Conversational Onboarding for Manodayam: 60% Fewer Drop‑offs With a Voice‑Assisted Chatbot

About the company

Manodayam is a digital mental health platform that delivers accessible, evidence‑informed support through friendly, low‑friction experiences. The team serves users at scale across web and mobile, with a strong emphasis on privacy, consent, and data integrity. Because intake quality directly impacts care outcomes, Manodayam treats onboarding as a clinical gateway: it must engage users, capture complete baseline data, and route them to the right next step without creating unnecessary barriers.

The problem

Manodayam’s onboarding relied on static forms that felt dull and non‑interactive. Completion rates suffered for two reasons:

• High friction from mandatory questions and the preliminary PHQ‑9 screener that blocked progress if any item was skipped.

• Low engagement due to form fatigue and lack of guidance or clarification for sensitive questions.

Operationally, data from forms required manual handling to reach backend systems, which added delay and introduced the possibility of errors.

The solution

We built a conversational AI chatbot with optional voice assistance that guides users through onboarding in a friendly, step‑by‑step flow while capturing all required data directly into Manodayam’s backend database.

What the chatbot does

• Collects essential details such as name, age band, contact preferences, consent, and prior support history.

• Administers the PHQ‑9 in a conversational format, with clear, neutral language and progress cues to reduce abandonment.

• Answers FAQs about the company, the platform, privacy, and how assessment results are used.

• Validates inputs in real time and saves state so users can pause and resume without losing progress.

• Sends structured payloads to the backend via secure APIs, eliminating manual data entry and ensuring audit‑ready logs.

Experience and compliance features

• Voice and text modes so users choose their preferred modality.

• Accessible UI patterns including readable contrast, error hints, and multi‑language prompts.

• Privacy controls such as explicit consent prompts and data‑handling disclosures.

• Escalation triggers that surface resource links or next steps based on responses, following Manodayam’s policies.

Success metrics

• 60% reduction in user drop‑offs during onboarding after launching the chatbot.

• Higher data completeness for required fields and PHQ‑9 items, with fewer partial submissions.

• Faster handoff to next steps (e.g., program selection or resource routing) due to immediate backend writes.

Business impact: More users complete onboarding on the first attempt, clinicians receive cleaner, structured data, and support teams spend less time on manual follow‑ups.