Blinko

Keep the customers you are about to lose.

We find which customers are likely to leave, when, and why, then measure which retention actions actually keep them. Survival models, machine learning and causal testing, built around how your business makes money.

See how it works

Clients include Fractal, Solventis and OmniXM.

Illustrative retention readout: three accounts entering their risk window with the main driver and a likely time frame, and a save-offer test where a 31% reported save rate corresponds to about 6 percentage points of customers actually kept compared with a holdout.

Retention readout

Illustrative
Entering risk windowRiskLikely in

  • Northline Pay

    Volume down 23%, no contact in 21 days

    78%~5 wks

  • Harbor Dental Group

    4 of 10 seats idle, price rise next month

    64%~8 wks

  • Fairhaven Cabinetry

    Support tickets up 3× this quarter

    52%~11 wks

Save-offer test, last 90 days

Reported save rate31%
Kept because of the offer+6 pts (2–10)

Against a holdout that saw no offer, most saves would have stayed anyway.

Track record

All case studies
~$500K+revenue protected a year by finding at-risk accounts before they left (estimate)Fractal, construction fintech
1.5×better churn detection than the previous process, with a survival modelPerimeter 81, before Blinko
$100M+in yearly transactions watched for early signs of declineFractal, construction fintech

What a churn report doesn't show

A save offer that reports a 31% save rate looks like it works. Compare it with customers who never saw the offer, and most of those saves turn out to be customers who were staying anyway.

31%

of customers who tried to cancel stayed after the offer. That is the number most tools report.

  • Stayed after the offer
  • Cancelled
Illustrative numbers: 100 customers who tried to cancel in one quarter.

  • Customers leave before they cancel

    Usage, seats or payment volume drift down for weeks. A churn report shows it only after the revenue is gone.

  • A risk score is not a reason

    Teams get a red flag with no driver and no next step, so the flag gets ignored.

  • A saved customer may never have left

    Without a holdout, a discount can look like it saves customers when it mostly pays the ones who were staying.

How we work it out

Retention is a business problem first and a modeling problem second. Each step builds on the one before, and every action is tested against customers who did not get it.

  1. Business understanding

    We map how revenue actually leaves your business, then agree what churn means and what each customer is worth.

    • Cancellations, downgrades, failed payments and falling volume
    • One customer record across CRM, billing and support
    • A dollar value on every account at risk

    Techniques: Revenue mapping, Cohorts, Unit economics, Data cleaning

  2. Survival models

    Not just whether a customer will leave, but when. Time-to-churn models show which accounts are entering their risk window.

    • Risk across each customer's lifetime
    • New and long-standing customers modeled differently
    • Seasonal dips kept apart from real decline

    Techniques: Survival analysis, Hazard models, Time-varying signals, Backtesting

  3. Machine learning with reasons

    Risk models that learn from your history and explain every score, so teams know what to do about it.

    • A risk score for every account, refreshed daily
    • The top drivers behind each score
    • Accuracy tested on past periods first

    Techniques: Gradient boosting, SHAP drivers, Calibration, Drift monitoring

  4. Causal measurement

    We test which offers, calls and fee changes actually cause customers to stay, against a holdout that got nothing.

    • Lift per retention action, with error bars
    • What each kept customer really costs
    • Offers that pay customers who were staying anyway

    Techniques: Holdout tests, Uplift modeling, Causal inference, Experiment design

Then the results reach daily work: alerts to account owners, AI-drafted renewal briefs and CRM tasks, with a person approving each one.

See the AI tools we build

See who is likely to leave, when, and why.

Every account gets a risk score, a likely time window and the drivers behind it, so the right person can act while there is still time.

Explainable
Every score comes with the drivers behind it.
Tested on your history
We backtest on past periods before anyone relies on a prediction.
Built into daily work
Scores reach the CRM, the app, or an alert, not a slide deck.
See every model we build

Health score · 8-week forecast

Illustrative

at risk below 50actualforecasttoday 41

Northline Pay · health score

41 / 100

Down from 82 · What is moving the score

  • No contact for 21 days−18
  • Payment volume down 23%−14
  • Support tickets up 3×−9
  • Renewal due, no meeting booked−6
  • Customer for 4 years+7

In the client's words

Blinko delivered exceptional work on a highly complex problem. Deep expertise in data science, machine learning, and business domain knowledge – exceeded all expectations.
Steve Conn, Analytics Director, Fractal & Sky Systems

Nearly 100 client engagements delivered since 2023. Fiverr Pro and Top Rated.

Case study, construction fintech

Fractal runs $100M+ a year in transactions on one live system we built

  • 30days until the whole team was using it
  • 10–12hours saved per person every week
Read the case study

Three ways to start

Each one stands on its own. Most clients start with the scan and add the rest when it pays off.

How we work, step by step

  1. Revenue Risk Scan

    2 weeks

    We find the accounts most likely to leave, put a dollar value on the risk, and show what is driving it.

    Best when you suspect churn costs more than your reports show.

  2. Retention offer audit

    2 to 6 weeks

    We measure whether your save offers, discounts and win-back campaigns actually keep customers, and what each kept customer costs.

    Best when you spend on retention offers but cannot say which ones work.

  3. Retention system

    First version in 4 to 8 weeks

    A live early-warning system: survival and risk models, alerts to account owners, and holdouts that keep measuring what works.

    Best when you want retention run on evidence every week, not once.

Tell us where you are losing customers.

Describe how customers leave today and what you have tried. We will reply honestly about what can be measured and what it would take.

No pitch, just an honest answer.