Since the competition in auto service industry is becoming more and more severe, the retention rate in Monro has been decreasing since 2015. Selecting the useful data from Monro system database with SQL, using RFM model to group customers and identify the loyal customers and developing merketing mix strategy, we increase the rentention rate by 24.7%.
People have different response rate to the promotion email, we should find the customer groups that have the highest responsive rate. Causal forest analysis can help us find the most valuable customers based on their purchase records, we use this method to select the target customer for our email compaign.
Conjoint Analysis is a very useful analytics tool to decide the characteristics of the new product. We use this tool to design the new products' features based on the real business world survey data, Finally we decide to launch two products based on the data and our business sense.
TURF analysis is an analytical tool to find the next best product, it's a very important method to estimate the market simulation and decide new products. Based on the system data from SQL database and survey data collected from taste test, we find the next best yogurt flavor to launch.
Adobe Analytics is very useful tool in digital marketing analytics, we participated Adobe Analytics Competition last year, with the data from MLB, we find some flaw in the site and gave MLB some recommdations on how to improve the website in order to increase retention rate.