Stop guessing your decisions: steer your business with predictive analytics
Leverage your historical data to anticipate demand, optimise your stock levels and spot opportunities before the competition.
From forecast to action
Making strategic decisions “on gut feeling”
Before choosing an algorithm, we define the target, when action remains possible, the cost of errors and the person who retains the final decision.
Oversized stock levels or repeated stockouts
Without anticipation, you tie up cash or lose sales.
Unable to anticipate customer churn
You find out a client is leaving when they cancel their contract, with no chance to act beforehand.
A mountain of untapped data in your databases
You accumulate gigabytes of history without ever drawing clear trends from it.
How we turn your raw data into predictive models
Predictive analytics means applying statistical and machine learning models (Machine Learning) to your past data to identify recurring patterns and calculate the probability of future events.
Concrete use cases and applied models
Sales forecasting and automatic replenishment (Time-Series Forecasting)
Mathematical models (Prophet, XGBoost) combining your sales history with seasonality, weather or economic trends to tell you the exact quantity of products to order.
Customer churn risk detection (Churn Prediction)
An algorithm analyses your customers’ usage behaviour (drop in login frequency, fewer orders, support tickets). If it detects abnormal behaviour, the tool alerts your sales team so they can reach out to the client in time.
Predictive equipment maintenance
Analysis of sensor data or operating cycles to anticipate the failure of a machine or vehicle before it happens.
Integration into simple dashboards (Looker Studio / Custom Dashboards)
Results don’t stay locked in a data engineer’s file. We integrate them directly as visual charts and actionable alerts within your day-to-day management software.
Custom decision-support models
Fine-grained demand and sales forecasting
Our algorithms analyse your history and external factors to give you reliable projections.
Early detection of weak signals (Attrition / Churn)
Identify the customer behaviours that precede a cancellation so you can step in at the right moment.
Clear, actionable decision-making dashboards
No metrics only data scientists can understand: we display concrete recommended actions.
Delivery stages of your Data project
Cleaning and structuring your historical data
Development and training of the algorithmic models
Validating prediction accuracy against your historical data
Integrating alerts and dashboards into your daily routine
Strategic results
- Optimised stock levels and freed-up cash flow.
- Reduced customer churn rate thanks to preventive actions.
- Fast decision-making backed by verified figures.
They trust us
“I recommend Play Digital because of their ability to understand the needs of the market in which clients operate and incorporate this dimension into their solutions. The support goes beyond purely technical thinking; it includes strategic considerations and elements of marketing and communication. Play Digital has allowed us to develop a custom solution in a complex market due to numerous regulations. The flexibility of the chosen solution allows us to constantly adapt to changing demands and market constraints.”
Frequently asked questions
01How much historical data do we need for the predictions to be reliable?
The longer the history, the better the accuracy. Generally, 12 to 24 months of operational or sales history is enough to train a highly effective predictive model.
02What is the difference between a traditional dashboard and predictive analytics?
A traditional dashboard shows you what happened yesterday (observation). Predictive analytics uses your past data and algorithms to tell you what is likely to happen tomorrow (anticipating stock levels, customer churn risk).
03How can an algorithm help me reduce my storage costs?
The algorithm combines seasonality, your supplier lead times and sales trends to tell you exactly when and how much to order, thereby avoiding overstocking or stockouts.
04Do I need a Data Scientist on my team to use your tools?
Not at all. We translate the mathematical complexity into very simple recommendations on your screen: visual alerts (“Reorder 50 units of product X before Thursday”) and clear charts.
05What happens if the market undergoes a sudden, unpredictable change?
Our models are continuously retrained on recent data. If a trend reverses sharply, the algorithm readjusts its projections as early as the following week.
Start with one decision
Evaluate the value of a signal before industrialising a model
Describe the decision, available data, action frequency and consequences of an error so we can define an initial evaluation protocol.
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