Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
Data analysis in Marketing:
General decision-making framework:
Example 1: TV Advertising for political campaign
Using electoral maps to get a view on what a candidate’s goal should be for each of the states.
How to win the voters over?
Example 2: Performing Arts Centres
Decisions to be made:
Example 3: Salesforce management
Decisions to be made:
Marketing to customers:
Free samples, discounts, savings à Are these going to turn prospects into customers? Turn lower value customers into higher value customers? Which alternative will have more impact on customers? What will be the magnitude of the impact?
Organizing data
Data are often organized into a data table.
Observations can be grouped together by state, for example, to compare sales months over month.
Seasonal trends can be observed for the same.
Linking data with a relational database
Data can be spread across several columns, thereby making it difficult to locate it. A solution for that is to relate tables to each other.
How is this helpful?
- updating customer/product info once is sufficient….if all the data was in a single table, every entry would have to be updated. Example à if a customer changes address, it is sufficient to update in customer table. Example à If the price of a product changes, it is sufficient to change that price in items table
Variable Types
Example: Viewer shares for one hour of television for 2 demographic groups:
Categorical variables: Demo Group 1, Demo Group 2, Networks
Quantitative variables: Ratings
- Behavioural targeting and customer segmentation
- Donor development
- Constructing a portfolio of investments
- Demand forecasting
- Planning and allocating resources
General decision-making framework:
- Define the business problem
- Collect and organize the relevant data
- Examine the relationship among different factors and the extent of uncertainty
- Develop an evaluation model (control price and advertising to predict sales)
- Evaluate potential solutions (change advertising message/change price by 5%, for e.g. and check the impact on sales)
- Recommend a course of action
Example 1: TV Advertising for political campaign
Using electoral maps to get a view on what a candidate’s goal should be for each of the states.
How to win the voters over?
- TV advertising
- Internet
- Feet on the ground
Example 2: Performing Arts Centres
Decisions to be made:
- Performance schedules
- Pricing subscriptions and seating tiers
- Ticket bundling
- Fundraising campaigns
- Advertising mix
Example 3: Salesforce management
Decisions to be made:
- How many (and which) employees need to be working at a particular time?
- Which tasks should be completed by which employees?
- Compensation packages to attract and retain top talent?
Marketing to customers:
Free samples, discounts, savings à Are these going to turn prospects into customers? Turn lower value customers into higher value customers? Which alternative will have more impact on customers? What will be the magnitude of the impact?
Organizing data
Data are often organized into a data table.
Observations can be grouped together by state, for example, to compare sales months over month.
Seasonal trends can be observed for the same.
Linking data with a relational database
Data can be spread across several columns, thereby making it difficult to locate it. A solution for that is to relate tables to each other.
How is this helpful?
- updating customer/product info once is sufficient….if all the data was in a single table, every entry would have to be updated. Example à if a customer changes address, it is sufficient to update in customer table. Example à If the price of a product changes, it is sufficient to change that price in items table
Variable Types
- Quantitative
- Natural numerical meaning
- Already a number
- Some arithmetic makes sense
- Has an appropriate unit
- Categorical
- No natural numerical meaning
- May appear in a data table as a number
- Arithmetic makes no sense
Example: Viewer shares for one hour of television for 2 demographic groups:
Categorical variables: Demo Group 1, Demo Group 2, Networks
Quantitative variables: Ratings
