Overview

• Promotion Analytics: Intuition• Model Specification• Interpretation of Estimated Coefficients• Estimation• Limitation and Improvement

Scanner Data-Based Promotion Analytics: Key Idea

• Essentially “Counterfactual” analyses– Baseline sales: Normally expected volume for the product in

absence of any store level promotional activity (estimated through econometric modeling)

– Incremental sales: Additional volume due to in-store promotions

• Incremental sales = Actual (Observed) – Baseline (Estimated)

• Profitability of promotion can be assessed by combining costs of promotion with incremental revenue from promotion

The Analytic PathMost issues can be addressed by drilling down this path

Issue

Base Volume Incremental Volume

Distribution Velocity

% ACV(Breadth)

# of Items(Depth)

Base Price

Competitive Activity

Other Factors

Promotion Support

(Quantity)

Promotion Effectiveness

(Quality)

Level of Support

Promo Mix

Promo Price

Price Discount

Competitive Activity

Baseline Calculation: Intuition170

week 1 week 2 week 3 week 4 week 5

Unit Sales

75 75 75 75

In Week 4 Baseline estimate would be 75 units based on pre and post week sales (non-promoted week sales)

75

DisplayWeek

Baseline Volume Includes Marketplace Conditions that Affect Sales of a Product

0

5,000,000

10,000,000

CategoryTrends Long-Term

SeasonalityMarket-Level

Effects

BrandTrends

Baseline

PipelineInventories

Trade Promotions Model

TradePromotions

Manufacturer’sShipments

OtherFactors

Consumer Sales

RetailerPromotions

Other Factors

Trade Promotion Model Manufacturer’s Shipment Model:

Shipmentst = f1 (inventoryt–1, trade promotionst, other factorst)

Retail Promotions model: Retail Promotionst = f2 (trade promotionst, trade promotionst–1, inventoriest–1)

Consumer Sales model:Consumer Salest = f3 (retailer promotionst, other factorst)

Inventory model:Inventoryt = f4 (inventoriest–1, shipmentst, consumer salest)

Note that the Inventory model is simply an accounting equation, as: Inventoryt = Inventoryt–1 + Shipmentst – Consumer Salest

Focus for today’s workshop

Consumer Sales Models for Promotion Analytics: Types

Focus for today’s workshop

• 1. Regression-based model– e.g. A.C.Nielsen’s SCAN*PRO, IRI’s Promoter

• 2. Time-series-based model – VARX (Vector autoregressive models with exogenous variables)– e.g. MarketShare

• 3. Discrete-choice-based model – e.g. IRI’s category optimizer, Berry-Levinshon-Pakes

(Econometrica, 1995)

Sales Model Specification: Multiplicative• For brand j, j = 1,….,n at store k in week t:

Interpretation of estimated coefficients• For brand j, j = 1,….,n at store k in week t:

• : price discount (deal) elasticities (own-brand if , cross-brand if • : feature-only (), display-only (), feature & display () multiplier • : seasonal multiplier for week t for brand j (seasonality)• : store k’s regular (base) unit sales for brand j if the actual price equals

the regular price and there are no promotion activities for any of the brands r

Log-Transformation• For brand j, j = 1,….,n at store k in week t:

• Seemingly Non-linear: Taking log on both sides of the sales model makes it as a linear model!

• After log-transformation:

• Simplification: Define , ,

Two Brand Example and Simplification• Non-price promotion: Only consider own-effects (No cross-effects)

• Two Brand Example (after simplification)

Two Brand Example: Interpretation

Week dummy

Store dummy

Residual error

Feature only indicator

Display only indicator

Feature-display indicator

Temporary price reduction: brand 1

Temporary price reduction: brand 2

Own price elasticity Cross price elasticity

Feature multiplier Display multiplier Feature-display multiplier

Seasonality Difference in baseline sales across stores

Estimation

• Since the log-transformed model in linear in variables: simple OLS (ordinary least square) will be enough for estimation

• However, if endogeneity problem can be expected, instrumental variable regression method (IV regression) needs to be used

• Endogeneity problem (bias in estimates) happens most with price elasticity estimates: wholesale prices can be good instruments for retail prices

Calculating Baseline and Incremental Sales

• Turn off promotions (no TPR, display, feature, etc)

• Include cross-price effects (if there are promotions from competing brands)

• Calculate (counterfactual) baseline sales (without promotion)

• Incremental sales = Actual sales (observed) – Baseline sales (estimated)

Limitation

• Curse of dimensionality: Not very scalable in the case of categories with many SKUs -> J SKU’s: J x J parameters for each marketing mix

• Homogeneity in response parameters: More flexible models allow heterogeneity in responses across chains/stores

• No consideration of dynamics: lags and leads of prices can be included for dynamics

• Log-linearity assumption on deal effect: More flexible (semi-parametric) models can be developed

• Potential endogeneity (bias in estimated effects) if there are systematic allocation of promotion based on market/store conditions: instrumental variable regression can be considered

Evolutionary Model Building: Example