Digital Marketing Application of Optimization
Dataset: a table where the variables, which are also called features or attributes, are in the columns and the observations are in the rows. This means that all the data values are in the body of the table.
Dimension Reduction (process of reducing the number of variables):
Why do we need to reduce number of variables?
Example: HR department of a company creates an instrument to measure job satisfaction
Aim of study: HR manager wants to predict an employee’s intention to quit
Questions asked to be rated (one means that they strongly disagree with the statement and seven means that they strongly agree):
In the example, a principal component analysis would identify two components. PCA would transform the original seven values into two scores, one for each component.
Dimension Reduction (process of reducing the number of variables):
Why do we need to reduce number of variables?
- Redundancy among the variables in a dataset.
- Thus, it is possible to reduce the number of dimensions without losing critical information.
Example: HR department of a company creates an instrument to measure job satisfaction
Aim of study: HR manager wants to predict an employee’s intention to quit
Questions asked to be rated (one means that they strongly disagree with the statement and seven means that they strongly agree):
- My supervisor treats me with consideration.
- My supervisor consults me concerning important decisions that affect my work.
- My supervisor gives me recognition when I do a good a job.
- My supervisor gives me the support I need to do my job well.
- My pay is fair.
- My pay is appropriate, given the amount of responsibility that comes with my job.
- My pay is comparable to the pay earned by other employees whose job are similar to mine.
- Items one to four are measuring a single construct that could be labelled “satisfaction with supervision”.
- Items five to seven are measuring a different construct that could be labelled “satisfaction with pay”.
In the example, a principal component analysis would identify two components. PCA would transform the original seven values into two scores, one for each component.
Model:
The second constraint has a shadow price of minus $1 which indicates that net revenue will decrease by $1 for each additional dollar that we insist on spending in display ads.
Conversely, for the constraint which limits spend on Gmail ads, there is a $4 shadow price which means that net revenue would increase by $4 for every additional dollar that we allow the model to allocate to Gmail ads.
Conversely, for the constraint which limits spend on Gmail ads, there is a $4 shadow price which means that net revenue would increase by $4 for every additional dollar that we allow the model to allocate to Gmail ads.