Follow Us

Intrinsicly evisculate emerging cutting edge scenarios redefine future-proof e-markets demand line

Gallery Posts

Working Hours

Retail Analytics Model Catalogue

Retail-Analytics-Model-Catalogue

Business Challenges:

Small and mid-sized enterprises (SMEs) have a greater need for predictive capabilities and less margin for error. The ability to project what will happen—and make decisions that change those trends for improved revenues and margins—is key. Qualex uses predictive analytics to:

    • Make real-time offers

    • Drive product recommendations

    • Improve sales forecasts

    • Increase profitability

    • Optimize marketing campaigns
       

Qualex leverages the power of predictive analytics accessible to SMEs in real time. We provide customers with powerful and actionable insights, combining the latest technologies and third-party data to support scenarios such as better sales forecasting, improved anticipation of demand and better predictability across the business.

Qualex offers a complete predictive analytics solution and a modern analytics development platform for SMEs.

Benefits:

Qualex fuels digital transformation across the business with predictive analytics:

    • Customer Churn. For small businesses, losing customers and the resultant revenue can be very expensive, particularly when it comes to replacing the customer. Predictive analytics can help identify characteristics related to customer churn and use that information to address issues like dissatisfaction or product quality and make the changes required to protect that revenue and keep customers happy.

    • Product Propensity. Companies can leverage data outside their organization (for example, online behaviour metrics like social media and product sentiment) and combine it with operational data and customer purchasing patterns to gain a significant competitive advantage. This approach can help companies predict which products customers are likely to buy and devise new ways to maximize those channels.

    • Logistics & Operations. Leveraging historical data and training predictive models to identify risk (particularly with IoT data) can help companies move from monitoring operations to predicting future events before they impact them and help them improve logistics.

  • Finance. The ability to forecast at a fine-grained level to gain a clearer view of orders, revenue, and inventory demand can introduce more accurate predictions that can improve the bottom line.

Comments are closed