Customer Retention Engine
"Keep who matters. Win back who left."
Identifies high-risk customer segments before they churn. It maps behavioural decay across feature usage, support tickets, and payment failures to trigger precise intervention workflows.
Key Applications
+ 3 Additional Industry Applications
Strategic Outputs
- Live churn risk scores (High / Medium / Low)
- LTV-ranked customer lists
- Segment-specific retention playbooks
- Win-back campaign triggers
- Cohort retention waterfall
Ecosystem Integration
Decision Framework
Managed intelligence layers that scale with your enterprise operations and data complexity.
Descriptive Analysis
Strategic parameters & pipeline architecture
Real implementation stack — Survival Analysis × XGBoost + SHAP × Neo4j graph layer
Ingest
Behavioural Event Logs signals batched via async pipeline into staging layer
Transform
Graph relationships built — XGBoost + SHAP applied on entity nodes
Serve
Scored outputs streamed to SaaS & Subscription endpoints in real-time
# Customer Retention Engine — ingestion pipeline
import asyncio
from neo4j import AsyncGraphDatabase
from pydantic import BaseModel
class XGBoost+SHAPRecord(BaseModel):
entity_id: str
behavioural_event_logs_score: float
metadata: dict[str, str]
async def run_pipeline(
records: list[XGBoost+SHAPRecord],
uri: str,
auth: tuple[str, str],
) -> None:
driver = AsyncGraphDatabase.driver(uri, auth=auth)
async with driver.session(database="neo4j") as session:
await session.execute_write(
_merge_entities,
[r.model_dump() for r in records],
technique="Survival Analysis",
)
await driver.close()
async def _merge_entities(tx, batch, technique):
await tx.run("""
UNWIND $batch AS row
MERGE (e:Entity {id: row.entity_id})
ON CREATE SET e.created = datetime(),
e.technique = $technique
ON MATCH SET e.updated = datetime(),
e.score = row.behavioural_event_logs_score
""", batch=batch, technique=technique)Strategic Implementation Kit
Access production-ready Python pipelines, optimized Cypher queries, and validated Pydantic schemas. Available after a technical discovery session.
- Full Neo4j schema + seed data
- Production Python pipeline
- FastAPI + Redis serve layer
- Docker Compose setup
Stack
Intelligence Engine Sandbox
See the engine in action
Strategic Perspective
Read our analysis of the operational philosophy and strategic metrics behind the Customer Retention Engine framework.
Transform your Retail operations
Get a custom strategic roadmap, ROI projection, and delivery plan tailored to your enterprise landscape.
