promotion analytics - module 2: model and estimation

17
Overview • Promotion Analytics: Intuition • Model Specification • Interpretation of Estimated Coefficients • Estimation • Limitation and Improvement

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Modeling and estimation details for log-linear demand model (SCAN*PRO model by AC Nielsen)

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Page 1: Promotion Analytics - Module 2: Model and Estimation

Overview

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

Page 2: Promotion Analytics - Module 2: Model and Estimation

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

Page 3: Promotion Analytics - Module 2: Model and Estimation

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

Page 4: Promotion Analytics - Module 2: Model and Estimation

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

Page 5: Promotion Analytics - Module 2: Model and Estimation

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

Page 6: Promotion Analytics - Module 2: Model and Estimation

PipelineInventories

Trade Promotions Model

TradePromotions

Manufacturer’sShipments

OtherFactors

Consumer Sales

RetailerPromotions

Other Factors

Page 7: Promotion Analytics - Module 2: Model and Estimation

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

Page 8: Promotion Analytics - Module 2: Model and Estimation

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)

Page 9: Promotion Analytics - Module 2: Model and Estimation

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

Page 10: Promotion Analytics - Module 2: Model and Estimation

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

Page 11: Promotion Analytics - Module 2: Model and Estimation

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 , ,

Page 12: Promotion Analytics - Module 2: Model and Estimation

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

• Two Brand Example (after simplification)

Page 13: Promotion Analytics - Module 2: Model and Estimation

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

Page 14: Promotion Analytics - Module 2: Model and Estimation

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

Page 15: Promotion Analytics - Module 2: Model and Estimation

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)

Page 16: Promotion Analytics - Module 2: Model and Estimation

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

Page 17: Promotion Analytics - Module 2: Model and Estimation

Evolutionary Model Building: Example