Sayit.pro, AI-powered therapy for a happier, healthier you
UI/UX Case Study
More details in my Medium story
Brief
Harness the potential of Chat GPT or other emerging technologies to develop an innovative product or service through the Lean methodology, which emphasizes failing fast and cheaply, failing by investigating. This time, the research phase took a backseat, giving priority to launching a Minimum Viable Product (MVP) within a short period to test its popularity in the market.
Design Process
Using Lean Methodology, we formulated assumptions about business and user aspects, which were then prioritized and transformed into hypotheses for validation. Protopersonas were developed to understand our potential users and a Lean Canvas was utilized to consolidate the business model. User stories guided the identification of our Minimum Viable Product (MVP) essential functionalities.
Solution
Sayit is a subscription-based AI therapy platform that leverages cutting-edge AI algorithms and emotional recognition technology to deliver personalized and effective therapy. Furthermore, the solution was backed by renowned mental health experts, ensuring a scientifically grounded and rigorous approach.
Assumptions
and Hypotheses
Our assumptions centered on users' need for on-demand therapy at affordable prices, particularly for those who face barriers to traditional therapy. The hypotheses revolved around the platform's appeal to both initial and potential users, offering flexible therapy options that cater to individual needs.
Our Hypotheses
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
The Lean methodology uses protopersonas, initial user models that evolve over time, to guide the design.
For Sayit, we developed two protopersonas: Sandra, 22 years old, is interested in mental health and experiences recurrent anxiety attacks. Marcial, 68 years old, deals with early signs of dementia and feels somewhat lonely. Sandra seeks affordable therapy and tools to manage anxiety, while Marcial needs reminders for medical appointments and medication.
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
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.
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
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.
I invite you to explore further Sayit through my Medium page in both English and Spanish.