Kai Nurture — How belonging gets built

Kai product: Data Fields Guide — Data Fields Guide — personas & funnel signals

Kai product: Templates Guide — Templates Guide — engagement & message flow

Kai product: Rituals Guide — Rituals Guide — community rituals & language

A living model built entirely from your own community.

Initial

The Learn portion of our 3-day partnership kickoff: Rituals Baseline Workshop (traditions and ritual taxonomy), Language Baseline Workshop (templates for ritual vs. non-ritual moments), and current-student interviews that inform staff sessions. Everything lands as your Learn baseline in the product.

Ongoing

Semester ritual reviews (2×/year) after each term — performance, taxonomy, and new rituals for recruiters. Language & messaging workshops (4×/year) for ritual campaigns; Kai messaging refreshes from what’s working.

Output

A living ritual taxonomy with matched messaging and unified templates across suggested nudges — rituals, lines, and stories Kai draws on to recommend recruiter action.

Initial

Diagnose: the funnel pass that maps each student’s behaviors into Kai’s models and sets your behavioral baseline. We walk through findings with leadership in a working session to shape the partnership scope.

Ongoing

CRM → Kai sync on a steady cadence — new signals in, updated nudges out. Plus an annual diagnostic refresh for aggregate behavior and correlations as your funnel shifts, revisited with leadership at each refresh.

Output

Engagement actions that stay aligned with your data and feed Build as recruiter-facing nudges.
Initial

Diagnose provides the same behavioral map; personas are the segments that emerge when students cluster in the model.

Ongoing

Personas update as data updates. We re-cluster students so Build and K-score actions match who is in each segment now.

Output

Each student receives a summarized persona to help recruiters understand the unique K-score signature of that student.

↳ see this in action for one student

Kai product: Students roster with segment filters and K-scores — Build — students list, segments, and filters

Kai product: Nudges dashboard with student queue, priority, and K-scores — Build — Nudges queue with priorities and recommendations

Kai product: Student Nudges view with timing and recommended email action — Build — Student nudge with priority, timing, and recommended action

Kai product: Select ritual and template with AI email preview — Build — ritual and template selection with email preview

Belonging-driven student intelligence and next-best-action recommendations — the right student, the right action, the right moment.

Initial

We pull from Learn’s 3-day Language Baseline Workshop.

Ongoing

Quarterly language & messaging workshops to refine language associated with outreach actions.

Output

Suggested messaging and talking points, with AI that helps every message sound like your team — for every outreach action.
Initial

We inventory the action types your team already runs, then map them to corresponding rituals and engagement categories.

Ongoing

We work with recruiters in weekly 1-on-1 sessions to facilitate learnings, take action, and derive insights for improvement.

Output

Recruiters get a clear, concise outreach action per student with rationale tied to where belonging is slipping and what to do about it.

Initial

We train Kai to blend prediction signals and K-scores into a single belonging-driven priority score — including which students need engagement, and when.

Ongoing

Continuous learning from new behaviors keeps priority and timing recommendations sharp as the funnel evolves.

Output

A clear “who to contact first” ranking with recommended timing — recruiters get actions only for students who need them, at the moments they’ll land.
Initial

We configure intuitive tags, risk signals, and custom views for the segments your team revisits — plus alert-worthy shift detection without the noise.

Ongoing

Tags, K-score thresholds, and shift-detection models stay aligned and refine as new behaviors and risk signals land.

Output

One-click filters recruiters can work down as a queue — plus clear cues for what’s shifted in a student’s belonging and the patterns worth acting on.

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Kai product: recommendation effectiveness — Measure — recommendation effectiveness

Kai product: recommendation adoption — Measure — recommendation adoption

Kai product: K-score trends — Measure — K-scores

Kai product: cohort analysis — Measure — cohort analysis

Clear visibility into belonging — and whether your interventions are moving students toward enrollment.

Quarterly Steering Committee briefings and monthly leadership check-ins synthesizing cohort behavior patterns, engagement effectiveness, and team adoption. Monthly recruitment team reviews — ignore patterns, what’s working, and refinements for the next cycle. Below: the K-score, cohort, effectiveness, and adoption views in Kai that we pull into those conversations.

Initial

We measure each of the five K-scores against an individual student’s behavior, so the numbers reflect how belonging is being built at the individual level.

Ongoing

We monitor K-scores in real time to help inform the Build phase, and run continuous model calibration so the scores stay sharp as your funnel evolves.

Output

A K-score signature for each individual student that helps teams understand belonging (KBI), engagement (KES), affinity (KAI), momentum (KMS), and connection (KCS).
Initial

We analyze each student’s individual K-score and then up-level to the whole funnel to understand distribution ranges and averages.

Ongoing

We monitor and update each student’s K-score in real-time, rolling up to the cohort level.

Output

Belonging, engagement, and momentum made visible at the cohort scale.
Initial

We configure the Recommendation Effectiveness Report — baselining K-scores and wiring CRM data so every recommendation can be measured against response and enrollment outcomes.

Ongoing

We monitor K-score lift between responders and non-responders, plus performance by channel and timing — surfaced in the monthly recruitment team reviews and fed back into Build.

Output

A single dashboard showing K-score lift by tier (KBI, KES, etc.), response and enrollment rates, and the channels and timing windows that drive belonging — so Build can re-weight confidently.
Initial

We instrument the ignore signal — when a recommendation isn’t taken, the ignore becomes data; acceptance is captured when it is.

Ongoing

Monthly recruitment team reviews (by role and campus location) covering ignore patterns, what’s working, and what to refine in the next cycle — the single place we close the loop with the people running engagement.

Output

A clear read on ignore patterns and recruiter behavior — direct input back into Build’s next refinement.

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Three motions. One community transformation.

Diagnose

Amplify

Measure