Priya
Age: 41
Occupation: Marketing director (laid off after 15 years)
Platform: iOS
Tier: Plus
Grief type: Career-loss grief (laid off after 15 years at the same company)
Her Story
Priya had been with the same company since she was 26. She built their digital marketing department from a team of two to a department of fourteen. When the layoffs came — her entire department, eliminated in a restructuring — she received a severance package and a 30-day transition period and two decades of professional identity that suddenly had nowhere to land.
She didn't call it grief at first. She called it "a setback." She made a spreadsheet of companies to apply to. She updated her LinkedIn. She scheduled coffee chats with former colleagues. She was very busy and very fine.
Six weeks in, she stopped sleeping. She'd wake up at 3 AM thinking about the team meeting she used to run every Monday — fourteen people who looked to her for direction, who didn't have that anymore because of a decision she had no part in. She was angry at the company. She was ashamed in ways she couldn't explain. She searched "grief after job loss" at 3 AM and found out that career loss was a recognized grief type, and that what she was experiencing had a name.
She downloaded Grief Companion the same night.
How She Uses the App
Priya upgraded to Plus within two weeks. She wasn't using the AI check-in or the therapist integration — she upgraded because she wanted the full data analytics and the ability to export her check-in and journal data to review herself.
She journals every day, sometimes twice. In six months, she's written 64 entries. Her early entries were tight, professional — things she might have put in a status update. "Applied to 3 positions. Got 2 rejections. Feeling unmoored." Her recent entries are longer and less organized and more honest.
The AI-personalized check-in named the Monday morning pattern within her sixth week of use. Her previous company's all-hands was every Monday at 9 AM. She had run those meetings for six years. The AI noticed that her emotional check-ins on Monday mornings were consistently in the "Heavy" or "Difficult" range — and that this pattern appeared even on weeks when the rest of her check-ins were lighter. She hadn't connected those two things. The AI did.
She's tracked in the analytics section how her emotion tags have shifted: the proportion of entries tagged "shame" has dropped from about 40% to under 10% over six months. Entries tagged "determination" have appeared in the last two months and are now her second most used tag after "grief." She checks this the way she used to check campaign performance metrics.
Her Personality and Tone
Priya is driven, analytical, and high-functioning. She is also genuinely suffering in ways she didn't expect and has found surprising comfort in having something to measure. She's good at measuring things. She's less good at sitting with ambiguity. The app gives her both: the data she can work with, and the prompts that push her into the ambiguity.
She's not embarrassed about using a mental health app. She is embarrassed about how long it took her to admit she was grieving. She's working on that. She's mentioned it in three journal entries.
She does not have a therapist yet. She's looked at the directory. She keeps telling herself she'll call when things settle down. Things have been unsettled for six months.
What She's Found Helpful
- AI-personalized check-in — named the Monday pattern she'd been carrying without seeing
- Emotion tag analytics — shows her the data of her own recovery in a way she can engage with
- Journal filtering — she regularly filters by "shame" and "determination" to track the shift
- Weekly self-care summaries — she cross-references with her journal on Sunday evenings
- Data export — she has a spreadsheet with her own data, because of course she does
Where She Is Now
Six months in, Priya is interviewing. She's not rushing. The analytics showed her that the weeks when she applied to things reflexively — out of panic rather than intention — her emotional check-ins were consistently worse. She's using that information. She's applying to fewer places more carefully.
She tagged one of last week's entries "hope." It was the first time.
Priya's persona is used to test the Plus analytics features, AI-personalized check-in, and data export. When building data features, she's the primary user to design for: she wants specificity, historical depth, and the ability to see her own patterns clearly.
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