In this model, onboarding prompts (such as tool-tips, pop-ups, or emails) are shown or sent based on how long it has been since the user signed up. This strategy for onboarding is most commonly used for onboarding emails. For instance, an enterprise SaaS business may have a sequence of onboarding emails. The first email is sent as soon as the user signs up. The subsequent emails are then sent on the second day after they signed up, the fifth day after they signed up, and so on.

The key advantage of this strategy is that it ensures that you stay in contact with all your sign-ups - including those who did not initially engage with your app. This strategy is also easy to scale, so you can use it whether you have 10 users or 10,000 users. However, the big disadvantage of this strategy is the inability to personalize the onboarding flow for each user. Since all users get the same messages on the same day, regardless of their activity, there is no option to customize. This inevitably results in some users receiving irrelevant emails. After all, the eighth email in an eight-email sequence is unlikely to be useful for a user who signed up and then never touched the app.

Action-Based Onboarding

This model involves sending onboarding messages to your users based on the actions they have (or have not) taken in your software. This could be via email, or in-app. The advantage of action-based onboarding is the increase personalization which is possible. By triggering onboarding messages with specific actions, the messages your users receive are far more likely to be of value to them. This is likely to make your users more likely to read the messages, and to increase retention! This also allows you to contact lapsed users via email with messages specifically designed to re-engage them. This method tends to be more effective than time-based onboarding for precisely this reason. It's far preferable to send your users relevant messages that they will actually read.

A customer's general satisfaction is their satisfaction with your brand or company as a whole. This is also known as their relational satisfaction, as it refers to a customer's overall relationship with your brand. This is the measure that the American Customer Satisfaction Index (ACSI) uses in their annual reviews. The general Customer Satisfaction question is a good customer feedback survey question because it measures someone's overarching attitude towards your brand, rather than their specific experiences with a product or service. In some ways, this question is similar to the NPS question, since it is attitudinal rather than specific. Your general satisfaction score gives you an idea of where you sit, which provides a good benchmark for more specific measures.

Product/service satisfaction

This measures a customer's satisfaction with a specific product or service. For example, if the general customer satisfaction measures somebody's satisfaction with Apple, this question measures their satisfaction with the iPhone. This is the first step in "drilling down" into a general satisfaction measure. Measuring how satisfied customers are with individual products means you can compare across products. It also allows you to identify whether certain products have a significantly lower satisfaction rating than others or the brand overall. This is also a good place to identify services which may need improvement, such as a website or customer support. This is known as transactional satisfaction, as the sentiment measured here is related to a specific transaction or experience a customer has recently had.

Attribute satisfaction

This question gets right down to the nitty-gritty details and asks about the customer's satisfaction with particular features (attributes) of a certain product. In the Apple example from earlier, this question would ask about satisfaction with the iPhone's screen, battery life, or audio quality (for instance). This is the most granular of these three measures. This question allows you to drill down even further into your customer satisfaction ratings.

Why do we need all three?

Asking about just one type of customer satisfaction could tell you something about how satisfied your customers are. However, what it can't tell you is why they are or are not satisfied, and what you should do to improve. This is where combining these three types of questions comes in handy! Gathering data about customer satisfaction throughout different levels will point you to what is making your customers dissatisfied. This data will allow you to conduct a driver analysis.

How to do a Driver Analysis?

Tutorial: CSAT Driver Analysis in Displayr

The welcome email is a perfect opportunity to start personalizing. Something as simple as using the person's first name in the subject line can have huge effects. It's been shown that personalized emails have a 26% higher open rate than other marketing emails!

However, that's just the tip of the iceberg when it comes to using email for personalized onboarding. An even more effective strategy is to use the information you have about your users - such as their location, company, or job title - to segment your onboarding process. After all, people in different roles will want to achieve different things with your software. The sooner you can show your users how to achieve what they want to achieve, the better! For example, if you're making a product that might be used by people in a variety of job roles, customizing onboarding messages based on job title could be highly effective. This would increase the likelihood that onboarding material will be relevant for the user, making them more likely to read it and take action.

Subsequent onboarding emails can also be personalized based on certain actions that a user has or has not taking in your software. This is known as action-based onboarding. This allows you to nudge users who have not yet taken key actions to complete those actions, while users who have can be shown additional features to improve their experience.

Personalized onboarding journeys

Of course, emails are not the only way you'll be onboarding your new users. The in-app onboarding process is arguably the most important way for your users to get to grips with using your software. This, too, can benefit from personalization. In particular, many SaaS products are complex, and there are any number of features you could choose to introduce to new users. A great idea in this situation can be to allow your users to choose how much detail they want in their onboarding. Offering different onboarding streams, such as "beginner" or "expert", allows you to tailor the process based on what users want to get out of it.

If you have more data about your users, you can tailor their experience even further, rather than presenting generic welcome screens and introducing features. If you know why people have come to your product, you can get them started right away with whatever their specific needs are. For instance, Displayr solves a huge variety of problems. These range from creating basic charts through to doing advanced data analysis. If somebody clicks through to the product from a page about creating bar charts, they probably have different needs from somebody who came from a page about choice-based conjoint analysis. Based on this information, we can customize the first experience these two users have. This makes the experience of the product far better for the user.

So, personalized onboarding makes it easier for your users to see the value in your product, creates a smoother onboarding journey, and leads to higher retention rates. If you aren't personalizing your user onboarding, you and your users are missing out!

Use template

Use template

If you want to get started right away, click the button above - there are instructions in the template to help you set it up! If you want a little more guidance, keep reading.

Formatting and adding your data

To use your email data with the template, your data needs to be in an Excel spreadsheet. Each row represents an onboarding email sent to a user. The columns of your spreadsheet should contain the following information:

1. The email address or user ID of the person the email was sent to
2. The title/subject of the email
3. The date the email was sent
4. The date the email was opening
5. The date a link within the email was clicked

Once you have your data in the correct format, add it to the template by clicking the + button next to Data Sets on the left of the screen. (These instructions are in the template as well, so don't worry if you don't remember all this!)

The free version of Displayr supports data with up to 1000 rows. If you have more data than this, fear not - you can still use the template! Just follow the prompts to sign up for a free trial.

Connecting your data

Once you've got your data uploaded, it's time to hook it up to the data! You can do this on the third page of the template by selecting the correct variables on the left side of the page (see below).

You can choose what dates and emails you want to include, as well as whether you want to see the data by week, by month, or by quarter.

This controls all the visualizations in the dashboard, so you don't have to update every individual chart if you want to change anything - just change this page and all the other pages will update automatically! This makes it easy to view your data at different time scales, update your data if needed, or view different campaigns.

Publishing your dashboard

Once your dashboard is all set up, it's time to export it! To publish your dashboard as a web page, go to Export in the ribbon and click Web Page. This will create your published dashboard. You can share this link with anyone you like, and they can navigate through the dashboard. If you need to go back and change anything, you can just click Embed > Update All and your published dashboard will update.

Handy hint: exporting data from Intercom

Here at Displayr, we use Intercom to manage our email campaigns. Here's how we export our email data in the correct format for this template:

1. Log in to Intercom.
2. Select Messages > Report.
3. Scroll down and select Export message data.
4. Choose the time frame when you'd like to see your data.
5. Click Apply.
6. Click Export.
7. Wait for the email from Intercom to show up, then click Download. 

Once you have your data as a spreadsheet, you can cut and paste it into the format specified above. Then just follow the steps to add it to the template!

Use template

Perhaps the worst mistake you can make in your onboarding process is to not do enough to onboard your users. The worst possible outcome is the dreaded Blank Screen Syndrome, where your users end up staring at an empty page with no idea what to do next. If a significant number of users are exiting your software without taking any action, or without taking any meaningful action, you're probably suffering from Blank Screen Syndrome. This means that you need to give your users more indication of what they should do next, rather than abandoning them in the deep end.

Back-seat driving

Almost as fatal a mistake as not providing enough support to new users is providing way too much. Everyone has experienced an over-onboarding nightmare - unskippable tours, six tooltips on one screen, or a tutorial about features we're not interested in. It can even be something as simple as explaining features that should be obvious - you probably don't need to tell your users that the big red button that says "SAVE" lets them save their work.

This is a trap that's very easy to fall into - particularly if you have previously struggled with Blank Screen Syndrome. The problem is that this robs your users of any autonomy in exploring your software. Overly long tours and tutorials force them to wait before they can actually start using your software, and too many tooltips distract from the interface that you've worked so hard on. The key is to find the middle ground. Focus on keeping your onboarding integrated, not distracting, and making sure it guides users towards success without being overly prescriptive.

Overpromising

If you're going to make promises to potential users to convince them to start using your software, you'd better make sure you can deliver on them. Saying that your software can solve a certain problem when it can't is a very quick way to chase away new users who wanted to solve that problem. Similarly, telling people that your signup process will take 2 minutes, when it's really more like 10, increases the chance of your users becoming frustrated and giving up. It's all about managing expectations. Being honest with your users about your signup process and the capabilities of your software will lead to a better experience for them and a higher retention rate for you.

Not measuring your onboarding properly

It's crucial to measure your onboarding in a way that lets you see whether or not it's working. After all, how will you improve your onboarding process if you don't know what the pain points are? You can do this by tracking user actions within the software, monitoring your support channels for common issues, or surveying your users specifically about the onboarding process. A combination of these approaches should give you a good idea of how you can make your onboarding better.

Abandoning users after they've "completed" onboarding

Congratulations, your user has achieved their first small success! This is where your job ends, right? That user may have technically completed their onboarding process, but staying in communication with them will help them on their journey to becoming a power user. Keeping in touch via email is a great way to stay in their mind and encourage them to discover new problems they can solve with your product. Letting users know where they can find any in-app tutorials is also a great way to avoid leaving them stranded. This lets them go back later and remind themselves of anything they've forgotten, and independently develop their skills.

A common strategy for software products is to include part or all of the onboarding process within the software. This strategy has the advantage of keeping the whole process within the app, rather than relying on external channels. This means that users are learning about your software during their first time using it, making the onboarding process more streamlined and keeping them in the software. A good in-app onboarding process seamlessly teaches your users how to use your software, without them even realizing it.

There are many ways you can implement in-app onboarding. Some of the most common are tours, tooltips, and bots. These methods all have strengths and weaknesses, which can be anywhere from amazing to catastrophic. It all depends on how you use them!

• Product tours are a great way to give your user a broad overview of how everything works. They allow your user to already know where to find core features before they've even started using the software. Tours can take the form of introductory screens, text pop-ups, or videos. However, they also come with a big caveat. If your tours are too long, and there's no option for the user to skip them, they'll probably do more harm than good! You can have too much of a good thing, and many users will give up rather than sit through an unskippable, detailed tour.
• Tooltips allow you to highlight key features and add extra information. Unlike a tour, which shows the user these features in isolation, tooltips let users discover them within the context of using the app. They can be less obtrusive than product tours and can help users learn better by allowing them to learn while doing. However, like tours, they should also be used in moderation. Having multiple tooltips on the same screen, or having too many, will annoy your user rather than educating them.
• Bots in onboarding got off to a rough start with Clippy, the infamously annoying Microsoft Office Assistant. Paperclips notwithstanding, this can be a helpful way to help out your users during their onboarding process. Well-designed bots can provide your users with answers to common questions they may have, lowering support costs and allowing them to solve their problems faster. But a word of warning: bot designers should learn from Clippy's downfall. What's super useful for a user the first time they do something becomes infuriating if you're still offering to help on the twentieth time!

Beware of bots who won't shut up!

Using content for onboarding

In-app onboarding is a great way for users to find their way around your software. However, what if they want to go back weeks or months later and find a tip they saw during their onboarding process? It could be difficult for them to remember where it was or how to access it. A great way to get around this is to create content that will provide users with tips and guidance in using your product. This could take the form of blogs, videos, tutorials, infographics, or any other kind of content you want to create. This allows new users to go through and find tips at their own pace, as well as meaning seasoned users know where to come back and find specific help.

Having said that, don't confuse a blog for a complete onboarding strategy. It's definitely a super helpful resource for your users. However, it's also far too much effort to expect new users to trawl through your blog before they use your software for the first time. A blog or video series can't replace other onboarding strategies; what it can do is provide a great backup and give your users the autonomy to solve problems on their own.

Demos and training

If you're running a smaller business, or you have a particularly valuable prospect, one-on-one demos or training are a way to provide a truly phenomenal onboarding experience. Giving your users a real person to talk to and who is an expert in your software is an onboarding experience that just can't be replicated by an automated onboarding sequence. There are obvious drawbacks to this method. It's expensive, it's not scalable, and it's impractical once your business grows beyond a certain size. However, if you really want to close a particular sale, this is a great tactic.

Welcoming your new users

The first email you send as soon as someone signs up - the welcome email - is among the most important emails you will send. Welcome emails typically have significantly higher open rates than other marketing emails, so this is by far your best shot at getting your new users in. A good welcome email should do three things:

1. Warmly welcome your new user to your community - make them feel as though you care
2. Remind them why they signed up - let them know about the amazing benefits that await them
3. Clearly tell them what they should do next and how to do it, including a single, simple call to action

The email onboarding process

After your welcome email, you need to stay in contact with your new users. This is true of those who have embraced your product and are becoming power users, as well as those who seem to have fallen by the wayside. Your onboarding sequence can be either time-based or action based. That is, you can send your emails based on how long it's been since a user signed up, or they can be triggered based on certain actions that users have or have not taken in the software. Time-based onboarding is much simpler to set up. However, you can use action-based onboarding to tailor and personalize a user's experience.

Your onboarding email sequence will largely depend on your product and your business model. A free social media platform will send very different onboarding emails to an enterprise analytics software with a free trial period. However, these emails will typically introduce users to the product and provide tips on how they can get the best value out of it. A good rule of thumb is that you should only introduce one tip or feature per email. Otherwise, you risk confusing or boring your users.

In order to send a good onboarding sequence, you need to figure out the action that users need to take in order to see the value of your product. All your onboarding emails should direct them to this goal. Doing this will help your users to be successful in the most straightforward way possible and lead to a higher rate of successful onboarding.

Dos and don'ts for email onboarding

• Do send emails regularly. This helps to remind your users that you exist, and of the benefits that they're missing out on if they're not using your software!
• Do make sure you provide value in your emails. Show your users how to use your product, share tips and tricks, and make their path to success as smooth as possible.
• Do keep your focus on the user. Emphasize all the awesome benefits that your product will bring them.
• Do personalize as much as possible. Show your user that they're not just a faceless number in a database. Personalized onboarding emails, especially based on user needs or actions, are far more effective at guiding your users to that critical first success.
• Don't spam your users. Sending streams of generic marketing copy doesn't help your users achieve success, and does more harm than good.
• Don't use your onboarding emails to talk up how amazing all the features of your product are. Knowing about cool features without knowing about the core workflow doesn't help your users be successful.
• Don't be inconsistent in your emails. This will leave your customers not knowing what to expect and lead to lower open rates.
• Don't confuse your users. Onboarding emails are supposed to help them use your software!

Measuring your onboarding emails

Now that you've put all this work into crafting the perfect email onboarding campaign, you need to know whether it's working! Metrics which are useful to measure include number of emails sent, open rates, click rates, and campaign engagement. Tracking these metrics over time will give you a sense of how successfully your emails are engaging your users.

Have your email data but can't find the best way to visualize it? This free template is a great way to get started. It shows emails sent, opened, and clicked, as well as your overall engagement rates. All you have to do is add your data and the visualizations will update automatically!

In order to dive into the mysterious world of SaaS onboarding, we need to understand why customer onboarding is important for SaaS in particular. Software as a service - commonly known as SaaS - is a model of software delivery where users pay a recurring subscription to access software hosted on the internet. This is opposed to models where users purchase a license and install the software on their own computer or server. As a result, SaaS typically has substantially lower upfront costs for the user. SaaS subscriptions are most commonly monthly or annual, and typically include a certain amount of usage, as well as support.

What is customer onboarding?

Customer onboarding is the process of introducing new customers, or potential new customers, to a product. In particular, it involves gradually introducing new users to key features and guiding them through the process of using the product. The aim is to enable new users to be successful in using the software, as successful users are more likely to purchase a subscription after a free trial or renew their subscription in the future.

The reason customer onboarding is so crucial for SaaS is that the entire SaaS business model depends on customers renewing their subscriptions. SaaS businesses don't just have to convince people to buy their product - they have to convince them to keep paying for it over a long period of time. This means that the job is not done once a customer has bought the product. This is especially true for products which offer a free trial. In this scenario, you only have 30 or 14 or 7 days to convince your triallist that your product is so amazing that they should pay for it. And the stats are sobering - 40-60% of users who download a free trial will use it once and never return. To avoid becoming part of this statistic, it's crucial to onboard customers rather than throwing them in the deep end and hoping they learn to swim.

Strategies for customer onboarding

The strategies that SaaS businesses use to onboard their customers vary widely. Common strategies for onboarding include:

• Email onboarding. This typically consists of a sequence of emails to users who have recently signed up, beginning with a "welcome" email immediately after they sign up. These emails are intended to introduce users to features within the software and to show them how they can achieve their goals within the software. They can be sent at predetermined time intervals after a user signs up, or they can be triggered by the user taking certain actions within the software.
• In-app onboarding. This can consist of welcome screens, pop-ups, tutorials, tool-tips, or any other feature which is integrated into the interface of the software. This is intended to guide the user through exploring the software. This strategy has the advantage of allowing users to do all their onboarding in one place (the product), and if done well, users will start using the product as part of the onboarding process. An great example of in-app onboarding is Slack, which onboards users by having them use Slack to chat to "slackbot", guiding them through the set-up process.
• Content. Creating blogs and videos that walk your users through how to use your product allows them to take a "learn at your own pace" approach. This kind of "on demand" learning is a great resource for your users, but is best paired with a more high-touch strategy such as emails or in-app onboarding. Otherwise, you can bet that most your users will give up rather than search through your blog with no guidance.
• In-person training. For smaller businesses, or for exceptionally valuable prospects, in-person training is a great way to show your new users the value of your product. This allows new users to connect with a real person, have all their questions answered, and see for themselves what is possible with the software.

Measuring onboarding

So, it's pretty easy to see why onboarding new users is so important. What can be trickier is knowing whether you're doing a good job. Many companies ask for feedback specifically about their onboarding process. An obvious metric of whether your onboarding process is successful is the number of users who convert to paying customers at the end of their trial. However, this by itself doesn't tell you a lot about what makes your process successful or unsuccessful. Some other useful metrics for measuring onboarding success are:

• User metrics - tracking the number of users currently onboarding and the number of active users will give a "headcount" view of user onboarding
• Session metrics - tracking the number of sessions per user and session duration can indicate whether users are engaged with the onboarding process. If users keep coming back to your product or are spending long periods in the product, you're probably doing well! Conversely, if users have multiple sessions on the same step of your onboarding process or the same section of your product, they may be struggling to figure it out.
• Event metrics - this metric involves tracking the number of times users take specific actions within your product. This is one of the best ways to measure whether users have been successfully onboarded.

It can be useful to visualize these measurements using techniques such as heatmaps, funnel charts, or time series modeling.

The Customer Satisfaction Score is the most straightforward measure of customer satisfaction. Customer satisfaction is calculated by asking customers a question, along the lines of "How satisfied were you with your experience today?" Responses are collected on a scale, usually 1-3, 1-5 or 1-10. The final CSAT score can be calculated using several different methods. If used correctly, collecting your customer satisfaction can help you identify key areas where your customers are less than satisfied. From here, you can make improvements accordingly. This is key to long-term customer retention and ensuring that your customers are happy with your service.

In order to get the best possible insights from your customer feedback survey, it's important to understand what it does and doesn't measure. Because of the nature of the customer satisfaction question - which asks about immediate satisfaction - responses are likely to indicate sentiment towards the customer's most recent interaction with your brand or service. This is not necessarily a disadvantage, as it means you can gauge sentiment towards individual touch points throughout the customer life cycle. However, it also means that customer satisfaction cannot generally be used to infer broader attitudes toward your brand or the customer's experience.

How should I use Customer Satisfaction?

A key advantage of customer satisfaction is its simplicity. The rating scale can be modified to suit your particular business needs, as can the question. For instance, you can ask how satisfied a customer was with their overall experience, or you can ask them about something more specific. For instance, a hotel might ask their customers how satisfied they were with their stay. However, they could also ask how satisfied they were with their room, the food, the spa, or the service. These more granular questions help to reveal if there are any specific aspects which are less satisfying than the others. For this reason, it's advantageous to ask customers to rate their overall satisfaction, as well as their satisfaction with multiple attributes. Otherwise, you will not know what changes you should make to increase customer satisfaction.

This illustrates how one of customer satisfaction's big advantages - its simplicity - is also one of its big disadvantages. In isolation, one customer satisfaction score tells you very little about how you're doing. It is most useful when you can differentiate between different aspects of your product or service, thus allowing you to make improvements. It can also be useful to track your customer satisfaction score over time, or against industry benchmarks. Customer satisfaction is ultimately most useful as a comparative measure. When used well, it allows you to use your customers' feedback to keep them coming back for more!

Tutorial: Measure Customer Satisfaction in Displayr

Get started

You can import your data directly from SurveyMonkey by clicking New Dataset in Displayr. This means you can start analyzing your data right away without needing to go through any other steps.

With the joint capabilities of SurveyMonkey and Displayr, you will be able to dig deeper into your data and share the insights you've uncovered from your survey responses. You can build automated reports, create charts and tables, and perform advanced statistical analysis - all for free.

Easily import your SurveyMonkey surveys into Displayr

You can import your data directly from SurveyMonkey by clicking New Dataset in Displayr. This means you can start analyzing your data right away without needing to go through any other steps.

Identify statistically interesting results

Displayr automatically highlights significant results in tables and charts with colors and arrows, meaning you can see what's going on at a glance.

Visualize your data

Displayr has a wide range of tables and charts that you can use to display your data. Best of all, they are all completely customizable. These include classics such as pie charts, bar charts, and line charts, as well as some you've probably never heard of, like palm tree charts or moonplots! You can also create custom word clouds, which are a great way to visualize data from open-ended survey questions.

So what are you waiting for? Click the button below to start analyzing your survey data!

Get started

Where have meteorites fallen?

First, let's see where on the globe all these meteorites have fallen. A lighter line indicates that the fall happened longer ago; a darker line indicates a more recent fall. The larger the meteorite, the longer the line it is represented by. You can interact with the globe with your mouse, or view and edit the original dashboard and R code by clicking the button below.

Explore the original dashboard

This seems to show that meteorite hits are concentrated around certain areas. This raises the question: are meteorites more likely to fall in certain places? There is some research to suggest that meteorites might be more likely to fall closer to the equator. However, this hypothesis doesn't appear to be borne out by our data. There is no obvious clustering around the equator visible on our globe.

Meteorites and land area

So, our meteorites don't seem to be more likely to fall at the equator. Here's another hypothesis: they're falling completely at random. If this was true, we would expect a reasonably even distribution around the globe. By this logic, the bigger a country's land area, the more meteorites should fall there. To examine this, let's chart each country by the number of meteorites in our dataset that fell there.

Avoid the United States! Our dataset contains 145 meteorites that have fallen there. India also looks dangerous, with 124 meteorites having fallen there.

This map makes it pretty clear that there's not an even distribution according to land area. If our hypothesis was correct, Russia and Canada should both have a higher number of meteorite hits than the United States.

We can chart the land area of countries against the number of meteorites that fell there. From the scatterplot below, we can see that there is a weak relationship. If the number of meteorites fallen was correlated with land area, we would expect to see a linear relationship. Clearly, this is not the case.

If we chart the number of meteorites we would expect to see if meteorites fell in a linear relationship with land area, this is what it looks like.

Compared to the map made from the real meteorite data, this looks very different. Obviously, land area does not determine how many meteorites fall in certain countries. But if that's the case, then what does?

Meteorites and reporting bias

Rather than being related to land area, it actually seems as though meteorite strikes are related to population size. India and the United States, the two countries with the most meteorite hits, are both densely populated. This is likely indicative of a reporting bias, as meteorites are more likely to be seen and reported if they fall in densely populated areas.

Just as there's a large reporting bias in favour of meteorites that fall in densely populated areas, there's also a bias in favour of larger meteorites. This is logical, as a one kilogram meteorite is going to be much more noticeable than a ten gram meteorite. The chart below shows all the meteorites that have fallen in the last 10 years. As you can see, there are far more meteorites over 1 kilogram (shown in red) than under 1 kilogram (50 g - 1 kg is shown in yellow; under 50 g is shown in green).

Explore the original dashboard

However, it's not all doom and gloom! The odds of being hit by a meteorite are extremely low. You're far more likely to die in a car crash or a fire than you are to die from a meteorite strike. It's also more likely that you'll be killed by lightning or a tornado - both of which are extremely rare. However, there's bad news too - you have a higher chance of being hit by a meteorite than you do of winning the lottery.

All the visualizations in this post were made by writing R code in Displayr. You can create online, shareable dashboards with R code - and best of all, it's free!

Start creating your dashboard

Learn more about creating online dashboards here!

An obvious way to measure music's popularity is through the charts. Billboard has been charting the "Hot 100" songs weekly since 1958. Nowadays, chart rankings are based on a combination of sales (physical and digital), radio play, and online streaming in the United States. Billboard states that the approximate weighting is 35-45% for sales, 30-40% for airplay, and 20-30% for streaming. An annual "year end" chart is also released, ranking that year's songs by calculating a cumulative total of sales, airplay, and streaming. The table below shows the 2017 Year-End chart.

But that's not the only way we can see which songs are popular. 140 million people worldwide are active users of Spotify, with around half of those using the paid Premium service. The Swedish streaming service has a vast catalogue of 20 million songs. Each year, Spotify releases a playlist of the 100 most streamed songs for that year. These are the most streamed songs of 2017.

We can see that there are similarities, but that the lists are far from identical. We can visualize the differences in the rankings using a slope graph.

Ed Sheeran's Shape of You wins out on both lists! In fact, this pop hit has been streamed over a billion times since its release in January 2017.

After that, though, the lists diverge almost immediately. While there are almost no songs in the top 10 on either list that don't appear anywhere on the other list, the orders are far from identical. Luis Fonsi's Despacito is another winner of these rankings, appearing at number 2 on Billboard and numbers 2 and 3 on Spotify (courtesy of the Daddy Yankee remix). Kendrick Lamar's HUMBLE is also a clear favorite at 3rd on Billboard and 5th on Spotify. After that, it's more contentious. Bad and Boujee by Migos, 4th on the Billboard list, is only at number 36 on Spotify! Even more dramatically, Sam Hunt's Body Like a Back Road is 6th on Billboard but 71st on Spotify! Clearly, the most popular songs depend on who you ask.

The most popular artists

If you're a fan of some of the songs we've been discussing, you might have noticed that there are several artists whose songs appear multiple times on one or both lists. Is there one artist who stands out as the most popular artist?

Answer: kind of, but not by much. This chart displays all the artists who have at least 2 songs on at least 1 list. Ed Sheeran and The Chainsmokers both have 4 songs on the Spotify list, but The Chainsmokers inch ahead by scoring 3 songs on Billboard as opposed to Ed's 2. Perhaps as we would expect, most artists who score at least 2 top 100 hits appear on both lists. The only notable exceptions are Martin Garrix, with 3 songs on Spotify but none on Billboard, and Gucci Mane and Rae Sremmund, both with 2 hits on Billboard but none in the Spotify top 100.

The most popular genres

What about genres? Does one style of music dominate the charts? Fortunately, we can use Spotify's Web API Console to find out. This nifty tool allows you to search for artists and see which genre Spotify classifies their music into. These genres range from the obvious ones like pop, hip hop, and EDM, through to genres you didn't even know existed. (One artist in this dataset - Starley - is classified into the somewhat nebulous genre of "aussietronica". If anybody can enlighten me as to what, exactly, this means, please let me know!)

Genres - Spotify

Genres - Billboard

We can see that the Spotify list is more disposed towards pop music, with almost half the list classified as pop. Conversely, the Billboard list contains more hip hop (35% as opposed to 19%), as well as more modern rock, R&B, and country.

We can see these differences more clearly if we plot the genres next to each other (you can see a larger version here).

If we had to pick a winner, it'd be pop, but both lists have a range of genres represented.

So, can we in fact quantify the most popular music? The answer would have to be - sort of. Ed Sheeran, if you're reading this, the data says that you're the winner! (Also hi, I'm a fan). But really, there's a whole world of music out there. Go forth and explore it!

All these visualizations were made in Displayr; you can make your own for free! You can also check out more of our cool Data Stories.

Kickstarter describes itself as a platform to help "artists, musicians, filmmakers, designers, and other creators find the resources and support they need to make their ideas a reality." Since the launch of the crowdfunding platform in 2009, almost 150,000 projects have been successfully funded. 15 million people have backed projects on Kickstarter, pledging a total of USD $3.8 billion. Some Kickstarter projects have raised thousands or even millions of dollars. But what are the odds of your project being successfully funded?

For this analysis, we looked at a dataset of over 300,000 Kickstarter projects from 2009 to 2018. It contains data including the project's name, category, state (outcome), goal, deadline, and how much was pledged. From this data we can start to look at some of the factors that influence the outcome of a Kickstarter project.

The projects

Kickstarter is designed specifically to fund creative projects. The platform provides 15 categories which projects must fit into. The first thing we can do is look at which categories most projects fall into.

We can see that projects on Kickstarter are diverse, with no one category dominating the field. The biggest categories are Film & Video and Music, but they're closely followed by Publishing, Games, Technology, Design, and Art.

Project Success

Sadly but perhaps not surprisingly, here we see that more Kickstarters fail than succeed. Over half the projects in this dataset failed, and there's only a 35% success rate.

However, all hope is not lost! On the Kickstarter website, they state that 78% of projects that raised at least 20% of their goal were successful. The chart below shows that this statistic is supported by our dataset, with 77% of projects which raised 20% or more of their goal succeeding.

We can also break this down by category to see which categories are most likely to succeed. Here we've excluded projects that were cancelled, suspended, are undefined, or were live when this data was collected. This leaves only projects which either failed or succeeded, which comprise 88% of the total dataset.

Dance, which comprises only 1% of all projects on the site, leads the pack with a 65% success rate! This is closely followed by Theater and then Comics and Music. These are the only four categories in which a project is more likely to succeed than fail. At the other end of the scale, Technology and Journalism look dire, with 76% of these projects failing.

Create your Column Chart in Displayr

The impact of your goal

What could be the reason for this? Kickstarter allows users to set the goal for their projects at whatever they want. In this dataset, goals range from $0.01 to over $160 million. (It's likely that most of the projects with goals this high or low are joke projects, but judging from the project names, at least some of them seem to be serious.) We can compare the median goal of each category to its success rate to see whether they are correlated.

Technology, tied for the lowest success rate, stands out with a median goal of $20,000 - twice as high as the next highest category. We can't say for sure that this is the reason for its low success rate, but it does seem logical that it would play a part. The four categories with a success rate of more than 50% - Dance, Theater, Comics, and Music - all have relatively low median goals of under $5000. However, Crafts, with the lowest median goal at just $2345, has the third worst success rate. This is interesting, but clearly not the whole story.

If we exclude projects which are undefined, live, suspended or cancelled, Kickstarter projects have a 60% failure rate.

The visualizations below show how the rate of success changes depending on the goal of the projects.

The impact of your backers

Projects on Kickstarter are funded by backers - users who pledge money towards a project's goal. While creators can certainly take steps to improve their chances of success, ultimately every Kickstarter project lives or dies by its backers (or lack thereof). However, the visualizations below show just how important it is to have a large number of backers.

Will your Kickstarter succeed?

Success or failure on Kickstarter depends on a wide range of factors. It's extraordinarily difficult to predict what will go viral, and it's impossible to predict with certainty which Kickstarter campaigns will succeed. However, we can make a guess based on the variables we've discussed in this article. Below is a decision tree visualized as a Sankey diagram. This visualization shows whether a campaign is likely to succeed or fail based on its category, goal, and number of backers. You can zoom in by scrolling, and hover over the forks to see the statistics.

Create your own CART decision tree

Ultimately, what can we say about how best to succeed in your Kickstarter campaign? Make sure you're trying to fund a Dance project and that you don't need more than $1000. Oh, and make sure you have at least 1000 friends who are willing to back you!

Make your own column chart here, or a Sankey decision tree here! Check out the rest of our Data Stories for more.

Basal metabolic rate

An organism's metabolic rate is the amount of energy needed per second to keep that organism alive. For humans, this is about 2,000 calories per day, or about 90 watts. This means that a human's metabolism could power one 90 watt lightbulb.

An animal's basal (resting) metabolic rate differs depending on the size of the animal. A rat's basal metabolic rate is about 1.5 watts. From this, we can figure out how many rats are equivalent to one 90 watt lightbulb.

If you're good at math, you would have already worked this out. It takes the combined metabolisms of 67 rats to power one 90 watt light bulb! No doubt once this knowledge becomes widespread, RatLights^TM will take over the lighting industry. Finally, a convenient source of renewable energy. Take that, solar power!

But if it takes 67 rats to power a lightbulb and only one human to power the same lightbulb, what other creatures can we use? An average rat weighs between 0.3 and 0.5 kg. An average adult human, on the other hand, weighs anywhere from 55 to 80 kg. How many light bulbs could we power with an animal that weighs, say, ten times more?

Cowpower

Here we encounter an interesting feature of metabolic rates:  they don't scale in a linear relationship with body size. An average adult cow weighs 750 kg - roughly ten times as much as an average adult human - but a cow can't power ten times as many light bulbs as a human. A cow's basal metabolic rate is about 400 watts. Rather than the 10 light bulbs we'd expect if metabolic rate scaled perfectly with weight, we can only power 4.4 light bulbs with poor old Bessie. Given how impractical it would be to keep a cow in your living room to power your lights, I'm not sure the extra 3.4 light bulbs is worth it. We'll have to think bigger.

Whalepower

Now we're talking! The blue whale, the largest animal in the world at over 150 metric tonnes, has a metabolic rate of 12,000 watts! This equates to a staggering 133 light bulbs! Forget about RatPower - the true energy of the future is clearly WhalePower^TM. The power of only one blue whale can provide light for you and both your neighbors! It's OK, you can thank me later.

Whalepower (artist's impression).

This piece is inspired by Scale, by Geoffrey West. 

All the pictographs in this post were created in Displayr. You can learn how to make your own here, or check out more of our Data Stories!

Creating the first table

Add your data as a dataset in Displayr. In the Data Sets section on the left, click Insert a Data Set. You can then drag and drop your data, select a file from your computer, or use another source such as a website. My dataset was in an Excel spreadsheet, so I imported that file and Displayr split it into variables for me.  From here, there are two ways to create a table. The easiest way is to drag and drop the data onto the document. You can also click Insert  > Paste Table or Enter Table to enter the data manually or paste it from a spreadsheet.

Word frequencies

Once we've inserted the data into Displayr, we want to analyze it. In this instance, I wanted to know the most common words which were used on each of the three web pages. To run text analysis, go to Insert > More > Text Analysis > Setup Text Analysis and drag and drop the dataset you want to analyze. Press Calculate. 

As you can see, we've made a table that shows the words most frequently used on the page for the iPhone X. But it's very short. This is because it only displays words which are mentioned at least 5 times. Since our sample size is quite small, I want to display words which are used at least 3 times. To do this, change the Minimum Frequency under Inputs to 3 (or whatever you want).

Now we have 24 entries instead of 8. We now need to remove a few words that are irrelevant for our analysis. Displayr automatically discounts words like "the", "and", "is", and "to." In this table, we can see that "000" was used 3 times. This is in the context of large numbers, such as 1,000,000. This is obviously not useful, so we want to remove it from the table. To do this, under the Inputs section, we can type words or phrases we want to remove in the Remove words/phrases bar, separated by commas. We're also going to remove "cent", which is used in the context of "per cent". This leaves us with the 22 words which are used 3 or more times, organized by frequency. Repeat this process for each of the datasets.

Creating the word clouds

To create a word cloud, go to Insert > Charts > Word Cloud. Drag the dataset you want to visualize from the Data Sets menu, or paste or type your data in the spreadsheet under Inputs. Click Calculate to show your word cloud.

Now to clean it up! The first thing to do is to remove any irrelevant words, the same way we did for the word frequency table. You can do this by dragging the word you want to delete to the Ignore tab on the right side of the word cloud. I'm going to delete "000", "It's", "Just", "per", "cent", "that's", and "instead" from our word cloud.

Now, I want to combine some of the words - for example, we can see that "iPhone" and "X" appear as separate words in the cloud. When we click on "X" in the cloud, we can see that it only ever appears following "iPhone". To combine them, drag "X" onto "iPhone". This is essentially telling Displayr that "iPhone" and "iPhone X" are synonyms.

We can also create phrases in our word cloud. For example, "Face" and "ID" appear as separate words, but the only context in which the word "ID" appears is in the phrase "Face ID". To add this as a phrase, click on "ID" and type in "Face ID" as the new phrase. This will change the word cloud.

That's how we created our word clouds!

You can access the original Displayr dashboard and have a play, or check out our other tips about using Displayr!