=Paper=
{{Paper
|id=Vol-1746/paper-09
|storemode=property
|title=Predictive Sales Analysis According to the Effect of Weather
|pdfUrl=https://ceur-ws.org/Vol-1746/paper-09.pdf
|volume=Vol-1746
|authors=Alessandro Massaro,Donato Barbuzzi,Valeria Vitti,Angelo Galiano,Melisa Aruci,Giuseppe Pirlo
|dblpUrl=https://dblp.org/rec/conf/rtacsit/MassaroBVGAP16
}}
==Predictive Sales Analysis According to the Effect of Weather==
https://ceur-ws.org/Vol-1746/paper-09.pdf
Predictive sales analysis according to the effect of weather
Alessandro Massaro, Melisa Aruci Giuseppe Pirlo
Donato Barbuzzi, Valeria Vitti, Departmenti i Informatikës Dept. of Informatics
Angelo Galiano Fakulteti i Shkencave të Natyrës, Bari University,
Dyrecta Lab srl Tiranë, Albania 70125 Bari, Italy
ResearchInstitute, melisa.aruci@gmail.com giuseppe.pirlo@uniba.it
70014 Conversano (BA), Italy
alessandro.massaro@dyrecta.com
Our approach is consistent with the aforementioned
studies, which analyzes the direct link between the
Abstract weather variables and the customers’ behavior in order
This work presents an empirical simulation to to predict the sales of a particular product in the next
estimate the extent to which weather affects future. This is important to establish optimal business
consumer spending. For the spending intelligence rules, i.e. for warehouse management. The
prediction purpose, the regression technique work begins with an analysis of daily sales in one shoe
has been applied on the historical daily data of store, which have an effect onweather forecasts. The
the following meteorological parameters: paper is organized as follows: Section 2 reports an
temperature, snow, sun, rain and humidity. overview on the effects that weather can have on
The experimental results demonstrate the consumer behavior. Section 3 describes the technique
effectives of the model for predicting sales for sales prediction based on Regression; Section 4
trends and for applying optimal business presents the experimental results. The conclusion of the
intelligence rules. paper and some of the most interesting future
perspectives are reported in Section 5.
1Introduction 2 Weather effects: An overview
Weather has strong effects on human behavior and a lot
In literature, the weather effects have been investigated
of scientific research was devoted to the investigation
for the consumer behavior analysis. Three different
on the direct link between weather and social activities.
consumer categories, affected by: (1) bad weather; (2)
For instance, [Coh90a] and [Coh90b] reported that
seasons and (3) his moods, were considered.
higher temperatures are correlated with increases in
violent assaults and homicides. Also [Bar94] and In the first category (1), the rain, the snow and extreme
[Sto99] studied the number of suicides related to the temperature keep people at home. In this case,the
barometric pressure and demonstrated their decrease weather negatively affects both sales and store
related to the wind. traffic[Par01].An intelligent marketing solution would
be to focus on online sales. The second set of
Weather also influences human behavior in the sales.
consumers (2) influences both sales volume and store
For instance, we buy warm clothing in winter and cool
traffic in particular product categories [Mau95]. For
clothing in summer. Moreover, in the finance field,
example, when temperatures fall, ice cream sales
weather variables can affect human behavior and his
decrease, while sales of oatmeal increase. Similarity,
mood [Goe05], [Hir03], [Sau93], [Tro97]. Coca-Cola
people tend to purchase more clothing and footwear in
company proposed a dynamic pricing strategy based on
the winter and more food and drinks in the summer
changes in the environment temperature, so the vending
[Ros00].Finally, it has been suggested that weather can
machines increase the price of a soda as the weather
influence sales by affecting consumers’ internal states
gets hotter [Kin00]. Nevertheless, the effect of weather
(3). Although there is very little research forthis third
on consumer spending has received only limited
category of effects, few studies have
attention in the marketing literature [Par00] [Par01]
providedpreliminarysupport for this idea. In [Par00],
[Ste51].
the authors present a global climate-based model of the
effect of weather on consumer behavior, which predicts
variation in consumption patterns in response to Table 1: Meteorological and sales data for the
different temperatures and exposure to sunlight. They calculation of regression coefficients
argue that consumers do adapt to changes in the Feb
Sales Temp Snow Sun Rain Humid TempSun Temp2
environment by modifying their purchasing behavior to 2010
both maintain physiological homeostasis and to achieve 1 7 8,7 0 66,75 14,79 69,54 573,92 77,21
3 12 7,1 0 72,5 0 57,38 493,58 56,17
optimal stimulation levels. In [Mur10], the authors
4 15 9,13 0 88,46 0 74,75 796,54 85,36
report an empirical evidence of how weather can 8 3 8,37 0 35,67 39,38 79,33 312,33 70,71
impact consumer spending. In particular, they claimed 10 1 13,08 0 29 55 83,38 431,71 176
that: 11 2 10,25 0 48,67 42,08 76,83 492,17 105,5
12 10 9,71 0 72,42 8,13 69,79 687,21 96,96
Weather variables and, sunlight in particular, 13 6 7,29 0 59,21 1,88 68,46 433,17 54,04
affect consumer spending; 15 3 9,08 0 46,04 22,29 81,75 429,21 83,17
16 5 11,83 0 21,13 42,29 82,79 209,42 143,25
17 3 13 0 37,04 51,25 87,75 502,42 169,75
Exposure to sunlight reduces negative affect; 19 14 14,96 0 79,21 1,25 78,71 1209,29 237,38
22 3 10,83 0 44 41,04 81,63 469,79 119,67
As negative affect decreases, consumer 23 8 13,25 0 70,13 21,25 80,5 928,38 180,25
spending increases; 24 2 13,25 0 55,33 82,5 85,21 752,83 177,75
25 10 12 0 72,88 12,08 82,54 879,25 144,58
Negative affect mediates the effect of sunlight 26 12 12,96 0 76,58 6,88 78,25 1002,38 176,38
on consumer spending.
Starting from the value of Table 1, the regression
Their research demonstrates a cause-and-effect coefficients have been computed with Microsoft Excel
relationship between exposure to sunlight and an tool. So, Table 2 shows thisspecific information. More
increased willingness to pay for common product. specifically, it reports:
Coefficient. This column shows the least
3Sales Predictive Analysis by Regression squares estimate.
Standard Error. This column shows the least
In this section, we will provide some aspects squares estimate of the standard error.
concerning the approach used for sales prediction based T Statistic. This column shows the T Statistic
on weather effect. for the null hypothesis vs. the alternate
For the purpose, we consider the empirical model for hypothesis.
the iterative calculation of the regression coefficients:
P Value. This column shows the p-value for
the hypothesis test.
Salesij=aij+b1Tempij+b2Snowij+b3Sunij+b4Rainij+
Lower 95%. This column shows the lower
+b5Humidij+b6SunijTempij+b7Temp2ij+eij(1)
boundary for the confidence interval.
Table 1 shows the averagemeteorological values,in Upper 95%. This column shows the upper
February 2010, related to: boundary for the confidence interval.
Temperature;
Table 2: Calculation of regression coefficients
Snow;
Standard Lower Upper
Sun; Coefficients
Error
t Stat P-value
95% 95%
-13,28 11,54 -1,15 0,28 -39,38 12,82
Rain; Intercepts
-1,68 2,26 -0,75 0,47 -6,79 3,42
Humidity.
x1
x2 0,00 0,00 65535 #NUM! 0,00 0,00
x3 0,44 0,15 2,97 #NUM! 0,11 0,78
Itreports thesimulated sales data in that day. x4 -0,11 0,03 -3,47 0,01 -0,18 -0,04
x5 0,17 0,17 1,03 0,33 -0,21 0,55
x6 -0,03 0,01 -2,38 0,04 -0,06 0,00
0,16 0,10 1,57 0,15 -0,07 0,40
x7 Table 4 describes the ANOVA parameters. An
The formula (1) becomes: ANOVA test is a way to find out if survey or
Salesij=-13,28-1,68x1+0x2+0.44x3-0.11x4+0.17x5- experiment results are significant. More specifically,
-0.03x6+0.16x7+1.67(2) these parameters are used to accept or to reject the null
hypothesis for the alternate hypothesis. In particular:
Table 3shows regression statistics. These measures df. This measure explains degrees of freedom.
show how well the calculated regression equation fits SS. This measure explains the sum of squares.
data. In particular: Regression MS. This measure is computed
Multiple R. This is the correlation coefficient. dividing Regression SS with Regression
It explains how strong the relationship is. A degrees of freedom.
value of 1 means a perfect positive Residual MS. This measure is computed
relationship and a value of zero means no dividing Residual SS with Residual degrees of
relationship at all. It is the square root of r freedom and it explains the mean squared
squared. error.
R squared. This is rxr, the Coefficient of F. This measure explains overall F test for the
Determination. It explains how many points null hypothesis.
fall on the regression line.The best value for Significance F. This measure explains the
this measure is 80%. It means that 80% of the significance associated P-Value.
variation of y-values around the mean is
explained by the x-values. Table 4: ANOVA Analysis
Adjusted R squared. The adjusted R-square
ANOVA Analysis
adjusts for the number of terms in a model.
Users have to use this measure instead of R df SS MS F Significance F
squared, if there are more than one x variable. Regression 7 311,4610 44,4944 16,0119 0,0002
Standard Error of the regression.This Residual 9 25,0096 2,7788
measure is an estimate of the standard Total 16 336,4706
deviation of the error μ. This is not the same
as the standard error in descriptive statistics.
The standard error of the regression is the
4 Prediction Results
precision that the regression coefficient is Figure 1 shows the sales prediction, in the next fifteen
measured; if the coefficient is large compared days, concerning a store (Store 1)located in
to the standard error, then the coefficient is Conversano (BA). Figure 2 and Figure 3 report weather
probably different from 0. forecast in February 2016 related to the city.
Observations. Number of observations in the
sample.
Table 3: Statistical Regression Performance
Regression Statistics
Multiple R 0,962117902
R Squared 0,925670858
Adjusted R squared 0,867859304 Figure 1: Sales prediction in Store 1
Standard Error of 1,666985637
regression
Observations 17
Figure 2: Temperature Forecast in Conversano (BA) Figure 6: Sun, Snow, Rain and Humidity Forecast in
Milano
Data of Figure 1 andFigure 4 have been calculated
respect to the Equation (2). More specifically, the
weather forecasts in Conversano (BA) and Milano have
been used to draw the trend line, respectively.
5 Conclusion
Figure 3: Sun, Snow, Rain and Humidity Forecast in A predicted model based on weather variables has been
Conversano (BA) introduced in order to optimize business intelligence
rules. The empirical simulation shows the direct link
Figure 4 shows the sales prediction in Store 2, located between the temperature, the snow, the sun, the rain
in Milano. Figure 5 and Figure 6 report weather and the humidity and the customers’ behavior in order
forecast in February 2016 related to the city. to predict the sales of a particular fashion product in the
next future. Finally, the sales prediction for two weeks
has been calculated respect to the historical data in the
month. In the future other models such as the “Decision
Tree” in the Data Mining field will be compared with
the regression approach.
Acknowledgments
The work has been developed within the framework of
the project titled: Sistema di Business Intelligence di
Figure 4: Sales prediction in Store 2 tipo “embedded” basatosu prediction real time in
sistemi Big Data orientate al settore di calzature e
accessorimoda “Predishoes”(Business Intelligence
Embedded Systembased on real time prediction in
BigData systems oriented on fashion”Predishoes”).
Figure 5: Temperature Forecast in Milano
[Par01] Parsons, A.G., 2001. The association between
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