Sayit, AI-powered therapy for a happier, healthier you
The challenge
Using Chat GPT, or other emerging technologies, to develop an innovative product or service using the Lean methodology, which emphasizes failing fast and cheaply. This time, the research phase took a backseat, prioritizing the launch of a Minimum Viable Product (MVP) into the market in a short period to test its success.
Research & Ideation
After conducting light research to clearly understand what cutting-edge technologies can offer and how we could use them to create a product or service with market potential, we focused on ideation to find a solution that would improve the lives of our users with artificial intelligence (AI).
Thanks to a quick brainstorming session, ideas quickly emerged. To prioritize the winning idea, we used a classic Eisenhower double matrix with variables of known/unknown and high risk/low risk, focusing on the least common idea, meaning the most unknown and risky.
The winning idea stood out as the most innovative, aiming to explore the potential of cutting-edge technologies in the therapeutic field. It resulted in a subscription-based therapy platform based on artificial intelligence and accessible through virtual assistance devices, which we named Sayit.
Assumptions and Hypotheses
The Lean methodology emphasizes a rapid and efficient work process to achieve business objectives in the shortest possible time. We discarded requirements and created a problem statement based on assumptions that would help generate hypotheses.
Assumptions are based on what we know about our users; they may be incorrect, but they form our starting point.
We created assumptions based on two categories:
- Business assumptions
- User assumptions
Once the assumptions were stated, we used a prioritization matrix again to filter the most innovative and unknown ones. This helped us identify the problems that could pose obstacles to the project’s development.
Three assumptions were prioritized. By adding the validation mechanism, each assumption statement became a hypothesis statement.
Hypotheses are an excellent way to establish what you believe about your users and what they need. The goal is to validate them so that we know the strengths and weaknesses of our idea and project.
- Hypothesis #1 We believe that our potential customers need on-demand therapy at affordable prices, and our product/service will satisfy this need, especially for those who cannot afford traditional therapy or avoid seeing a psychologist due to reasons such as shame, guilt, or simply laziness.
- Hypothesis #2 We believe that, for those reasons, our initial customers are (or will be) young and older adults because they face more precariousness or stigma when it comes to traditional therapy or are vulnerable due to issues such as loneliness or bullying, and harassment.
- Hypothesis #3 We believe that the factor of being able to do therapy wherever and whenever they want may be very appealing, not only to our initial customers but to anyone interested in improving their mental health.
Creating protopersonas
Understanding users’ needs is crucial to creating an effective product.
Lean methodology offers an agile and efficiency-oriented approach, but unlike other methodologies like Design Thinking, it does not directly focus on in-depth user knowledge from the beginning. Instead, it uses so-called protopersonas, which are initial user models that evolve over time toward final product users.
Based on the assumption statement, we built two protopersonas, defining who we believe will initially use Sayit and why.
Lean Canvas
The Lean Canvas consolidates and visualizes all aspects of the business model. It included ideas, functionalities, problems, and solutions.
By concentrating on the most relevant information from each stage, I outlined a concrete plan to develop an MVP and validate my ideas and hypotheses through clear objectives.
Building an MVP
To validate the viability of our product and service, we chose to quickly launch an MVP in the form of a landing page with the essential functionalities and basic interactions. This way, we could measure its success on the market in just a few days and accelerate the development process.
Through user stories, we understood the needs and expectations of our target audience. These stories helped us identify the essential functionalities for Sayit’s MVP, and with the MoSCoW method, we prioritized our backlog.
We created a detailed sitemap to visualize and organize our landing page: Sayit.pro. Additionally, we used low-fidelity wireframes to sketch the user interface and interactions. These tools allowed us to iterate quickly and see how the different sections of the landing page would be organized and function.
We have divided our landing page into the following sections:
- Hero, We introduce the idea and the service with a clear and direct summary of Sayit’s main mission and features.
- How it works?, We list the main innovative benefits of the platform. Partners: Partners who support Sayit.
- Partners, Supporters of Sayit.
- Testimonials, reviews, and success stories, users who have already tried it highlights the power of this alternative therapy, explaining how they use it and how it has improved their lives.
- Guided by the best professionals, in this section, we emphasize professional support, guaranteeing a truthful and appropriate approach.
- Choose your plan, explanation of the three plans we offer, and the launch offer.
- Download our App, we emphasize the added value our app offers to users.
- Contact form: At this stage, our goal is lead generation and evaluating the level of interest from our audience. Thanks to the form, we can measure this and, at the same time, listen to the concerns, questions, or doubts of our potential buyers.
- Footer, We use this space to communicate and ensure our privacy commitment. Additionally, we clarify when it is not advisable to use Sayit, such as in crisis or emergency situations. We also provide the emergency phone number for those in need.
Check out our active platform at www.sayit.pro
KPIs and Metrics
First things first, before launching our landing page, we established our objectives and KPIs to later measure the results and, ultimately, the success of our concept. A KPI is a measurable value that demonstrates the effectiveness of an action aimed at achieving a specific goal.
We set three objectives:
- Evaluation of user interest in Sayit
- Lead generation
- Evaluation of Intent to use and contract Sayit
We created the following table to validate the results and achievement of our KPIs.
Results Analysis
Hotjar and Google Analytics were used to assess our metrics.
Hotjar
Thanks to heatmaps and recordings, we could analyze user behavior on the landing page and verify that the scroll depth was high. However, we fell short of the set KPI by 4%.
The heatmap of taps revealed that the highest number of touches was on the CTA’s (Call-to-Action) Waiting List and I Want to Try It, demonstrating users’ interest in Sayit.
Analyzing desktop vs. mobile results, we can conclude that more people visited our contact form through their mobile devices. Additionally, more people tapped on the Send CTA to leave their email addresses.
Google Analytics
Based on an analysis of 2 weeks, we found:
- We obtained a total of 223 visits.
- Our bounce rate has been particularly good: 0% of our users left the landing page without taking any action, meaning that every user who visited us interacted with the page either through clicks or scrolls.
- Our mobile audience is significantly larger than the desktop audience, which we interpret as a positive sign due to the geek nature of our idea.
- By creating different events, we were able to determine that the click rate on the I Want to Try It CTA was 7%, while for the Advanced Plan it decreased to 3%.
- The average time a user spends on our landing page has been 1 minute and 5 seconds.
Through the contact form, we were able to recruit 45 leads.
Conclusion
Is the world ready for AI-powered therapy? It’s hard to say, but from my observation, it generates a great deal of interest and curiosity, as well as debate, which was precisely this project’s goal.
Thanks to the fail fast, fail cheap approach, Sayit reached the users in just two weeks, generating valuable feedback that allows us to continue iterating functionalities until we have a product ready to hit the market.
In my opinion, well-directed new technologies can contribute to mental health and I bet they will be common in the near future.
Thank you for reaching this point! I’d love to hear your thoughts on Sayit.
