Promotion Analytics - Module 2: Model and Estimation
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Transcript of Promotion Analytics - Module 2: Model and Estimation
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