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Forecasting Consumer Market Share with SAP HANA Cloud

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Improve market share forecasting accuracy and visibility using SAP HANA Cloud

Business analysts work closely with sales and marketing to help establish strategies through market share analysis.

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Improve accuracy of market share prediction with advanced machine learning models

Establish a sustainable analytical process to accelerate data-driven innovation. Analyse market share patterns to develop more segmented, differentiated marketing strategies. A shift toward a strategic approach is based on granular insight, easy access to market share information by an extended user group, and an architectural blueprint that can be reused for other analytics use cases.
  • Reduced manual work for extracting and reporting forecasting value
  • Optimised forecasting cycle
  • Improved forecasting accuracy
  • High accuracy prediction through advanced ML intuitive dashboards to communicate in real-time

Why use SAP Business Technology Platform

Elevate data and analytics insights with new predictive capabilities to quickly identify trends and sales flow.

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Accelerate data-driven innovation

Machine learning technology and predictive analytics improve market share forecasting accuracy and enable segmentation analysis.

  • SAP HANA Cloud offers a single database-as-a-service foundation for modern applications and analytics across enterprise data.
  • SAP Analytics Cloud enables integrated planning and analysis processes

Value driven results

To gain a holistic view of its equipment, Motor Oil Group embraced machine learning and predictive analytics using SAP HANA Cloud and SAP Analytics Cloud. 

>77%

accuracy in explaining abnormal events from 120 to 20 hours in advance using root-cause analysis of historical data.

24x7 

forward-looking time-series forecasting that enables accurate prediction of future sensor measurements.

Gaining accuracy in understanding historical and future measurements

Motor Oil Group used SAP HANA Cloud, application development services from SAP BTP and SAP Analytics Cloud to build predictive models for abnormal events based on sensor data and to provide the results through user-friendly dashboards and e-mail notifications. They achieved up to 77% accuracy for explaining abnormal events based on historical data and up to 70% accuracy for predicting future sensor measurements using root-case analysis and time-series analysis, respectively.

We have a complete view of our refinery  equipment that will help us reduce unexpected downtime and lower maintenance costs.

Dimitrios Michalopoulos, 
Industrial Applications Head of IT Division, 
Motor Oil (Hellas) Corinth Refineries S.A. (Motor Oil Group)

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