Predicting consumer product demands via Big Data: the roles of online promotional
marketing and online reviews
Abstract
This study aims to investigate the contributions of online promotional marketing and online
reviews as predictors of consumer product demands. Using electronic data from Amazon.com,
we attempt to predict if online review variables such as valence and volume of reviews, the
number of positive and negative reviews, and online promotional marketing variables such as
discounts and free deliveries, can influence the demand of electronic products in Amazon.com.
A Big Data architecture was developed and Node.JS agents were deployed for scraping the
Amazon.com pages using asynchronous Input/Output calls. The completed web crawling and
scraping datasets were then preprocessed for Neural Network analysis. Our results showed
that variables from both online reviews and promotional marketing strategies are important
predictors of product demands. Variables in online reviews in general were better predictors
as compared to online marketing promotional variables. This study provides important
implications for practitioners as they can better understand how online reviews and online
promotional marketing can influence product demands. Our empirical contributions include
the design of a Big Data architecture that incorporate neural network analysis which can used
as a platform for future researchers to investigate how Big Data can be used to understand and
predict online consumer product demands.
Keywords: Product demands; online reviews; promotional marketing; online marketplace; big
data; neural network
1.0 Introduction
Businesses today operate in an increasingly competitive and dynamic environment.
Traditionally, manufacturers compete with each other by lowering their production costs and
having better product qualities. However, competition on costs and product qualities are
becoming more challenging as manufacturers move from offering standardized products and
services to one that focuses on customizations (Dobrzykowski et al. 2014). In order to achieve
competitive advantages over their rivals, manufacturers now aim to improve the efficiencies of
their supply chain, and many achieved this by having better understanding of their customer
demands (Chong, Ooi, and Sohal 2009). Bullwhip effect, a traditional challenge faced by
manufacturers, can be overcome by having a better understanding and forecasting of customer
demands.
The advent of Information Technology (IT) and Data Sciences has empowered companies with
the ability to understand and predict customer demands more accurately using quantitative
approaches. For example, Radio Frequency Identification Tag (RFID) allows companies to
obtain real-time inventory data and their spatial mobility so as to help companies understand
and improve product demand forecasts. An emerging IT trend which has captured the attentions
of researchers and practitioners is the application of Big Data to better understand business
processes and performances. Big Data technologies have the ability to help companies
understand complex business relationships by providing useful information that were
previously not available to them (Bollen, Mao, and Zeng 2011). The use of data analytics to
better understand business processes is not new. Companies such as Wal-Mart and Kohl use
various sales, pricing, economic, and demographic data to understand customer behaviors and
product demands. Big Data technologies and the Internet however, provide companies with
enhanced abilities to obtain and analyze data from multiple channels, resulting in opportunities
for discovering untapped business information. An example of how Big Data can provide new
insights can be found in a recent study by Bollen, Mao, and Zeng (2011), who found that the
moods of Twitter can influence and predict stock market.
Previous studies have shown that a manufacturer can obtain competitive advantage over its
rivals by having an efficient supply chain, and this can be achieved by better understanding the
demands of products. An area which has not been studied extensively by previous researchers
is how data from online marketplace or e-commerce offers manufacturers the opportunity to
better understand product demands (Cao and Schniederjans 2004). Specifically, could
information from online user-generated data be useful for manufacturers to understand and
forecast product demands better? Online user-generated contents are beginning to have a
bigger influence on consumer decisions than traditional media such as newspaper and
television. Statistics have shown that as many as 53% of posters on Twitter recommend
products or brands in their tweets, and 48% of those who receive the tweets follow through on
the recommendations (Flannagan 2011). The state of online retail is that when products are
being sold online, the inclusion of user reviews has become a common practice. Besides online
reviews, companies selling products online will also include product information such as price
and descriptions, as well as promotional marketing information such as the availability of
discounts, or ‘savings’ from discounts of products. The volume of available data related to
products, marketing promotions, and online reviews can potentially be used to predict the
demand of products and help companies plan their logistics better.
The goal of this study is to examine the comparative influence of promotional marketing
strategies such as discounts and the provision of free delivery options, and online reviews
information such as the ratings of the products and the percentage of positive and negative
reviews on products. The demand of products in this study is measured by a product’s online
sales rank. We designed a series of Big Data algorithms which sit on a Big Data architecture
used for Web data and social media analytics (Ch’ng, 2014). The algorithms use asynchronous
I/O (input/output) to request, extract and preprocess data in real-time from Amazon.com. The
categories used for our study are electronic devices such as camera, computers and televisions.
Besides examining the influence of online promotional strategies and online reviews on
product demand, this study also examines if their interaction effects (e.g. when both of them
are being offered concurrently online to users) can improve the demand of products (Lu et al.
2013). Recent articles have stated that manufacturers, through the collection of social media
chatter, can help understand the real time demand and trends of their products, and thus help
to mitigate bullwhip effect (Suominen, 2014). In particular, it is challenging for companies to
detect sudden input swings in demand in real time for even small number of items which can
result in bullwhip effect, but this can be solved with Big Data (Chase, 2013). Our proposed
approach will allow a better understanding of customer demand through the use of online
marketplace information, which in turn reduce the risks of bullwhip effect. One of the reasons
why our approach reduces bullwhip effect risk is that new and real time data can be extracted
and feed into our architecture, and predictions and decisions can be made instantly instead of
having the delays of compiling data from various sources which we may find in many legacy
systems.
This research makes several important contributions. First, although understanding and
predicting product demand is an important topic in operations management, few studies have
examine whether the Internet and e-commerce data can help predict product demand. Second,
this study examines whether combining marketing promotional strategies together with online
reviews can result in better predictions of product demand. Third, this study demonstrates how
Big Data technologies and architecture can be applied to extract data online, in combination
with neural networks to predict how product demands can be influenced by promotional
marketing and online reviews variables.
2.0 Literature Review
2.1 Online promotional marketing
The shorter product life cycles today, especially in the electronic industry, means that
manufacturers are faced with a greater pressure to sell their products in a shorter span of time.
As compared to the past, the increase in product information means that more options are
available to consumers. For example, consumers are now able to efficiently compare product
prices and features online. Due to these business pressures, companies are spending significant
amount of their resources to promote and advertise their products online. A popular
promotional marketing strategy implemented by companies is to offer price discounts.
2.1.1 Discount Value
Price discounts are popular as they are able to stimulate short term, immediate increase in sales
of a product (Gendall et al. 2006). Transaction posit utility theory stated that consumers’
demand for a product will increase when the product is being given a higher discount, resulting
in consumers believing they have received a bargain (Lichtenstein, Netemeyer, and Burton
1990). The effects of price discounts are often measurable, and because they are able to
increase the store traffics, they are able to support relationships between manufacturers and
retailers, and ensuring that a particular brand is well stocked and has adequate shelf space in
the retail stores (Gendall et al. 2006; Liu et al. 2015). Despite numerous studies on the
influence of discount on product demands, results have shown inconsistent and contradictory
outcomes on the effects of price discounts on product demands (Drozdenko and Jensen 2005).
Marshall and Leng (2002), for example, found that although product sales increased when
discount is being offered from 10% to 50%, additional increase at between 60 to 70% have no
effect on product sales. Consumers may occasionally use a product’s price information to
determine the quality of the product instead of just using it to determine monetary gain or loss
(Suri, Manchanda, and Kohli 2000). Thus a high price with no discount may indicate high
product quality, while a product without discount could mean high monetary sacrifice to the
consumers (Suri, Manchanda, and Kohli 2000).
On the other hand, based on attribution theory, when information is being thoroughly
processed, consumers are able to rely on attribute information besides price to evaluate a
product’s quality (Drozdenko and Jensen 2005). When purchasing products online, there are
various information about the product, as well as online reviews which may result in customers
viewing discount as a monetary gain, and may increase the intention to purchase the product.
Furthermore, Jensen et al. (2003) when comparing the prices of online stores versus brick and
mortar store, found that having an external reference price has a lesser effects for online stores.
These contradictory findings have not been verified in the context of an online environment
(The study used price discount as a predictor of a consumer’s intention to purchase product).
This is important for manufacturers as discounts are often used as a strategy by companies
when they are trying to clear their stocks for example, or when they have less stocks on a
particular product, and will use discounts to encourage customers to buy the alternative product
under the company, thus giving them more time to replenish their inventory.
2.1.2 Discount Rate
Another predictor used in the present research is the ratio of discount when compared to the
actual price of a product. A consumer’s perception of price in terms of absolute or relative
sense has the ability to influence their price discount perceptions (Chen, Monroe, and Lou
1998). The “psychophysics-of-price-heuristics” theory stated that consumers’ psychological
utility derived from saving a fixed amount of money is inversely related to an item’s price
(Chen, Monroe, and Lou 1998). Therefore for a company, they can either provide the absolute
price discount, or the relative price discount in %. However, it is still unclear whether a $20
savings or a 20% discount offer would be more appealing to a consumer on a $100 jacket. The
question then will be, in the presence of both of these information, would one of these lead to
better prediction of customer demand of a product?
2.1.3 Free delivery
Besides using price discounts, consumers are likely to purchase a product online if free delivery
is offered. (Doern and Fey 2006) in their studies on e-commerce developments and strategies
in Russia, found that offering free delivery will result in better customer loyalty and trust. Yip
and Law (2002) in examining users’ preferences for web site attributes also found that besides
special discounts, free delivery is a feature that will attract online users. However, not all
websites that use free delivery as an incentive are successful. Smith and Rupp (2003) found
that free delivery promotions resulted in the failure of online companies such as Kozmo.com
and Urbanfetch.com. The main reason cited in their studies was that online websites which
used free delivery to attract users and increase their user base, will find it difficult to retain their
customers when they remove the free delivery promotion. With e-commerce becoming mature,
offering free delivery may also be something that is expected by consumers, thus those who do
not offer free delivery may lose out to their competitors. Based on the discussions, we will be
using free delivery as a predictor of customer demand of products, in particular in the presence
of other promotional strategies such as price discounts.
2.2. Online Reviews
With the growth of online media, contemporary users regularly and actively share their
opinions on products and services with others on various online platforms such as product
reviews, blogs, twitter and wikis (Tirunillai and Tellis 2012). This type of communication is
presented in “reviews” and the contents are regarded as Word of Mouth. Word of mouth is
defined as “all informal communications directed at other consumers about the ownership,
usage, or characteristics of particular goods and services or their sellers” (Westbrook 1987).
Compared with traditional advertising such as television and newspaper ads, eWOM is
perceived by consumers as being more credible than private signals, and the information are
much more easilly accessible through social networks (Davis and Khazanchi 2008). Unlike
traditional word of mouth, online word of mouth or electronic word of mouth (eWOM) has far
greater reach to other users, and offer much more richness in contents. Users are able to share
their online reviews using pictures and even videos. Furthermore, eWOM is able to aggregate
both positive and negative information on an online review website from different sources,
while traditional word of mouth is only able to capture a single piece of either positive or
negative information (Lu et al. 2013).
Previous studies have shown that eWOM is able to affect the sales of products (Chevalier and
Mayzlin 2006; Duan, Gu, and Whinston 2008). In these studies, some of the most commonly
used attributes of eWOM include the valence, volume, and dispersion of reviews (Lu et al.
2013). Duan, Gu, and Whinston (2008) developed a dynamic simultaneous equation system
to capture the relationships between eWOM and motion picture sales. Their studies found that
although the valance of eWOM does not directly affect the sales of movie tickets, higher
valence was able to generate higher eWOM volume, which in turn increase sales of movie
tickets. Chevalier and Mayzlin (2006) examined the effects of eWOM on book sales, and
found that customer online reviews are able to influence the sales of books. Lu et al. (2013)
examined three year panel data set from an online restaurant review website, and found support
between the relationships of valence and volume and product sales.
2.2.1 Online review valence (average rating)
Online review valence is defined as the evaluation score of a specific products or services in
eWOM (Lu et al. 2013). Although researchers in the past have proposed that online review
valences to have persuasive effects on consumers’ purchasing decisions (Cheung and Thadani
2012), the findings on such relationships have been inconsistent. Liu (2006) in his study on
the relationships between eWOM and box office revenue, found that although there are
significant relationships between the two, most of the explanatory power were derived from
the volume of eWOM and not from its valence. Duan, Gu, and Whinston (2008) extended the
study by Liu (2006), and found that valence by itself was not able to influence the sales of
movies. However, Duan, Gu, and Whinston (2008) found that valence has an indirect
relationship with movie sales, as it was able to influence eWOM volume, which in turn
influence movie sales. Similarly, Davis and Khazanchi (2008) in their study on multi-product
categories online, found that valence of eWOM does not influence the product sales.
On the other hand, researchers such as Lu et al. (2013), Zhu and Zhang (2010) and Chevalier
and Mayzlin (2006) found support for the relationship between online review valence and
product sales. The ratings of a product and service is increasingly important in an eWOM
environment as consumers today are more likely to use make decisions based on wisdoms of
the crowds (Chen and Singh 2001). Dellarocas, Awad, and Zhang (2004) also found that
valence is one of the strongest predictor of sales among all the other word of mouth attributes.
Besides the inconsistent in previous findings, most of these existing studies have examined the
role of eWOM on experience products such as movies and books. Studies on search products
however have remained sparse. Search products include electronics where a consumers can
evaluate the specific attributes of the product before purchasing (Cui, Lui, and Guo 2012).
When consumers purchase electronic products, they are more likely to apply a systematic
decision making process by evaluating specific attributes of the product. In comparison,
customer purchasing movie tickets or books are more likely to make their decisions based on
extrinsic attributed related cues (Cui, Lui, and Guo 2012). Therefore when purchasing an
electronic product, a customer will evaluate the product’s technical aspects and performances.
In an online information, such information are readily available, and the ratings of the products
are prominently displayed to the customers (Cui, Lui, and Guo 2012). Therefore valence may
be a predictor of electronic product sales, and not in experience products such as in the studies
conducted by Liu (2006) and Duan, Gu, and Whinston (2008). Based on the discussions and
inconsistent previous findings, this study include valence as a predictor of the electronic
product sales.
