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• Conversion Rate Optimisation Statistics for 2024 and Beyond

  21 novembre 2023, par Erin — Analytics Tips

  Driving traffic to your website is only half the battle. The real challenge — once you’ve used a web analytics solution to understand how users behave — is turning more of those visitors into customers.

  That doesn’t happen by accident. You need to employ conversion rate optimisation strategies and tools to see even a small lift in conversion rates. The good news is that it doesn’t take much to see massive results. Raising your conversion rate from 1% to 3% can triple your revenue. 

  In even better news, you don’t have to guess at the best ways to improve your conversion rate. We’ve done the hard work and collected the most recent and relevant conversion rate optimisation statistics to help you. 

  General conversion rate optimisation statistics

  It appears the popularity of conversion rate optimisation is soaring. According to data collected by Google Trends, there were more people searching for the term “conversion rate optimization” in September 2023 than ever before. 

  As you can see from the chart below, the term’s popularity is on a clear upward trajectory, meaning even more people could be searching for it in the near future. (Source)

  

  Do you want to know what the average landing page conversion rate is ? According to research by WordStream, the average website conversion rate across all industries is 2.35%. 

  That doesn’t paint the whole picture, however. Better-performing websites have significantly higher conversion rates. The top 25% of websites across all industries convert at a rate of 5.31% or higher. (Source)

  Let’s break things down by industry now. The Unbounce Conversion Benchmark Report offers a detailed analysis of how landing pages convert across various industries.

  First, we have the Finance and Insurance industry, which boasts a conversion rate of 15.6%. 

  On the other end, agencies appears to be one of the worst-performing. Agencies’ landing pages convert at a rate of 8.8%. (Source)

  

  What about the size of the conversion rate optimisation industry ? Given the growth in popularity of the term in Google, surely the industry is experiencing growth, right ?

  You’d be correct in that assumption. The conversion rate optimisation software market was valued at $771.2 million in 2018 and is projected to reach $1.932 billion by 2026 — a compound annual growth rate (CAGR) of 9.6%.

  Statistics on the importance of conversion rate optimisation

  If you’re reading this article, you probably think conversion rate optimisation is pretty important. But do you know its importance and where it ranks in your competitors’ priorities ? Read on to find out. 

  Bounce rate — the number of people who leave your website without visiting another page or taking action — is the scourge of conversion rate optimisation efforts. Every time someone bounces from your site, you lose the chance to convert them.

  The questions, then, are : how often do people bounce on average and how does your bounce rate compare ? 

  Siege Media analysed over 1.3 billion sessions from a range of traffic sources, including 700 million bounces, to calculate an average bounce rate of 50.9%. (Source)

  

  Bounce rates vary massively from website to website and industry to industry, however. Siege Media’s study unveils an array of average bounce rates across industries :

  • Travel – 82.58%
  • B2B – 65.17%
  • Lifestyle – 64.26%
  • Business and Finance – 63.51%
  • Healthcare – 59.50%
  • eCommerce – 54.54%
  • Insurance – 45.96%
  • Real Estate – 40.78%

  It won’t come as much of a surprise to learn that marketers are determined to reduce bounce rates and improve lead conversion. Today’s marketers are highly performance-based. When asked about their priorities for the coming year, 79% of marketers said their priority was generating quality qualified leads — the most popular answer in the survey. (Source)

  Just because it is a priority for marketers doesn’t mean that everyone has their stuff together. If you have a conversion rate optimisation process in place, you’re in the minority. According to research by HubSpot, less than one in five marketers (17%) use landing page A/B tests to improve their conversion rates. (Source)

  When it comes to personalisation strategies – a common and effective tool to increase conversion rates — the picture isn’t any rosier. Research by Salesforce found just over one-quarter of markets are confident their organisation has a successful strategy for personalisation. (Source)

  Conversion rate optimisation tactics statistics

  There are hundreds of ways to improve your website’s conversion rates. From changing the color of buttons to the structure of your landing page to your entire conversion funnel, in this section, we’ll look at the most important statistics you need to know when choosing tactics and building your own CRO experiments. 

  If you are looking for the best method to convert visitors, then email lead generation forms are the way to go, according to HubSpot. This inoffensive and low-barrier data collection method boasts a 15% conversion rate, according to the marketing automation company’s research. (Source)

  Where possible, make your call-to-actions personalised. Marketing personalisation, whether through behavioral segmentation or another strategy, is an incredibly powerful way of showing users that you care about their specific needs. It’s no great surprise, then, that HubSpot found personalised calls-to-actions perform a whopping 202% better than basic CTAs. (Source)

  If you want to boost conversion rates, then it’s just as important to focus on quantity as well as quality. Yes, a great-looking, well-written landing page will go a long way to improving your conversion rate, but having a dozen of these pages will do even more. 

  Research by HubSpot found companies see a 55% increase in leads when they increase the number of landing pages from 10 to 15. What’s more, companies with over 40 landing pages increase conversion by more than 500%. (Source)

  

  User-generated content (UGC) should also be high on your priority list to boost conversion rates. Several statistics show how powerful, impactful and persuasive social proof like user reviews can be. 

  Research shows that visitors who scroll to the point where they encounter user-generated content increase the likelihood they convert by a staggering 102.4%. (Source)

  Other trust signs can be just as impactful. Research by Trustpilot found that the following four trust signals make consumers more likely to make a purchase when shown on a product page :

  • Positive star rating and reviews (85% more likely to make a purchase)
  • Positive star rating (78%)
  • Positive customer testimonials (82%)
  • Approved or authorised seller badge (76%)

  (Source)

  Showing ratings and reviews has also increased conversion rates by 38% on home appliances and electronics stores. (Source)

  And no wonder, given that consumers are more likely to buy from brands they trust than brands they love, according to the 2021 Edelman Trust Barometer Special Report. (Source) 

  A lack of trust is also one of the top four reasons consumers abandon their shopping cart at checkout. (Source) 

  Traffic source conversion rate statistics

  What type of traffic works the best when it comes to conversions, or how often you should be signing up users to your mailing list ? Let’s look at the stats to find out. 

  Email opt-ins are one of the most popular methods for collecting customer information — and an area where digital marketers spend a lot of time and effort when it comes to conversion rate optimisation. So, what is the average conversion rate of an email opt-in box ?

  According to research by Sumo — based on 3.2 billion users who have seen their opt-in boxes — the average email opt-in rate is 1.95%. (Source)

  Search advertising is an effective way of driving website traffic, but how often do those users click on these ads ?

  WordStream’s research puts the average conversion of search advertising for all industries at 6.11%. (Source)

  The arts and entertainment industry enjoys the highest clickthrough rates (11.78%), followed by sports and recreation (10.53%) and travel (10.03%). Legal services and the home improvement industry have the lowest clickthrough rates at 4.76% and 4.8%, respectively.

  

  If you’re spending money on Google ads, then you’d better hope a significant amount of users convert after clicking them. 

  Unfortunately, conversion rates from Google ads decreased year-on-year for most industries in 2023, according to research by WordStream — in some cases, those decreases were significant. The only two industries that didn’t see a decrease in conversion rates were beauty and personal care and education and instruction. (Source)

  The average conversion rate for search ads across all industries is 7.04%. The animal and pet niche has the highest conversion rate (13.41%), while apparel, fashion and jewelry have the lowest conversion rate (1.57%). (Source)

  What about other forms of traffic ? Well, there’s good reason to try running interstitial ads on smartphone apps if you aren’t already. Ads on the iOS app see a 14.3 percent conversion rate on average. (Source)

  E-commerce conversion rate optimisation statistics (400 words)

  Conversion rate optimisation can be the difference between a store that sets new annual sales records and one struggling to get by. 

  The good news is that the conversion rate among US shoppers was the highest it’s ever been in 2021, with users converting at 2.6%. (Source)

  If you have a Shopify store, then you may find conversion rates a little lower. A survey by Littledata found the average conversion rate for Shopify was 1.4% in September 2022. (Source)

  What about specific e-commerce categories ? According to data provided by Dynamic Yield, the consumer goods category converted at the highest rate in September 2023 (4.22%), a spike of 0.34% from August. 

  Generally, the food and beverage niche boasts the highest conversion rate (4.87%), and the home and furniture niche has the lowest conversion rate (1.44%). (Source)

  If you’re serious about driving sales, don’t focus on mobile devices at the expense of consumers who shop on desktop devices. The conversion rate among US shoppers tends to be higher for desktop users than for mobile users. 

  

  In the second quarter of 2022, for instance, desktop shoppers converted at a rate of 3% on average compared to smartphone users who converted at an average rate of 2%. (Source)

  Increase your conversions with Matomo

  Conversion rate optimisation can help you grow your subscriber list, build your customer base and increase your revenue. Now, it’s time to put what you’ve learned into practice.

  Use the advice above to guide your experiments and track everything with Matomo. Achieve unparalleled data accuracy while harnessing an all-in-one solution packed with essential conversion optimisation features, including Heatmaps, Session Recordings and A/B Testing. Matomo makes it easier than ever to analyse conversion-focused experiments.

  Get more from your conversion rate optimisations by trying Matomo free for 21 days. No credit card required.

• Visualizing Call Graphs Using Gephi

  1er septembre 2014, par Multimedia Mike — General

  When I was at university studying computer science, I took a basic chemistry course. During an accompanying lab, the teaching assistant chatted me up and asked about my major. He then said, “Computer science ? Well, that’s just typing stuff, right ?”

  My impulsive retort : “Sure, and chemistry is just about mixing together liquids and coming up with different colored liquids, as seen on the cover of my high school chemistry textbook, right ?”

  
  
  

  In fact, pure computer science has precious little to do with typing (as is joked in CS circles, computer science is about computers in the same way that astronomy is about telescopes). However, people who study computer science often pursue careers as programmers, or to put it in fancier professional language, software engineers.

  So, what’s a software engineer’s job ? Isn’t it just typing ? That’s where I’ve been going with this overly long setup. After thinking about it for long enough, I like to say that a software engineer’s trade is managing complexity.

  A few years ago, I discovered Gephi, an open source tool for graph and data visualization. It looked neat but I didn’t have much use for it at the time. Recently, however, I was trying to get a better handle on a large codebase. I.e., I was trying to manage the project’s complexity. And then I thought of Gephi again.

  Prior Work
  One way to get a grip on a large C codebase is to instrument it for profiling and extract details from the profiler. On Linux systems, this means compiling and linking the code using the -pg flag. After running the executable, there will be a gmon.out file which is post-processed using the gprof command.

  GNU software development tools have a reputation for being rather powerful and flexible, but also extremely raw. This first hit home when I was learning how to use the GNU tool for code coverage — gcov — and the way it outputs very raw data that you need to massage with other tools in order to get really useful intelligence.

  And so it is with gprof output. The output gives you a list of functions sorted by the amount of processing time spent in each. Then it gives you a flattened call tree. This is arranged as “during the profiled executions, function c was called by functions a and b and called functions d, e, and f ; function d was called by function c and called functions g and h”.

  How can this call tree data be represented in a more instructive manner that is easier to navigate ? My first impulse (and I don’t think I’m alone in this) is to convert the gprof call tree into a representation suitable for interpretation by Graphviz. Unfortunately, doing so tends to generate some enormous and unwieldy static images.

  Feeding gprof Data To Gephi
  I learned of Gephi a few years ago and recalled it when I developed an interest in gaining better perspective on a large base of alien C code. To understand what this codebase is doing for a particular use case, instrument it with gprof, gather execution data, and then study the code paths.

  How could I feed the gprof data into Gephi ? Gephi supports numerous graphing formats including an XML-based format named GEXF.

  Thus, the challenge becomes converting gprof output to GEXF.

  Which I did.

  Demonstration
  I have been absent from FFmpeg development for a long time, which is a pity because a lot of interesting development has occurred over the last 2-3 years after a troubling period of stagnation. I know that 2 big video codec developments have been HEVC (next in the line of MPEG codecs) and VP9 (heir to VP8’s throne). FFmpeg implements them both now.

  I decided I wanted to study the code flow of VP9. So I got the latest FFmpeg code from git and built it using the options "--extra-cflags=-pg --extra-ldflags=-pg". Annoyingly, I also needed to specify "--disable-asm" because gcc complains of some register allocation snafus when compiling inline ASM in profiling mode (and this is on x86_64). No matter ; ASM isn’t necessary for understanding overall code flow.

  After compiling, the binary ‘ffmpeg_g’ will have symbols and be instrumented for profiling. I grabbed a sample from this VP9 test vector set and went to work.

  ./ffmpeg_g -i vp90-2-00-quantizer-00.webm -f null /dev/null
  gprof ./ffmpeg_g > vp9decode.txt
  convert-gprof-to-gexf.py vp9decode.txt > /bigdisk/vp9decode.gexf
  

  Gephi loads vp9decode.gexf with no problem. Using Gephi, however, can be a bit challenging if one is not versed in any data exploration jargon. I recommend this Gephi getting starting guide in slide deck form. Here’s what the default graph looks like :

  
  
  

  Not very pretty or helpful. BTW, that beefy arrow running from mid-top to lower-right is the call from decode_coeffs_b -> iwht_iwht_4x4_add_c. There were 18774 from the former to the latter in this execution. Right now, the edge thicknesses correlate to number of calls between the nodes, which I’m not sure is the best representation.

  Following the tutorial slide deck, I at least learned how to enable the node labels (function symbols in this case) and apply a layout algorithm. The tutorial shows the force atlas layout. Here’s what the node neighborhood looks like for probing file type :

  
  
  

  Okay, so that’s not especially surprising– avprobe_input_format3 calls all of the *_probe functions in order to automatically determine input type. Let’s find that decode_coeffs_b function and see what its neighborhood looks like :

  
  
  

  That’s not very useful. Perhaps another algorithm might help. I select the Fruchterman–Reingold algorithm instead and get a slightly more coherent representation of the decoding node neighborhood :

  
  
  

  Further Work
  Obviously, I’m just getting started with this data exploration topic. One thing I would really appreciate in such a tool is the ability to interactively travel the graph since that’s what I’m really hoping to get out of this experiment– watching the code flows.

  Perhaps someone else can find better use cases for visualizing call graph data. Thus, I have published the source code for this tool at Github.

• Generating test data – Introducing the Piwik Platform

  9 octobre 2014, par Thomas Steur — Development

  This is the next post of our blog series where we introduce the capabilities of the Piwik platform (our previous post was How to create a command). This time you’ll learn how to generate test data.

  Developers are developing on their local Piwik instance which usually does not contain useful data compared to a real Piwik installation in production (only a few test visits and a few tests users and websites). The ‘VisitorGenerator’ plugin lets you generate any number of visits, websites, users, goals and more. The generator makes sure there will be data for each report so you can easily test anything.

  Getting started

  In this series of posts, we assume that you have already installed Piwik. If not, visit the Piwik Developer Zone where you’ll find the Installation guide for developers.

  Installing the VisitorGenerator plugin

  The easiest way to install the plugin is by using the Marketplace in Piwik itself. It is accessible via Settings => Marketplace => Get new functionality. There you’ll find the plugin “VisitorGenerator” which you can install and activate in one click.

  If your Piwik instance is not connected to the internet you can download the plugin from the VisitorGenerator page on the Marketplace. Afterwards you can install the plugin by going to Settings => Marketplace => Uploading a plugin and uploading the previously downloaded ZIP file.

  If you have already installed the plugin make sure it is activated by going to Settings => Plugins.

  Generating websites

  After you have installed the plugin you can add as many websites as you need. This is useful for instance when you want to test something that affects many websites such as the ‘All Websites’ dashboard or the Websites manager. To generate any number of websites use the following command :

  ./console visitorgenerator:generate-website --limit=10

  This will generate 10 websites. If you need more websites simply specify a higher limit. In case you are wondering the names and URLs of the websites are randomly generated by the Faker PHP library.

  Generating goals

  In case you want to test anything related to Goals you should execute the following command :

  ./console visitorgenerator:generate-goals --idsite=1

  This will generate a few goals for the specified site. The generated goals are defined in a way to make sure there will be conversions when generating the visits in the next step.

  Generating visits

  To generate visits there are two possibilities. Either via the Piwik UI by going to Settings => Visitor Generator or by using the command line. The UI is a bit limited in generating visits so we recommend to use the command line. There you can generate visits as follows :

  ./console visitorgenerator:generate-visits --idsite=1

  This will generate many different visits for the current day. Don’t worry if it takes a while, it will insert quite a few visits by default.

  In case you want to generate visits for multiple days in the past as well you can specify the --days option.

  ./console visitorgenerator:generate-visits --idsite=1 --days=5

  Providing your own logs

  Half of the generated visits are randomly generated and half of the visits are based on real logs to make sure there is data for each report. If you want to generate visits based on your own logs for a more realistic testing just place your log files in the plugins/VisitorGenerator/data folder and make sure the file name ends with .log. You can find a few examples in the VisitorGenerator data folder.

  To generate visits based only on real log files then use the --no-fake option.

  ./console visitorgenerator:generate-visits --idsite=1 --no-fake

  All generated visits will come from the logs and no random visits nor random fake data will be used.

  Advanced features

  We are regularly adding new commands, tools and runtime checks to make your life as a developer easier. For instance you can also generate users and annotations. In the future we want to extend the plugin to create visits in the background to make sure there will be constantly new actions in the real time report.

  Are you missing any kind of generator or any other feature to make your life as a developer easier ? Let us know by email, we are listening !

  Would you like to know more about the Piwik platform ? Go to our Piwik Developer Zone where you’ll find guides and references on how to develop plugin and themes.