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Grocer's Guide to Sales Strategy

This document describes a sales-promotion decision model developed by a grocer. The grocer varied price, coupons, and advertising expenditures over 16 weeks across three stores and recorded the resulting sales. The grocer then used business analytics tools to develop a model where sales are a function of price, coupons, and advertising. The model estimates, for example, that an increase in price of $1 would decrease weekly sales by 0.05 units, while offering coupons would increase weekly sales by 30 units. The model provides a good estimate of the actual sales but does not account for variability or error. Still, the grocer can use the model to evaluate different pricing and promotion strategies to maximize sales and profits.

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Abiola Ajayi
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294 views2 pages

Grocer's Guide to Sales Strategy

This document describes a sales-promotion decision model developed by a grocer. The grocer varied price, coupons, and advertising expenditures over 16 weeks across three stores and recorded the resulting sales. The grocer then used business analytics tools to develop a model where sales are a function of price, coupons, and advertising. The model estimates, for example, that an increase in price of $1 would decrease weekly sales by 0.05 units, while offering coupons would increase weekly sales by 30 units. The model provides a good estimate of the actual sales but does not account for variability or error. Still, the grocer can use the model to evaluate different pricing and promotion strategies to maximize sales and profits.

Uploaded by

Abiola Ajayi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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Chapter 1 Introduction to Business Analytics 23

ExAMPLE 1.8 A Sales-Promotion Decision Model

Advertising
Week Price ($) Coupon (0,1) ($)
1 6.99 0 0
2 6.99 0 150
3 6.99 1 0
4 6.99 1 150
5 6.49 0 0
6 6.49 0 150
7 6.49 1 0
8 6.49 1 150
9 7.59 0 0
10 7.59 0 150
11 7.59 1 0
12 7.59 1 150
13 5.49 0 0
14 5.49 0 150
15 5.49 1 0
16 5.49 1 150
In the grocery industry, managers typically need to
know how best to use pricing, coupons, and
advertising strategies to influence sales. Grocers
often study the relationship of sales volume to these
strategies by conducting controlled experiments to
identify the relationship between them and sales
volumes.1 That is, they implement different
combinations of pricing, coupons, and advertising,
observe the sales that result, and use analytics to
develop a predictive model of sales as a function of
these decision strategies.
For example, suppose that a grocer who operates
three stores in a small city varied the price, coupons
(yes = 1, no = 0), and advertising expenditures in a
local newspaper over a 16-week period and observed
the following sales:

1 Roger J. Calantone, Cornelia Droge, David S. Litvack, and C.


Anthony di Benedetto. “Flanking in a Price War,” Interfaces, 19, 2
(1989): 1–12.
To better understand the relationships among
price, coupons, and advertising, the grocer might
have developed the following model using business
analytics tools: sales = 500 − 0.05 × price + 30 ×
coupons + 0.08 × advertising + 0.25 × price ×
advertising
In this model, the decision variables are price,
coupons, and advertising. The values 500,−0.05, 30,
0.08, and 0.25 are effects of the input data to the
model that are estimated from the data obtained from
the experiment. They reflect the impact on sales of
changing the decision variables. For example, an
increase in price of $1 results in a 0.05-unit decrease
in weekly sales; using coupons results in a 30-unit
increase in weekly sales. In this example, there are no

uncontrollable input variables. The output of the


model is the sales units of the product. For example, if
the price is $6.99, no coupons are offered and no
advertising is done (the experiment corresponding to
week 1), the model estimates sales as

sales = 500 − 0.05 × $6.99 + 30 × 0 + 0.08 × 0


+ 0.25 × $6.99 × 0 = 500 units

We see that the actual sales in week 1 varied


between 481 and 510 in the three stores. Thus, this
model predicts a good estimate for sales; however, it
does not tell us anything about the potential variability
or prediction error. Nevertheless, the manager can use
this model to evaluate different pricing, promotion,
and advertising strategies, and help choose the best
strategy to maximize sales or profitability.

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