Machine Learning & Artificial Intelligence in the Meditation Industry

Modern technology is far beyond simple chatbots that provide automatic responses to different inquiries. Two of the biggest fields where technology is already disruptive but still has a lot of potential to release are Machine Learning (ML) and Artificial Intelligence (AI).

Additionally, Machine Learning can process in-app user behavior and biometric indicators (heart rate, strains, etc.) to make personalized course/class recommendations. Recommendations don’t have to be regarding your products only. You can increase your personalization level and report users in case your tools spot any abnormal body activities.

If you’d like to learn about the importance of personalization in meditations apps and how it influences the whole app’s functionality more, feel free to follow the link to our meditation app development article:

To show you how the use of such tools can (positively) influence your business metrics and get into more details in terms of implementing them, we’re going to review how Calm, one of the most popular meditation and mindfulness companies, uses machine learning.

Calm Case Study: Machine Learning 🧘

Calm is one of the world’s most widespread meditation & mindfulness apps. It contains hundreds of meditations targeted at different needs from fighting anxiety to increasing the sense of your own value. Users can choose out of unguided and voice-guided sessions that last 3-30 minutes.

Without further ado, let’s dive into how Calm uses machine learning. In general, most of the user data that Calm gathers then gets transferred to a third-party machine learning system. The results of machine learning processing are then used by multiple departments in the company — Marketing, Customer Experience, Engineering.

The marketing department, for example, found out that what brings Calm most users was actually their sleep stories (bed stories for adults, as they call it) — they got this information via the machine learning tools. Also, the tools showed them that it’s Calm’s daily meditation content that makes users stay. Surely, such information is essential for most of any company’s marketing activities as it can be used for determining strong and weak sides, developing marketing campaigns, updating already existing content, etc.

Looking at general information is useful so as to get some ideas that you could implement into your own workflow. However, we wouldn’t recommend taking Calm’s case as the gold standard. It’s likely that what worked for them might not work for you the same way. With that in mind, let’s take a closer look at how Calm implemented and is currently using [Amazon Personalize]](https://aws.amazon.com/personalize/?nc1=h_ls){ rel="nofollow" target="_blank" .default-md} — a machine-learning-based tool for personalizing the user experience.

Calm & Amazon Personalize

Calm’s journey to implementing Amazon Personalized started when they partnered up with celebrities to expand the content base. As cool as it may look, soon it turned out that such an unexpectedly rich content diversity can confuse users. Some of them had a hard time finding what they were interested in!

How did the suggestion feature work before the ML implementation?

The app was recommending the most popular sessions while removing the ones a user has already seen. However, the results of metrics analysis showed that while recommended sessions might be the most clicked, they rarely got completed. Meaning that users had low engagement & interest in them. Once Calm found out that there are some issues, they turned to Amazon Personalize.

Two of the main goals that Calm wanted to achieve by using Amazon Personalize were:

• Increase user engagement. It was measured by the average number of days it takes a user to complete a certain piece of content. Such a metric was chosen consciously since Calm’s goal is helping users build healthy relationships with mindfulness, which might mean less time using the app itself. In other words, 5-10 minutes of meditation every day was a better sign for Calm than 1 hour 2 times a week.
• Diversify the content users consume. While the content Calm provides is really diverse, many users tend to choose the same meditation over and over, which isn’t bad, of course. However, they still wanted to draw users’ attention to other content. That’s why the number of repeated plays was limited so that the ML tool doesn’t recommend the same session multiple times, even though a user loves it.

For such goals, Amazon Personalize offered Calm to use a sequence model that is able to learn from the step-by-step process of users choosing and following a piece of content. For example, one of the first things that the ML tools had to learn was that new content is highly popular when first added, but then the interest goes down. That meant that Amazon Personalize needed to be able to catch rapid changes in users’ interests and adjust to them with the help of a real-time tracker.

To better understand how Amazon creates such a learning sequence, let’s take look at its structure:

To measure how the implementation of Amazon Personalize impacted Calm, the company ran some user testings and came to the following results:

• Compared to hand-picked “What’s New” email recommendations, users who received the ones generated via Machine Learning were 1% more likely to finish the suggested content within 24 hours, which might sound like not that different but statistically is a significant increase.
• In another test, Calm tested three groups — with user experience before implementing Machine Learning, “Recommended for You” based on the most popular content, and machine-learning-based recommendations. The last group showed 3,4% higher engagement, which is again a big success.

As you can see, Machine Learning is a great way to optimize personalization processes in your meditation and mindfulness product. However, the challenge in such industries as meditation, personal growth or coaching, lies in the fact that the progress significantly depends on users’ inner state. Luckily, Machine Learning has some tricks to help companies “get into users’ heads.”

Mood Control using Machine Learning 😀

To track users’ inner state, it’s sufficient to monitor their mood regularly — Machine Learning will then adjust the recommendations to the user data and take a bunch of external factors into account. A great example of such an approach is Mindwell, which is also a meditation & mindfulness app that highlights the importance of aiming towards a more stable mood.

Companies with their own infrastructures normally have several teams that are exclusively responsible for certain parts of machine learning. Meaning that you would have to build an additional communication infrastructure and new business processes specifically for the matter of machine learning.

Taking all those factors into account, our verdict would be that it’s not really worth it since there are enough decent third-party providers. This way, you significantly reduce the time and costs needed for machine learning system implementation. Moreover, vendors in this industry normally offer a decent level of customization. However, the final decision is always up to you!

⚙️ Tech Stack, ML & AI Tools for Meditation

In this section, we’ll talk about one of the most useful technologies that a сompany could use to either optimize business processes or create a unique user experience so as to stand out and be ahead of the competition.

Consumer-Grade Electroencephalogram (EEG) Device

This device with an overly complicated name is basically a headband intended to provide real-time metrics to users while they’re meditating. The main idea is to help them understand whether what they’re doing is correct since it’s quite a challenge to get a sense of Zen right at the beginning of any mindfulness journey.

Such devices can be used in multiple industries, however, for meditation and mindfulness, it normally looks the following way:

1. They track brain activity and record wave patterns using electrodes.
2. The data gets processed.
3. Then, the device sends the data to a smartphone and/or app.

This is your place to be creative. For example, Muse transforms this data into sounds of nature. If the device determines that a user’s mind is calm, the app will play something respectively calming like rain or gentle breeze. This way, users get informed that they’re on the right track.

In case a user is anxious, the app will play something rather chaotic so that a user would know that they need to concentrate on positive thoughts more. Even though it’s debatable whether such sounds are suitable for a meditation app, at least a user would know what state they’re actually at.

You could surely think of another implementation way with a completely different approach. But the fact that it’s actually possible for you to show users their progress on a neurobiological level is inspiring.

AI Content Creation

In case you want to create content for your business with lower resource consumption, automate as many business processes as possible, or need help because you simply run out of ideas for the meditation and mindfulness sessions creation, the AI market has quite a lot of solutions to offer.

Artificial Intelligence for creating content normally does it with the help of NLP (Natural Language Processing) and NLG (Natural Language Generation) models. The first one allows machines to understand human language both verbally and in written form. NLG then helps them produce content in human language.

These models are what’s behind the scenes when you type in a request and an AI then gives you a response or any other type of output. In our case, you type in a topic and AI creates a piece of content based on all available data, which might include large numbers of articles on the Internet or some internal resources.

What’s great is that the content would be unique — AI doesn’t simply gather different information from all over the Internet but makes its own piece based on what it has learned. Here are some of the AI providers: Jarvis,
Frase AI Writer, Automated Insights, HypeWrite.

Additionally, there are AIs that can convert the text into natural speech — for example, WaveNet. Here’s a video of a meditation that was created by an AI:

See how you can integrate AI into an app specifically tailored for this industry!

Sound Stimulation for Meditation Practice

Almost every meditation app uses some sort of sound to help users be present, relax, structure their thoughts, separate important from minor — with anything that meditation is intended to do. To make the effectiveness higher, there are a lot of sound stimulative devices.

For example, there’s a device from Welltiss that uses special technology called PEMF (Pulsed Electro-Magnetic Field). It’s used in many industries. For meditation and mindfulness specifically, the device is able to create sounds at a certain frequency so that the brain is practically forced to synchronize with it and fall into much more relaxed but conscious states.