trying to improve your demand-generation efforts or conversion rates at the BoFu stage. But to get meaningful results from CRO tests, you need to select measurable, representative objectives.

  For example, split testing different pricing strategies for low-priced, frequently purchased products makes sense as you can run an experiment for a couple of weeks to get a statistically relevant sample. 

  But if you’re in a B2B SaaS product, where the average sales cycle takes weeks (or months) to finalise and things like “time-sensitive discounts” or “one-time promos” don’t really work, getting adequate CRO data will be harder. 

  To see tangible results from CRO, you’ll need to spend more time on test ideation than implementation. Your team needs to figure out : which elements to test, in what order, and why. 

  Effective CRO tests are designed for a specific part of the funnel and assume that you’re capable of effectively identifying and tracking conversions (goals) at the selected stage. This alone can be a complex task since not all customer journeys are alike. For SaaS websites, using a goal like “free trial account registration” can be a good starting point.

  A good test also produces a meaningful difference between the proposed variant and the original version. As Nima Yassini, Partner at Deloitte Digital, rightfully argues :

  “I see people experimenting with the goal of creating an uplift. There’s nothing wrong with that, but if you’re only looking to get wins you will be crushed when the first few tests fail. The industry average says that only one in five to seven tests win, so you need to be prepared to lose most of the time”.

  In many cases, CRO tests don’t provide the data you expected (e.g., people equally click the blue and green buttons). In this case, you need to start building your hypothesis from scratch. 

  At the same time, it’s easy to get caught up in optimising for “vanity metrics” — such that look good in the report, but don’t quite match your marketing objectives. For example, better email headline variations can improve your email open rates. But if users don’t proceed to engage with the email content (e.g. click-through to your website or use a provided discount code), your efforts are still falling short. 

  That’s why developing a baseline strategy is important before committing to an A/B testing tool. Google Optimize appealed to many users because it’s free and allows you to test your split test strategy cost-effectively. 

  With its upcoming depreciation, many marketers are very committed to a more expensive A/B tool (especially when they’re not fully sure about their CRO strategy and its results). 

  Matomo A/B testing is a cost-effective, GDPR-compliant alternative to Google Optimize with a low learning curve and extra competitive features. 

  Discover if Matomo A/B Testing is the ideal Google Optimize alternative for your organization with our free 21-day trial. No credit card required.

• What is Funnel Analysis ? A Complete Guide for Quick Results

  25 janvier 2024, par Erin

  Your funnel is leaking.

  You’re losing visitors.

  You’re losing conversions and sales.

  But you don’t know how it’s happening, where it’s happening, or what to do about it.

  The reason ? You aren’t properly analysing your funnels.

  If you want to improve conversions and grow your business, you need to understand how to properly assess your sales funnels to set yourself up for success.

  In this guide, we’ll show you what funnel analysis is, why it’s important, and what steps you need to take to leverage it to improve conversions.

  What is funnel analysis ?

  Every business uses sales funnels, whether they know it or not.

  But most people aren’t analysing them, costing them conversions.

  

  Funnel analysis is a marketing method to analyse the events leading to specific conversion points. 

  It aims to look at the entire journey of potential customers from the moment they first touch base with your website or business to the moment they click “buy.”

  It’s assessing what your audience is doing at every step of the journey.

  By assessing what actions are taking place at scale, you can see where you’re falling short in your sales funnel.

  You’ll see :

  1. Where prospects are falling off.
  2. Where people are converting well.

  By gaining this understanding, you’ll better understand the health of your website’s sales funnels and overall marketing strategy.

  With that knowledge, you can optimise your marketing strategy to patch those leaks, improve conversions and grow your business.

  Why funnel analysis is important

  Funnel analysis is critical because your funnel is your business.

  When you analyse your funnel, you’re analysing your business.

  You’re looking at what’s working and what’s not so you can grow revenue and profit margins.

  Funnel analysis lets you monitor user behaviour to show you the motivation and intention behind their decisions.

  Here are five reasons you need to incorporate funnel analysis into your workflow.

  

  1. Gives insights into your funnel problems

  The core purpose of funnel analysis is to look at what’s going on on your website.

  What are the most effective steps to conversion ?

  Where do users drop off in the conversion process ?

  And which pages contribute the most to conversion or drop-offs ?

  Funnel analysis helps you understand what’s going on with your site visitors. Plus, it helps you see what’s wrong with your funnel.

  If you aren’t sure what’s happening with your funnel, you won’t know what to improve to grow your revenue.

  2. Improves conversions

  When you know what’s going on with your funnel, you’ll know how to improve it.

  To improve your conversion funnel, you need to close the leaks. These are areas where website visitors are falling off.

  It’s the moment the conversion is lost.

  You need to use funnel analysis to give insight into these problem areas. Once you can see where the issue is, you can patch that leak and improve the percentage of visitors who convert.

  For example, if your conversion rate on your flagship product page has plateaued and you can’t figure out how to increase conversions, implementing a funnel analysis tactic like heatmaps will show you that visitors are spending time reading your product description. Still, they’re not spending much time near your call to action.

  

  This might tell you that you need to update your description copy or adjust your button (i.e. colour, size, copy). You can increase conversions by making those changes in your funnel analysis insights.

  3. Improves the customer experience

  Funnel analysis helps you see where visitors spend their time, what elements they interact with and where they fall off.

  One of the key benefits of analysing your funnel is you’ll be able to help improve the experience your visitors have on your website.

  For example, if you have informational videos on a specific web page to educate your visitors, you might use the Media Analytics feature in your web analytics solution to find out that they’re not spending much time watching them.

  This could lead you to believe that the content itself isn’t good or relevant to them.

  But, after implementing session recordings within your funnel analysis, you see people clicking a ton near the play button. This might tell you that they’re having trouble clicking the actual button on the video player due to poor UX.

  In this scenario, you could update the UX on your web page so the videos are easy to click and watch, no matter what device someone uses.

  With more video viewers, you can provide value to your visitors instead of leaving them frustrated trying to watch your videos.

  4. Grows revenue

  This is what you’re likely after : more revenue.

  More often than not, this means you need to focus on improving your conversion rate.

  Funnel analysis helps you find those areas where visitors are exiting so you can patch those leaks up and turn more visitors into customers.

  Let’s say you have a conversion rate of 1.7%.

  You get 50,000 visitors per month.

  Your average order is $82.

  Even if you increase your conversion rate by 10% (to 1.87%) through funnel analysis, here’s the monthly difference in revenue :

  Before : $69,700
  After : $76,670

  In one year, you’ll make nearly $80,000 in additional revenue from funnel analysis alone.

  Different types of funnel analysis

  There are a few different types of funnel analysis.

  How you define success in your funnel all comes down to one of these four pillars.

  Depending on your goals, business and industry, you may want to assess the different funnel analyses at different times.

  1. Pageview funnel analysis

  Pageview funnel analysis is about understanding how well your website content is performing. 

  It helps you enhance user experience, making visitors stay longer on your site. By identifying poor performing pages (pages with high exit rates), you can pinpoint areas that need optimisation for better engagement.

  2. Conversion funnel analysis

  Next up, we’re looking at conversion funnel analysis.

  This type of funnel analysis is crucial for marketers aiming to turn website visitors into action-takers. This involves tracking and optimising conversion goals, such as signing up for newsletters, downloading ebooks, submitting forms or signing up for free trials. 

  The primary goal of conversion funnel analysis is to boost your website’s overall conversion rates.

  3. E-commerce funnel analysis

  For businesses selling products online, e-commerce funnel analysis is essential. 

  It involves measuring whether your products are being purchased and finding drop-off points in the purchasing process. 

  By optimising the e-commerce funnel, you can enhance revenue and improve the overall efficiency of your sales process.

  How to conduct funnel analysis

  Now that you understand what funnel analysis is, why it’s important, and the different types of analysis, it’s time to show you how to do it yourself.

  To get started with funnel analysis, you need to have the right web analytics solution.

  Here are the most common funnel analysis tools and methods you can use :

  1. Funnel analytics

  If you want to choose a single tool to conduct funnel analysis, it’s an all-in-one web analytics tool, like Matomo.

  

  With Matomo’s Funnel Analytics, you can dive into your whole funnel and analyse each step (and each step’s conversion rate).

  

  For instance, if you look at the example above, you can see the proceed rate at each funnel step before the conversion page.

  This means you can improve each proceed rate, to drive more traffic to your conversion page in order to increase conversion rates.

  In the above snapshot from Matomo, it shows visitors starting on the job board overview page, moving on to view specific job listings. The goal is to convert these visitors into job applicants.

  However, a significant issue arises at the job view stage, where 95% of visitors don’t proceed to job application. To increase conversions, we need to first concentrate on improving the job view page.

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  2. Heatmaps

  Heatmaps is a behaviour analytics tool that lets you see different visitor activities, including :

  • Mouse movement
  • How far down visitors scroll
  • Clicks

  You can see which elements were clicked on and which weren’t and how far people scroll down your page.

  

  A heatmap lets you see which parts of a page are getting the most attention and which parts go unnoticed by your users.

  For example, if, during your funnel analysis, you see that a lot of visitors are falling off after they land on the checkout page, then you might want to add a heatmap on your checkout page to see where and why people are exiting.

  3. Session recordings

  Want to see what individual users are doing and how they’re interacting with your site ?

  Then, you’ll want to check out session recordings.

  A session recording is a video playback of a visitor’s time on your website.

  

  It’s the most effective method to observe your visitors’ interactions with your site, eliminating uncertainty when identifying areas for funnel improvement.

  Session recordings instill confidence in your optimisation efforts by providing insights into why and where visitors may be dropping off in the funnel.

  4. A/B testing

  If you want to take the guesswork out of optimising your funnel and increasing your conversions, you need to start A/B testing.

  An A/B test is where you create two versions of a web page to determine which one converts better.

  

  For example, if your heatmaps and session recordings show that your users are dropping off near your call to action, it may be time to test a new version.

  You may find that by simply testing a different colour button, you may increase conversions by 20% or more.

  5. Form analytics

  Are you trying to get more leads to fill out forms on your site ?

  Well, Form Analytics can help you understand how your website visitors interact with your signup forms.

  You can view metrics such as starter rate, conversion rate, average hesitation time and average time spent.

  This information allows you to optimise your forms effectively, ultimately maximising your success.

  Let’s look at the performance of a form using Matomo’s Form Analytics feature below.

  

  In the Matomo example, our starter rate stands at a solid 60.1%, but there’s a significant drop to a submitter rate of 29.3%, resulting in a conversion rate of 16.3%.

  Looking closer, people are hesitating for about 16.2 seconds and taking nearly 1 minute 39 seconds on average to complete our form.

  This could indicate our form is confusing and requesting too much. Simplifying it could help increase sign-ups.

  See first-hand how Concrete CMS tripled their leads using Form Analytics in Matomo.

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

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  Start optimising your funnels with Matomo today

  If you want to optimise your business, you must optimise your funnels.

  Without information on what’s working and what’s not, you’ll never know if your website changes are making a difference.

  Worse yet, you could have underperforming stages in your funnel, but you won’t know unless you start looking.

  Funnel analysis changes that.

  By analysing your funnels regularly, you’ll be able to see where visitors are leaking out of your funnel. That way, you can get more visitors to convert without generating more traffic.

  If you want to improve conversions and grow revenue today, try Matomo’s Funnel Analytics feature.

  You’ll be able to see conversion rates, drop-offs, and fine-tuned details on each step of your funnel so you can turn more potential customers into paying customers.

  Additionally, Matomo comes equipped with features like heatmaps, session recordings, A/B testing, and form analytics to optimise your funnels with confidence.

  Try Matomo free for 21-days. No credit card required.

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