International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number5–Nov2014
Identification and Extraction of Parameters Influencing Commerce
Trends Based on Data Mining in Shopping Centres
Theresa Rani Joseph1, Smitha Jacob2
1
PG Scholar, Department of Computer Science and Engineering, St. Josephs College of Engineering and
Technology Pala, Kottayam
2
Assistant Professor, Department of Computer Science and Engineering, St. Josephs College of Engineering and
Technology Pala, Kottayam
Abstract—The paper is the initial effort towards a
proposed prescriptive analysis of commerce trends based
on data mining in shopping centres. The prescriptive
analysis is proposed as the second phase of our previous
work on commerce recommender systems for improving
customer relationship management in shopping centres.
The first phase which contained the design of commerce
recommender system was intended for providing
personalized as well as generic recommendations to the
customers of the shopping centre. The second phase will
concentrate on formulating optimal solutions and
marketing strategies for business needs of managers and
admins based on predictive and descriptive analysis of
customer data. As a preliminary step towards the bigger
analysis, we attempt to identify and extract relevant
predictive and descriptive parameters that influence
commerce trends based on the first phase of the work we
carried out.
Index Terms — Prescriptive Analysis, Descriptive
Analysis, Predictive Analysis,
I.
INTRODUCTION
The research significance of commerce trends
in shopping centres mostly comes from the role a
shopping centre plays in the commerce world. Being a
part of the economy of all towns and cities, shopping
centre is a good domain for carrying out data mining
studies. Data mining can be of predictive or descriptive
nature. Predictive data mining can be used to forecast
almost precise values, based on patterns determined
from known results where as descriptive data mining
describes a data set in a concise way and presents
interesting characteristics of the data without having
any predefined target [11].
Descriptive analytics is used to intelligently
group or classify customers, or to simply better
understand the composition of a population [12].
Descriptive analysis is often used as the primary step in
segregating a population for detailed analysis. It helps
to organize the database into well defined segments or
groups.
problem. The actual predictive analysis comes after the
above mentioned steps. Predictive analysis tracks down
and filters necessary data with the aim of producing
valuable results. The valuable results usually guess the
likelihood of occurrence of an event or possible
outcomes. Predictive analysis has the power to
augment customer relationship management systems
by analyzing customer data.
Prescriptive
analysis
combines
both
descriptive and predictive analyses. Prescriptive
analysis explores a set of possible actions and suggests
actions based on descriptive and predictive analyses of
data [13]. Uncertainty associated with a problem is
accounted and ways to reduce the risks arising from it
are suggested. It makes use of optimization and
mathematical models to make recommendations and
suggestions.
Customer Relationship Management is an
important aspect in commerce world. It is analysed that
the marketing success of an enterprise is founded on a
continuous dialogue with user leading to real
understanding of product or service [2]. Good CRM
usually includes the following key points (1)
Presenting a single image of the organization; (2)
Understanding who customers are and their likes and
dislikes; (3) Anticipating customer needs and
addressing them proactively; and (4) Recognizing
when customers are dissatisfied and taking corrective
action [1]. It is interesting to note that CRM uses
predictive and descriptive analyses to proactively
understand customer purchase habits and product
demands.
II.
RELATED WORK
The related work will briefly deal with
descriptive and predictive analyses, RFM analysis as
well as customer relationship management.
A. Descriptive and Predictive Analyses
Wikipedia says that predictive analytics
encompasses a variety of statistical techniques from
modeling, machine learning, and data mining that
analyze current and historical facts to make predictions
about future, or otherwise unknown, events. It is
imperative to identify the business problem and to
determine the parameters that address the business
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Descriptive analysis for the purpose of
customer relationship management addresses a list of
routine business needs [12]. The primary consideration
here is the determination of distinct types of customers
of a shop. It will analyse what differentiates a set of
customers from others. Price sensitivity is also
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International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number5–Nov2014
addressed and the impact of discounts and incentives
are evaluated. Segmentation, clustering and profiling
are three common techniques used for descriptive
analysis. Clustering helps to find best groupings and
specifically in CRM, clustering helps to clarify long
held assumptions about customer groups. The process
of dividing records into predefined groups is termed as
segmentation. Profiling will include deeper
understanding of attributes and corresponding values
within a defined segment.
Predictive analysis uses data associated with
past events to predict if a similar event will occur in
future [12]. Predictive analysis has many applications
within CRM. It will determine the chances of a
customer becoming a repeat buyer and final evolution
into a high value customer. It can reduce promotion
costs by eliminating unproductive marketing strategies.
Classification, regression, pattern recognition etc are
techniques for predictive analysis. Predictive analytics
have the power to significantly optimize customer
relationship management systems. They can help
enable an organization to analyze all its customer data
before exposing patterns that predict customer behavior
[14]. A predictive analytic solution can be made part of
a CRM system for effective use of in-session data.
those who are not appropriate customers because they
are no longer part of the target market; or those who
may have shifted their purchases to competing
products.
III.
PROPOSED SYSTEM
The basic framework of the system consists of
a web application at shopping centre side and an
android application at the customer side. A brief
description of the first phase of the work now follows
[17]. The first phase of the work was focused on
providing a recommender system solely for the use of
customers. The web application supports four kinds of
users namely web admins, shop admins and customers.
Shops can register in the website by providing
necessary details and they need to be approved by web
admins. Customers can register in the website and can
download the android recommender application from
the website. The android application will have a login
for each registered user. The user can then search for
shop products and avail recommendations.
B. RFM Analysis and CRM
CENTRALISED DATABASE
The concept of RFM was introduced by Bult
and Wansbeek (1995) and has proven very effective
(Blattberg et al., 2008) when applied to marketing
databases [13]. RFM stands for Recency, Frequency
and Monetary value. A marketing technique used for
analyzing customer behavior such as how recently a
customer has purchased (recency), how often the
customer purchases (frequency), and how much the
customer spends (monetary) is termed as RFM
analysis. It is a useful method to improve customer
segmentation by dividing customers into various
groups for future personalization services and to
identify customers who are more likely to respond to
promotions [15].
.
Customer Relationship Management is
defined by four elements of a simple frame-work:
Know, Target, Sell and Service. CRM requires the firm
to know and understand its markets and customers
[16]. This involves detailed customer intelligence in
order to select the most profitable customers and
identify those no longer worth targeting. CRM also
incorporates development of the offer including which
products to sell to which customers and through which
channel. As far as customer life cycle is concerned,
CRM identifies four important stages. Prospects are
people who are not yet customers but are in target
market. Responders are prospects who have shown an
interest in a product or service. Active customers are
people who are currently using the product. Former
customers may be customers who incurred high costs;
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Customer Specific
Purchase & Visit
Info
Inventory info
for each shop
Frequent Pattern
Mining &
Prediction
Similarity
Measuring
Model
Prescriptive Analysis of Commerce Trends
Fig 1: Proposed System Design
A dual recommender system was envisioned
and implemented for customer end. The dual system
involves a personalized recommender system and for
implementing this, it is mandatory to have the purchase
information for each registered user. This will be
available only at the shop database that stores the
billing information. In addition the search patterns of
the customers need to be tracked using the android
recommender application. The design also involved a
generic recommender system, the implementation of
which required the complete inventory information of
each shop, complete with at least four levels of
categorization information. Hence it is necessary to set
up a centralized database containing the consolidated
purchase, visit and inventory info from each shop. It
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should be noted that each registered shop will be
assigned a unique shop id by the web application.
The second phase of the work concentrates on
performing prescriptive analysis of commerce trends
for the use of shop admins or managers. Prescriptive
analysis will help to figure out optimal solutions for
different business problems faced by the shop manager.
Prescriptive analysis combines both predictive and
descriptive analysis. Predictive analysis involves
prediction or forecast of results based on existing data.
Descriptive analysis on the other hand involves concise
representation of existing data and figuring out
interesting facts from data. This paper is an initial
effort to track some of the decisive parameters for
prescriptive analysis based on the customer specific
and inventory specific data mining process carried out
in first phase. The identification of some high level
commerce parameters as well as the ways of extracting
these specific parameters will be the focus of this work.
A. Identification of decisive parameters
In this preliminary phase, we identify six
classes of commerce parameters. These parameters
may have descriptive or predictive nature with regard
to the centralized database. We assume a centralized
database containing the consolidated purchase, visit
and inventory info from each shop within a shopping
centre. The following are the parameters we consider in
this paper.
1.
2.
3.
4.
5.
6.
Temporal parameters
Customer categorization parameters
Product based parameters
Price and offer based parameters
Search and browsing based parameters
Linked parameters
1) Temporal parameters: Temporal parameters as
the name suggests are related to time. RFM analysis
typically deals with recency and frequency. Recency
shows the most recent date and time of purchase by the
customer. Frequency counts the number of times the
customer has made the purchase. Apart from these two
parameters, we are interested in certain specific
temporal parameters of the framework we have
discussed.
First we consider temporal parameters for
granularity of a single day. The most favourite
purchase hour of the customer is one such attribute.
This might vary with customers. It will be useful to
map customers with their favourite purchase hour.
Again this will be a function of frequency of purchase
by the customer during the chosen hour of the day.
Instead of doing an hourly analysis, it is sufficient to
divide a day into time frames like morning, midday,
noon, afternoon, evening and night. There could be
specific interest groups during each of these time
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frames. Associative data will be useful for finding out
the existence of commonality if any between customers
of a specific time frame. The most busy and least busy
time frame of a day is another interesting attribute.
From granularity of a single day, we consider
granularity of a week. The days with maximum,
minimum and average sales in a week are parameters
coming under this category. We can extend the
granularity form week to month and to year and can
track similar parameters.
Predictive parameters deal with the likelihood of
occurrence of events based on past data. We have
already discussed about rush sale hours and not so rush
hours at a shop during a single day. This comes under
descriptive analysis. One predictive parameter is to
check whether it will be productive to allow express
checkout lanes during rush hours. It may not be
productive for granularity of a single day, but it could
give better results if we allow express checkout lanes
for busy day or holidays in a week. Similarly we can
check the best date and time for release of offers.
2) Customer categorisation parameters:
Customer categorization is a typical descriptive
analysis procedure. The RFM model is always useful
for customer categorization. We can always rate the
customer on the basis of their recency, frequency and
monetary value. The best valued customer will be the
ones with maximum scores for all three parameters.
There could be interesting predictive parameters
associated with a customer group. We can always
direct our best offers and promotional mails to these
customers. It will not be wise to ignore the least valued
customers. But we have to come up with specific
strategies for each customer group.
Grouping customers based on associative data is
another way of classification. Associative data refers to
a mix of information about the customer. It could
include demographic information (age, marital status,
nature of job, income etc), geographic information etc.
3) Product based parameters: Product based
parameters are dependent on inventory data. There are
lots of possibilities for deriving descriptive parameters
from product data depending on the type of shop and
range of products supported. These can include the
most sold out product, the least sold out product and
products with average sales. We can group products
into different sections and can find best sellers in each
section. We can also find out products that are gaining
new attention from customers and those products
which are slowly ignored by the customers.
A valuable predictive parameter is to check
how beneficial it is to have additional stocks of the
most sold out item and to introduce different brands of
the most sold out products. Brands play a big role in
the sale of a product. The store managers have to
wisely choose the brand with highest demand and have
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to recognize brands with diminishing demand from
customers.
A very famous analysis concept related to
products is called market basket analysis. It tries to find
sets of items that are brought together often during a
single purchase by the customer. Bread, butter and
milk bought during the same purchase are one such
example. Market basket analysis actually helps to
predict items for combo offers.
4) Price and offer based parameters: Price based
parameters are inevitable for commerce trend analysis.
The role of price in commerce is often tricky. The
general trend is to move towards cost effectiveness.
Low priced products are always attractive to most
customer groups. But at times the decisive factors are
the budget and the urgency of the customer group. It is
a general practice in certain shops to maintain products
of varying quality and expense for targeting more
customer groups to their shops.
But there are
trademark shops which often target a specific customer
groups. One descriptive parameter to check is the price
ranges of the most sold out brands of products. It is
also noteworthy to understand the effect of price
variations in the sale of a product. Offers inherently
calls for increased sales. But it is important to notice
the offers that gave the maximum sales, the period
during which the offer was introduced, its duration and
the customer groups involved.
5) Search and browsing based parameters: The
search and browsing info give useful insights about the
tastes of each customer group. Moreover it is a sure
indicator of the recent trends of the market. The most
searched item in a shop is a good descriptive
parameter. Introducing offers for the most searched
product is a predictive parameter. The recency of
search is also important as its frequency. The
diminishing utility of a product can be understood by a
depreciation in search or browse activities surrounding
that product.
6)
Linked parameters: Most of the above
mentioned parameters are linked to one another. Price
based parameters are closely related to temporal and
product based parameters. A good example is the
introduction of combo offers at peak sale hours.
Combo offers combine more than one favorite product
of the customers. Sale of combo products at low price,
and at peak sale hours definitely creates a very positive
response from customers. Synergy of multiple factors
is at work in this case. Customer categorization
parameters can be easily linked with product
parameters. In a similar way market basket analysis of
different customer groups might yield surprising
results.
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B. Extraction of identified parameters
A brief discussion regarding the means of
extraction of the identified parameters will give us an
idea about the feasibility of the proposed prescriptive
analysis for our designed framework.
Our framework is so designed that a new
pattern id is created each time the user logs into the
android app [17]. The time between one login and log
off is considered as the time interval of a pattern. The
pattern ids and customer ids are maintained in a table
called customer_patterns. Two separate tables are
maintained for tracking purchase pattern and browsing
pattern. Each time the user purchases an item the
maximum pattern id or the latest pattern id for that
customer is chosen from the customer_patterns table.
This is based on the assumption that user will be
logged into the android app during the time of visit to
the mall. Since the pattern ids are created based on
time, we are amply equipped with means to capture
temporal parameters.
For collecting associative data for customer
categorization, we can make use of the registration data
available in the database. Each customer has to register
in the website to download the android app. For data
regarding recency, frequency and monetary value of
customer, it is enough to query customer_patterns
table, purchase and browsing info table.
Product based parameters can be easily
tracked from the detailed inventory data available with
each shop owner. Price and offer based parameters can
be tracked partly from inventory data and partly from
purchase info table of each shop. The android
application allows customers to check in to a shop to
view recommendations and offers. The user can
additionally search for a product and then checks in to
a shop offering the product to view further
recommendations and offers. The browse and search
history of the user is saved in visit_info table. So it is
feasible to extract parameters regarding search and
browse data of customers.
For finding out repeated purchase or browse
patterns, we can access the user pattern data structure
which is a hash map [17] that contains pattern id as key
and the transaction array list corresponding to each
pattern id as the value of the hash map. Frequency
mapping is performed on this dynamic data structure to
find out repeated patterns.
IV.
CONCLUSION
The identification and ways of extraction of
decisive parameters for prescriptive analysis have been
discussed. The key to improving customer relationship
management is to analyze the descriptive parameters
that are available through various means like customer
registration, purchase info ,billing info, browsing info
etc and to derive predictive parameters and finally to
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move on to prescriptive analysis for finding out
optimal solutions for business needs. . We have
identified six categories of commerce parameters. Each
category will involve predictive and descriptive
parameters relevant to our framework. The design and
implementation of a prototype commerce recommender
system for a shopping centre [17] has been the basis for
studying the extraction of the identified parameters.
V.
FUTURE WORKS
[13] http://cdn.intechopen.com/ pdfs-wm/13162.pdf
[14] http://huaat.net/download/DMtechniques.pdf
[15] http://www.dataminingarticles.com/info/data-miningintroduction/
[16]https://faculty.washington.edu/socha/css572winter2012/ASA_Int
roduction_to_Analytics.pdf
[17] Theresa Rani Joseph and Smitha Jacob,“A Commerce
Recommender System for Improving Customer Relationship
Management in Shopping Centres,” in International Journal of
Engineering Trends and Technology, vol. 13, no. 4, Jul. 2014.
The discussion we have done in this paper is
the preliminary step towards building a strong
customer relationship management system for a
shopping centre. From the parameters we have
identified, our aim is to move towards the complete
prescriptive analysis of business needs associated with
a shopping centre. It is extremely useful to provide
store owners with analytical reports which summarize
specific customer requirements, behavioral patterns
and solutions for business needs.
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