customer profiling of online grocery shoppers- a

CUSTOMER PROFILING OF ONLINE GROCERY SHOPPERS- A COMPARISON

OF TWO TECHNIQUES

Seema Sambargi

Research Scholar, Adarsh Institute of Management and Information Technology

Research Centre, Bangalore University, Bangalore, India

Anitha Ramachander

Research Guide, Adarsh Institute of Management and Information Bangalore,I ndia

Technology

Research Centre, Bangalore University,

Uma Devi Ananth

Adarsh Institute of Management and Information Technology, Bangalore, India

R K Gopal

PES University, Bangalore, India

Corresponding author contact details:

Seema Sambargi,

Adarsh Institute of Management and Information Technology

5th

Main Chamarajpet, Bangalore 560085

Email: [email protected]

ADALYA JOURNAL

Volume 9, Issue 1, January 2020

ISSN NO: 1301-2746

http://adalyajournal.com/243

ABSTRACT

Consumer profiling is gaining a lot of traction in the recent past among marketers .The

availability of techniques and computing power to harness big data to profile

prospective customers is enabling them get additional insights into customers and to

help to identify and gain a deeper understanding of the target market. This empirical

study compares two statistical techniques- one traditional, binomial logistic regression

and other modern, neural networks in profiling the online women grocery customers.

The results are encouraging and lend credibility to new age statistical techniques like

neural networks

Keywords: Customer profiling, Neural Networks, Binomial logistic regression, Profiling

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Introduction

Among the various sectors contributing to the changing life standards of consumers in

India, an important one is the retail sector. This sector has been characterized by a shift from

the unorganized to an organized one, which included a shift from the kirana store format to

the formats like department stores, hypermarkets, supermarkets and specialty stores across

the different range of product categories. The use of Internet is catching up as alternative

channel for retailing in India, and it is now an acknowledged and important part of the retail

experience. The momentum and growth of the Indian retail industry coupled with the

development of the requisite infrastructure and increasing awareness of online shopping

would give a further boost to the online shopping industry.

While online retail has been a considerable success in airlines, train or movie tickets

on the internet and, online marketing of books and music have also been considerably

successful despite the fact that internet penetration in India is very low, e-tailing in sectors

like grocery and FMCG has not met the same kind of success. In this context, it is important

for marketers to understand which kind of customers have a higher propensity to shop

groceries on line. Considering that grocery shopping is a chore traditionally carried out by

women it is important for online grocers to understand the profile of online women grocery

shoppers.

Review of Literature

In an exploratory research through survey of literature to find the prospects of grocery

e-tailing, profile of online grocery customer, sustainability of e-tailers, the key success

factors and impediments to success by (Keh & Shieh, 2001)it was found that grocery

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shopping online appeals to time pressed, elderly, infirm .But characteristics like impulse

buying, browsing ,instant gratification and product freshness are hard to replicate online .In

their opinion ,in all likelihood both e-tail and retail would co-exist.

Lynch & Beck (2001) in their study to find if internet buyers' beliefs, attitudes and

internet behavior will differ among world regions, between countries within a world region,

as well depend on the amount of time they spend on the internet, found that there is a need to

micro market to different niches because of differences in culture albeit this study did not

include India.

In a study involving survey of US consumers Hansen (2005), found that online

grocery shopping adopters had higher house hold income than the non-adopters.

Brashear, Kashyap, Musante, & Donthu (2009)in their study across six countries to

understand the characteristics relating to attitudes, motivations and demographics in six

countries(USA, UK, NZ, China, Brazil & Bulgaria) ,and the differences between internet

users and online shoppers showed with the help of statistical tools like ANOVA and chi

square tests that online shoppers across the countries show similar traits-desire for

convenience, impulsive, favorable attitude towards direct marketing and ads, wealthier and

are heavy users of both email and internet.

In an India specific study, to profile Indian online shoppers, Parikh (2006) the results

suggested strong association between length of Internet surfing and actual Internet shopping.

Also, a strong association was revealed between Internet usage and actual Internet shopping.

In addition to this, prior experience of Internet shopping had a multiplying impact on future

intention to shop through the Internet. Contrary to expectations there were no significant

associations between the shopping segments and demographic variables.

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In line with the findings of the above study, another study conducted in Delhi on

college going students by (Handa & Gupta, 2009) also found that gender has no influence on

the innovativeness of online shoppers.

In a comprehensive survey(Zhou, Dai, & Zhang, 2007) , an attempt to identify the

convergent factors that were highlighted from 35 empirical researches regarding online

shopping behavior it was opined that with increasing competition in online business,

business needs to devise strategies that are based on sound consumer demographics and

psycho graphics recognized by consumer behavioral research.’

Walters & Bekker (2017) describe the on-going development of a proposed simulator

and demonstration tool that includes big data analytics to reveal patterns within the customer

dataset, and hence generate a customer profile.Fares, Lebbar, & Sbihi (2018) opine that data

science and machine learning tools are the latest trend in big data analysis, with short

calculation time.

Yoseph, Malim, & AlMalaily (2019) applied soft clustering Fuzzy C-Means (FCM)

and hard clustering Expectation Maximization (EM) algorithms to classify individual

consumers who exhibit similar purchase history into specific groups.

The review of extant literature shows that studies to understand the behavior of

women online shoppers have not been documented so far. Also, studies on online

FMCG/grocery retailing are few and far between in India. No studies have been undertaken

to profile women internet shoppers in India.

Objectives of the study

i. To profile women who shop groceries online based on demographic

characteristics

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ii. To compare the results of two techniques – neural networks and binomial

logistic regression in profiling online grocery shoppers

Methodology

A survey research was conducted by collecting cross-sectional data from randomly

selected 490 women. Responses were sought through a well-structured questionnaire that had

items capturing information on variables like online buying behavior, age, qualification,

hours online, number of children, type of family, type of residence, industry they belong to,

possession and use of a smart phone. Two statistical techniques –Neural networks and

Binomial logistic regression were used to try profile the consumers who shoppen online for

groceries

Neural Networks

The Algorithm

The Multilayer Perceptron (MLP) procedure of Neural Networks of SPSS produces a

predictive model for one or more dependent (target) variables based on values of the

predictor variables (SPSS, 2012).Here we are trying to predict the ‘online buying behavior’

of working women using many consumer characteristics like age ,qualification, hours

online, number of children, type of family, type of residence, industry they belong to,

possession and use of a smart phone. In other words, we are trying to identify characteristics

that are indicative of working women who are likely to buy FMCG/ groceries online.

A multilayer perceptron algorithm was used to train a neural network to predict the

dependent variable ‘Bought online’ using the 7 factors- Qualification, Industry, Marital

status, Family life cycle stage, Residence type, Family type and possession of smartphone

and 4 covariates-Age, number of hours online, Annual Income and number of children.

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Case processing summary

The case processing summary as seen in Table 0.1 shows that 306 cases were

assigned to the training sample and 141 to the hold out sample. 42 cases were excluded from

the sample.

Table 0.1

Neural networks case processing summary

1.1.1 Network information

The network information table (See Table 0.2) displays information about the neural

network (See Figure 0.1) and is useful for ensuring that the specifications are correct. In

particular we can note that

The number of units in the input layer is the number of covariates (4) plus total

number of factor levels i.e., 5 in Qualification, 6 in industry, 3 in marital status, 5 in

life cycle stages, 2 in family type, 5 in residence type and 2 in possess smart phone

which makes it a total of 32 units. None of the categories are considered redundant.

A separate output unit is created for each category of ‘Bought online’ (Yes and No)

for a total of two nits in the output layer.

Automatic architecture selection has chosen six units in the hidden layer.

All other network information is default for the procedure.

N Percent

Sample Training 323 72.1%

Testing 125 27.9%

Valid 448 100.0%

Excluded 42

Total 490

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Table 0.2

Neural Network Information

Input Layer

Factors

1 qualification

2 Industry

3 marital status

4 Family Life Cycle Stage

5 Type of family

6 Type of residence

7 Possess a smartphone

Covariates

1 Hours/day online

2 age

3 Annual income

4 number of children

Number of Unitsa 32

Rescaling Method for Covariates Standardized

Hidden

Layer(s)

Number of Hidden Layers 1

Number of Units in Hidden Layer 1a 6

Activation Function Hyperbolic tangent

Output Layer

Dependent

Variables 1 Bought groceries online

Number of Units 2

Activation Function Softmax

Error Function Cross-entropy

a. Excluding the bias unit

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Figure 0.1 Neural network for online buying of FMCG/groceries

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Table 0.3

Neural Network model summary

Training

Cross Entropy Error 108.888

Percent Incorrect

Predictions 16.4%

Stopping Rule Used

1 consecutive

step(s) with no

decrease in errora

Training Time 0:00:00.25

Testing

Cross Entropy Error 51.839

Percent Incorrect

Predictions 20.0%

Dependent Variable: Bought groceries online

a. Error computations are based on the testing sample.

The model summary shows two positive signs-

The percentage of incorrect predictions is less in the hold out sample than in the

training sample.

The estimation algorithm stopped because the error did not decrease after a step

in the algorithm.

This suggests that the specified testing sample is keeping the network ‘on

track’.

The classification table, (See Table 0.4) using the pseudo-probability cut off

for classification, the network does considerably better at predicting the non-buyers

than buyers.

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Table 0.4

Neural Networks classification

Sample Observed Predicted

No Yes Percent

Correct

Training

No 176 34 83.8%

Yes 19 94 83.2%

Overall Percent 60.4% 39.6% 83.6%

Testing

No 57 19 75.0%

Yes 6 43 87.8%

Overall Percent 50.4% 49.6% 80.0%

Dependent Variable: Bought groceries online

The importance of an independent variable is a measure of how much the network’s

model-predicted value changes for different values of the independent variable. Normalized

importance is simply the importance values divided by the largest importance values and

expressed as percentages. See Table 0.5

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Table 0.5

Neural network- Independent variable importance

Importance Normalized

Importance

Qualification .099 27.6%

Industry .069 19.2%

Marital Status .100 28.0%

Family Life Cycle Stage .053 14.7%

Type of family .020 5.5%

Type of residence .071 19.9%

Possess a smartphone .086 24.2%

Hours/day online .358 100.0%

age .061 17.1%

Annual income .059 16.6%

number of children .023 6.6%

The importance chart is a bar chart of the values in the importance table, sorted in

descending value of importance. It appears that variable related to the number of hours a

working woman is online per day has the greatest influence on how the network classifies

them as ‘buyers’ or ‘non-buyers’. See Figure 0.2

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Figure 0.2

Normalized Importance

Binomial logistic regression

H1:: The buying behavior is not impacted by a linear combination of the various

demographic variables

A logistic regression was performed to ascertain the effects of age, hours online,

Qualification, Type of family, Stage in Family Life cycle, Marital status, Annual income,

Industry and No. of Children on the likelihood that respondents bought online. The logistic

regression model was statistically significant, χ2 (19) = 174.082, p < .05.(See Table 1.6). The

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model explained 41.2% (NagelkerkeR2) (See Table 1.7) of the variance in 'bought online' and

correctly classified 76.0% of cases. Sensitivity was 62.1%, specificity was 84.4%, positive

predictive value was 70.625 % and negative predictive value was 78.70% (using Table 0.8),

Of the eight predictor variables only one-hours online, was statistically significant (as shown

in Table 4.12). Hours online had 1.547 times higher odds to buying online. Increasing hours

online was associated with an increased likelihood of buying online.

Table 1.6

Binomial logistic regression omnibus tests of model coefficients

Chi-square df Sig.

Step 1

Step 174.082 19 .000

Block 174.082 19 .000

Model 174.082 19 .000

Table 1.7

Binomial logistic regression model summary

Step -2 Log

likelihood

Cox & Snell

R Square

Nagelkerke

R Square

1 466.819a .302 .412

a. Estimation terminated at iteration number 5

because parameter estimates changed by less

than .001.

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Table 0.8

Binomial Logistic regression classification table

Classification Tablea

Observed Predicted

Bought groceries

online

Percentage

Correct

No Yes

Step 1

Bought groceries

online

No 255 47 84.4

Yes 69 113 62.1

Overall Percentage 76.0

a. The cut value is .500

Table 0.9

Binomial Logistic regression -variables in the equation

B S.E. Wald df Sig. Exp(B

)

95% C.I.for EXP(B)

Lower Upper

Step 1a

ONLINE .432 .045 91.569 1 .000 1.541 1.410 1.684

AGE .020 .020 1.009 1 .315 1.020 .981 1.060

QUAL 2.118 3 .548

QUAL(1) .418 .743 .317 1 .574 1.519 .354 6.512

QUAL(2) -.366 .311 1.392 1 .238 .693 .377 1.274

QUAL(3) -.059 .299 .039 1 .844 .943 .525 1.694

IND 9.741 5 .083

IND(1) .302 .632 .228 1 .633 1.352 .392 4.666

IND(2) -.527 .561 .881 1 .348 .591 .197 1.774

IND(3) -.246 .689 .127 1 .721 .782 .203 3.016

IND(4) -.228 .585 .151 1 .697 .796 .253 2.505

IND(5) .568 .600 .894 1 .344 1.764 .544 5.722

MARTSTAT 5.209 2 .074

MARTSTAT(1) -1.135 1.425 .635 1 .426 .321 .020 5.246

MARTSTAT(2) 1.076 .849 1.605 1 .205 2.934 .555 15.502

ANNINC -.063 .072 .757 1 .384 .939 .816 1.082

FLCSTAGE 1.407 5 .924

FLCSTAGE(1) .021 1.039 .000 1 .984 1.022 .133 7.823

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FLCSTAGE(2) .363 1.052 .119 1 .730 1.437 .183 11.293

FLCSTAGE(3) -.448 .663 .457 1 .499 .639 .174 2.341

FLCSTAGE(4) -.294 .647 .206 1 .650 .745 .210 2.651

FLCSTAGE(5) -.191 .587 .106 1 .744 .826 .261 2.609

FAMTYP(1) -.060 .277 .047 1 .829 .942 .547 1.621

Constant -3.157 1.333 5.607 1 .018 .043

a. Variable(s) entered on step 1: ONLINE, AGE, QUAL, IND, MARTSTAT, ANNINC,

FLCSTAGE, FAMTYP.

Summary and discussion:

Using the Multilayer Perceptron procedure we constructed a network for predicting

the probability that a working woman will buy FMCG/groceries online. The network was

able to achieve more than 80% correct classification and indicated that the number of hours

online had the greatest influence on how the network classifies them as ‘buyers’ or ‘non-

buyers’.

A logistic regression was performed to ascertain the effects of age, hours online,

Qualification, Type of family, Stage in Family Life cycle, Marital status, Annual income,

Industry and No. of Children on the likelihood that respondents bought online. Among them,

only one-hours online, was statistically significant. Increasing hours online was associated

with an increased likelihood of buying online.

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Handa, M. & Gupta, N. (2009). Gender influence on the innovativeness of young urban

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