FORECASTING EFFECTS OF PROMOTIONS WITH
CAUSAL METHODS IN GROCERY RETAILING
11/26/2015
1
Henri Nikula
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Agenda
1. Introduction and Motivation
2. Demand Forecasting and Data aggregation
3. Research Desing
4. Case Data
5. Results
6. Conclusions
RELEX provides comprehensive solutions for supply chain management and optimisation
– Forecasting and demand planning
– Store replenishment and inventory management
– Actionable analytics and reporting
3
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1. Introduction and Motivation
• Importance of promotions has grown
– Wider product assortments => promotions to attract customers
– Increased competition => have to do promotions as competitors do
• Sales impact of promotions can be very large
– These impacts need to be forecasted in order to maximize the availability and to minimize the spoilage and warehousing costs
• Current RELEX offering has module for forecasting promotions but it is based on a heuristic model
– Model does not take into account price changes or the effects of multiple different promotions
The goal of this thesis is to find more advanced causal methods to better forecast the effects of different promotional activites
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1. Introduction and Motivation
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1.1 Research questions
Question 1: How accurate forecast can be achieved with SCAN*PRO, Base Times Lift and Support vector regression models?
Question 2: Can we improve the forecasting accuracy of SCAN*PRO, Base Times Lift or Support vector regression models by changing the seasonal variables from weeks to months, adding the number of promotion days on a week as parameter or including a baseline forecasts into the models?
Question 3: Can we improve the forecasting accuracy using different levels of aggregation for training the forecasting models?
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2. Demand Forecasting
Forecasting Techniques
Qualitative Techniques
Based on the subjective
expectations and experiences of a
person
Quantitative Techniques
Time series based forecasts
Causal and Machine Learning
based forecasts
Figure 1: Qualitative and quantitative forecasting techniques
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2. Data aggregation
Figure 2: Three dimensional structure of demand data*
*H. Stadtler and C. Kilger, Supply chain management and advanced planning, Third Edit. 2000.
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3. Research design
• Comparison of different forecasting model – parameter – aggregation combinations
• Forecasts done on week level sales and compared with MPE and MAPE metrics
• Selected forecasting models:
– Baseline forecast with time series methods (BASE)
– Historical averages model (HIST)
– Base times lift model (LIFT)
– Multivariate regression model SCAN*PRO (LM)
– Support vector regression (SVR)
• Forecasts with BASE and HIST models were calculated in RELEX, others were done with R
1. Select test and
train data
2. Choose used
forecasts models
3. Choose additional
parameters
4. Select the
aggregation level
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3.1 Baseline (BASE) and Historical averages models (HIST)
• The baseline forecast is calculated for the normal sales with time series methods
– Idea is to forecast the normal sales without any promotions or events
– Really accurate for that purpose
• The Historical averages model searches for similar promotions in the past and uses the average sales uplift as the forecast for the promotion
– Searches from multiple different aggregation dimensions
– Proven to work in actual production environments
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3.2 SCAN*PRO model (LM)
m number of different types of promotions;
𝑇 number of weeks in a year;
K number of stores;
𝑆𝑘𝑗𝑡 unit sales of a product 𝑗 in store 𝑘, time 𝑡;
𝑝𝑘𝑗𝑡 unit price for product 𝑗 in store 𝑘, time 𝑡;
𝑝𝑘𝑗𝑡 median of the normal unit price for product 𝑗 in store 𝑘, time 𝑡;
𝐷𝑙𝑘𝑗𝑡 an indicator variable for different promotion types: 1 if product 𝑗 is promoted by
promotion type 𝑙 in store 𝑘, in time t; 0 otherwise;
𝑋𝑡 an indicator variable: 1 if observation is in time t;
𝑍𝑘 an indicator variable: 1 if observation is in store k;
𝛽𝑗 the price discount elasticity for product 𝑗;
𝛾𝑙𝑗 promotion type multipliers for product 𝑗 and promotion type 𝑙;
𝛿𝑗𝑡 week multiplier for product j and week t;
𝜆𝑘𝑗 store multiplier for product j and store k;
휀𝑘𝑗𝑡 disturbance term.
𝑆𝑘𝑗𝑡 =𝑝𝑘𝑗𝑡
𝑝𝑘𝑗𝑡
𝛽𝑟
𝑙=1
𝑚
𝛾𝑙𝑗
𝐷𝑙𝑘𝑗𝑡
𝑡=1
𝑇
𝛿𝑗𝑡𝑋𝑡
𝑘=1
𝐾
𝜆𝑘𝑗𝑍𝑘 𝑒𝜀𝑘𝑗𝑡
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3.3 Base times lift model (LIFT)
𝑆𝑘𝑗𝑡 = 𝐹𝑏𝑘𝑗𝑡𝑝𝑘𝑗𝑡
𝑝𝑘𝑗𝑡
𝛽𝑗
𝑙=1
𝑚
𝛾𝑙𝑗
𝐷𝑙𝑘𝑗𝑡
• The forecast for time periods with promotions is calculated by multiplying
the baseline forecast 𝐹𝑏𝑘𝑗𝑡 with the price elasticity multiplier 𝑝𝑘𝑗𝑡
𝑝𝑘𝑗𝑡
𝛽𝑗and
the different promotion type multipliers 𝛾𝑙𝑗
𝐷𝑙𝑘𝑗𝑡.
• Baseline forecast is the normal corrected forecast calculated in RELEX system
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3.4 Support vector regression model (SVR)
Figure 3. Architecture of the support vector regression machine*
*A. Smola and B. Schölkopf, “A tutorial on support vector regression,” Stat. Comput., vol. 14, pp. 199–222, 2004
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3.5 Aggregation levels and additional parameters
Figure 4. Amount of data used for estimation and training
on different levels of aggregation. Size of the rectangle
represents the amount of a data used in an abstract
situation.
• Additional parameters:• Baseline forecast as parameter• Number of promotion days in a
week• Months instead of weeks as
seasonal indicators
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4. Case data
Table 1. Number of products and product-locations in product categories
Group level 1 Number of sub
groups Number of products
Number of product-locations
Alcoholic Beverages 38 1 289 5 520
Beverages 29 812 3 627
Yogurts 35 495 2 012
Desserts 8 334 1 487
Cakes 28 692 2 841
Confectionary 52 1 890 7 458
Soft Drinks 26 1 047 4 455
Total 216 6 559 27 400
Table 1. Sales and promotional statistics for each product category
Group level 1
Average number of sales days
Percentage of sales days from total
Average number of promotional
sales days
Percentage of sales days on
promotion
Alcoholic Beverages
204 33 % 72 33 %
Beverages 258 33 % 51 20 %
Yogurts 472 66 % 305 63 %
Desserts 324 53 % 83 25 %
Cakes 190 40 % 35 32 %
Confectionary 219 40 % 40 22 %
Soft Drinks 335 43 % 111 32 %
Average 286 44 % 100 32 %
• Casa data from European grocery retailer• Sales and promotion data
between 01/11/2013-27/05/2015
• 5 stores and 6 product categories
• 10 different promotion types
• Test period:• Promotional weeks between
2014-09-1 – 2014-10-31
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5.1 Results: Basic models
Table 1. Forecasting accuracy measurements for different methods estimated on product
level measured from forecasts for 675 different product-locations.
MPE MAPE MAPE 50% Percentile
MAPE 90% Percentile
MAPE 99% Percentile
SVR nu radial -36 % 60 % 43 % 106 % 286 %
LIFT -2679 % 2743 % 53 % 142 % 1791 %
LM -370 % 413 % 47 % 286 % 7311 %
HIST -201 % 218 % 116 % 401 % 2058 %
BASE -305 % 312 % 126 % 466 % 1668 %
Model Accuracy measurements
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5.2 Results: Additional informationTable 1. Forecasting accuracy measurements for models with different modifications
estimated on product level. In the additional parameters columns 1 means that the
modification was used and 0 that it was not.
Model MO
NT
HS
NB
R O
F P
RO
MO
DA
YS
BA
SELI
NE
MP
E
MA
PE
MA
PE
50
% P
erc
enti
le
MA
PE
90
% P
erc
enti
le
MA
PE
99
% P
erc
enti
le
LM 0 0 0 -370 % 413 % 47 % 286 % 7311 %
LM 1 0 0 -277 % 317 % 44 % 186 % 3798 %
LM 0 1 0 -816 % 859 % 47 % 324 % 10879 %
LM 0 0 1 -683 % 724 % 46 % 343 % 14358 %
LIFT 0 0 0 -2679 % 2743 % 53 % 142 % 1791 %
LIFT 1 0 0 -40 % 105 % 51 % 123 % 1187 %
LIFT 0 1 0 -331 % 397 % 53 % 137 % 4877 %
SVR nu radial 0 0 0 -36 % 60 % 43 % 106 % 286 %
SVR nu radial 1 0 0 -22 % 51 % 38 % 100 % 265 %
SVR nu radial 0 1 0 -31 % 55 % 40 % 103 % 263 %
SVR nu radial 0 0 1 -36 % 58 % 42 % 106 % 293 %
Model Additional
parameters Accuracy measurements
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5.3 Results: Effects of aggregation levelTable 1. Forecasting accuracy measurements for models estimated on different aggregation
levels.
Model Aggregation level M
PE
MA
PE
MA
PE
50
% P
erc
enti
le
MA
PE
90
% P
erc
enti
le
MA
PE
99
% P
erc
enti
le
LIFT GROUP 1 -1 % 55 % 40 % 73 % 342 % LIFT GROUP 2 -2607 % 2676 % 48 % 91 % 528 % LIFT PRODUCT -2679 % 2743 % 53 % 142 % 1791 % LIFT PL -12037 % 12094 % 46 % 131 % 5807 % LM GROUP 1 -27 % 65 % 40 % 119 % 546 % LM GROUP 2 -69 % 108 % 45 % 161 % 1209 % LM PRODUCT -370 % 413 % 47 % 286 % 7311 % LM PL -20990 % 21034 % 48 % 374 % 45276 % SVR nu radial GROUP 1 -14 % 64 % 42 % 109 % 518 % SVR nu radial GROUP 2 -19 % 55 % 42 % 104 % 254 % SVR nu radial PRODUCT -36 % 60 % 43 % 106 % 286 % SVR nu radial PL -45 % 65 % 48 % 124 % 357 %
Model and aggregation level Accuracy measurements
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5.4 Results: Best models
Table 1. Forecasting accuracy measurements for best parameter-aggregation level
combinations for each model type ordered by the MAPE metric
Mo
del
Agg
rega
tio
n le
vel
MO
NT
HS
NB
R O
F P
RO
MO
DA
YS
BA
SELI
NE
MP
E
MA
PE
MA
PE
50
% P
erc
enti
le
MA
PE
90
% P
erc
enti
le
MA
PE
99
% P
erc
enti
le
SVR nu radial GROUP 2 1 1 1 -11 % 46 % 36 % 72 % 225 %
LM GROUP 1 1 1 1 -22 % 51 % 38 % 89 % 325 %
LIFT GROUP 1 0 1 0 3 % 53 % 40 % 70 % 332 %
HIST - 0 0 0 -201 % 218 % 116 % 401 % 2058 %
BASE - 0 0 0 -305 % 312 % 126 % 466 % 1668 %
Model and aggregation level
Additional information
Accuracy measurements
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5.4 Results: Best models
Figure 1. Density of MAPE values amongst the 675 product-locations for the best
parameter-aggregation level combinations for each model type. The dotted lines represent
the median of the MAPE values and the color of the line indicates the model.
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6. Conclusions
Finding 1: Forecasting accuracy can be improved for majority of the product-locations by using causal methods compared to the time series and Historical Average model.
Finding 2: Forecasting accuracy of the tested causal methods can be improved with the tested additional information, adding months and the baseline forecasts both improve the forecasting accuracy.
Finding 3: Aggregation level has a significant impact on the overall forecasting performance on all of the tested models. Higher aggregation levels give better forecasts.
Finding 4: Combining the selection of aggregation level and the model modifications further improves the forecasting accuracy with all the tested methods.
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Questions?