Marketing Optimization Augmented Analytics Use Cases - Smarten
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The document discusses a marketing optimization predictive analytics use case that enables companies to evaluate the effectiveness of marketing and advertising campaigns on sales. It illustrates how to identify the relationship between advertisement expenses and sales using correlation and regression algorithms to optimize the marketing budget for improved ROI. The findings emphasize using the correlation coefficient and r-square values to determine model accuracy and predict sales based on various influencing factors.
Marketing Optimization Augmented Analytics Use Cases - Smarten
- 1. Marketing Optimization Predictive Analytics Use Case
- 2. A company can determine the influence of the marketing and advertising campaigns on sales and identify whether marketing expenses are effective for the business and optmize the marketing budget to increase sales or ROI. Marketing Optimization Sample Application Description
- 4. • Advertisement expense • Vacancy • Wholesale gap Influencing Factors Marketing Optimization Sample Application
- 5. To solve this problem, we first need to identify the relationship between advertisement expense and sales using a correlation algorithm. Correlation is a statistical measure that indicates the extent to which two variables fluctuate together. • A positive correlation indicates the extent to which those variables increase or decrease in parallel. • A negative correlation indicates the extent to which one variable increases as the other decreases. Algorithm(s) Marketing Optimization Sample Application
- 8. Marketing Optimization Sample Application Correlation Result The correlation coefficient value indicates the strength of the relationship.
- 9. Once the relationship is identified, we can further predict sales using regression algorithm. • Multiple Linear Regression is a technique that attempts to explore the relationship between two or more independent variables and one dependent variable. • The higher the R-square value of a model(>=0.7), the better the accuracy of the model. • A lower R-square value of a model(<0.7) means the model needs to be rebuilt using different input parameters or the input dataset doesn't exhibit a structure suitable for regression. Algorithm(s) Marketing Optimization Sample Application
- 16. Marketing Optimization Sample Application Regression Result Sales prediction with accuracy value (R-square value) based on advertisement expense, wholesale gap and vacancy can be performed using APPLY functionality as shown below
- 18. Marketing Optimization Predictive Analytics Use Case For more information, contact us today. www.Smarten.com contact@Smarten.com Smarten – Marketing Optimization Use Case - 2019