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Solution Framework

Customer Retention Engine

"Keep who matters. Win back who left."

Retail
SaaS
Subscription

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

Churn Prediction Analysis
RFM Segmentation
Customer Lifetime Value Calculation
Retention Rate Analysis
First Purchase to Repeat Analysis
Customer Segmentation for Personalization

+ 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

CRM (Salesforce, HubSpot)
Email/SMS platforms
CDP (Segment, mParticle)
Loyalty systems

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

01

Ingest

Behavioural Event Logs signals batched via async pipeline into staging layer

PythonBehavioural Event LogsKafka
02

Transform

Graph relationships built — XGBoost + SHAP applied on entity nodes

Neo4jCypherXGBoost + SHAP
03

Serve

Scored outputs streamed to SaaS & Subscription endpoints in real-time

FastAPIRedisWebhook
SIMULATED
# 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)
Full Access

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

Neo4jPython 3.12FastAPIPydantic v2RedisDocker

Intelligence Engine Sandbox

See the engine in action

Customer Retention Engine
$ # Behavioral signal stream
Customer: Enterprise cohort Q1–Q3. Signals: login frequency, support ticket cadence, contract renewal window = 47 days.
AI Engine: Orchestrating Agents...

Strategic Perspective

Read our analysis of the operational philosophy and strategic metrics behind the Customer Retention Engine framework.

Read Strategic Analysis

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