Predictive Modeling

Predictive modeling leverages statistical techniques, algorithms, machine learning, and artificial intelligence to forecast future outcomes based on historical data. It answers the question, "What could happen in the future?" Its goals are to:

Anticipate future events and behaviors.
Support strategic decisions by providing data-driven forecasts.

A few examples...

Attrition & Retention
Forecasting customer churn probabilities and identifying actions to enhance loyalty.
Cross-Sell/Upsell
Identifying target customers and opportunities for cross-selling or upselling to maximize customer value.
Sales Forecasting
Using statistical models to anticipate future sales, enabling better planning and resource allocation.
Customer Lifetime Value (CLV)
Estimating the total value a customer will bring to the company over the course of their relationship, enabling prioritization of marketing and customer service investments.
Marketing Mix
Evaluating the impact of marketing spend—both online and offline—as well as external factors (seasonality, economic trends, competition) on sales, and measure the ROI of each marketing initiative.
Attribution
Assessing the contributions of each digital touchpoint in the customer journey to conversion, optimizing marketing investments accordingly.

Contact us

500-355 Sainte-Catherine St W
Montréal (Québec)
H3B 1A5 Canada

Telephone: 514-237-5307
[email protected]