This document describes how a cheese manufacturer (Borden) can allocate their marketing resources like display advertising and price discounts across 88 retailer stores. It recommends using a Bayesian demand model to estimate demand parameters at the store level to overcome limitations of individual store analysis. The model finds most stores have a price elasticity around -0.954, meaning a 10% price decrease increases sales by 9.54% on average. It also estimates how much increasing display coverage to 100% would increase sales 462.94% on average. The document advises prioritizing display budgets for stores with high estimated display lift and price discounts for stores with high price elasticity.

1. How to allocate marketing
resources across retailers?
Example: Cheese Manufacturer (Borden brand)

2. Data
Cheese manufacturer (Borden)
88 stores in the U.S, 65 weeks
Sales (VOLUME), PRICE, DISPLAY (in-store advertising,
percentage of ACV on display), store ID variable
(RETAILER)
!
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Data source: R bayesm package
More details available : Bayesian Statistics and Marketing,
Peter E. Rossi, Greg M. Allenby, Rob McCulloch, p73, WILEY

3. Business Problem
How should a Borden manager allocate
marketing resources across retailers (88 stores)?
DISPLAY advertising budget
PRICE discount promotion (coupon) budget
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4. Method Selection
Click “Marketing” and select “Bayesian Demand Model”
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5. Why should we use Bayesian?
Running log-linear regression for each store has limitations
to estimate demand parameters at store level
Some variables might have not much variations at store level
No variation Can’t estimate at all
Little variation Could produce wrong sign estimates and
outliers
“Bayesian” estimation overcomes these drawbacks by
combining store level information and pooled information
across stores
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More details: Bayesian Statistics and Marketing, Peter E. Rossi, Greg M. Allenby, Rob
McCulloch, p73, WILEY

6. Sample Data
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Choose Sample (cheese, 88 retailers in the U.S.)
!
The 88 retailers sample data is only available for
“Advanced plan”
You can get similar results with 13 retailer sample
data under “Basic plan”

7. Variable Selection
Select the variables for Sales, Price, Store, and Promotion, and click the "Run" button
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8. Price Elasticity
8 Then you can see the store level demand analysis. The interactive
histogram shows "price elasticity" across 88 stores.
Price elasticity = % change in Sales
% change in Price
When price is decreased by 10%,
the sales will be increased by 9.54%
=price elasticity x % change in price
=(-0.954)*(-0.1)

9. Sales Lift by DISPLAY
DISPLAY Sale Lift score shows how much sales could be lifted given DISPLAY in-store ad
is 100%
Sales Lift by DISPLAY = {exp(Est_DISPLAY x DISPLAY)-1}x100, when DISPLAY = 1 (100%)
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When in-store DISPLAY ad coverage is 100%,
the sales is increased by 462.94%
Base Sales Lift score is 100

10. Results Table
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You can get the summary table by clicking the other tab ("Table" , "Est", "R output").
The impact of DISP on sales is (exp(Est_DISPLAY)-1)x100 % given
Display is 1 (100%)

11. What if analysis
In-store Display ad simulation
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What if analysis:
When in-store DISPLAY ad coverage is 10%,
the sales is increased by 18.86%
[exp(1.728 * 0.1) -1 ]*100=18.86
Est_DISPLAY is 1.728

12. Resource Allocation
A marketer should prioritize their
DISPLAY advertising budgets to the most
sensitive stores (DISP Lift scores are
high), and allocate price discount
promotions (like discount coupons) to the
most price sensitive markets where the
price elasticity is highest (absolute value)
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