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Case Study

Developed an ML-powered Churn Prediction Solution ‘RetainIQ+’ for Sonny’s

We built RetainIQ+ for Sonny’s, pairing ML-powered churn prediction with AI-driven marketing to reduce the churn rate and boost profits.

218,037 Active customers monitored
9,868 At-risk customers flagged
$85,229 Revenue at risk surfaced
17.88% Projected 90-day churn
Overview

The brief that revealed a much bigger problem.

We were already working on Sonny’s Data Platform Modernization requirement. In parallel, we also helped them build a solution that could identify and predict where they were going to lose money. Our ML-powered Churn Prediction solution helped them identify layers that were probably costing them thousands of dollars, if not millions.

Appinventiv built RetainIQ+ for Sonny’s, pairing ML-powered churn prediction with an AI marketing CRM so car wash operators can predict membership churn and act on it before the revenue walks out the door.

Challenge

Operators could see churn only after it cost them.

We uncovered why operators were losing members before they had the visibility to step in, then mapped the gaps RetainIQ+ needed to close.

Churn stayed invisible until the cancellation landed

Churn stayed invisible until
the cancellation landed

Operators only found out a member was leaving once they had already gone, which left no early warning and no window to step in and save the relationship.

Churn stayed invisible until the cancellation landed

Marketing ran on instinct,
not data

Every campaign was built and sent by hand, with nothing in place to target members by churn risk, visit behavior, or lifetime value.

Churn stayed invisible until the cancellation landed

Member data sat trapped
across sites

Every location held its own records. No single view tied a member's visits, spend, and payment health together into one risk picture.

Churn stayed invisible until the cancellation landed

Blanket promotions were
burning the list

Generic offers went to everyone. They drove unsubscribes and missed the members who actually needed saving.

Solution

A marketing CRM that pairs ML prediction with AI-driven retention.

We built RetainIQ+, the predictive retention layer for Sonny's. Machine learning scores every member for churn risk, while AI groups them automatically and recommends the right campaign to win them back. The models read behavioral, transactional, and engagement signals across the network and turn them into action.

RetainIQ+ is the heartbeat of the platform, where machine learning forecasts active members, at-risk members, revenue at risk, and projected churn, and refreshes every day. A churn probability distribution sorts the entire base into low, medium, and high risk.

A top risk factors view explains the why behind the numbers, ranking signals like early usage risk, decreasing wallet share, visit decline, billing issues, and low engagement. A predicted at-risk sites list points operators to the locations bleeding the most revenue.

The CRM turns those risk signals into ready-made audiences. No query building, no spreadsheets.

Each AI segment arrives sized and risk-scored, from billing issue risk to low engagement to decreasing wallet share. Operators move from a segment straight into a campaign with a single click.

The CRM recommends ready-to-launch campaigns for each risk segment, including payment recovery, member re-engagement, spend revival, new-signup outreach, and visit-frequency boosts. Each suggestion shows audience size and risk level before launch

Operators manage email and text campaigns from one workspace, with live status, delivery, open metrics, and territory filters.

Automation rules can also trigger the right message after failed payments, missed visits, or new signups, keeping retention running without constant manual effort.

Prebuilt and shared templates cover the moments that repeat: license-plate updates, payment-method changes, coupon offers, and purchase confirmations.

Personalization tokens and coupon codes drop in member names and offers automatically, so every send feels one-to-one.

Checkout and membership forms deploy across hundreds of sites from a single place. They capture clean, structured data and send it straight back into the CRM.

That closes the loop between the moment a member signs up and the work of keeping them.

Tech Stack

Engineering Stack Behind Retain IQ Plus

Python 3.12
Python 3.12
Tavily
Tavily
FastAPI
FastAPI
LangChain
LangChain
Azure Cloud
Azure Cloud
Azure Container
Azure Container
LLM
LLM
XG Boost
XG Boost
GPT 4o
GPT 4o
GPT 4.1
GPT 4.1
MongoDB
MongoDB
Redis
Redis
Docker
Docker
Kubernetes (AWS EKS)
Kubernetes (AWS EKS)
AWS ECR
AWS ECR
GitLab CI
GitLab CI
GPT 4o Mini
GPT 4o Mini
Pydantic
Pydantic
AWS Secrets Manager
AWS Secrets Manager
GPT 4.1 Nano
GPT 4.1 Nano
Result

From reactive to predictive, and from churn to retention.

Operators stopped guessing. The platform flags at-risk members early, hands marketers the exact audience to reach, and recommends the message that wins them back. And every operator now sees exactly where the risk sits.

16.65%
Active-customer decline
caught early
83.92%
Of churn risk from
early usage
5
Risk factors ranked
per customer
3
Highest-risk sites
pinpointed

RetainIQ+ became more than a tool for blasting promotions. It turned into the retention engine of Sonny's customer-experience offering, giving every operator on the network a data-driven way to protect the revenue they already earned.

Building something similar?

Talk to us about ML-powered churn prediction, AI marketing CRM development, or retention automation for your platform.