> ## Documentation Index
> Fetch the complete documentation index at: https://docs.prisme.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Adoption
> User segments, personalization depth, quality, sentiment, and AI-generated recommendations to grow each segment.
Adoption answers: *who is using our agents, how deeply, and what should we do to grow each segment?*
## User segments
A donut chart segments your active users into three buckets:
| Segment | Description |
| ----------------- | ------------------------------------------------------------------------------------------------ |
| **Power Users** | High-frequency, high-engagement users — typically returning daily and using more than one agent. |
| **Regular Users** | Users with sustained but moderate engagement. |
| **Basic Users** | Light users — a few interactions and rarely returning. |
The card shows total users, the count and percentage in each segment, and a trend badge against the previous period.
If the org has no user data yet, the card shows a "No user data" empty state.
## Personalization
Four metrics that summarize how much the agents are learning about users:
| Metric | What it measures |
| ---------------- | ------------------------------------------------------------------------------------------------------ |
| **Instructions** | Standing instructions users have given (*"always reply in French"*, *"keep answers under 100 words"*). |
| **Preferences** | Captured preferences (formats, channels, tone). |
| **Memories** | Total memories of any type. |
| **Avg Per User** | Memories divided by active users — a proxy for personalization depth. |
A **type breakdown** is plotted below the metrics: Facts / Preferences / Instructions / Relationships, with progress bars and percentages. Hover any label for a tooltip describing what each type stores.
## Quality
Four quality metrics rolled up across analyzed conversations:
| Metric | What it measures |
| -------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Quality Score** | A 0–100 rollup that combines evaluation scores, resolution rate, and feedback into a single number. Color-coded — green ≥70, amber 40–69, red \<40. |
| **Resolution Rate** | Percentage of analyzed conversations the LLM judged as resolved. |
| **Feedback Like Rate** | Share of feedback items that are likes (vs dislikes). |
| **Sentiment distribution** | A horizontal bar showing the positive / neutral / negative split across analyzed conversations. |
If no conversations have been analyzed yet, this card shows "No data".
## Common patterns
A short table of recurring usage patterns the analytics pipeline detected — for example "users asking the same workflow question across multiple agents". Each row shows the pattern, the number of associated instructions, and how many distinct users it covers.
## Recommendations
Priority-ranked actions to grow adoption. Each recommendation has:
* A **priority** badge (high / medium / low).
* A short action string.
* An optional target segment (e.g. *"Basic Users"*).
* An optional metric value (e.g. *"+18% personalization"*) showing the projected impact.
Recommendations are advisory — the platform doesn't apply them.
## Aggregation freshness
Adoption metrics are computed from a daily aggregation. After a large analysis run or fresh memory capture, the page may show stale numbers until the next cycle. A "Waiting for aggregation" banner is shown when the most recent aggregation hasn't completed yet.
You can force a recomputation from the [Organization dashboard](/products/ai-insights/dashboard) using the **Recalculate metrics** menu action.
## Where to go next
The memory store the personalization metrics are built on.
Likes, dislikes, and their categorized reasons.
What users are talking to your agents about.
The agent fleet whose adoption you're measuring.