Topics in pricing analytics
This is a general overview of the most relevant pricing and analytics topics and how they apply to your company. While the selection of materials each time is made as to reflect the audiences interest, the shown data and examples are not taken specifically from your company. Possible topics include
Pricing Basics
- The importance of pricing
- Measuring price elasticity and optimal pricing of single products
- Substitution and pricing of multiple products
- Ways to measure price elasticities
Price Differentiation
- Price differentiation basics
- Product differentiation
- Differentiation in time: Promotions
- Differentiation by channel
- Analysing and optimising differentiation strategies
Pricing and CRM
- Customer lifetime: acquisition, development, retention
- Customer behavioural and –value KPI
- Value to customer
- Customer segments
- Loyalty schemes and campaign management
Dynamic Pricing
- Basics of revenue management
- Markdown strategies
- Online pricing
Direct data to decision
We discuss business issues and gather the data sets in advance of the workshop. Core analyses are prepared and discussed in the workshop. Further deep dives are made directly during the workshop. Best effectiveness is obtained with smaller groups
Your own data
- Sales, revenue, cost time series
- Promo data
- Media data
- Google Analytics
- Stocks, shipments
- etc.
Analyses
- with R (by presenter) and Excel Pivot tables:
- Calculation of elasticities
- Simple A/B tests
- Simple forecasting
- Mini business driver analysis
- etc.
R in the Company
Based on data provided by the participants, we go through several full data analyses in R. The full procedures are fully explained and documented in the form of R notebooks.
Your own data
- Sales, revenue, cost time series
- Promo data
- Media data
- Google Analytics
- Stocks, shipments
- etc.
Analyses
- with R and RStudio (cloud-based if required)
- Data loading
- Data cleaning and "tidying" / formatting
- Programming of reports
- Visualising results with ggplot
- Some statistical models Regression / Classification
- etc.