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How Data Analytics Helps in Decision Making


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This presentation is focusing two real life use cases - the waste reduction in a chain of pastry and cake stores, where the analytical solution achieved 71% waste drop, as well as the increase in the efficiency of a sampling campaign conducted by The Coca Cola Company where the reach of target audience was improved by more than 20%.

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How Data Analytics Helps in Decision Making

  1. 1. - 71% + 20%
  2. 2. The big treasure in the small chest: How data helps in decision making
  3. 3. 3 DEMOCRATISATION OF DATA ANALYTICS Providing affordable, easy to understand and use analytical solutions is not only possible but the future of our industry Our team has 75+ years cumulative experience in data analytics
  4. 4. Industry: Pastry and cakes retail and production Size: 30 + retail locations Revenue: € 9M (2016) Cakes = Limited shelf life NEDELYA Business Specifics
  5. 5. Nedelya wanted to know how many cakes and pastries would sell in the future. Nedelya needed to open new locations fast and easy. NEDELYA Business Goals
  6. 6. Connection with POS Data analysis & modeling Correlation hypothesis testing and validating INSIGHTS delivery NEDELYA Approach
  7. 7. A tool capable of forecasting future product sales on a daily basis NEDELYA Solution
  8. 8. Waste: 71% decrease More efficient store managers Precise sales forecasting tool Easy opening of new stores NEDELYA Results
  9. 9. “A4E helped us to turn the raw data sets into valuable information, which helped us to make the right business decisions. We’d highly recommend their services to any business in need to master the art of the numbers“ Zdravko Mintchev, Nedelya CEO NEDELYA Feedback
  10. 10. New product pack launch Focus on 2/3 member house holds Sampling campaign visiting families for dinner and giving away the new product pack Coca-Cola Business Specifics
  11. 11. Geo-targeting for more effective sampling campaign Logistics support Survey quality Coca-Cola Business Goals
  12. 12. Previous sampling campaign. Demographic data. Property values within residential districts. Age distribution per district, etc. Coca-Cola Approach and Data Sets
  13. 13. Analytical model evaluating the likeliness to locate a prospective member from the target group within a particular residential district. The model assessed, ranked and recommended the most feasible residential districts in which targeted marketing campaign should be run. Coca-Cola Solution
  14. 14. “A4E team was very proactive in finding the proper efficiency boosting solution and displayed extremely high level of work performance. They were full with alternative approaches in reaching the project objectives. Our expectations for valuable ideas and proactive work were definitely exceeded. Stoyan Ivanov, Coca-Cola Franchise Country Manager BU/HR/BA/SI Coca-Cola Feedback
  15. 15. ANY QUESTIONS? Contact us at: +359 (2) 4411 243 16 Follow us at: