Analyzing the impact of Sales promotion

A t a n a s L u i z o v

А т а н а с Л у и з о в

Burgas Free University

Business Faculty

www.bfu.bg

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The importance of evaluation

Sales promotion effects measurement is limited in practice. Reasons:

- Managers neither have time or have required skills to build models that can

measure SP effects.

- The software for building marketing models may not be suitable.

- Managers may not want to measure SP effects.

- Managers may not have access to data of acceptable quality for measurement.

- Managers may decide it is too costly to collect data for measurement.

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Empirical generalizations (1)

1. Temporary price reduction substantially increase sales.

2. Higher market-share brands are less deal elastic.

3. The frequency of deal changes the consumer’s reference price.

4. The greater the frequency of deals, the lower the height of the deal spike.

5. Cross-promotional effects are asymmetric.

6. Retailers pass less than 100 percent of trade deals through.

7. Display and feature advertising have strong effects on item sales.

8. Advertised promotion can result in increased store traffic.

9. Promotions can effect sales in complementary and substitute categories.

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Empirical generalizations (2)

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Basic concepts (1)

The Baseline sales

… an estimate of sales in absence of specific promotional activity for specific

product and for determined time period.

Incremental sales

… these are sales that are directly attributable to the promotion during the

period.

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Total sales = Baseline sales + Immediate incremental sales Incremental sales = f (temporary price reduction, displays, features, …) Baseline sales = f (regular shelf price, advertising stock, distribution, competitive activity, seasonality, …)

Baseline sales

Incremental sales

Post-promotion deep

Basic concepts (2)

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Effects resulting from sales promotion (1)

1. Brand switching: purchasing a different brand

2. Timing acceleration: purchasing earlier.

3. Quantity acceleration: purchasing more.

4. Purchase acceleration: general term for timing and quantity acceleration

together.

5. Stockpiling: having higher stock at hand due to timing- or quantity

acceleration.

6. Anticipatory responses: deferring the purchase until the anticipated

promotion week.

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Effects resulting from sales promotion (2)

7. Store switching: purchasing in a different store.

8. Deal-to-deal purchasing: purchasing only on promotion.

9. Increased consumption: purchasing more and consumption it faster.

10. Repeat purchasing: trying a brand on promotion and repurchasing it.

11. Category switching: substituting purchases between categories.

12. Complementary effects: buying product from other categories as

complements to the promoted brand.

13. Store-traffic effects: choosing a store because of a sales promotion, and

purchasing other, non-related product in that same store.

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The time frame of promotional effects

1. The immediate effects of promotions are reflected in short-term changes in

sales.

2. The adjustment effects of promotions refer to the transition period between

the short-term response and the resulting equilibrium, which be either

means reversion or a new sales level.

3. The permanent effects of promotions require that a proportion of the event’s

impact is carried forward and sets a new trend.

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Fig. 1: The time frame of promotional effects

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Sales promotion experiments

Time series quasi-experiments Time series quasi-experimental deign can be diagrammed as follows: O1 O2 O3 O4 X5 O6 O7 O8 O9 The estimation for the promotion effect is: Op- Opre

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Example

10 20 30 40 50 90

Op- Opre = 90 – 30 = 60

Op- Opre = 90 – 60 = 30

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Sales promotion experiments

Two-group pre-post experiments The two-group pre-post experiment can be diagrammed as follows: The estimation for the promotion effect is: (Op – Cp) -(Opre – Cpre)=( Op- Opre)-( Cp- Cpre)

Promotion group O1 O2 O3 O4 X O5 O6 O7 O8

Control group C1 C2 C3 C4 C C5 C6 C7 C8

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Example

Pre-promotion Promotion Post-promotion

Promotion

group 100 105 110 105 170 140 120 110 100

Control

group 90 95 100 95 120 130 110 100 90

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Regression analysis applied to sales promotion

Data Experimental vs. nonexperimental Panel vs. store Consumer sales vs. warehouse withdraws

Level of aggregation Temporal Across stores Across brand size/style/flavor Across consumer

Dependent variables Category sales Brand sales Market share

Independent variables Promotion Advertising Distribution Price Lags Interactions Competition Other variables (seasonality, weather)

Functional form Linear Multiplicative Attraction Semilog

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Example: dependent variables

Period Category sales Brand sales

1 1000 300

2 1000 300

3 1000 300

4 (promotion) 1200 600

5 850 200

6 1000 300

7 1000 300

8 1000 300

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Example: independent variables

Deal-discount

price_discountedprice_regularoff_amount

100*price_regular

off_amountpercentage_discount_deal

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Example: independent variables

Advertising support

weeks_of_number_total

only_)display(feature_with_weeks_of_number)display(feature_of_freaquency

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Time series analysis applied to Sales promotion

Univariate time series analysis

SALES = f (T, S, C, I) T – trend S – seasonality C – cycle I - irregularity

Models: additive, multiplicative, mixed

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Time series analysis applied to Sales promotion

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The PROMOTER methodology

The PROMOTER model:

ttttttt pb*X*SI*TS

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The PROMOTER methodology

Data are adjusted for seasonality, trend, and any factors whose effects are known a priori.

An initial baseline is estimated.

Clearly “contaminated” promotion periods are removed from the data.

“Abnormal” outlier sales periods are identified and weighted to reflect how much influence they should have in computing the baseline.

A new baseline is computed.

If the new baseline is theoretically unreasonable, corrective action is taken.

Is the new baseline close to the previously calculated baseline?

The baseline is readjusted for seasonality, etc. and these data are compared to the actual to ascertain the effects of promotion.

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Example

t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

20 22 18 19 23 20 21 19 41 10 19 20 22 21 23 18

t 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

b + e

20 22 18 19 23 20 21 19 - - 19 20 22 21 23 18

b 20 19,7 20 21,3 22 20,7 20,2 19,7 19,3 19,8 20,3 21 22 20,7

We use three periods moving average.

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Thank you!

Contact:

Atanas Luizov BFU

louizov@abv.bg