IRJET- Customer Buying Prediction using Machine-Learning Techniques: A Survey

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 10 | Oct 2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 931
Customer buying Prediction Using Machine-Learning
Techniques: A Survey
Mr. Shrey Harsh Baderiya1, Prof. Pramila M. Chawan2
1M.Tech Student, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
2Associate Professor, Dept of Computer Engineering and IT, VJTI College, Mumbai, Maharashtra, India
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Abstract:- Nowadays, customers have become more
interested in the quality of service (QoS) that
organizations can provide them. Services provided by
different vendors are not highly distinguished which
increases competition between organizations to maintain
and increase their QoS. Customer Relationship
Management systems are used to enable organizations to
acquire new customers, establish a continuous
relationship with them and increase customer retention
for more profitability. CRM systems use machine-learning
models to analyze customers’ personal and behavioral
data to give organization a competitive advantage by
increasing customer retention rate. Those models can
predict customers who are expected to churn and reasons
of churn. Predictions are used to design targeted
marketing plans and service offers. This paper tries to
compare and analyze the performance of different
machine-learning techniques that are used for churn
prediction problem. Ten analytical techniques that belong
to different categories of learning are chosen for this
study. This study focuses more on inventory led e-
commerce companies, however the model can be
extended to online marketplaces without inventories.
Facilitated by statistical and machine learning models the
study seeks to predict the purchase decisions based on
adaptive or dynamic pricing of a product. Different data
sources which capture visit attributes, visitor attributes,
purchase history, web data, and context understanding,
lays a strong foundation to this framework. The study
focuses on customer segments for predicting purchase
rather than on individual buyers. Personalization of
adaptive pricing and purchase prediction will be the next
logical extension of the study once the results for this are
presented. Web mining and use of big data technologies
along with machine learning algorithms make up the
solution landscape for the study.
Key Words: Frequent Item set Mining, Recommendation,
Machine Learning, Prediction system
1. INTRODUCTION
Purchase decision process describes the sequence of
actions performed by a customer when deciding to
purchase a particular product or service. It can also be
described as a process of problem solving, where a
consumer satisfies his needs after thoughtful
consideration. The outcome of a purchase decision process
is a decision whether a customer will buy a given product
or service or not. Basically system distinguish between
three different types of purchase decisions. They differ in
value and frequency of purchase, covering different
intensity levels of involvement and time invested in the
purchase decision: (i) routine response behavior (for
frequently purchased, low involvement products and
services), (ii) limited decision-making (unfamiliar brand
choices in the known category of products and services),
and (iii) extensive decision-making (high involvement,
high value and low frequency of purchasing). There are
several factors affecting buying behaviour, such as
cultural, social and personal decision elements. Cultural
factors include cultural context and belonging to a certain
social class or subculture. Social factors are defined with
position and role of the individual, his family and
reference groups, which have a direct or indirect impact
on buying behavior. Personal factors are determined with
individuals lifestyle, occupation, property status,
personality and self-esteem.
2. LITERATURE SURVEY
Research [1] indicates that the cost of retaining a
customer is less than attracting new ones. This is due to
marketing costs required to appeal to new customers. For
this reason, together with the increase of competition it
has become pivotal that the current customers base is
retained. Normally, customers churn gradually and not
abruptly. This means that by analyzing customers historic
buying patterns one can adopt a proactive approach in
predicting churn. Since all transactions are inserted
through POS and stored in databases, understanding
customers’ needs and patters is possible as data is
accessible.
According to [2], executives are dedicating marketing
budgets to focus on customer retention campaigns.
Various models designed to predict churn focus on
statistical and renowned machine learning algorithms
including Random Forest and Logistic Regression. This
paper focuses on two aspects when predicting churn
within the grocery retail industry. The first is based on the
features which will be passed on to the model. Instead of
using customers buying trends to cluster the individuals,
these values will be created as features and are passed to
the model. Therefore, for each customer various features
are created to allow the model to learn and identify
patterns per individual. For this reason, two datasets are
created to test and evaluate how data should be
represented to predict churn. The second aspect is the
implementation of the algorithms. The novelty of this

study is the use of deep learning to predict churn within
the grocery industry. To our knowledge, this is the first
study which implements deep learning within this
industry. The strength of using deep learning is that it can
reveal hidden patterns within the available dataset.
An important aspect within the business is to have a good
understanding of customers’ needs, whereby holistic
views of their patterns may be analyzed. When customers
are satisfied with the service or products, customer loyalty
increases [3].
Authors [4], further discuss that revenues and margins
will increase if customers are retained when compared to
the cost of attracting new customers. Applying statistical
techniques and machine learning algorithms on available
data may guide companies in identifying hidden trends
and customer behavioral patterns. Implementing data
mining techniques to predict churn may give companies a
competitive edge in improving the relationship with
customers. Using customer churn models which correctly
classify churn, companies have added value.
Churn is a term used within the marketing field to indicate
that a customer has moved to a competitor or has stopped
transacting. Churn may be defined as customers who have
a high probability to stop transacting with the company
[1] or as described by [6]: churn may be identified when a
customers purchasing value falls beneath a threshold
across a predefined period of time. Within the Grocery
Retail Industry, the identification of the exact moment a
customer will churn is hard to define.
The output of this layer is sent to a max pooling layer [7]
discusses that this layer creates a smaller and concise
feature map of the input. Next, is a fully connected layer
whereby all nodes from the max pooling layer are
connected to the neurons within this layer. The number of
fully connected layers differs on the depth of the data.
Yuta Kaneko et. Al [8] proposed A Deep Learning
Approach for the Prediction of Retail Store Sales system
used deep learning to construct a model that predicts the
increase and decrease in the sales of a retail store and
tested its usability. System used three years of POS data
from supermarkets for the analysis, treating 29 months of
it as data for learning, while the remaining 7 Months of
data were used for verification. As a result, the predictive
accuracy of the increase decrease in sales for the following
day varied between 75% to 86% according to the changes
in the number of product attributes. The predictive
accuracy was highest when the model was constructed
using the Category 1 data, which consisted of 62
attributes.
Yi Zuo et. Al. [9] proposed Prediction of Consumer
Purchasing in a Grocery Store Using Machine Learning
Techniques. System employs two representative machine
learning methods: Bayes classifier and support vector
machine (SVM) and investigates the performance of them
with the data in the real world. It also carried out a
method for extracting consumer purchasing behavior.
Utilizing RFID data acquired from individuals in a
Japanese supermarket, we examined several important
methodological issues related to the use of RFID data in
support vector machines (SVMs) to predict purchasing
behavior.
Liu Bing and Shi Yuliang [10] Prediction of User's
Purchase Intention Based on Machine Learning. System
present stage, a naive Bayesian algorithm has the
advantages of simple implementation and high
classification efficiency. However, this method is too
dependent on the Distribution of samples in the sample
space, and has the potential of instability. To this end, the
decision tree method is introduced to deal with the
problem of interest classification, and the innovative use
of Local storage technology in HTML5 to obtain the
required the experimental data. Classification method uses
the information entropy of the training data set to build
the classification model, through the simple search of the
classification model to complete the classification of
unknown data items.
Jinggui Liao, Yuelong Zhao and Saiqin Long have proposed
MRPrePost-A parallel algorithm adapted for mining big
data [11]. It is a parallel calculation which is actualized
utilizing the Hadoop stage. The MRPrePost is an enhanced
Pre-Post calculation which utilizes the map reduces
structure. The MRPrePost calculation is utilized to
discover the affiliation runs by mining the vast datasets.
The MRPrePost calculation has three stages. In the initial
step the database is isolated into the information squares
called the shards which are allotted to every specialist
hub. In the second step the FP-tree is developed. In the last
advance the FP-tree is mined to acquire the successive
item sets. Trial comes about have demonstrated that the
MRPrePost calculation is the quickest.
Sheela Gole and Bharat Tidke Frequent item set Mining for
Big Data in social media using ClustBig FIM algorithm [12].
Substantial datasets are mined using the Map reduce
framework in the proposed estimation. Enormous FIM
figuring is changed in accordance with get the ClustBig
FIM computation. ClustBig FIM computation gives
adaptability and speed which are used to get
accommodating information from far reaching datasets.
The profitable information can be used to settle on better
decisions in the business development. The proposed
ClustBig FIM estimation has four essential steps. In the
underlying advance the proposed computation uses K-
infers count to make the bundles. In the second step the
relentless item sets are mined from the gatherings. By
building up the prefix tree the overall TID summary are
gotten. The sub trees of the prefix tree are mined to get the
standard item sets. The proposed ClustBig FIM figuring is
wound up being more successful appeared differently in
relation to the Big FIM calculation.
R. Priyanka and S. P. Siddique I. proposed A Survey on
Infrequent Weighted Itemset Mining Approaches [13].

This paper handles the issues of finding the exceptional
and weighted item sets. The intermittent itemset mining
issue is discovering item sets whose repeat of the data
isn't precisely or equal to most outrageous edge. This
paper audits distinctive procedure for mining infrequent
itemset. Finally, relative strategy for each method is
shown. Data Mining is described as Extraction interesting
illustrations or gaining from colossal measure of data".
Data burrowing is the procedure for discovering data from
different points of view and gathering into accommodating
information. Finding of typical illustrations concealed in a
database expect a pivotal part in a couple of data mining
task. There are two anticipate sorts of models in data
mining. One is insightful model which uses data which
uses data with known result td develop a model that can
use explicitly to expect esteems. Another is clear model,
which depicts the case in existing data. Course of action is
a model or classifier is created to anticipate class name. It
made out of two phases: coordinated learning of a
planning set of data to make a model, and after that
gathering the data as demonstrated by the model. It relies
upon perceptive model. Backslide examination is a
quantifiable theory that is often used for numerical desire.
It is used to expect missing or difficult to reach numerical
data esteems instead of class marks. Various certifiable
data mining applications can be seen as reckoning future
data states in light of past and current data. Clustering,
Summarization, Association represent and course of
action disclosure are illustrative in nature. Packing
incorporates recognizing a constrained course of action of
characterizations to depict the data. Each part in a
gathering should be in a general sense the same as other
part in its cluster and not at all like other group.
Continuous disclosure is used to choose progressive
licenses in data. These illustrations rely upon a period
gathering of exercises. Alliance oversee mining is common
and especially inspected data burrowing technique for
finding interesting connection between factors in an
extensive database.
Surendar Natarajan and Sountharrajan Sehar Distributed
FP-ARMH Algorithm in Hadoop Map Reduce Framework
[14]. The proposed calculation uses the gatherings
effectively and helps in mining general case from broad
databases. The workload among the gatherings is
administered using the hadoop scattered structure. The
hadoop passed on report structure stores the broad
database. Three map reduce livelihoods have been used to
mine the customary cases. The FP-tree is conveyed in the
central map reduce work. The FP-tree is secured in the
show data structure plan. The FP-tree bunch data
structure is given as the commitment for the second map
reduce work. The second map reduce work produces
condition configuration base as yield for all the thing sets.
The third guide diminish work takes the condition
configuration base as information and convey visit
configuration identifying with the thing set to which the
unexpected case base has been made. In third guide
decrease program, the guide occupation would convey the
prohibitive FP-tree for unforeseen case base and diminish
work would make visit outline from the relating
prohibitive FP-tree. The unforeseen FP-tree is in like
manner set away in show data structure. From the
unforeseen FP-tree the ceaseless cases are gained. The
proposed ARMH figuring utilizes the hadoop group
reasonably to obtain the unending case from expansive
databases.
Figure 1: The proposed framework for predicting online
purchase by a customer based on Dynamic Pricing for
online
Xiaoting Wei, Yunlong Ma, Feng Zhang, Min Liu and
Weiming Shen have proposed Incremental FP-Growth
Mining Strategy for Dynamic Threshold Value and
Database Based on Map reduce [15]. The huge scale
information is handled utilizing a map reduce system. The
proposed incremental estimation is convincing when edge
regard and special database change meanwhile. The
parallel incremental FP improvement computation
incorporates seven stages. There are four map reduce
stages. In the underlying advance the database is parceled
into little irregularities and the nonstop summary is
obtained. In the second step the FP-tree is assembled and
the area visit item sets are gotten. The third step is to add
up to the persistent item sets mined. In the fourth step the
database is redesigned. The fifth step is to overhaul the
progressive thing list. In the sixth step the FP-tree is
produced and the close-by progressive item sets are
mined. In the last walk the adjacent general item sets are
totaled. The computation ends up being more powerful in
the incremental databases.
3. PROPOSED SYSTEM
One of the most common financial decisions that each of
us makes on a nearly daily basis involves the purchasing of
various products, goods, and services. In some cases the
decision on whether or not to make a purchase is based
largely on price but in many instances the purchasing
decision is more complex, with many more considerations
affecting the decision-making process before the final
commitment is made. Retailers understand this well and
attempt to make use of it in an effort to gain an edge in a
highly competitive market. Specifically, in an effort to
make purchasing more likely, in addition to balancing the
salability and profit in setting the selling price of a
product, companies frequently introduce additional
elements to the offer which are aimed at increasing the
perceived value of the purchase to the consumer.

Figure 2: System Architecture Overview
4. CONCLUSIONS
We will do research on following area of recommendation
as well as pricing. We will try to consider both user and
providers concerns of changing demand and its cost. This
will ensure both provider and customers benefit. Apart
from this we will consider competitive prices and its result
on pricing. We will study best fit auction based pricing to
support optimized fine grained scheme. Also partial waste
issue is a area of study which can result in reduced prices
using precise scheduling of users’ job. User scheduling
behaviors and partial usage waste will be brainstormed to
find an effective solution
REFERENCES
[1] C. Jie, Y. Xiaobing, and Z. Zhifei, “Integrating OWA and
data mining for analyzing customers churn in e-
commerce,” The Editorial Office of JSSC and Springer-
Verlag Berlin Heidelberg, vol. 28, pp. 381-391 2015.
[2] The science behind customer churn. [Online].
Available:http://financeinbusinesslife.info/the-
sciencebehind-customer-churn/
[3] S. Neslin, S. Gupta, W. Kamakura, L. Junxiang, and C. H.
Mason, “Defection detection: Measuring and
understanding the predictive accuracy of customer churn
models,” Journal of Marketing Research, vol. 43, pp. 204-
211, 2006.
[4]IBM. [Online]. Available:
https://www.ibm.com/developerworks/library/badata-
mining-techniq ues/, Developer works, Accessed:
November 2016
[5] E. Siegel, Predictive Analytics, The power to Predict
Who Will Click, Buy, Lie or Die, Wiley, 2013.
[6] V. Migueis, D. V. den Poel, A. Camanho, and J. Falcao,
“Modelling partial customer churn: On the value of first
product-category purchase sequences,” Expert Systems
with Applications, pp. 11250-11256, 2012.
[7] M. Nielson, Neural Networks and Deep Learning,
Online Book, 2016.
[8] Zuo Y, Yada K, Ali AS. Prediction of Consumer
Purchasing in a Grocery Store Using Machine Learning
Techniques. InComputer Science and Engineering (APWC
on CSE), 2016 3rd Asia-Pacific World Congress on 2016
Dec 5 (pp. 18-25). IEEE.
[9] Kaneko Y, Yada K. A deep learning approach for the
prediction of retail store sales. InData Mining Workshops
(ICDMW), 2016 IEEE 16th International Conference on
2016 Dec 12 (pp. 531-537). IEEE.
[10] Bing L, Yuliang S. Prediction of User's Purchase
Intention Based on Machine Learning. InSoft Computing &
Machine Intelligence (ISCMI), 2016 3rd International
Conference on 2016 Nov 23 (pp. 99-103). IEEE.
[11] Jinggui Liaoat. Al. proposed A parallel algorithm
adapted for mining big data‖ IEEE Workshop on
Electronics and Computer Applications in year 2014
[12] Sheela Gole and Bharat Tidke, proposed a system
Frequent Itemset Mining for Big Data in social media using
ClustBigFIM algorithm‖, International Conference on
Pervasive Computing in 2012.

[13] Siddique Ibrahim at. Al. A Survey on Infrequent
Weighted Itemset Mining Approaches‖ , IJARCET, Vol.4,
pp. 199-203 in 2015.
[14] Surendar Natarajan and Sountharrajan Sehar
proposed a idea of Distributed FP-ARMH Algorithm in
Hadoop Map Reduce Framework for IEEE, in 2013.
[15] Xiaoting Wei at. Al. define the, Incremental FP-
Growth Mining Strategy for Dynamic Threshold Value and
Database Based on Map reduce proposed a idea
Proceedings of the 18th IEEE International Conference on
Computer Supported Cooperative Work in Design in 2014
AUTHORS
Mr. Shrey Harsh Baderiya
M.Tech Student, Dept of
Computer Engineering and IT,
VJTI College, Mumbai,
Maharashtra, India
Prof. Pramila M. Chawan
Associate Professor, Dept of
Computer Engineering and IT,
VJTI College, Mumbai,
Maharashtra, India

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