Introduction

Introducing our Mood Tracker template — a daily experience that helps users record and monitor how they're feeling. Mood tracking is a powerful psychological technique to keep an eye on a person's emotions while encouraging them to learn more about themselves along the way.

Mood trackers are great examples of a sticky and repetitive skill that can drive users back consistently. With this template and a little help from voice technology, mood journaling has never been easier. This experience allows users to instantly record what they're feeling and save it in a database, giving them the option to retrieve that info at a later time. This encourages users to keep a running log of their mood to help identify and manage their health.

It's immediate, in-the-moment, and hands-free. Not to mention it's an effective way to build an experience with a high retention rate.

What are we covering in this video?

In this project, we'll be storing the user's moods in Airtable — a spreadsheet database software. There are a few separate instances where we use Airtable throughout this project, and so If you've never used it, not to worry. We have a separate video that we've linked to in the project that explains how to use Airtable with Voiceflow. This tutorial includes linking your account to your project and saving and retrieving data.

In this video, we'll guide you through the following steps:

1. Overview of template design and vital components
2. Customizing positive and negative Intents (i.e. moods)
3. Using the IF and SET step
4. Saving and retrieving responses to a database
5. Testing the experience

Design Walkthrough of Template

When breaking down our design, we can see that the core of the project can be divided into four main phases.

1. The first phase is setting up your Airtable and introducing the user to the project.
2. The 2nd phase is using the intent, if and set steps to decipher the user's mood
3. The third phase is saving the user's mood using the API step
4. And the final phase is retrieving the user's previous mood entry - also using the API step. Let's walk through each of these phases.

Phase 1 - Setting up your Airtable

In order for the Mood Tracker to work as desired, you're going to need to connect your Airtable. You can do this inside the step we've labelled Airtable Setup. Once you've connected your Airtable, the assistant will welcome the user to the experience, explain what it is, and then ask the user how they're feeling today. This represents the main prompt in the project.

Phase 2 - Using the Intent, IF and Set steps to decipher the user's mood

This now leads us to the second phase of the template, which starts with a feeling intent. If we open up the interaction model by clicking M on our keyboard, you can see all of the utterances we've included inside this intent, along with two different slots: positive and negative.

If we head over to the slots tab, you'll see both slots show up: positive which we've labelled green, and negative which we've labelled red. If we click into the positive slot you can see we've pre-filled moods that are positive in nature such as appreciative, cheerful, and thankful.

We've done the exact same thing with the negative slot. You can even add new moods of your own if you scroll to the bottom of the slots page. All you need to do is click add custom value.

In order for the assistant to recognize the user's mood as either positive or negative, we've employed an IF step to do a simple comparison for us. The IF step states if positive does not equal 0, then the positive slot hasn't been filled. This means we head down the according set step where the mood variable is set to negative. If positive does equal zero, then the positive slot has been filled. This means we'll be re-directed to the next set step where the mood variable is set to positive.

Now you might be asking yourself — why does this matter? Why didn't you just use one mood slot instead of two? The idea here is that by using a positive and a negative slot we can create a sense of compassion or empathy towards the user and how they're feeling. This helps foster trust and comfortability between the user and the experience.

What this looks like in practice is being able to redirect the user to the correct corresponding dialogue that speaks to their given mood.

For example, if the slot value in the user's utterance matches the negative slot, we want the assistant to say something like, "we're sorry about that." If they match with the positive slot, we want to encourage the user and say something like, "it sounds like you're in a good mood. That's great to hear."

Phase 3 - Using the API step to save data

This leads us to the third phase of this template which deals with saving the user's mood. In order to save the user's mood, we're using an API step to make a request to our Airtable database using the POST URL. We're also using the API key variable in the VALUE field. If you remember, we set this earlier in the project in the step labelled Airtable Setup.

Lastly, If we jump back into the step labelled "Save Mood" and click on the body tab, you can see that we're only sending the user's mood to the Airtable.

Phase 4 - Using the API step to retrieve data

After this, we move into the fourth and final phase of the project. This is where we're doing two things: confirming that the mood has been saved, and whether the user wants to hear their previous mood. If they say yes, then the GET request triggers which works to retrieve the information from the Airtable. Finally at the bottom of the editor under "Transform into Variables", we can see that this has been set up to grab the last record in the table. Again, to better understand how to fill out these API steps when working with Airtable, check out our Airtable tutorial.

Now, once the assistant responds with the user's previous mood, we come to the end of the template, where we ask the user if there is anything else we can do. Here the user can either exit the skill, repeat the journey by adding another mood, or recall their previous mood.

Testing the Experience

We're going to test this experience using Voiceflow's one-click sharing — which makes it super easy to share your project with anyone. To test this experience using Voiceflow's share view, we can simply click the share button at the top right of the screen and then set the test type to Voice and Transcript. Now we're all set to prototype the experience.

Assistant: Welcome to your mood journal. You can record your mood and keep track of trends just by letting us know how you're feeling.
‍Assistant: How are you feeling today?
‍User: I'm feeling great
‍Assistant: Do you want me to save great as your today's mood?
‍User: Yes
‍Assistant: Would you like to know yesterday's mood?
‍User: Ok
‍Assistant: Your previous mood was confident. Anything else I can help you with?
‍User: No, I'm good.
‍Assistant: Okay. That's all for today. Talk to you later.

Conclusion

That's an overview of our Mood Tracking template. Mood Tracking is a really great way for users to monitor their emotions and identify negative triggers. It also pushes them to develop strategies relating to their well-being, so they can make positive changes in their life. We hope this template sets the foundation for creating your own journaling experience.

If you want to learn about more ways you can build with Voiceflow, check out our templates library! It's entirely free and serves as a great resource to help build out the vision of your next project.