forecasting same store salesforecasting same store sales · consumer behavior forecasting “drive...

Forecasting Same Store SalesForecasting Same Store Sales

ForecastIQ® is a service of Prosper Technologies, LLC.

BIGresearch® is an authorized marketing representative © 2011, Prosper Technologies, LLC

How do changes in consumer behavior and intentions affect your decisions?your decisions?Assortment/MerchandisingMarketing/PromotionMarketing/PromotionForecasting Sales



The ProblemConnecting consumer purchase intentions to actual purchases is difficult to do.◦ Stores differ in positioning and merchandisingmerchandising

◦ Consumers differ in outlook and disposable income

◦ Consumers have choicesPredicting consumer

h i d i i ddpurchasing decisions adds enormous value to trading and merchandising decisions



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ChallengesIntentions data have not been found to directly predict marketplace behavior

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marketplace behavior◦ Many leading researchers believe that intensions data are not trustworthy

Statistical predictions are often based on extrapolations of p ppast patterns in the data◦ Like driving a car through the rear‐view mirrorPublished retailer expectations of retail same store sales tend

bto be too conservative◦ It’s hard to be bold and not occasionally wrong◦ Only reflect retailer’s side of the story: subjective



The ForecastIQ SolutionQ

Enhanced forecasts of same store sales and industry consensus



ForecastIQ Inputs

Automatically integrates state‐of‐the‐art Bayesian

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statistical methods with:

Consumer Intentions and Actions® (CIA®)( )Survey Data

◦ Survey of intentions and sentiment of more than 8,000 thconsumers every month

◦ Has powered the CIA Engine of Insights since 2001◦ Questions focus on intentions and sentiment

Example: “Do you plan to spend more, the same, or less for women’s clothing over the next 90 days?”Example: “Are you concerned about being laid off?”



Example: Are you concerned about being laid off?

Qualified Respondents

ForecastIQ removes respondents who are uncertain

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about future economic conditions◦ Statistically shown to provide unreliable statements about future spending behaviorfuture spending behavior

For each retailer, ForecastIQ only includes respondents who are current customersForecastIQ weights responses relative to household income, giving greater weight to households with greater spending flexibility



ForecastIQ MethodologyForecastIQ employs an iterative Monte Carlo chain

h d i i il i

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method to estimate a Bayesian quartile regressionSeparately calculates endpoints of the confidence interval rather than the expected value with standardinterval rather than the expected value with standard errors◦ Different sets of variables explain the upper and lower parts of the confidence intervalthe confidence interval◦ Allows for asymmetric intervals◦ Robust to statistical outliers (i.e. to the “Black Swan”)G i l b f i f h d h◦ Greater signal because focus is on corners of the dataset – the people most likely to switch up from a discounter to a department store, or vice versa



ForecastIQ Outputs

Forecasts of same‐store sales increases and decreases.

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Short‐term enhancement of industry consensus forecasts.45‐ and 75‐day ex‐ante forecastsDriver analysis indicating product categories associated with store level forecastswith store‐level forecastsPrediction intervals for volatility assessment



ForecastIQ Predictions

% change in same‐store sales for the next two months

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45‐days 75‐days

Forecasts are granular by business and by monthC l t 50% 70% i t tilCan select 50% or 70% interquantile range



Enhanced Estimate Calculator

Short‐term analyst estimates are currently available as industry consensus forecasts.ForecastIQ adjusts these estimates by comparing it to

d l t t th t fl t t i t timodel output that reflects customer intentions.The enhancement produces up to a 40% improvement in short‐term forecasting accuracy (depending on thein short‐term forecasting accuracy (depending on the retailer).



ForecastIQ Historical Predictions

View historical comparison of ex ante sales predictions

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to actual values◦ Granular by business and by forecast period (45‐ or 75‐day)



ForecastIQ “Drivers” View

Drill down into the

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product categories that drive the same‐store l di ti fsales prediction for

each business◦ Purchase intentionsPurchase intentions drawn from C.I.A. survey◦ “BIG factor” indicates the i f i dimpact of increased purchase intentions on the selected business



Drivers ExampleFamily Dollar for November 2008.

Historically when consumers anticipate spending less on



Historically, when consumers anticipate spending less on groceries, Family Dollar experiences a increase in sales.


According to the Consumer Intentions and Actions survey



According to the Consumer Intentions and Actions survey, consumers anticipate lowering their spending on groceries.


Our conclusion for this product category is a positive effect



Our conclusion for this product category is a positive effect on Family Dollar revenues

Empirical Findings of Interest

1. When respondents’ purchase intentions increase for

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a product category, upscale department stores achieve greater sales and discounters experience decreases in salesdecreases in sales.

2. In periods of economic uncertainty (e.g., when the economy is contracting) consumers follow througheconomy is contracting), consumers follow through on their intentions with greater regularity than when times are good.

3. Discounters like Wal‐Mart are much easier to forecast than department stores like Macy’s



ForecastIQ AdvantagesHighly sensitive to upturns and downturns in the

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economyIncorporates the perspective of the consumer and combines this signal with retailer expectationscombines this signal with retailer expectations Forecast periods of 15‐, 45‐, and 75‐daysBreakdown of same‐store sales prediction by product categoryModels based on unique set of monthly consumer intention data collected since January 2002intention data collected since January 2002Transparency: CIA data available through syndication; models academically published



Broad Retailer Coverage

33 retailers and growing

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A New ParadigmTraditional Methods

h d d b h bl f

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Mathematicians and statisticians mine existing datasets for signals

Bring new data to bear on the problem of consumer behavior forecasting

“Drive the car by looking in the rear‐view mirror ” Detect trends in same store

Ask consumers what they plan to do, before they do it Research shows theymirror. Detect trends in same‐store

sales.before they do it. Research shows they do what they say they’re going to do.

Regression analysis with point estimates and standard errors.

Quartile regressions robust to outliers and focused on consumers on the marginand standard errors. focused on consumers on the margin between discounters and department stores



Dr. Greg M. AllenbyHelen C. Kurtz Chair in Marketing Fisher College of

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Marketing, Fisher College of Business, Ohio State UniversityProfessor of Marketing and StatisticsFellow of the American Statistical AssociationCo‐author of Bayesian Statistics and Marketingand Marketing◦ Best seller in the Wiley series of probability and statistics

◦ Used in doctoral programs nationwide



forecasting same store salesforecasting same store sales · consumer behavior forecasting “drive...

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