Make decisions
Managers need to make many hard decisions. It helps when they have some data to back up the decision-making process.
This class of problem generally requires 108.30.30 Data Analysis - Prescriptive analysis Here are some of the kinds of decisions they can use data science to help with:
- Impact of packaging, upsells, new products
- Would bundling services increase overall revenue?
- Would launching a mobile app impact usage on the web site?
- RFM segmentation, and reaching out to best customer groups can be useful here 108.30.10.20.20.10 Data Analysis - Segments - RFM
- Impact of promotions
- Are customers who use coupons borrowing from future purchases?
- Will sale buyers become loyal purchasers?
- Impact of displays
- Which type of displays (e.g. End of aisle) work better?
- Within and across category
- Which categories are substitutes / complements?
Note, these are slightly different than the case of 108.20.10 Data Analysis - Optimize.
Graph:
- 108.20.20 Data Analysis - Make decisions to 108.20.10 Data Analysis - Optimize
- 108.20.20 Data Analysis - Make decisions to 108.30.30 Data Analysis - Prescriptive analysis
- 108.20.20 Data Analysis - Make decisions to 108.30.10.20.20.10 Data Analysis - Segments - RFM
- 108.20.07 Data Analysis - Customer analysis primary objectives to 108.20.20 Data Analysis - Make decisions
- 111.32 Epic - Causal inference and brand impact to 108.20.20 Data Analysis - Make decisions
- 131.005 Leadership - Napoleon was across every detail to 108.20.20 Data Analysis - Make decisions