Profile
Complete customer state
Identity, orders, subscriptions, support history, economics, owner, and consent come together before the next reply.
Unified Customer Memory for AI and Humans
Give AI agents and human teams a unified profile, memory, and timeline for every customer
Memory
Preferences and prior wins
Source-backed context travels across future conversations so agents remember what the customer values and what worked last time.
Groups
Segments with economics
VIPs, at-risk subscribers, high-cost customers, and save audiences update continuously with LTV, cost, CSAT, and revenue impact.
Actions
Next step, owner, outcome
Recommendations include the reason, approved path, owner, status, and result so the CRM learns from every intervention.
Recall the customer before the reply is written.
Applied CRM retrieves the few source-backed memories that change how an agent responds in the moment.
Ava Collins
Source-backed customer memory
Company
Northstar Outfitters
Segment
VIP subscriber
Owner
Maya Tan
Channel
Email + SMS
Consent
Retention offers approved
Open issue
Delayed exchange / order #8421
Retrieved for reply
Fit preference
Exchange note
Tall length, soft fabrics, no boxed replacement.
Next reply
Policy + order
Use prior save context, loyalty credit, and preferred size.
Build groups around value, risk, and relationship history.
CX teams ask for the customers that matter now and see economics, service cost, and save movement without exports.
Live customer groups
Segments update around value, risk, service cost, and save movement.
Matched customers
428
live audience
Save value
$412k
protected LTV
Movement
+23
saves this week
Audience movement
Risk becomes a save-ready audience.
At riskSave ready
At-risk VIP subscribers
184 customers / $412k
High-cost refund loop
67 customers / $96k
First-order fit risk
119 customers / $138k
Loyalty recovery ready
58 customers / $74k
Turn memory into a governed action and outcome.
Each recommendation carries evidence, classification, owner, status, and writeback so the next interaction starts smarter.
Recommended action
Keep the exchange, protect the subscription, and write the result back.
Customer
Ava Collins / VIP subscriber
Evidence
Order #8421, transcript, fit preference, consent
Owner
Maya Tan / retention queue
Controls and writeback
Policy path allowed
Credit below approval limit
Consent and source attached
Status
Ready for approval
Writeback
Outcome, save reason, next preference
One readout for memory, action, and revenue.
The CRM should make the practical business case visible: what the system knows, what action it recommended, what changed, and what value was protected.
Readout
Customer intelligence
Profile, timeline, source-backed memory, consent, and relevant context before the reply.
Readout
Business outcome
Revenue saved, LTV protected, cost to serve, save rate, CSAT, and refund leakage.
Readout
Risk decision
Abuse risk, churn risk, operational failure, and loyalty recovery are separated before action.
Readout
Closed-loop learning
Action, owner, outcome, and what worked feed future recall and recommendations.
| Readout | What Applied ties together |
|---|---|
| Customer intelligence | Profile, timeline, source-backed memory, consent, and relevant context before the reply. |
| Business outcome | Revenue saved, LTV protected, cost to serve, save rate, CSAT, and refund leakage. |
| Risk decision | Abuse risk, churn risk, operational failure, and loyalty recovery are separated before action. |
| Closed-loop learning | Action, owner, outcome, and what worked feed future recall and recommendations. |
Works across the Applied agent family.
Use the same context and controls across support, save, conversion, and help desk workflows.
Trusted by millions of customers
