AI & Intelligent Solutions · Service 09 of 14

Predictive Analytics Services forecasts your team can act on, with accuracy shown

O Clock Software, headquartered in Chennai, India, builds predictive analytics that forecast demand, churn, risk and capacity from your historical data. Predictions are delivered into the dashboards and tools your team already uses, and each one shows how accurate it has been.

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  • 2009Founded in Chennai
  • 1,000+Projects delivered
  • 12+Countries served
  • 100+Engineers in-house
  • 4.6★Google rating · 59 reviews

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What we build

Predictive Analytics: what we build

Forecasts for demand, churn and risk built on your historical data. Here are the six kinds of predictive analytics services work we are asked for most often.

Data readiness

Is your data ready for predictive analytics?

Most forecasting projects are decided by data, not algorithms. This is the readiness check we run in the first week of every engagement.

Usually ready when

  • History covers seasonal patterns, usually one to two years
  • The outcome you want to predict is recorded consistently
  • Key drivers such as promotions, holidays and capacity are captured
  • Data can be accessed regularly, not only as one-off exports
  • Someone owns the decision the forecast will inform

Needs work first when

  • Outcomes are recorded differently by different teams
  • Data sits in spreadsheets with conflicting definitions
  • A major process change makes old history unrepresentative
  • Nobody will change a decision based on the forecast

How we deliver

How we deliver Predictive Analytics

  1. 01

    Decision and measure

    The decision the prediction informs and how accuracy will be judged.

  2. 02

    Data audit and features

    Clean, join and enrich the data; agree definitions.

  3. 03

    Baseline first

    A simple benchmark every model must beat.

  4. 04

    Model and backtest

    Test on past periods the model has never seen.

  5. 05

    Deliver and monitor

    Predictions in your tools, with accuracy tracked over time.

Technology

Technology for Predictive Analytics

Chosen per project for fit, maintainability and your team's skills.

Data

  • PostgreSQL
  • BigQuery
  • Snowflake
  • Databricks

Modelling

  • Python
  • scikit-learn
  • XGBoost
  • LightGBM
  • Prophet
  • statsmodels

Delivery

  • Power BI
  • Metabase
  • Grafana
  • APIs into CRM and ERP

Operations

  • MLflow
  • Airflow
  • dbt

Straight answers

Is Predictive Analytics right for you?

We would rather recommend less than sell you more. Here is how we judge it.

A good fit when

  • A recurring decision depends on what happens next
  • Enough consistent history exists
  • The outcome is recorded
  • Someone will act on the forecast

  • There is little or no history

    New products or processes need rules, comparable cases or expert estimates until data accumulates.

  • Nobody will act on it

    A forecast that does not change a decision is just a report. We start from the decision.

  • The past no longer represents the future

    After major changes, models need recent data or explicit adjustments.

Use cases by industry

Predictive Analytics by industry

The same capability, shaped by the data, rules and users of each sector.

Related work

Where we have shipped this

Platforms in production that use this capability. See all work

Related services

Often combined with Predictive Analytics

Solutions built on this service

Prefer engineers in your own team?

The full service area

FAQ

Predictive Analytics FAQ

What is the difference between predictive analytics and business intelligence?

Business intelligence reports what has already happened, such as last month's orders; predictive analytics estimates what is likely to happen next, such as next month's demand. Most teams need both, and predictions are often delivered inside BI dashboards.

How much historical data do we need?

For seasonal forecasts, one to two years of consistent history is a good starting point; for scoring models such as churn, several hundred past examples of the outcome you want to predict. The data audit shows what your data can support.

How accurate will the forecasts be?

Accuracy depends on how predictable the underlying behaviour is. We backtest models on past periods they have not seen, compare them with a simple baseline, and report the expected error range alongside every forecast.

Can you explain why a prediction was made?

Yes. We show the main drivers behind each score or forecast, using techniques such as SHAP values, so people can check that the reasoning makes sense before acting.

Where do the predictions appear?

Wherever your team works: a dashboard, your CRM or ERP, a daily email or an API used by other systems. Predictions refresh on a schedule that matches how often the decision is made.

Do predictive models need maintenance after launch?

Yes. Behaviour changes over time, so we monitor accuracy, alert when it drifts, and retrain on recent data on a schedule or when performance drops.

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