PREDICTIVE CUSTOMER RETENTION

Why react to churn when you can prevent it from happening?

We automate early churn detection using AI-driven analytics – transforming your existing data directly into measurable results and concrete next steps. Do you know which customers are most likely to leave next month, and why?

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How our approach differs

The Reactive Way

✗React after the customer has already left

✗Gut feeling drives retention decisions

✗All churners treated the same

Churn

Signal

Notice

React

Too late

The Hopmann Approach

✓Predict churn 60 to 90 days in advance

✓AI-powered risk scores per individual customer

✓Targeted actions for each risk tier

Signal

Detected

Action

Card sent

Retained

~5×

lower cost of retaining a customer compared to acquiring a new one

70%

prediction accuracy from day one using AI-driven analytics

< 5 days

fast-track deployment on your existing data – no lengthy setup required

20 years

expertise in transactional data and genuine client partnership

Katja Bulaeva
Data Visualization Specialist &
Expert for Predictive Churn

+49 89 219 099 021

Book your free 30-minute initial consultation now.

Contact

We know your pain points.

The 4 most common reasons companies lose customers they could have saved.

1. Churn is noticed too late

A common problem: When a contract is not renewed, the decision was made months earlier. The signals were there – transaction history, declining engagement, support tickets – they were just never systematically analyzed.

Our approach: Our ML model detects churn patterns 60 to 90 days in advance and delivers individual risk scores per customer – before any obvious signal appears.

2. Retention is treated as a fire drill

A common problem: Discounts and calls only go out after a customer complains or cancels. There is no proactive, systematic process in place – only reaction.

Our approach: Prioritized Action Cards give your team concrete recommendations per customer and risk tier – well before things escalate.

3. No internal data science capacity

A common problem: Mid-market companies often lack the internal team to build, validate, and maintain predictive analytics models on an ongoing basis.

Our approach: Our pre-built ML pipeline runs on your existing data. No internal data science team required – results delivered within 5 business days.*

4. CRM data is rich but unused

A common problem: Transaction history, engagement patterns, and support tickets sit in systems without ever being turned into actionable risk signals.

Our approach: We connect all relevant data points from your existing sources and translate them into a Power BI report your team can act on immediately – no new software needed.

Our Approach

Predictive Customer Retention that goes well beyond a dashboard.

Most BI tools tell you what happened last quarter. With our AI-driven approach, you can see which customers are at risk of leaving next month – and what your team should prioritize next.

Machine learning, not historical averages

Standard analytics look backward. Our machine learning (ML) models are trained on your own behavioral and transactional data. They identify which individual customer is at risk and why – before that customer signals it themselves.

Results in days, not months

We use a pre-built pipeline that runs on your existing data. No lengthy setup project, no new software to purchase. Your team sees the first at-risk customers and recommended actions within 5 business days.

Answers, not just numbers

Our team combines data science, marketing analytics and commercial context. The output is not just a model report – it is a prioritized action list your team can execute immediately, without a data science background.

With our Predictive Customer Retention we are able to identify at-risk customers often before they show any obvious signals. What used to take weeks of manual analysis now runs automatically on the client’s own data and delivers results in days.

Team Lead Analytical CRM, Hopmann Marketing Analytics

Roadmap & Milestones

From first data to a live retention system.

Our process runs in three phases. Each has a clear goal, defined deliverables, and a measurable outcome before the next phase begins.

Phase 1: Churn Assessment and Data Readiness

Churn Assessment & Data Readiness

Goal: We map your churn reality, quantify the financial impact, and evaluate which customer data is available and usable.

Result: A clear picture of your churn situation, a data readiness scorecard, and prioritized retention levers – before any model is built.

Phase 2: Predictive Model and Action Cards

Predictive Model & Action Cards

Goal: We run our pre-built ML pipeline on your data, validate the results, and translate risk scores into concrete, role-specific Action Cards.

Result: A Power BI report with individual risk scores and Action Cards – delivered within 5 business days.*

Phase 3: Live Implementation and Team Enablement

Live Implementation & Team Enablement

Goal: We deploy automated scoring, a live retention dashboard, and enable your team to run the system independently.

Result: Your investment delivers ongoing value – not a one-time report, but a continuously operated system.

More data alone does not create better decisions.

Why Hopmann is the right partner for you.

Companies today have access to extensive data – but rarely to the bridge between a data point and a concrete action. A reporting tool or another dashboard does not close this gap. The difference lies in how data is translated into targeted guidance and measurable outcomes.

Capability	Hopmann Predictive Retention	BI / Analytics Consultancy	In-House Data Team	SaaS Churn Tool
Speed & Time-to-Value
First results on client data within 5 business days	✓	✕	✕	(✓)
Pre-built pipeline – no lengthy setup project required	✓	✕	✕	✓
Analytical Depth
ML model trained on the client’s own behavioral and transactional data	✓	(✓)	✓	✕
Individual risk score per customer – not segment averages	✓	✕	✓	(✓)
Works with any transactional or behavioral data source	✓	(✓)	✓	✕
Operationalization & Support
Action Cards: prioritized next steps per customer and risk tier	✓	✕	✕	✕
Delivered as a Power BI report – no new software purchase needed	✓	(✓)	(✓)	✕
Multi-specialist team: data science + marketing analytics + BI	✓	✕	✕	✕
Fixed cost – no long-term headcount commitment	✓	(✓)	✕	✓

Proven engagement formats from 20 years of practice.

Our Offer

Our offer provides structured formats to make your retention projects plannable and controlled from the start. All three modules deliver concrete, actionable output from day one. We recommend a short alignment call to determine the right starting point for your situation.

Churn Assessment Workshop

Entry point · fixed fee

A structured 1-day session that maps your current churn reality: which customers are at risk, which data is available, and what the financial impact of inaction looks like.

Duration: 1 day

Get in touch

Predictive Retention Quick Start

On request · output-based

We run our pre-built ML pipeline on your data and deliver a Power BI report with individual risk scores and concrete Action Cards per customer – within 5 business days.*

Duration: from 5 business days*

Request a sample analysis

Ongoing Retention Intelligence

On request

An ongoing partnership with automated pipeline, monthly model recalibration, live retention cockpit, and regular insight reviews. Not a one-time report – ongoing, measurable value.

Individual scope based on objectives and data availability.

Get in touch

From data point to action – in days, not months.

Your Retention Cockpit at a glance.

Once implemented, your team has a live dashboard that converts raw data into usable retention intelligence. Delivered as a Power BI report, ready to use from day one.

Churn Risk Overview

A fast read of your customer base segmented by ML-based risk tiers, automatically updated from your existing data sources.

Action Cards per Customer

Every high-risk customer receives a tailored recommendation. Concrete, prioritized, and ready to act on.

Retention Trend Tracking

Follow how your retention rate develops over time, which segments are improving, and where actions are taking effect.

CRM-Compatible Output

Risk scores and Action Cards integrate directly into your existing CRM or marketing automation tool. No new software required.

Request a sample analysis

Illustrative concept – Retention Cockpit

Active Customers

1,284

↑ +3.2% vs. last quarter

High Churn Risk

Require immediate action

↑ 6.8% of customers

Retained this Q.

via Action Cards

↑ €128k ARR secured

Churn Risk Trend · 12 months

Risk by Segment

Action Cards · High-Risk Customers

Customer	Score	Risk	Recommended Action
Company 1	94	High	Management call within 7 days
Company 2	81	High	Offer loyalty pricing review
Company 3	67	Medium	Send usage tips + check-in email
Company 4	42	Low	Include in Q2 NPS survey

Churn Risk Score by Customer Segment

Enterprise

SaaS

Retail

Insurance

SMB

Automotive

Illustrative concept – actual design adapted to client environment

Why AI and not just historical averages

Standard BI tools look backward: churn rates, renewal trends, last quarter’s numbers. Our approach uses machine learning (ML) trained on behavioral and transactional patterns to identify which individual customer is at risk and why – before they signal it themselves.

See your churn risk on your own data within 5 days*. Request a sample analysis.

Katja Bulaeva
Data Visualization Specialist &
Expert for Predictive Churn

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What our customers often want to know.

FAQ on Predictive Customer Retention

What is Predictive Customer Retention and how does it work?

Predictive Customer Retention uses AI-driven analytics to identify which customers are likely to churn before they show obvious signals. Hopmann builds a machine learning model on your existing data and delivers individual risk scores and prioritized Action Cards within 5 business days.

How is the Hopmann approach different from off-the-shelf churn software?

We build individual models tailored to your specific data and business context. The result is not segment averages but individual risk scores per customer – combined with concrete recommendations your team can act on immediately.

What data do I need – and does my current data qualify?

In most cases it does. You need customer master data and transactional or behavioral records such as purchase history, login frequency, or support interactions. Before the project starts, Hopmann runs a data readiness assessment to confirm what is available and usable.

How is Predictive Retention different from standard BI reporting?

Standard BI shows what has already happened. Hopmann’s AI-driven approach identifies which customers are statistically at risk and delivers a prioritized action list your team can execute immediately – without a data science background.

Does this work for both B2B and B2C companies?

Yes. For B2B, the model delivers individual risk scores per account. For B2C, we provide ongoing segmentation and cohort-level predictions. The approach is industry-agnostic and trained on your specific data.

What happens after the Quick Start?

You receive a joint debrief and decide whether to continue with the Ongoing Retention Intelligence module – which includes model recalibration, a live retention cockpit, and regular insight reviews. There is no obligation to proceed.

* The <5 business day delivery timeline applies when the client provides complete and validated transaction data in the required CSV format, completes the business context questionnaire, and confirms the project scope within 1 business day of kick-off. Timelines may vary depending on data quality and availability.