Information about Statistics applied to the interdisciplinary areas of marketing

Optimising price and marketing mix.

Concept of learning. When an account/product has too little sales data, bayesian shrinkage allows us to borrow information from other accounts.

Deals with outliers, by shrinking estimates towards each other.

Allows one hierarchical model instead of multiple models.

More robust, stable estimates with significant regional and account variation in estimates that cannot be done in a classical linear model.

Provides price elasticity measure that shows the impact of price changes on volume

Concept of learning. When an account/product has too little sales data, bayesian shrinkage allows us to borrow information from other accounts.

Deals with outliers, by shrinking estimates towards each other.

Allows one hierarchical model instead of multiple models.

More robust, stable estimates with significant regional and account variation in estimates that cannot be done in a classical linear model.

Provides price elasticity measure that shows the impact of price changes on volume

Introduction Statistical Model Interpretation Recommendations/Strategy Issues/ Research Opportunities

How much do sales increase when we run a temporary price reduction? When I promote do I cannibalise my own sales? When my competitor promotes do they take sales from me? ......If so, how much? What effect would a 4% price rise have on my sales ?

Regression Model - Nonlinear - Multiplicative - Based on Bayesian Shrinkage

Concept of learning. When an account/product has too little sales data, bayesian shrinkage allows us to borrow information from other accounts. Deals with outliers, by shrinking estimates towards each other. Allows one hierarchical model instead of multiple models. More robust, stable estimates with significant regional and account variation in estimates that cannot be done in a classical linear model.

Multilevel/hierarchical model Centering is a helpful way of parameterising models so that the results are easily interpreted. Fixed effect and random effect. SAS Proc Mixed to fit the hierarchical model.

We use the Restricted Maximum Likelihood (REML) as the estimator and the Newton-Raphson as the search algorithm. To gauge the fit of the model, we use Akaike’s Information Criterion (AIC) and Schwarz’s Bayesian Criterion (BIC). The estimator generates reasonable MAPEs at the total National level of about 1% to 6%.

Measures the impact of price changes on volume Price Elasticity = %Change in Sales %Change in Price For example, if an item had a price elasticity of -1.5,it would lose 15% of its volume if it raised its price by 10%, (β1= -1.5).

For example, the lift due to catalogue is: Example Catalogue Vol = Vol*exp(Beta) Beta (β8 ) 0.49 exp(Beta) 1.49 Lift 49%

Marketing activities that are not found statistically significant by the model are deemed to be ineffective. Based on the size of the promotional price elasticity, clients are recommended to continue/discontinue temporary price reductions. Based on the ‘Return of Investment’ for each marketing activity, clients are advised which marketing activities best increase their sales. A simulation tool, using the coefficients from the model, is presented to clients so that they are able to simulate and plan their future strategy, so that they obtain the best profit possible.

Price Elasticity - Challenge: A declining sales in salad dressing. A regression on historical data suggested that reducing price would significantly boost sales - A category manager was told by market research that the price elasticity for one of the products was -2. The product, however, still had strong sales in spite of several increases. (Competitive Reaction and time horizon).

There is most times insufficient variation in base price to accurately estimate its elasticity with scanner data. A major source of controversy is the appropriate level of aggregation at which to study advertising effects with regression. While some agents argue in favour of store or account level analysis, third party consultant argue in favour of market level analysis. A careful comparative study of the estimation of advertising effect sizes with store level versus market level data would be of substantial interest to practitioners who come into contact with this issue.

What is the duration or length of the long term effect of advertising. Some researchers say 6months others show a duration of well over 6months and others show that duration might last several years.

Studies are needed to determine whether or not base price elasticity’s can be reliably estimated from scanner data and, if so, what is the extent of natural price variation or number of observations needed. These estimates could also be compared with and tested against those obtained from survey-based methods such as discrete choice analysis.

Research is needed to resolve aggregation issues in assessing advertising effects. Determining the best approach to assess advertising effects with scanner data could resolve some of the methods and data aggregation debate in this area. Manufacturers and retailers could benefit from methods to determine the costs and benefits of broader versus narrower product assortment (e.g., Number of flavours and/or varieties)

Establishment of a ‘Methods Standards’ for Scanner Data Analysis. Determine the generalizability of empirical results (e.g., meta-analysis of price elasticity or advertising elasticity) which helps provide reasonable bounds when estimating models.

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