Broadscale Predictive Modeling and Marketing Optimization in Retail Sales

Salford Systems © Copyright 2012


Felipe FernándezDan Steinberg

May, 2012

Broad Scale Predictive Modeling and Pricing Optimization in Retail Sales

Managing Complexity


1. The ProblemManaging Complexit

y


Problem Statement

Typical grocery store stocks about 50,000 SKUs Managing this inventories requires sophisticated

and automated logistics operations Managing daily and promotional prices requires

daily field information feeding automated tools Requirements:

Assure that products remain available to customers

Stock-outs of 3% to 10% of promoted products are common

Keep margins under control and prices competitive

Managing Complexity


Estimated promotional event impact

Lift of promotions(positive or negative)

The problem Key question:

How many units of product x will we sell in store y next week?

What is the right price for product x to get margin y?

Managing Complexity


The problem Key question:

How many units of product x will we sell in store y next week?

What is the right price for product x to get margin y?

Mechanisms may be complex, but crude Standard industry practice:

Simple moving average up-lifts

Linear regression to extract demand curvesPredict sales as a function of price (own) and other factors

Quality of the data usually far lower than ideal

Financial impact Commercial impact

Me-too promotions

Repetitive promotions

Incoherent pricing

Perceive price gouging

Aging of stocks

Carrying cost

Loss of sales

Managing Complexity


Volatility of Sales Volume

Much more difficult to predict because substantial changes from business as usual Quantity sold may deviate substantially from historical averages

Running out of a promoted item risks alienating customers

Promotions:

an additional challenge

Spikes represent promos

Managing Complexity


How Relevant is the error in demand forecasting?

Error measured as a percentage of predicted sales (units sold) Typical absolute error of 70% to 90% is common place (outside the US) In September 2009 across 4 promotions the chain experienced

Typical overstock 43% Typical understock of 15% Tradeoff: can avoid risk of understock by overstocking everything heavily

Goal is to achieve optimal balance

67

jan 10

-16%44%

60%

dez 09

-19%56%

75%

nov 09

-46%

40%

86%

out 09

-27%36%

63%

set 09

-15%43%

59%

Absolute error ( % of units sold)

% of absolute error

Forecast<ActuallForecast>Actual

Managing Complexity


How Relevant is elasticity for pricing strategies?

For this product elasticity is low above R$11 54% of sales during last year the product was sold above R$11 averaging R$12,5 If that product would had been sold at R$15, unit sales would had remain the same

but But sales in R$ would had grown up to 13% and margin up to 40%

Managing Complexity


1I. The CaseManaging Complexit

y


More limited than the management of stocking for all products Focus on a subset of 7,000 frequently promoted items Promotions involve different media (TV, newspaper, flyers delivered to home) Advertised discounts (median: 7%; maximum: 70%)

Managing Complexity

The Project: Promotional Sales


Media (TV is generally more powerful in inducing a boost in sales) Discount (the law of demand) Calendar (Summer vs. Winter, weekday vs. weekend, holidays, payday, etc.) Prior promotions (how recent, how frequent, previous discounts) Cross-price effects (sales of Product A in response to a discount on Product B) Assortment effects (the fact that Product A and Product B are both promoted)

Managing Complexity

Factors Influencing Sales


Weather (rain or snow storms can severely disrupt transportation) Traffic (unusually principal traffic routes can be paralyzed for five hours) Soccer matches (during World Cup matches, many stores shut their doors) Historical database (we did not attempt to augment the historical record) Retail science micro factors (positioning on shelves, product neighbor effects,

special aisle displays, etc.)

Such detail was not available in our database which covers sales volumes for a huge geographic region and stores that do not necessarily comply with central directives.

We were thus limited to making use of the types of factors listed earlier.

Managing Complexity

Factors Influencing Sales


Managing Complexity

Types of Goods Promoted Commodities (rice, beans, pasta, cooking oil,

coffee) Drug store items, personal hygiene (soaps,

shampoos, deodorants, etc.) Drinks (alcoholic: beer, wine, spirits;

nonalcoholic: soft drinks, ready made teas) Bakery items Perishables (fruits, vegetables, meat, fish) Deli items (sausage, cheeses, smoked fish,

pickles) Miscellaneous (non-food products)


• Matrix to the left is an array of products (rows) by stores (columns)

• Need to produce a recommended stock level for every store/product cell

• For a typical promotion there could be up to 166*2500 or 415,000 predictions

• These are total forecasts for the entire promotion period (4-10 days typically)

• New promotions are being launched continuously with different prices, product mix, and media

Managing Complexity

Recommendation Challenge

Stores

Products


Managing Complexity

Solution approach: Data Preparation We rolled-up daily sales to the promotion level

10-day promotion would be rolled up into a single row of data Non-promo periods were rolled up into “pseudo-promos” Some products promoted frequently with rich historical database (eg. 20,000

promotions), some rarely promoted products (eg. fewer than 100 promotions) Data quality is moderate at best: Data cleaning was certainly an option (eg.

nearest neighbor imputation would be reasonable for missing price information) We opted for no data cleaning Instead we opted for a robust learning machine that deals effectively

with the problems we inherited (outliers, missing values, anomalies) SQL Server extended with custom written C# modules as required Postgres version in development


Managing Complexity

Modeling Strategies: TreeNet boosted trees

Classical statistical regressions were frequently driven by outliers and often produced “wild” predictions

We choose Friedman’s stochastic gradient boosting (TreeNet)


Managing Complexity Data Mining Approach

Based on trees, the models are far less sensitive to outliers and data errors Achieved a dramatic improvement in R-Squared and other performance

measures (Mean Absolute Deviation (MAD), Percent Prediction Error) Naturally handles interaction detection Allows choice of Least Squares, Least Absolute Deviation, or Huber LS-LAD

hybrid objective criterion for models Our target variable is defined as percent deviation from normal sales

Yt* = (Yt – Y0) / Y0


Managing Complexity Daily vs. Promotion Level

Models

A product promoted on 100 occasions with each promotion lasting 10 days:

1,000 daily records (great uncertainty, substantial random fluctuation)

Only 100 promotion level summary records (dangerously small sample) We elected to build both types of models

Daily models were used to predict the pattern of sales (fraction sold each day)

Promotion level models used to predict volumes


1II. ConclusionsManaging Complexit

y


Managing Complexity Results -Dramatic improvement of error distribution, actual unwanted problematic

overstock was reduced of 75% and actual store’s stock outs improved of near 50%. -Problematic stock; requires more than 30 days to be cleared

Illustrative

ANTES <0,5 >0,5<1 >1<2 >2<3 >3<4 >4<5 >5<6 >6<7 >7<8 >8<9 >9 Total geralD P H 4% 21% 48% 18% 4% 2% 1% 0% 0% 1% 2% 100%LIQUIDA 1% 7% 47% 22% 5% 6% 4% 1% 0% 3% 4% 100%P.A.S. 2% 14% 37% 24% 11% 4% 3% 1% 1% 0% 2% 100%PERFUMARIA 3% 17% 39% 20% 9% 5% 2% 2% 1% 1% 2% 100%SECA 2% 13% 40% 22% 9% 4% 2% 1% 1% 1% 5% 100%Total geral 2% 13% 42% 22% 8% 5% 3% 1% 1% 1% 4% 100%

APOS <0,5 >0,5<1 >1<2 >2<3 >3<4 >4<5 >5<6 >6<7 >7<8 >8<9 >9 Total geralD P H 8% 39% 38% 7% 2% 1% 1% 0% 0% 0% 2% 100%LIQUIDA 4% 34% 43% 10% 3% 2% 1% 0% 0% 0% 2% 100%P.A.S. 16% 36% 37% 6% 2% 1% 0% 0% 0% 0% 1% 100%PERFUMARIA 12% 36% 38% 8% 2% 1% 1% 0% 0% 0% 1% 100%SECA 9% 40% 38% 7% 3% 1% 1% 0% 0% 0% 1% 100%Total geral 9% 37% 39% 8% 3% 1% 1% 0% 0% 0% 2% 100%

Dep 1Dep 2Dep 3Dep 4Dep 5

Dep 1Dep 2Dep 3Dep 4Dep 5

Forecast volume error distribution (times actual sales)

45% of problematic over stock

15% of problematic overstok

Before

After


% DO VOLUME PREVISTO Sem risco nem de falta nem de excesso (>0,5<2 EVENTOS)

MEDIA MOVIL % DO VOLUME PREVISTO Sem risco nem de falta nem de excesso (>0,5<2 EVENTOS)

70% 71%73%

77%

68%

79%77%

72%

60% 60%

66%69%

74%71% 71%

73%

58%

66%

69%

64%

70%

50%

55%

60%

65%

70%

75%

80%

85%

MAI I JUN I JUL I AGO I SET I OUT I NOV I DEZ I

EDIÇAO I

EDIÇAO II

EDIÇAO III

50%

55%

60%

65%

70%

75%

80%

85%

MAI I JUN I JUL I AGO I SET I OUT I NOV I DEZ I

EDIÇAO I

EDIÇAO II

EDIÇAO III

3 per. Mov.Avg.(EDIÇAO I)3 per. Mov.Avg.(EDIÇAO II)3 per. Mov.Avg.(EDIÇAO III)

Managing Complexity Evolution of accuracy

Over almost one year we saw continuous improvements of accuracy by tuning both our models and/or increments used in recommendations.

Accu

racy

10-20th day of the month




Mass Modeling SystemManaged in SQL Server

© Copyright Salford Systems 2011

Managing Complexity

Modeling FilesConstructed variablesStore Characteristics

Calendar DetailsPromotion Details/MediaOwn & cross price details

DataBaseDaily Sales

All Products

Data Mining Engines

Predictive Models

Prediction ScenariosPricing, promotion details

Forecasted Sales, RevenuesRecommended stock levels

If a model is rejected for poor predictive performance (test R2 >=.10)

we search for a replacement model using a similar product Poor performance can be due to insufficient or poor quality data Related product models can perform extremely well by comparison


Automated Modeling

A common modeling strategy applied to all products Main prediction system uses 30 predictors

TreeNet adapts raw inputs as needed Some products involve powerful interactions while others can be

safely predicted with purely additive models Models developed on demand as sufficient new data

becomes available Particular project normally required about 2500 models to be

developed overnight Special projects outside of normal promotion system required more

Managing Complexity


Example Product Model Summary

Ridge regression yields an R2 of .137 on test dataTreeNet reaches a test data R2 of .226

Managing Complexity


Managing Complexity

Sales Volume variation in some frequently promoted items

Distribution of Normal SalesHuge variation across similar sized stores


Managing Complexity Distribution of Deviation from

Normal Sales During Promotion

Normal sales are based on the union of promo and non-promo days Deviation from normal is our target variable (and is often negative) Distribution of sales deviation from normal displays a median close to zero Interquartile range (Q3-Q1) is close to 100% of normal sales for these products


Partial Dependency Plot %D Sales vs %D PriceManaging

Complexity

Fresh onion kg Rice Carolino 5kg T1

Detergent Omo 1kg Condensate Milk Mococa 395g TP

Reducing the price by just 15% from “normal” increases sales to more than 3x normal for detergent but nothing for fresh onion


Number of days of a promotion Managing Complexity




Impact of Most Recent DiscountManaging

Complexity




Seasonal Pattern

During the last 5 weeks of the year sales steadily increase to more than 70% above normal by New Years Day and then gradually fall back to normal by March

Managing Complexity


Locational Effects

Measure about a 15% difference between the most and least favorable regionsRecall that we already control for price and other key factors

Managing Complexity


Number of Days Before Next Holiday

Some consumers start planning as much as 10 days in advance but more wait until the start of the holiday or the day or two before

Managing Complexity


Managing Complexity Partial Dependency Plot%D Sales vs %D Competitor´s Price


Managing Complexity Price Optimization to Maximize

Profit This problem is very well understood and is typically solved by making use of the slope of the demand curve

Constraints may well be imposed Maximum price to be applied to

highly inelastic products Minimum price to control sales

volumes in the presence of supply limitations

The TreeNet models naturally capture nonlinearity and interactions

The complexity of the TreeNet model requires that optimal solutions be found via simulation and search


Managing Complexity Final Comments Retailers with more than 30 stores have problems to figure out the right amounts

of merchandise to supply each store Healthy turnovers of the stocks and right pricing strategies are key to the profitable

transition from 30 to 100s stores

Our solution have demonstrated on a massive retail chain that the problem of accuracy can be dramatically improved by using predictive analytics based on non-parametric modeling.


Thank you

Technology

Broadscale Predictive Modeling and Marketing Optimization in Retail Sales