Blinko
Predictive analytics & ML

Know what is likely to happen next, and why.

Machine learning, time series, and statistical models built on your own history. Each prediction comes with the reasons behind it and lands where your team already works.

See what we predict

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
What we predict

Models for the decisions you make every week.

Each one answers a specific business question, is tested on your past data, and explains its answers.

Customer success and revenue

Churn and retention

Answers“Which customers are likely to leave, and why?”

  • A risk score for every customer, updated regularly
  • The reasons behind each score, in plain language
  • Alerts routed to the account owner
CRMProduct usageSupportBilling

~$500K+

Annual revenue protected (estimate) · Fractal

Case study

Marketing, growth, and finance

Customer lifetime value

Answers“What will each new customer be worth?”

  • Early value predictions from a customer's first days
  • Value forecasts by cohort and acquisition channel
  • Inputs for budget and bidding decisions
TransactionsProduct eventsAcquisition source
Read how we build LTV prediction systems

Finance and operations

Time series forecasting

Answers“What will revenue, demand, or volumes look like next quarter?”

  • Forecasts with realistic ranges, not a single guess
  • Trends, seasonality, and holidays built in
  • Scenarios for pricing, hiring, or budget changes
Sales historyPipelineVolumesCalendar

Operations, finance, and risk

Time series anomaly detection

Answers“Is this spike or drop normal, or does someone need to act?”

  • Continuous checks on metrics against their expected range
  • Seasonality taken into account, so fewer false alarms
  • Alerts with the likely cause, sent to the right owner
TransactionsVolumesOperational metrics

Credit, risk, and finance

Credit and payment risk

Answers“Which applicants or accounts carry the most risk?”

  • Risk scores your credit team can explain
  • Monitoring that flags changes over time
  • Thresholds agreed with the people who make the decision
TransactionsRepayment historyCredit data

40%

More financial reviews completed · Apickle

Sales and marketing

Lead scoring and conversion

Answers“Which leads will convert, and what makes them convert?”

  • A conversion score for every lead, inside your CRM
  • The behaviours that predict a sale
  • Priority lists for each rep
CRMWebsite activityMarketing

Statistical analysis

Not every question needs a model.

Sometimes the answer is a well-designed test or a careful analysis. We measure whether a change actually worked, what really moves a KPI, and how confident you can be in the result.

  • A/B tests and experiments
  • Campaign and pricing impact
  • Driver analysis
  • Honest confidence levels
How we build models

From business question to a model your team uses.

  1. 1

    Frame the decision

    We agree the question, who acts on the answer, and how we will measure success.

  2. 2

    Prepare the data

    We connect and clean your history and check there is enough of it to learn from.

  3. 3

    Build and backtest

    We test the model on past periods and compare it with a simple baseline.

  4. 4

    Deploy and monitor

    Scores reach your CRM, app, or alerts, and we track accuracy over 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.

Have a question you want predicted?

Tell us the decision you are trying to make. We will tell you honestly whether your data can support a model, and what it would take.