How pricing of business intelligence and analytics SaaS applications can catch up with their technology
| Pages | 229-246 |
| Date | 10 August 2015 |
| DOI | https://doi.org/10.1108/JSIT-03-2015-0024 |
| Published date | 10 August 2015 |
| Author | Aaron Wolfgang Baur,Julian Bühler,Markus Bick |
| Subject Matter | Information & knowledge management,Information systems |
How pricing of business
intelligence and analytics SaaS
applications can catch up with
their technology
Aaron Wolfgang Baur, Julian Bühler and Markus Bick
ESCP Europe Business School, Berlin, Germany
Abstract
Purpose – The purpose of this paper is to investigate the development of software pricing, following
the advent of cloud-based business intelligence & analytics (BI&A) Software. A value-based conceptual
software model is developed to ignite and structure further research.
Design/methodology/approach – A two-step research approach is applied. In step one, the
available literature is screened and evaluated, and this is followed by ten semi-structured expert
interviews. With that input, a conceptual software pricing model is designed. In step two, this model is
validated and rened through discussions with representatives of the ve leading business intelligence
suites.
Findings – The paper sheds light on the value perception of customers and suggests a clear focus on
the interaction between customers and vendors, and less on technical issues. The developed
customer-centric, value-based pricing framework helps to improve pricing techniques and strategies.
Research limitations/implications – The research is focused on the pricing strategy of software
houses and excludes differentiations of technical specications and functionalities.
Practical implications – The research can support practitioners in the eld of BI&A in rethinking
their pricing methods. Placing the customer at center stage can lead to lower customer churn rates,
higher customer satisfaction and more pricing exibility.
Originality/value – This empirical study reveals the importance of a customer-centric pricing
approach in the specic case of BI&A. It can also be applied to other fast-developing sectors of the
software industry.
Keywords Business model, Business intelligence, Cloud computing, Pricing, Business analytics,
Software as a service (SaaS)
Paper type Research paper
1. Introduction and background
Cloud computing, virtualization and Software as a Service (SaaS) shift the delivery of
software from physical distribution and installation on local hardware to a provision via
the Internet. Analysts like Maynard from Credit Suisse therefore describe this shift
using strong words: “Traditional software is dead” (The Economist, 2006,p.1).
Examining the shift from a more sober, scientic perspective, however, it is likely that
rather than a switch to complete virtualization, there will be a harmonization of the
different extremes of pure on-premise and pure over-the-cloud delivery (Carraro and
Chong, 2006).
SaaS delivery has ignited a gradual estrangement from perpetual licenses, with its
traditional focus on a large sales force, up-front payments, physical product delivery
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1328-7265.htm
Analytics
SaaS
applications
229
Received 30 March 2015
Revised 9 April 2015
Accepted 11 April 2015
Journalof Systems and
InformationTechnology
Vol.17 No. 3, 2015
pp.229-246
©Emerald Group Publishing Limited
1328-7265
DOI 10.1108/JSIT-03-2015-0024
and a frequent and tedious updating process. Now, vendors can update their products
“on-the y”, receive a steady stream of revenue and focus on a closer client relationship
and greater penetration within the client’s organizations (Gruman et al., 2007).
These technological and business model changes inuence how vendors can set and
communicate their pricing policy. Due to the decreased variable costs of vendors, there
is a gap between the prices of software and the incremental costs of adding a new
customer. Rohitratana and Altmann (2012) show that this misalignment is perceived as
unfair and, therefore, criticized by software customers. Ironically, it is through the
pricing strategies themselves that software houses can prevent their customers from
focusing only on price as a choice parameter, although there is generally a high
sensitivity to the applied pricing techniques (Bertini and Wathieu, 2010). That is why
understanding the client is a key characteristic, as pricing should be designed upon the
variables that the buyer will use in measuring value realization (Bontis and Chung,
2000). This is what the customer is willing to pay, based on the actual benet. The result
should be a win-win scenario, in which customers see the value of the software reected
in their business processes and vendors benet from recurring payments (Gruman et al.,
2007).
Consequently, choosing the right pricing model is of high importance for software
vendors in attracting and retaining customers, as well as keeping competitors at bay. To
justify such a “cost-price gap” and to focus on the added value for the customer, pricing
models are now increasingly taking into consideration a customer-centric mindset, by
associating price perceptions with product congurations (Schneider, 2012).
Analyzing the real value that the software represents for the customer needs to be the
central focus. Hence, the price of the software must be aligned with the customer’s value
realization, i.e. the shift from cost-based software pricing to a more dynamic value-based
software pricing (Baker and Hatami, 2004). In the latter case, the price is continuously
adapted to the market and is demand-driven, based on a deep knowledge of the
customers (Lehmann and Buxmann, 2009).
Numerous studies have attempted to analyze pricing techniques in the SaaS age
(Heffron, 2013;Harmon et al., 2009;Choudhary, 2007). However, there has not yet been
an analysis of pricing techniques and their correlation with customer value realization
that has been specically applied to business intelligence & analytics (BI&A) solutions
for companies. BI&A tools may be the key to dealing with today’s data glut, and
customers place high expectations upon the performance and quality of these software
suites (Chen et al., 2012;Swoyer, 2013). They are of pivotal importance in the
management of a company, which explains the presence of hundreds of offered software
tools, low transparency, lack of consolidation and very high growth rates (see Figure 1).
According to Redwood Capital (2014), the BI market can be segmented into traditional,
mobile, cloud and social business intelligence, depending on product architecture and
user interface. Although growth of traditional BI is projected to slow to low single-digit
rates, newer BI technologies are expected to grow at rates of between 20 and 30 per cent
over the next ve years, starting from a smaller base. Among the fastest growing
segments, cloud-based BI is estimated to grow nearly fourfold, from $0.75 billion in 2013
to $2.94 billion by 2018, resulting in a Compound Annual Growth Rate of 31 per cent
Due to the complexity of the tools, business intelligence is the software stream for
which the virtualization process has been among the most challenging (Van Der Lans,
2012). Additionally, it is an area of the software industry that is faced with erce
JSIT
17,3
230
To continue reading
Request your trial