Big Data in Data-driven innovation: applications, prospects and limitations In marketing

e-Bi Lab
e-Bi Labe-Bi Lab
ICCMI 2016 | 4th International Conference on Contemporary Marketing
Issues, 22-24 June 2016, Heraklion, Greece
Big Data in Data-driven
innovation:
applications,prospectsand
limitationsIn marketing
Ioannis Kopanakis
Konstantinos Vassakis
George Mastorakis
Department of Business Administration
Technological Educational Institute of Crete
Agios Nikolaos, Crete, Greece
Introduction
Knowledge- Innovation - Performance
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
2
 In modern economies, characterized as “knowledge-based”,
the changing customer needs, technological evolutions and
the competitive market pressure have made innovation a
vital determinant of enterprises’ success (Daud, 2012).
 Hence, enterprises have to adopt strategies innovation-
oriented in order to build and sustain competitive
advantage in the globalized and technology changing
environment they operate.
 Innovation is dependent on the combination of
technologies and exploitation of knowledge.
Data to knowledge
3
 The capacity of enterprises to access information and create
valuable knowledge provides them competitive advantage
in the innovation race against rivals (Sarvan et al., 2011).
 In the era of Industry 4.0 (4th industrial revolution), Data
has major impact on businesses, since the revolution of
networks, platforms, people and digital technology
changed the determinants of firms’ innovation and
competitiveness (OECD, 2015).
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Source: https://goo.gl/6ciVPo
Data – Driven Innovation (DDI)
Knowledge to Innovation
4
 “Data-Driven Innovation” (DDI) -> techniques and technologies
for processing and analysing “big data” as the method to
innovate using data-based decision process.
 DDI’s economic value expecting to be enormous in the
following years and it has the capacity to introduce:
 new improved products & services,
 new improved production processes
 better organizational management
 more efficient R&D
 better supply chain management
 more efficient marketing
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Big Data
5
 Large streams of data generated through information and
communication technologies (ICT) and Internet of Things (IoT)
named “Big data” -> datasets with large volume that cannot be
captured, stored, managed and analysed by typical database
software tools (Manyika et al., 2011)
 BD is a major resource for enterprises to obtain new
knowledge, present added value, foster new products,
processes and markets. Hence, the ability to manage, analyse
and act on data is significant to enterprises
 An asset for enterprises’ indicating the significance of data-
driven approach within enterprises (Microsoft, 2016)
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Characteristics of Big Data
The 4Vs of Big data
6 ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Source: http://www.ibmbigdatahub.com/infographic/four-vs-big-data
Significance of Big Data
7
 The increased use of digital services and Internet has transformed
all the sectors in the economy. Through DDI, almost all the sectors
including retail, manufacturing and agriculture has become more
service-centred, adopting the term “servicification” (Lodefalk, 2013)
 The exploitation of big data provides enterprises in several
industry sectors, not only ICT firms (Tambe, 2014) -> added value
through the improvement in resources (physical & human)
supervision and allocation, reduction of waste, greater
transparency and facilitation of new insights.
 Εconomic benefits of big data in UK private and public sector
businesses: increase from £25.1 billion in 2011 to £216 billion in
2017, while data-driven innovation will lead to £24,1 billion
contribution to UK economy during 2012-2017 (Cebr, 2012) .
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Contribution-limitations
8
 Big data, similar to IT, has the ability to bring significant cost
reductions, delivery time, enhanced R&D and new/improved
products or services.
 However, little evidence exists on ROI for big data applications
in enterprises, showing promising issues (Davenport & Dyché,
2013)
 Empirical evidence related to the impact of data-driven
approach and its impact to enterprises performance are scarce
and limited mainly in research for large-multinational
companies
 In addition, no research exists in the impact of big data-driven
approach in marketing innovation
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Significance of study
9
 It is observed growing attention to big data and data-driven
approach from academics and professionals, since the analysis
of “big data” leads on valuable knowledge, promotion of
innovative activity transforming economy of countries (OECD,
2015).
 There is evidence that data-driven approach has a positive
impact in enterprises’ performance (Brynjolfsson, 2011;
Davenport & Harris, 2007; Lavalle, 2010; Bakhshi et al., 2014).
 The scope of this study is to provide a theoretical framework
examining the impact of big data exploitation to enterprises
marketing innovation performance.
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Data – driven decisions
10
•Data Acquisition & recording
•Extraction, cleaning & Annotation
•Integration, aggregation &
representation
Data Management
•Modelling & Analysis
•Interpretation
Data Analytics
Data Driven Decision
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
 Processes of leveraging big
data are divided into 2
phases:
 Data Management
 Data Analysis
(Gandomi & Haider, 2014).
Data-driven performance
Impact in enterprises’ performance
11
 Brynjolfsson et al. (2011) examine how adoption of data-driven decision making
induces firm performance using a dataset of 179 large publicly traded firms and
concluded that adoption of data-driven decision making approach provide 5-6%
higher performance (in terms of productivity) to firms. In addition, these firms
present better performance in asset utilisation, return on equity and market value.
 McAfee & Brynjolfsson (2012) examined the performance of data-driven companies
and whether big data intelligently improves business performance. Conducting
interviews with 330 executives of North American companies and combining them
with companies’ financial performance, found that top performers in the use of
data-driven decision making have 5% higher production and 6% higher profitability
than their rivals.
 Bakhshi & Mateos-Garcia (2012) in their empirical study for 500 UK businesses from
various industries operating on-line. Data sample divided into 2 categories: data-
driven decision making enterprises and experience-driven enterprises. They found
that data-driven companies present higher level of innovativeness launching new
products and services and making disruptive changes to their business processes.
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Applications of Big Data in Marketing
12
 Using online tools to provide trending topics i.e. Google
trends
 Monitoring trends to make Informed Decisions on
Marketing Strategy
 Maximize ROI via Budget management
 Defining Ideal Customer Profile -> personalized marketing
to optimize customer engagement
 Target Audience at the Right Time
 Efficient content marketing attracting customers
 Market & competitors analysis
 Predictive Analysis
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
DDI in Marketing
13
 “Data-Driven Marketing Creates Consumer-Driven Companies:
what customers want and need and engineering the company to
provide it. The more firms can use data to develop a 360 degree,
multi-channel view of what customers think and want, the more
the customer will truly be king” (Deighton and Johnson, 2013) .
 'Organizations that are ‘leaders’ in data-driven marketing report
far higher levels of customer engagement and market growth
than their ‘laggard’ counterparts. In fact, leaders are three times
more likely than laggards to say they have achieved competitive
advantage in customer engagement/loyalty (74% vs. 24%) and
almost three times more likely to have increased revenues (55%
vs. 20%).‘(Forbes, 2015)
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Big data & Marketing Intelligence
14
 In marketing intelligence, leveraging data for marketing
decision making has the ability to improve sales and marketing
performance by reducing inefficient marketing expenditures
and increasing consumer surplus with customised marketing.
 Therefore, leveraging big data can create the prerequisites for
marketing innovation -> the implementation of a new
marketing method involving significant changes in product
design or packaging, product placement, product promotion
or pricing (OECD, 2005).
 Recent evidence shows that 5Ps (People, Product, Promotion,
Price and Place) marketing mix (Goi, 2009) can be used in Big
Data management for marketing intelligence (Fan, 2015).
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Big Data & 5Ps Marketing mix
Developing insights from data for marketing decision-making
15 ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
(Fan et al., 2015).
Conclusions – further research
16
 In the data-driven era, transformed by the rapidly large
streams of data generated through ICT and IoT, knowledge
originates from big data processes provides the decision
makers the capability to innovate and increase their
performance gaining a competitive advantage against rivals.
 The insights by leveraging big data in marketing prospects
provide a competitive advantage in enterprise through new
ways of growth and consumer surplus
 What happened with SMEs? What are the obstacles facing in
adopting data-driven approach in marketing? What happened
with the ROI in data-driven marketing processes? Is marketing
innovation a result of data-driven approach?
ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June
2016, Heraklion, Greece
Thank you for your
attention!
ICCMI 2016 | 4th International Conference on Contemporary Marketing
Issues, 22-24 June 2016, Heraklion, Greece
1 of 17

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Big Data in Data-driven innovation: applications, prospects and limitations In marketing

  • 1. ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece Big Data in Data-driven innovation: applications,prospectsand limitationsIn marketing Ioannis Kopanakis Konstantinos Vassakis George Mastorakis Department of Business Administration Technological Educational Institute of Crete Agios Nikolaos, Crete, Greece
  • 2. Introduction Knowledge- Innovation - Performance ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece 2  In modern economies, characterized as “knowledge-based”, the changing customer needs, technological evolutions and the competitive market pressure have made innovation a vital determinant of enterprises’ success (Daud, 2012).  Hence, enterprises have to adopt strategies innovation- oriented in order to build and sustain competitive advantage in the globalized and technology changing environment they operate.  Innovation is dependent on the combination of technologies and exploitation of knowledge.
  • 3. Data to knowledge 3  The capacity of enterprises to access information and create valuable knowledge provides them competitive advantage in the innovation race against rivals (Sarvan et al., 2011).  In the era of Industry 4.0 (4th industrial revolution), Data has major impact on businesses, since the revolution of networks, platforms, people and digital technology changed the determinants of firms’ innovation and competitiveness (OECD, 2015). ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece Source: https://goo.gl/6ciVPo
  • 4. Data – Driven Innovation (DDI) Knowledge to Innovation 4  “Data-Driven Innovation” (DDI) -> techniques and technologies for processing and analysing “big data” as the method to innovate using data-based decision process.  DDI’s economic value expecting to be enormous in the following years and it has the capacity to introduce:  new improved products & services,  new improved production processes  better organizational management  more efficient R&D  better supply chain management  more efficient marketing ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 5. Big Data 5  Large streams of data generated through information and communication technologies (ICT) and Internet of Things (IoT) named “Big data” -> datasets with large volume that cannot be captured, stored, managed and analysed by typical database software tools (Manyika et al., 2011)  BD is a major resource for enterprises to obtain new knowledge, present added value, foster new products, processes and markets. Hence, the ability to manage, analyse and act on data is significant to enterprises  An asset for enterprises’ indicating the significance of data- driven approach within enterprises (Microsoft, 2016) ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 6. Characteristics of Big Data The 4Vs of Big data 6 ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece Source: http://www.ibmbigdatahub.com/infographic/four-vs-big-data
  • 7. Significance of Big Data 7  The increased use of digital services and Internet has transformed all the sectors in the economy. Through DDI, almost all the sectors including retail, manufacturing and agriculture has become more service-centred, adopting the term “servicification” (Lodefalk, 2013)  The exploitation of big data provides enterprises in several industry sectors, not only ICT firms (Tambe, 2014) -> added value through the improvement in resources (physical & human) supervision and allocation, reduction of waste, greater transparency and facilitation of new insights.  Εconomic benefits of big data in UK private and public sector businesses: increase from £25.1 billion in 2011 to £216 billion in 2017, while data-driven innovation will lead to £24,1 billion contribution to UK economy during 2012-2017 (Cebr, 2012) . ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 8. Contribution-limitations 8  Big data, similar to IT, has the ability to bring significant cost reductions, delivery time, enhanced R&D and new/improved products or services.  However, little evidence exists on ROI for big data applications in enterprises, showing promising issues (Davenport & Dyché, 2013)  Empirical evidence related to the impact of data-driven approach and its impact to enterprises performance are scarce and limited mainly in research for large-multinational companies  In addition, no research exists in the impact of big data-driven approach in marketing innovation ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 9. Significance of study 9  It is observed growing attention to big data and data-driven approach from academics and professionals, since the analysis of “big data” leads on valuable knowledge, promotion of innovative activity transforming economy of countries (OECD, 2015).  There is evidence that data-driven approach has a positive impact in enterprises’ performance (Brynjolfsson, 2011; Davenport & Harris, 2007; Lavalle, 2010; Bakhshi et al., 2014).  The scope of this study is to provide a theoretical framework examining the impact of big data exploitation to enterprises marketing innovation performance. ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 10. Data – driven decisions 10 •Data Acquisition & recording •Extraction, cleaning & Annotation •Integration, aggregation & representation Data Management •Modelling & Analysis •Interpretation Data Analytics Data Driven Decision ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece  Processes of leveraging big data are divided into 2 phases:  Data Management  Data Analysis (Gandomi & Haider, 2014).
  • 11. Data-driven performance Impact in enterprises’ performance 11  Brynjolfsson et al. (2011) examine how adoption of data-driven decision making induces firm performance using a dataset of 179 large publicly traded firms and concluded that adoption of data-driven decision making approach provide 5-6% higher performance (in terms of productivity) to firms. In addition, these firms present better performance in asset utilisation, return on equity and market value.  McAfee & Brynjolfsson (2012) examined the performance of data-driven companies and whether big data intelligently improves business performance. Conducting interviews with 330 executives of North American companies and combining them with companies’ financial performance, found that top performers in the use of data-driven decision making have 5% higher production and 6% higher profitability than their rivals.  Bakhshi & Mateos-Garcia (2012) in their empirical study for 500 UK businesses from various industries operating on-line. Data sample divided into 2 categories: data- driven decision making enterprises and experience-driven enterprises. They found that data-driven companies present higher level of innovativeness launching new products and services and making disruptive changes to their business processes. ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 12. Applications of Big Data in Marketing 12  Using online tools to provide trending topics i.e. Google trends  Monitoring trends to make Informed Decisions on Marketing Strategy  Maximize ROI via Budget management  Defining Ideal Customer Profile -> personalized marketing to optimize customer engagement  Target Audience at the Right Time  Efficient content marketing attracting customers  Market & competitors analysis  Predictive Analysis ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 13. DDI in Marketing 13  “Data-Driven Marketing Creates Consumer-Driven Companies: what customers want and need and engineering the company to provide it. The more firms can use data to develop a 360 degree, multi-channel view of what customers think and want, the more the customer will truly be king” (Deighton and Johnson, 2013) .  'Organizations that are ‘leaders’ in data-driven marketing report far higher levels of customer engagement and market growth than their ‘laggard’ counterparts. In fact, leaders are three times more likely than laggards to say they have achieved competitive advantage in customer engagement/loyalty (74% vs. 24%) and almost three times more likely to have increased revenues (55% vs. 20%).‘(Forbes, 2015) ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 14. Big data & Marketing Intelligence 14  In marketing intelligence, leveraging data for marketing decision making has the ability to improve sales and marketing performance by reducing inefficient marketing expenditures and increasing consumer surplus with customised marketing.  Therefore, leveraging big data can create the prerequisites for marketing innovation -> the implementation of a new marketing method involving significant changes in product design or packaging, product placement, product promotion or pricing (OECD, 2005).  Recent evidence shows that 5Ps (People, Product, Promotion, Price and Place) marketing mix (Goi, 2009) can be used in Big Data management for marketing intelligence (Fan, 2015). ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 15. Big Data & 5Ps Marketing mix Developing insights from data for marketing decision-making 15 ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece (Fan et al., 2015).
  • 16. Conclusions – further research 16  In the data-driven era, transformed by the rapidly large streams of data generated through ICT and IoT, knowledge originates from big data processes provides the decision makers the capability to innovate and increase their performance gaining a competitive advantage against rivals.  The insights by leveraging big data in marketing prospects provide a competitive advantage in enterprise through new ways of growth and consumer surplus  What happened with SMEs? What are the obstacles facing in adopting data-driven approach in marketing? What happened with the ROI in data-driven marketing processes? Is marketing innovation a result of data-driven approach? ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece
  • 17. Thank you for your attention! ICCMI 2016 | 4th International Conference on Contemporary Marketing Issues, 22-24 June 2016, Heraklion, Greece

Editor's Notes

  1. 1
  2. innovation plays crucial role concerning enterprises performance and competitiveness.
  3. Data provides knowledge about processes, customers, human capital and technology significant to enterprises leading to innovation.
  4. Big data is a buzzword nowadays Knowledge arising from the information given from data analysis processes.
  5. Volume: the sizes of data that are extremely huge- measured in exabytes Variety: heterogeneity of data types, (unstructured, semi-structured and structured). Velocity: the ratio of data generation and the speed needed for their analysis. Veracity: data uncertainty and the level of reliability correlated with some type of data 90% of the data in the world today was generated in just the last 2 years (IBM, 2016) -> the huge increase of sensors and connected devices known as “Internet of Things (IoT)”. since technological evolution allows enterprises concentrate various types of data The tremendous increase of smartphones and sensors led on a significant increase of data generation and a growing need of real-time analysis and instant decision making
  6. A significant obstacle to adopting data-driven approach through big data analytics is the high level of technical skills required to use and exploit these systems. People with specific skills and expertise in statistics, analysis and machine learning are required in order to get valuable insights from big data.
  7. The first is related with the processes and technologies for data generation, storage, mining and preparation for analysis, while the second refers to the methods and techniques to analyze and get valuable insights from big data
  8. 2. This is one of the easiest and simplest ways of using big data. It gives the user the trending topics that are related to the search word that they have entered. Marketers can filter the results based on who they want to target either globally or locally. It gives a broad range of options to set filter options that include location, regions, countries, interests among any others. Using a trending topic, you can associate it with your product to get more traction. Using Big Data, you can set Ideal Customer Profile. With Big Data, a market does not need to make an educated guess at this day and age of technology. Big Data delivers accurate information about people’s ages, demographics, and work profile of the targeted consumers. According to Avis Budget case study, the company managed to have an effective contact strategy because of big data. 4. With Big Data, a marker can reach its target audience at the right time. An effective marketing strategy needs timeliness and relevancy, and this is possible with Big Data. A good example is mobile ad networks. A marketer that understands its targeted audience through Big Data will know what time to show their ads in a mobile ad company because they give the option to set the time to show ads on their platforms. 5. With Big Data, it is possible to know the content that is effective in a marketing push. It is possible to get such information with content scoring tools that are available with some translation management system tools. For example, a marketer can get insights on which assets were successful and also gauge how efficient they were. With the information, a marketer will get content that resonated well with buyers. 6. Using a company’s base CRM data and other third-party Big Data providers, a marketer can generate a predictive lead score. The information can then be used to predict future lead behavior. This gives marketers a clear indication on digital behaviors that should be weighed more in lead scoring.
  9. Big data and its applications in business intelligence can contribute to business added value. Marketing intelligence refers to developing insights from data for marketing decision-making.
  10. - Marketing decision making based on big data … Data characteristics Methods-> data from various sources -> a variety of analytics methods are applied to convert raw big data to actionable marketing knowledge (intelligence). Finally, both data and methods are combined to support marketing applications with respect to each perspective of the marketing mix mode
  11. Research questions to further research…
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