Measuring the “carryover” effects of pricing

Pricing Using Dynamic Demand
Modeling
Measuring the “Carryover” Effects of
Joe Sakach
Director - Consumer & Customer Insights
Joy Joseph
Vice President, Analytics

2 Copyright © SymphonyIRI Group, 2010. Confidential and Proprietary.
Overview
• Standard pricing models evaluate point in time consumer price sensitivity,
looking at the relationship between consumption changes vis-à-vis pricing
or promotional changes one week at a time
• Consumer price sensitivity is a more dynamic phenomenon that extends
over time, with shocks on consumption reverberating several weeks
following a price change
• We use a Dynamic Time Series model to capture the contemporaneous as
well as lagged effect of pricing and promotions to capture this “carryover”
effect
• This can help marketers develop more effective promotion plans that
maximized effectiveness of own promotions and minimize the impact of
competitive promotions

Marketers Perceptions on Pricing
(Based on Survey Responses from SymphonyIRI Clients)
• 83% of responders used some level of econometric analysis including
elasticity models to drive pricing decisions
• 50% managed price (promotional and everyday) gaps to key competitors
• …but only 30% accounted for post-event factors influencing pricing effects
and lifts
71.4%
28.6%
Evaluate Pricing & Promotional Lifts
within week of change
Adjust lifts for Forward Buying &
Repeat Purchases

A Primer on Contemporary Pricing Models
Current pricing models
capture all the major
drivers that impact
sales, but they correlate
the impact of these
drivers on volume sales
within each week in the
analysis time period
separately
Volume
Sales in
Week
Base & Promoted
Price For Week
Quality Trade For
Week
Special Trade
Programs
(Multiples, Bonus
Packs, Retailer
Specific events)
in Week
Competitive Price
& Trade
For Week
Category
Trend, New
Product
Launches and
Seasonality
For Week

Why is this a problem?
• …because promotional and pricing effects “carry over” well-beyond
the week of the event itself and looking at just the event week will:
– Understate or overstate impact of the event
– Not reveal any insights on optimal time gaps (hiatus) between events,
leading to inefficiency
Initital Promotional Lift
generated by TPR
(Includes core buyers and
incremental triers)
Promotional Events are followed by
a dip resulting from “forward
purchase acceleration” effects
(a.k.a pantry-stocking”)
Short-term Post-Promotional Dip
could be followed by lifts from
incremental repeat buyers inlater
purchase cycles

So What About Competitive Cross Pricing Effects?
• Competitive pricing effects also carry over beyond the week of the
competitive event driven by
– Consumers taken out of normal purchase cycles due to competitive pantry-
stocking
– Consumers retained by competing brands through repeat purchases
Carryover negative effects from
proportion of switchers retained by
competitor in later purchase cycles
Initial negative impact from
competitive promotion
Hiatus period before repeat
losses set in

Solution: Dynamic Time-Series Modeling
• Dynamic Time Series modeling, especially Panel Vector-
Autoregressions look at the effects of drivers over multiple time-periods
• Output from these models measure the effect of consumption “shocks”
due to drivers over consecutive time periods
Lag in Weeks
StandardizedVolumeImpact

So what’s a Panel VAR?
• Ability to measure continuous demand over a period of time
• VAR models can handle only time-series data, “Panel VAR” can leverage
time periods across multiple geographies
• Week 1
• Week 2
• …Week n
Geo 1
• Week 1
• Week 2
• …Week n
Geo 2
• Week 1
• Week 2
• …Week n
Geo 3

Application to Trade Promotion Planning
• While just looking at the promotion week, Feature & Display could be most
profitable but other tactics may yield overall better cumulative incrementality
• The post-promotion dip driven by forward-buying is not an ideal period to be
promoting again as consumers are still working on the pantry they stocked
up in the previous promotions
• Ideal point to promote again is when the repeat buying and/or forward-
buying starts to taper off- in below instance it would be around week 6

Application to Competitive
Response Strategy
• Successful competitive promotions should not be immediately responded to, but
rather leverage modeled insights to determine the optimal hiatus during which
counter-promotions will not obtain the intended benefit as consumers already
have a stocked up pantry and your category is not on their shopping list

Case Study

Background
• Study objectives
– Understand promotional impacts beyond the week of
execution
– Use results to inform a more effective trade promotion
strategy
• Analysis framework
– Market-level data
– 4 years of weekly data
– Competitive set of both intra- and inter- category
products
– Control for other major drivers (e.g. Advertising and
Economic factors) that are not reported here

Demand Drivers Summary
• Managing competitive threat is key for this segment
-0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4
Category Comp
X-Category Comp
Non-Promoted Price
Distribution
Own Trade

Impulse Response Curves - Price
• Promoted discounts show strong forward-buying
behavior with minimal repeat purchases.
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0 2 4 6 8 10 12 14 16
Weeks
Discount Depth
Forward-Buying
Dip
Minimal Repeat
Purchases
Initial
Promo
Lift

Cumulative Trade Impact
• Cumulative impact of promotional price changes are
significantly lower than week zero impact would indicate
0 0.05 0.1 0.15 0.2 0.25
Cumulative Impact
Week 0 Impact

Impulse Response Curves - Trade
• Lagged effects for F&D and Disp offset initial promotional lifts
• Feature generates significant repeat purchasing
• Optimal hiatus of 8-9 weeks between promotions
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0 2 4 6 8 10 12 14 16
Weeks
Feat & Disp
Feat Only
Disp Only
Forward-Buying
Dip
Repeat Purchases
Optimal Hiatus ~ 8-9 wks

Cumulative Trade Impact
• Cumulative impact of Features exceeds that of Feat & Disp
• Initial promotion lift of Displays is almost offset by forward
buying impacts
0 0.05 0.1 0.15 0.2 0.25
Disp
Only
Feat
&
Disp
Feat
Only
Cumulative Impact
Week 0 Impact

Impulse Response Curves - Competitors
• Some competition has an immediate impact on the
modeled product but for others the effect is lagged
-0.03
-0.025
-0.02
-0.015
-0.01
-0.005
0
0.005
0 2 4 6 8 10 12 14
Competitor A Discount
Competitor B Discount
Competitor C Discount

Impulse Response Curves – Category Switching
• Switching to products outside of the category is also
likely occurring as a result of dealing
-0.016
-0.014
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
0 2 4 6 8 10 12 14 16
Category 1 Switching

Sources of Volume Change
The source of volume change produced by the analysis puts a slightly different light on what drove year
over year change than looking at week of execution alone would have suggested, with the Dynamic
approach explaining overall year-over-year change better
– Lesser gain due to everyday price change & Trade/ Depth of discount
– Greater loss due to Category Switching.
1.1 0.4 0.0 0.4 0.5 1.8 2.4 3.2
100.0
93.3
YearAgo
OwnTrade
DiscountDepth
Non-PromotedPrice
CategoryComp
Error
X-CategoryComp
Distribution
Other*
CurrentYear
*Other includes TV and Economic Factors
Week 0 Effect Only 1.3 1 0.5 0.3 2 0.9 2.4 3.2

Summary of Findings
• The cumulative impact of trade tactics are not the
same…Feature promotions outperform Feat & Disp
when taking into account lagged effects
• Significantly higher competitive and category-switching
effects than indicated by conventional models
• Pricing as a standalone lever may be less attractive than
previously hypothesized because of forward-buying
effects
• Pay attention to the timing of promotions to minimize
impact of forward buying and competitive impact

Further Pricing & Promotional
Innovation In Progress…
Depth of Discount Point of
Diminishing Returns
Consumer-level Promotional
Effects
ConversionPropensityIndex
Cost Index

Questions

Model Fit
• R-Square: 0.89
• Average Error: 2.7%
• Absolute Error: 8.3%
0
50000
100000
150000
200000
250000
300000
350000
12/31/06
03/31/07
07/01/07
10/01/07
01/01/08
04/01/08
07/01/08
10/01/08
01/01/09
04/01/09
07/01/09
10/01/09
01/01/10
04/01/10
07/01/10
10/01/10
Actual Predicted
-40.0%
-20.0%
0.0%
20.0%
40.0%
Error

Measuring the “carryover” effects of pricing

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