Measuring Marketing Performance Flashcards
Estimating the effect of price promotions using transactional data
- In order to estimate the effect of price promotions you need to have a control group
- Historical data alone usually doesn’t provide that
- Subjects need to be treated to different prices over time
When demand estimation goes wrong:
Gasoline example
Looking at historic data of gas prices, one could conclude that the prices should be raised in order to reach higher demand, as demand and prices were positively related in the past.
Look at historical data carefully: Always ask why there was a price variation in the first place.
Why do we have price variation?
Ice cream example
Ice cream sales depend on price, but also on other factors, like the weather. Good weather increases demand and this in turn prices. But raising prices does not increase demand.
In order to calculate the clean price variation, all other factors need to be held constant or to be parsed out by regression.
Good and bad price variation
- sales depend on price but also on other factors
- if the other factors can be observed, they can be parsed out using regression
- if they cannot be observed, there is no possibility to have a clean price variation
Measurement of marketing performance
Measurement of marketing performance usually requires a control condition:
- The goal is to determine the causal effect: The difference between running the campaign or not
- Historical data seldom provides control conditions
- Other variables will be confounding the effects
Two possibilities to get clean variation
Experiment:
- respects the ceribus paribus rule
- price movement is not correlated with other demand factors
- Estimation of price coefficient is not invalidated
Experiments are often costly to run
Instrumental variables:
- filters the good variation in the variable of interest
- the good variation is then independent of other demand factors
Approach requires skilled economists to be trusted
Instrumental variables
- Predict price based on good variation
- Regress quantity on predicted price from step 1