Marketing Mix Optimization through the Market Mix Model for Furniture Retailer in the US

Is it possible for businesses today to survive without paying attention to customer data flowing in from multiple digital and point-of-sale touch points? Absolutely not.

Our client, a leading furniture retailer from the Midwest US, discovered that their media advertising left a lot to be desired because they did not have a winning data-driven marketing strategy in place. The need of the hour was a marketing data analysis solution to assist the marketing and finance team in predicting the effectiveness of each media type on sales, calculate ROI of the initiative, and make recommendation on which channel to choose for which region/season.

Our Decision Sciences Lab experts were on the job in no time. We performed an exploratory data analysis on the ad-spent data and store sales data of the last 3 years and proposed a market mix model
to identify the impact of ad-spent on sales.

Our Approach

•  The transformed dataset was used for statistical modelling in Python and R
• 15 + statistical models built to finalize the best using Decision Sciences Lab’s Library
• A multiple linear egression model was conceptualized to identify the relationship of ad-spend incurred through Radio, TV, and Digital ads on the sales
• The results were summarized in a user-friendly dashboard using Power BI
• Using the model found the right channels for better ROI

Benefits

• The proposed model was able to predict which media spent had the highest impact on sales and
  which media spent had lowest impact with a high accuracy of 85%
• Hexaware’s Data Science team was also able to predict the combined effect of ad-spent channels on sales
• Cost Reduction – $ 400Ksavings on Yearly budget