To understand the Price Sensitivity buyer behavior of Consumer
Prof. Amit Shrivastava1, Dr. Sushil Kumar Pare2,
Dr. Saumya Singh3
Devi Saraf Institute of Management Studies
S.V. Road, Opp. Bajaj Hall,
Malad (W), Mumbai (India)- 400064
Mob No: +91 9768129299
2Assistant Professor, Marketing
Thakur Institute of Management
Studies and Research,
Thakur Shyam Narayan Marg,
Kandivali (E),Mumbai (India) - 400066 email@example.com
3Associate Professor, Marketing
Indian School of Mines,
Mob No: +91 94313 77737
Consumers in rural markets are exposed to ever
increasing expansion of options with reference to consumables in the last
decade of 21st century. Marketers desire to set a price that will
maximize demand and ensure a profitable business result in the rural areas.
Therefore, correct approximation of demand, with respect to price sensitivity
across the segments, ability to define distribution expectation of rural
markets is crucial for marketers. Understanding the price sensitivity of potential
buyers is a requirement for successfully segmenting the consumer so that as a
marketer, firms can design suitable value propositions instead of offering
similar marketing mix and under-utilize scared resources. This paper is an
attempt to establish and validate the price sensitivity of the rural buyer. Despite
an earlier intensive research work done by researchers on price and its various
a study on comparative
Price Sensitivity for Personal Care & Grocery Category specifically framed
for the FMCG Sector has been missing, particularly in the rural area. The study
aims to address this area. Comparative study was conducted to ensure that
respondent is able to register their sensitivity towards pricing corresponding
to both the categories with choice and fairness. The research design involved three
broad stages: item generation and selection assessment of reliability and
validity and developing a model of price sensitivity. The paper provides future
course of study of consumer’s price sensitivity for researchers and practitioners.
Key words: Price Sensitivity Scale, Price
Sensitivity Measurement, Consumer Behaviour, Personal Care, Grocery,
Price Sensitive, Value Segment
Fast Moving Consumer Goods (FMCG) goods are popularly
named as consumer packaged goods. Items in this category include all
consumables (other than groceries/pulses) people buy at regular intervals. The
most common in the list are toilet soaps, detergents, shampoos, toothpaste,
shaving products, shoe polish, packaged foodstuff, and household accessories
and extends to certain electronic goods. These items are meant for daily of
frequent consumption and have a high return.
India has been a consumption-driven economy for the
last many decades. Broadly categorised into urban and rural markets, Consumer
spending in the country is expected to increase about 2.5 times by 2025,
therefore, the Indian consumer segment is gaining high attention and pampering
from marketers across the globe.
overall fast moving consumer goods (FMCG) market is expected to increase at a
compound annual growth rate (CAGR) of 14.7 per cent to US$ 110.4 billion during
2012–2020, with the rural FMCG market expected to increase at a CAGR of 17.7
per cent to US$ 100 billion during 2011–2025 (Indian Consumer Market:
SNAPSHOT: Introduction, 2014). FMCG companies were largely
dependent on rural demand to garner volumes in 2013 when sales of discretionary
items were affected in a slowing market. The industry, which grew in high
double digits until 2012, was grappling with the slowdown in the past three
quarters. High input costs left no choice for consumer goods companies but to
increase prices and reduce unit pack sizes. A
non-food category experienced a higher degree of slowdown and is driven by hair
care and personal care. Essentials such as cooking oil and impulse foods saw
better growth compared with indulgent categories such as skincare. (Purvita Chatterjee, 2013) FMCG companies with categories such as household
insecticides, hair colours and fabric whiteners have been spared from the slow
growth that hit bigger categories such as soaps and detergents.
Despite a market of 100 Million ascribed to 400 towns,
FMCG companies have not been able to tap the market potential fully due to
limited understanding of consumers’ habit of deriving value against price, as
sourced by performance & measurement company AC Nielsen.
Companies have been struggling to get the numbers
attributed to ever rising inflation and weaker crippling consumer sentiment.
However, as per Nielsen reports, there is larger market begging for attention.
This proposes FMCG players to focus on the middle India (with population range
from 1 Lac to 10 Laces) & rural India in the long term. These two markets
together contribute to 3/4th of the market, by 2015. Middle India’s
per capita consumption stood at Rs.2800/-, which is significantly higher than
all-India average of Rs.1200/-. Euro monitor International's survey has found
that 68 per cent of personal care products were sold in rural India in FY 12 as
against 31 per cent in cities. Thus, markets in rural and semi-urban India are
on the edge to be the future growth drivers due to higher disposable incomes,
rising desires of people to own quality products and improved infrastructure
support extended by the Government for the development of these areas (Avinash
Rural markets, unaffected by the
economic slowdown, have created a complete turnaround in the mindsets of the
marketers. Rural India is and will remain a major consumer of goods and services,
its sheer size making it a force to reckon with. It is well known that 70 per
cent of the country’s population and 56 per cent of its overall consumption
comes from rural India. (Purvita Chatterjee, 2013).
Several factors have
led to an increase in rural purchasing power; the sector is excited about a
burgeoning rural population whose incomes are rising and which is willing to
spend on goods designed to improve lifestyle (S John Mano Raj, 2007). It is
also not uncommon to notice that growth in hinterland is also propelled by the
distribution push because search area for growth is ably supported by
manufacturers placing their brands on shelves across geographies and create
demand by sheer availability. Geographic presence and the availability of these
products in proximal markets now mean faster replenishment cycles that is
likely to translate into greater per capita consumption. A
leading consumer research firm has also identified that consumer readiness for
trial a category has also expanded the ambit of large number of consumers in
rural markets demanding for branded packaged goods (Prashant Singh, 2011). The
rural audience has matured enough to understand the communication developed for
the urban markets, especially with reference to FMCG products. Television has
been a major effective communication system; an advertisement touching the
emotions of rural folks has also driven quantum jump in the sales volume (S
John Mano Raj, 2007). Also with a near saturation and cut throat competition in
urban India, many producers of FMCGs are driven to chalk out bold new
strategies for targeting the rural consumers in a big way.
The increase in
procurement prices the government sets the minimum support price -- MSP -- for
many farm products has contributed to a rise in rural demand. A series of good
harvests on the back of several good monsoons boosted rural employment in
agricultural and allied activities. Government schemes like NREGS [National
Rural Employment Guarantee Scheme, which guarantees 100 days of employment to
one member of every rural household] reduced rural underemployment and raised
wages. Also, farmers benefited from loan waivers introduced in the last Union
Budget. Total income in rural India (about 43% of total national income) is
expected to increase from around US$220 billion in 2004-2005 to US$510 billion
by 2012-2013, a CAGR of 12%, lastly, rural consumer is nearly free from
spending on rent or food, therefore, rural way of living leaves the consumer
with higher amount that can be spent on other products.
Challenges of Rural India
(Pawan Kumar, August, 2013) The typical
structure of rural markets and behavior of rural consumer pose challenges to
marketers in reaching these markets. One of the concerns are no all weather
roads to connect the urban center with the rural areas, other infrastructure
handicap is warehousing, because of which, wastage is a perennial problem.
Inadequate media coverage and lack of power creates hindrance for communication
in rural areas, apart from this, there is relatively low literacy level,
inability to consume print media, and extremely scattered market pockets make
the job difficult for rural marketers.
Price sensitivity can be defined as
how consumer feels about the price of an offering (Goldsmith & Newell,
1997). Early AIO (activities, interest & opinion) measures of lifestyle
contained a subscale to measure price consiousness (Wells & Tigert, 1971). It is important for the marketers to
ascertain the range and the Price Sensitivity, because, if they miss the range
by miniscule margins, it will result into enormous consequences to their
bottom-line. Despite the concerns, the Price
Sensitivity Measurement remains a simple method; it is both easy to execute and
easy to understand. Although it is rarely proposed and recommended the Price
Sensitivity Measurement as a method for definitively selecting the price for a
product, it can be used as a tool for gauging consumers’ price perceptions and
expectations (bsaikrishna, 2012).
(SANGMAN HAN, 2001) states that, price
influence consumer purchase behavior and eventually firm’s sales and profits. Price plays a considerable role in
consumers’ formation of quality perception (Robin Raffard 1992). Managing price and
price perception is typically difficult, because price is often a quality cue
and really lower price may allow consumer perceive low price as a signal of
poor quality. The most common definition of value is the ratio or trade-off
between quality and price (Monroe, 1990). The relationship between Price-Value has been a common
expression. Some researchers have tried to find relationship beyond just
monetary value. The interpretation of price value must be based on the buyer’s
view of the relationship between price and value (Lewis & Shoemaker, 1997). It is also
interesting to note that consumers may be conditioned to expect deals and
insensitive to smaller on offers. (C M Lillis, 1974) found that out shoppers of
larger communities residents from smaller communities have a perception that
local prices are higher and quality to be poor viz-a-viz retail centers. Low
levels of income mean that rural markets are apt to be most concerned about
receiving value for money. This does not always mean a low price, but rather
that the product or service is deemed to offer appropriate beneﬁts for
the expenditure (Douglas, 2011).
(Vantrappen, 1992) commented that
perceived value will be critically important in 21st century; indeed
in retailing industry. (Burden, 1998), Successful retailers target their
offers towards two customer categories; those with an emphasis on value and
others with time pressure is key.
In nearly every market, particularly
rural market, there will be a segment, which is driven with price. Such a
segment is given nomenclature as “Value Segment”. The size of the ‘Value
Segment” inflates more during the inflationary period. It is risky to ignore
this segment, particularly in developing economies because of expanding
lower-middle class and steep recession can enhance the market value share of
‘value segment’. (Douglas, 2011) has suggested rural areas in emerging
markets require modified marketing mix in terms of low cost functional and
innovative pricing strategies. The value propositions designed for countries at
the upper end of the income spectrum seldom work sustainably for the needs of
the emerging rural population. The problem persists when companies try to
export value propositions developed for the Middle and Upper-Middle tiers, with
some customization, to the rural population (Shashank Tripathi, 2012).
Therefore, researcher proposes that companies need to develop a nuanced
understanding of the set of expectation; this consumer segment keeps developing
innovative propositions to meet its needs. While, lower prices are critical in
this segment, as rural markets have irregular bite-sized cash flow. Yet rural
marketer has to position the value proposition around performance and rising
aspirations, marketer must focus on what ‘job’ the consumer is ‘hiring’ the
product to perform, businesses must design and position their solutions on
other dimensions of value. Which signifies proposition around only low cost
will not lead to success.
Researcher ambition is to develop a
scale to measure price sensitivity of rural consumer, because as a marketer,
nuanced understanding of various segments with underpinning of their price
sensitivity will help in designing appropriate value proposition.
Development of Scale
The literature already discussed suggested distinct
aspects of price sensitivity. This section describes the process used to
establish the content for dimensions and validate the scale theoretically and
statistically. After the development of initial set of 23 items based on the
theoretical groundwork, sample of diverse set of rural consumers, balanced
between males and females and were from range of occupations such as farmers,
self-employed and business men. They were aged between 24-59 years. The
diversity of respondents was planned to represent buyers of consumable product
in study with the key intention to generate items measuring price sensitivity.
The respondents were asked to register their price sensitivity on
low-involvement category consumables such as paste & shampoo.
As a consequence of exploratory phase, many of the
items produced were generic and eliminating those alpha coefficients items
increased the reliability statistics of the scale to 0.921. An initial
quantitative procedure to reduce the number of items and to examine the scale’s
psychometric properties. Eight items were deleted from scales on the basis of
retest of correlation value of less than 0.50. After this 15 items remained.
Till this stage, researcher has not checked the
probable overlap of items across dimensions, keeping this in view, a Principal
Component Analysis with varimax rotation. A clear factor pattern emerged;
however, after the repeated iteration of removing items, which had highest
loading on inappropriate factor, or almost similar loading on more than one
factor, 7 items remained. Another exploratory factor analysis was on these 7
items showed an absolutely clear factor pattern.
5 Reliability & Validity of Scale
Since the other dimensions of the scale were
established, researcher conducted a confirmatory factor analysis, which was
tested against a one factor model suggested that observed variables represent a
single value dimension.
As the researchers are using active research, the
research design includes two basic things, first is designing a questionnaire
and administering the survey. The questionnaire was reviewed by three reviewers
experienced in consumer buying behavior research, out of which, one was blind
Before going for actual administration of the
questionnaire, first it was administered on a small sample to check and then it
is actually carried out on the sample population. Furthermore to enhance the
effectiveness a 20% of the sample is covered with structured interview.
To limit the scope of study the researchers has chosen
the personal care category and grocery as both the categories occupy major
portion in the monthly shopping list of the households.
The study was undertaken with the following objective:
understand the Price Sensitive buying behavior of Consumer
Development of study
After consulting relevant literature and discussion
with academicians and practitioners, 29 items were developed. These items were
discussed with three experts. After discussion, 23 items remained. These items
were converted into a structured questionnaire. These questionnaires were
administered on the sample size of 300. The data was then tabulated and
Quartile test was administered to establish the lower & upper limit for the
The reliability of an instrument is its ability
to produce consistent results each time. While administering the
instrument under similar conditions to the same population – similar the
results, higher the reliability. There are external and internal consistency
procedures for determining reliability. The present research considers the
internal consistency procedure for measuring reliability of the instrument in
personal care category.
Besides face validity, as all items of the scale are
related to the variable under focus, it has high content validity. It is
evident from the assessment and ratings of the judges /experts that items of
the scale are directly related to the concept of price sensitivity measurement.
In order to find out the validity from the coefficient of reliability (Garrett,
1981), the reliability index was calculated, which indicated high validity on
account of being 0.94.
The item-item correlation matrix is the important
component of item analysis. This matrix displays the correlation of each item
with every other item. This matrix provides important information about a
test’s internal consistency. As we all know, it should be correlated highly
with the other items measuring the same construct.
As only two items are negatively correlated with other
items, it can be safely assumed that first two items can be dropped without
compromising the reliability of the test. These two items viz. “checking price before
buying” and “checking at three different options before buying” mainly
Researcher can interpret that even a price
sensitive customer is not going for explicit “comparison” of the
alternate before buying or ‘comparison’ is not on top of the mind while taking
purchase decision for these categories.
Item Reduction and exploratory analysis
Analysis has been used as researcher has little idea about the underlying idea
about the underlying interplay of variables therefore how variable would
operate with one another (Matsunaga, 2010). Research has used EFA to identify a
latent factor that reconstructs the complexity of the observed variable into a
form, that solution extracted from EFA should retain all important variables
and covariance between the construct while redundant information are removed (R.K.Henson
& Roberts, 2006).
pool of items that are supposed to tap the construct, it was expansive with 23
items, since ambition was to not to miss any important aspect of the construct
thereby maximizing the face validity. Researcher collected quantitative data
interval and ordinal data using five point likert scale.
Cronbach's alpha is an index of
reliability associated with the variation accounted for by the true score of
the "underlying construct." Construct is the hypothetical variable
that is being measured (L.Hatcher, 1994).Once data are collected with
sample size of 333. The item total statistics table presents the ‘Cronbach’s
Alpha if item deleted’ as appearing in the table below:
Test of Reliability was
administered on 23 items which reported Cronbach’s Reliability Statistics of
0.871 with 23 items. The
last column presents the value that Cronbach's Alpha would be if that
particular item was deleted from the scale. We can see that removal of any
item, except item 1, 6, 8 & 15 would result in a lower Cronbach's alpha.
Therefore, we would not want to remove these questions. Removal of question 1,
6, 8 & 15 would lead to an improvement in Cronbach's alpha, and we can also
see that the "Corrected Item-Total Correlation" value
was low (below 0.40) for these items. This led the researcher to remove these
items. After removing the items when reliability statistics was reiterated, the
improved Cronbach’s Alpha is discussed below:
After 4 iterations improved Cronbach’s Alpha is 0.92
with 19 items for the intended construct.
7.3 KMO & Bartlett’sTest
Barlett's test of
sphericity is significant, thus the hypothesis that the inter-correlation
matrix involving these eight variables is an identity matrix is
rejected. Thus from the perspective of Bartlett's test, factor analysis
is feasible. As Bartlett's test is almost always significant, a more
discriminating index of factor analyzability is the KMO. For this data
set, it is .849, which is very large, so
the KMO also supports factor analysis. Kaiser's rule of retaining factors with Eigen
values larger than 1.00 was used in this analysis as the default.
7.4 Analysis of Communalities
Once the extraction of factors has been completed,
researcher examined the table of 'Communalities' which explains how much of the
variance in each of the original variables is explained by the extracted
communalities are desirable. If
the communality for a variable is less than 50%, it is an item for exclusion
from the analysis because the factor solution contains less then, which can be
Half of the variance
in the original variable, and the explanatory power of that variable might be
better represented by the individual variable. The table of Communalities for this analysis shows
communalities for three variables below 0.75. Since the researcher does not use
the factors as either dependent or independent variables in additional analysis,
removal of variables with low communalities is not an issue.
After removing item numbers reiterating the process,
the revised Communalities are mentioned below:
7.5 Exploratory Factor Analysis
With EFA, researchers usually decide on the number of
factors by examining output from a principal components analysis. Eigen
values are produced by a process called Principal Components Analysis (PCA)
and represent the variance accounted for by each underlying factor.
Using the rotated component matrix
gives few dimensions of price sensitivity.
In order to explore the underlying
factors, which structure the measure, there were few items removed based on
close cross loading or low loading among more than one factor, after three
iterations KMO & Bartlett’s Test was also administered to ascertain the
After continuing the iteration to establish the
factors that can ascertain the construct of measure, researcher removed items
Here, there are nine items to measure the construct,
however, since, two items do not define any factor therefore researcher has
safely removed these items to establish one factor model. In the subsequent
stage, researchers have performed confirmatory factor analysis to by measuring
goodness of fit indices as a construct to measure the price sensitive buyer
The Confirmatory Factor Analysis
With the help of SPSS; having established that the
scale is one factor model the other probabilities such as two factor model are
ruled out (sweeny & Southar, 2001). As suggested by Bollen (1989, as cited
by sweeny and southar, 2001), a null model, in which no factors were conserved
underlie the observed variables, correlations between observed indicators were
zero and variances of the observed variables were not restricted can be tested
against a series of models. In this case a one factor model (suggesting that
the observed variables represent a single value dimension) is the appropriate
Goodness of Fit Statistics
Minimum Fit Function Chi-Square = 30.57 (P = 0.0013)
Root Mean Square Error of
Approximation (RMSEA) = 0.078
90 Percent Confidence Interval for RMSEA = (0.045 ;
P-Value for Test of Close Fit (RMSEA < 0.05) =
Chi-Square for Independence Model with 21 Degrees of
Freedom = 2601.75
Independence AIC = 2615.75
Model AIC = 64.04
Saturated AIC = 56.00
Independence CAIC = 2648.42
Model CAIC = 143.37
Saturated CAIC = 186.66
Parsimony Normed Fit Index (PNFI) = 0.52
Comparative Fit Index (CFI) = 0.99
Incremental Fit Index (IFI) = 0.99
Relative Fit Index (RFI) = 0.98
Root Mean Square Residual (RMR) =
Standardized RMR = 0.019
Goodness of Fit Index (GFI) = 0.97
Adjusted Goodness of Fit Index (AGFI) = 0.93
Parsimony Goodness of Fit Index (PGFI) = 0.38
Covariance Matrix (Lisrel output)
Since all the parameters are in place. These measures of fit indicate that
the model provides a good fit to the data. Since this is one factor model so
all the variables are strongly suggest only one variable that is price
sensitivity of the consumer.
This study discusses the role of price
sensitivity in today’s scenario and establishes relationship of pricing
with various variables and their relative importance for buyer while making the
buying decision in FMCG industry. This research is also addressing the
precedence of the Factor Budget on all the other factors, which indicates
during pricing process, due consideration should be given to budgeting aspect
of consumer’s buying decision process.
Researcher’s endeavor is to help managers by finding
out variables having significant bearing on the buyer’s decision making process
while buying personal care and grocery category. Thus, manager will be able to
design the retail format keeping a purview and finding of this study.
As the finding suggests there could be three major
factors in assessing the consumers’’ price sensitivity namely budget, price and
the comparison. It may lead to some practical and interesting insight into
Indian consumer’s buying pattern.
Firstly, customer is more discerning as the finding suggests
- budget and price are two more important factors viz.-a-viz. “comparison” as a
factor, which means – while taking decision Indian customer is concerned with
their monthly budget for respective categories.
As it is evident that budget for monthly shopping is
big bracket in this country. Indian consumer is shopping for all kind of
products available, so there is probability in upgrading the consumer from one
platform (in terms of price point) to another.
Importance of budget and price factors also indicates
that Indian consumer is more value driven rather than communication, since
comparison as a factor is not in the high priority for buying decision in most
of the situations, therefore, the RTB i.e. reason to buy communication
should revolve around value vis-à-vis any other driver.
Scope for further study
The study can be extended to other sectors as
the brands are now-a-days getting commoditized, as a result price
sensitivity is increasing across all the product categories and even in service
With the exhibiting behavior, further study could be
conducted to design an absolute product or place proposition in line with the
consumer preferred factors such as Budget, Price, Comparison and availability.
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