How to allocate marketing resources across retailers?

Example: Cheese Manufacturer (Borden brand)

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

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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Method Selection

Click “Marketing” and select “Bayesian Demand Model”

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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

Sample Data 6

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”

Variable Selection

Select the variables for Sales, Price, Store, and Promotion, and click the "Run" button

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Price Elasticity8

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)

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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Base Sales Lift score is 100

When in-store DISPLAY ad coverage is 100%, the sales is increased by 462.94%

Results Table10

The impact of DISP on sales is (exp(Est_DISPLAY)-1)x100 % given Display is 1 (100%)

You can get the summary table by clicking the other tab ("Table" , "Est", "R output").

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

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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