Mapping Archetypes to Class Booking Suggestions (Gym / Studio Apps)

Class booking apps win when recommendations feel relevant, timely, and low-friction: not “more workouts”, but the right class today for how someone tends to live and train.

Sahha Archetypes are designed for this kind of personalization because they’re stable labels derived from weeks of behavior, not momentary signals.

Docs reference: Sahha Archetypes list and definitions: https://docs.sahha.ai/docs/products/archetypes

Prerequisites (recommended)

To get the best results, your product should be able to deliver journeys like:

• onboarding (“start here” suggestions),
• weekly nudges (“book your next class”),
• reactivation (“return with a low-friction option”),
• and contextual recommendations (“recovery-first week”).

That typically means using a customer engagement tool such as Customer.io or Klaviyo (alternatives: Braze, Iterable, etc.) to orchestrate messaging, segmentation, and journeys.

Important: As of 2 March 2026, Sahha does not provide built-in customer journey delivery (campaign orchestration / messaging) capabilities. Sahha provides the data outputs (e.g., archetypes) that you can route into tools like Customer.io or Klaviyo to trigger experiences and recommendations.

What this guide gives you

A practical approach you can implement quickly:

1. Normalize your schedule into a small set of generic class types
2. Use preference archetypes to choose the kind of class
3. Use capacity / recovery archetypes to choose the intensity and format
4. (Optional) Use sleep timing archetypes to choose the best time of day to suggest
5. A JSON mapping file to get you started (download below)

When to use Archetypes (and when not to)

Use archetypes for:

• “Suggested for you” modules and class recommendations
• Segmentation (e.g., “recovery-first” journeys)
• Lifecycle journeys (onboarding, habit-building, reactivation)

Avoid using archetypes for:

• Minute-by-minute nudges (“go now”) — archetypes are best treated as durable context.

Step 1 — Normalize your class catalog

Studios name classes creatively (“Hot Vinyasa Flow”, “Bootcamp X”), but recommendations work best when your system maps everything into a small, consistent set of generic types.

Use (or adapt) the taxonomy below and store it as class_type_normalized on each scheduled session.

Step 2 — Choose a simple recommendation strategy

A clean way to think about class booking recommendations is:

A) Preference (what they’re likely to enjoy)

Use:

• primary_exercise_type (categorical)
• optionally primary_exercise / secondary_exercise (most frequent exercise types)

B) Capacity / readiness (what they can likely sustain)

Use:

• activity_level + exercise_frequency

C) Recovery modifier (how hard to push)

Use:

• sleep_quality, sleep_duration, sleep_regularity
• mental_wellness, overall_wellness

D) Timing (when to suggest)

Use:

• wake_schedule, bed_schedule, sleep_pattern

Step 3 — Archetype → class type mapping tables

These tables are designed to be copy/paste implementable. Each row gives you:

• Best-fit class types (rank higher)
• Also works (rank lower; use for variety)

3.1 Activity & habit archetypes → class types

activity_level

exercise_frequency

primary_exercise_type

3.2 Wellness archetypes → class types

These are best used as intensity modifiers (how demanding to make the recommendation), not hard gates.

mental_wellness

overall_wellness

3.3 Sleep archetypes → class types

Sleep archetypes are particularly effective for protecting retention: if someone is under-recovered, pushing only high-intensity classes tends to reduce adherence.

sleep_quality

sleep_efficiency (requires wearable)

sleep_duration

sleep_regularity

3.4 Sleep timing archetypes → when to recommend (optional but high-impact)

Timing archetypes don’t change what someone likes as much as they change when suggestions convert.

wake_schedule

bed_schedule

sleep_pattern

Step 4 — Normalize primary_exercise / secondary_exercise (optional)

Sahha may return many exercise types. In a studio booking context, normalize the relevant ones into your class taxonomy:

Exercise types reference: https://docs.sahha.ai/docs/get-started/data-dictionary/exercise-types

Step 5 — A simple scoring recipe (easy to ship)

A practical default:

1. Start with preference
   • Use primary_exercise_type to pick a primary set of class types (rank highest).
2. Apply capacity
   • If activity_level = sedentary or exercise_frequency = rare_exerciser, down-rank HIIT/Boxing/Bootcamp.
3. Apply recovery modifier
   • If sleep_quality or overall_wellness is in the bottom band, bias toward Mobility/Yoga/Pilates and moderate Strength.
4. Add variety
   • Include 1–2 “Also works” types to avoid repetition.
5. Apply timing
   • Use wake_schedule/bed_schedule to decide when to send and what time slots to highlight.

This keeps the system explainable (“we suggested Pilates because you trend mind-body + your sleep has been inconsistent”), which helps user trust.

Example: What your app can show

Suggested classes for you:

• Pilates (primary)
• Mobility / Recovery (secondary)
• Strength (light) (variety)

Driven by archetypes like:

• primary_exercise_type = mind_body_oriented
• sleep_quality = fair_sleep_quality
• exercise_frequency = occasional_exerciser

Copy/paste implementation checklist

• Add class_type_normalized to your class/session objects
• Create a lookup mapping from archetype values → recommended class types (tables above)
• Decide your ranking logic (preference → capacity → recovery → timing)
• Route archetype outputs to your engagement tool (Customer.io / Klaviyo) for journeys and triggers
• Log exposure + bookings to tune ranking over time