Demand forecasting & churn prediction: Data & Predictive Analytics

Use the data you already have to see what's coming. We build forecasting and analytics systems that help you plan with confidence.

Definition

What is predictive analytics?

Predictive analytics uses historical data and machine learning to forecast what is likely to happen next, such as next month's demand for a product, which customers may churn or which transactions look unusual. The forecasts help teams plan purchasing, production and retention with evidence instead of gut feel.

Service

What we deliver

  • Demand, sales and inventory forecasting
  • Customer churn and lifetime-value models
  • Anomaly and fraud detection
  • Data pipelines and warehouse setup
  • Dashboards you can question in plain language

A good fit if

You have years of transaction history but still plan from gut feel and spreadsheets.

Capabilities

ForecastingMachine learningData engineeringBI
Process

How we build a forecasting system

01

Pick the decision

We start from the decision the forecast should improve, such as what to reorder and when.

02

Assess your data

We check how much history you have, how clean it is and what is missing, and tell you early if it is not enough.

03

Build and back-test

Models are tested against past periods and compared with your current method, so you can see whether they do better.

04

Put it in daily use

Forecasts reach the dashboards and tools your team already uses, and are monitored as conditions change.

Illustrative example

What it could look like.

Imagine a retailer planning purchases across several stores from spreadsheets. A demand model uses sales history, seasonality and promotions to forecast each product by store, then flags what to reorder and when, so buyers review exceptions instead of building the plan by hand.

Where to start

Your first project, and when to skip it.

A good first project is one forecast tied to one decision, such as weekly demand for your top-selling products. Most first projects begin as a proof of concept lasting two to four weeks. See ways to work with us

If you have little historical data, or your market has just changed sharply, a model has little to learn from. Clean reporting is often the better first step.

FAQ

Data & Predictive Analytics, explained.

Have a question we haven't covered?

Talk to us
How much historical data do we need?

It depends on the problem, but most forecasting projects start working with one to two years of transaction history. We assess your data in the discovery phase before you commit.

Our data is spread across spreadsheets and systems. Is that a problem?

No. Data pipelines and warehouse setup are part of what we deliver, so your data ends up in one reliable place.

Can non-technical staff use the dashboards?

Yes. We build dashboards you can question in plain language, so anyone on your team can ask about the numbers.

Data & Predictive Analytics

Start with a conversation.

Tell us about the process you want to improve. We'll recommend the simplest path to a result, and the first conversation is free.