Strategic communication is defined as biased and professionally pre-planned mass communication (Sweldens, Van Osselaer and Janiszewski, 2010). Hallahhan et. al, (2007) defined Strategic Communication as a process of planning and development of targeted communication activities in order to achieve organization’s mission. Smulowitz, S. (2015) defined strategic communication as a “distinct approach focusing on the process of communication which offers complimentary insights and open up new fields for interdisciplinary research” (p. 3). He explained the strategic communication process as a communication process that follow organizational strategy with a focus on the role of communication in achieving organizational strategic goals.
In this context we can relate content creation using inbound marketing concept for the known and targeted audience as a process of strategic communication since big data analysis is enabling organizations to collect user identities, their demographics, and psychographics and consent through IP address and cookies to identify the right target audience. So after knowing their audience/ potential customers, organizations are devising strategies for more targeted and interactive communication.
Academic research around using big data analysis to make informed decision has recently stepped into the field of strategic communication (Weiner & Kochhar, 2016). A large part of previous academic research work on big data analysis has focused on technical and computational aspects of big data analysis in marketing. There is limited knowledge about the impact of evaluating and using results of big data driven campaigns to predict and control upcoming campaigns.
This area just freshly started developing in relations to academic research. Weiner and Kochhar (2016) further explained that the research and academic discussions about big data recently started about how it is collected, what are the sources of big data, and how it is helping in making informed decisions in relation to strategic communication. Markus et. al, (2017) discussed the automation of strategic communication due to the big data analysis. They informed the communication practitioners with the challenges and benefits of big data by saying that big data have potential to bring dramatic changes to their jobs. They further descried that big data analysis and artificial intelligence can replace humans at work in the field of strategic communication with automation of processes.
Loebbecke and Picot (2015) said the same about automation in strategic communication as “digitization and big data analytics (. . .) impact employment amongst knowledge workers—just as automation did for manufacturing workers” (Loebbecke and Picot, 2015, p. 149).
Big data analysis
Earlier researchers defined big data analysis more as a mathematical process to make sense out of data and those definitions were formed due to its large size and problems with storing it in the computer disks. Cox and Ellsworth (1997) firstly used the big data as a term to explain their problem that “data sets do not fit in main memory (in core), or when they do not fit even on local disk” (p. 235) Further researchers defined big data from the management approach such as Laney (2001) defined big data with a focus on (3V’s) data volume, velocity, and variety. Lately these 3V’s as big data’s definition become largely accepted as Volume refers to large amount of data, velocity represents real-time streams and data motions, and variety elaborates the multi-faced nature of big data such as structured, semi-structured, or unstructured. Further on Gandomi & Haider (2015) criticized the big data methodologies and analytics and explaining the need for a structured methodology to handle big data. They added a fourth V of Veracity in big data’s definition by explaining uncertainty and inadequate reliability of big data being heterogeneous, noisy and huge in size. And this new definition is widely accepted currently by many researchers in the area. In an extensive study on different Big data sets in order to identify common attributes of Big data Rob Kitchin and Gavin McArdle (2016) discovered that most common traits of big data are exhaustivity (it means that big data read and consider whole system than selecting small samples, so for big data n= whole population) and velocity (real time processing). Crawford and boyd Mark Lycett (2013) called big data analysis as a process of sense-making driven by information technology. Other researchers work related big data analytics with digital communication technologies and datafication (Boyd & Crawford, 2012; van Dijck, 2014).
Boyd et.al, (2012) explained the process of utilizing big data analysis in the digital age by saying that more and more companies have realized that the abundance of real time data derived by information technology systems has the potential to present a knowledge base to understand current performance and to anticipate future. Stemming from this perspective, Big Data research actually give structure to online or offline information collected in abundance in the form of mathematical numbers to get a detailed picture of psychographics and demographics of people (audience), companies, places, and topics. Mayer and Cukier (2013) called this whole transformation process of data collection, generating knowledge system, and coding them into machine readable formats to discover patterns through data mining as big data analysis. The knowledge base derived from the process of dbig data analysis helps in development of communication strategies for inbound communication.
A large part of the academic work on big data (Banasiewicz, 2013; Couldry & Turow, 2014; Erevelles, Fukawa, & Swayne, 2016; Fulgoni, 2014; Micu et al., 2011; Tirunillai & Tellis, 2014) highlighted key concepts, identified opportunities and applications in the field of marketing. Many of them explained that by using big data analysis, companies can micro-target the customers and can co-create products and information which resulted in a more successful brand, product or communication and eventually generate more sales (Banasiewicz, 2013; Couldry & Turow, 2014; Erevelles, Fukawa, & Swayne, 2016; Fulgoni, 2014; Micu et al., 2011; Tirunillai & Tellis, 2014). Many other scholars focused on the opportunities of using big data and sensors to evaluate, measure, and control communication on social media and online platforms (Campbell, Pitt, Parent, & Berthon, 2011; Netzer, Feldman, Goldenberg, & Fresko, 2012; Rogers & Sexton, 2012). But their work mostly focused on the technical aspects of using big data. Wiesenberg, M., and others (2017) in their empirical study of big data analysis and strategic communication, finds out that there is a wide gap between perceived value and current practices. They mentioned that the lack of competence, knowledge, experience and some ethical issues are restricting the practitioners of strategic communication to use big data analytics. They further called for the need to explore the potential of Big data in many other research fields and dimensions. Van den Driest et al., (2016) also called the gaps on the individual, organizational, and professional level as a main hindrance between deploying Big data analytics in strategic communication.
Kitchin. R, (2014) discussed the commonly accepted definition of big data consisting upon 3V’s as huge in volume, high in velocity and diverse in variety, he further called big data as exhaustive in scope, fine-grained in resolution, relational in nature, and flexible. Here one cannot deny the role of big data in making informed decision being information rich and accurate. Mayer et.al, (2013) said that “Data is the oil of the information economy” (p. 16).
David (2016) in his book “The New Rules of Sales and Service” defined big data analysis as, “No matter if you call it rich data or big data, the concept involves using very large data sets and powerful analytics to generate real-time information that is valuable for making decisions.” (p. 52) He further said that the term and idea of using big data analysis was advocated by an American statistician and writer Nate Silver, who analyzed 2008 U.S. presidential elections using big data analysis and succeeded in predicting the outcomes of 49/50 American states. Many scholars have different views about the purpose of using big data. For some it is mainly to anticipate and to pro-act accordingly while for others big data analysis is to measure and react. Strong. C, (2015) said that big data agenda is “less about trying to ‘predict and control’ and more about ‘measure and react’ strategies.” (p. 198)
Using big data analysis brings many challenges and opportunities for the organizations. As Markus et. al, (2017) said that big data can change the jobs of communication practitioners in a dramatic way with the automation of strategic communication but big data research alone is also full of challenges. The literature review showed that some barriers are hindering the competitive advantages of big data specially the lack of competence in understanding the analytical part of handling big data. Big data being huge in size, unstructured, full of variety, and complex in nature demands smart handling to turn its complexities into valuable knowledge. And this process is known as big data analysis.
“….the exploitation of raw data in many different contexts—can be seen as an attempt to tackle complexity and reduce uncertainty. Accordingly promising are the prospects for innovative applications to gain new insights and valuable knowledge in a variety of domains…” (Strauß, S. 2015, p. 836)
Lycett. M, (2013) called datafication as a lens needed to “….. turn data into something of value” (p. 382). He defined datafication as a three step process in the light of Normann 20 innovative concepts of value creation of 2001, namely dematerialization, liquification, and density. Lycett explained dematerialization as the ability of datafication process to separate the informational aspects of big data sets and liquification is the second step after dematerialization to manipulate the collected information to place them into closely linked groups for communication and he called density as the “best (re)combination of resources, mobilized for a particular context, at a given time and place – it is the outcome of the value creation process.” (p.382)
Collecting audience’s psychographics and demographics data is not a new thing for communicators, marketers and brand teams but this behavioral data used to be collected through surveys, focus groups, audience interviews and other traditional data collection tools and such practices are still on-going. These traditional methods can still work for collecting demographic data but many researchers believe that unlike data collected from surveys, focused groups, interviews, laboratory and field observations online data which is collected under un-controlled conditions is more reliable and authentic about human behaviors.
Strong. C, (2015) explained that real time data has the potential to provide more accurate responses of audience as compared to the data collected retrospectively with more chances of less accurate information coming from respondents recalling their past activities.
He further said that “Big data analysis means we can see exactly when each activity has taken place and, where relevant, with whom and what was communicated. Survey data is still important but we are starting to see that it has a new role in the era of big data.” (p. 10)
Other researchers also believe that customers’ online data is more authentic than customers’ responses gathered by surveys and focus group studies. Morabito, V. (2015) differentiate social media data from surveys by saying that via online data collection, companies can collect spontaneous and response bias free information about their customers whereas one cannot avoid such biased results in data collected from surveys or in focus group methodology. The comparison of survey or other similar research methods with datafication is same as comparing human brain with machines.
Strong. C, (2015) replaced the focus from web 2.0 towards Big data in Scott Golder and Michael Macy’s research “the web sees everything and forget nothing”. Strong said that “the data sees everything and forget nothing” (p. 09). Strong. C, (2015) further compared Big Data research method with sampling and called Big Data more time consuming and costly process but he also defended Big Data or datafication being giving unbiased results and profitable knowledge but he concluded it by saying that its less about avoiding the biases and more about deciding that which bias researcher is willing to accept and which not and this makes objectivity illusionary. Strong. C, (2015) raises some questions around preferring big data instead of sampling by saying that Big Data is somehow objective and comes with unquestionable reliability. Mayer and Cukier (2015) said that big data analysis is beneficial for offering more freedom to explore, more in-depth details in a number of directions, and for uncovering new connections that would remain hidden with smaller samples.
Morabito. V, (2015) elaborates more the upsides of big data such as: “Big data can change the way companies identify and relate to their customer base. Undoubtedly, companies can boost the old marketing strategies using new big data tools and expertise. Market penetration strategies can leverage big data to feed marketers information on how to keep existing customers and improve repetitive sales.” (p. 30)
Strong.C, (2015) developed a deeper understanding about big data analysis and its impact on consumer insight. He explained that due to the increase in online activity by customer, cookies are tracking each and every move and creating data to support companies. Morabito, V. (2015) in his book “Big Data and Analytics” said that big data or datafication is enabling companies to identify and relate to their customers more effectively than before and can multiply the impact of their old marketing strategies by knowing their customers’ online behaviors. Big data driven communication is considered more efficient in targeting the right audience.
Marr, B. (2016) talked about big data driven campaigns that “there was no margin for error and every cent would have to be spent efficiently.” (p. 104)
This all shows the importance of big data in the identification of right target groups for communication. David (2006) said in his book that big data is used mainly in sales and customer oriented communication to analyze website traffic, clicks, and social media streams and search engine optimized word in real time. He further explained that by collecting and analyzing this invaluable big data, companies get clear and more accurate understanding of their existing and potential customers’ motivations and can also predict their future needs.
Big data analysis helps in distinguishing different target audience groups as per their needs and then in the creation of targeted communication content to fulfil their needs through inbound marketing. Inbound marketing is making it possible for businesses to achieve the customer centricity in digital content communication. Unlike outbound marketing where companies directly ask target audience to buy a product or service inbound marketing mainly market the content to attract customer to push it towards purchasing it (Patrutiu-Baltes, 2016). Inbound marketing is a digital way of business promotion through content marketing on websites, blogs, podcasts, eBooks, videos, SEO, and social media advertisements to attract customers as per their stages in the customer journey (Halligan, 2009). The idea behind inbound marketing is to produce marketing/communication content in a way that pull, engage, attract people by sharing relevant, useful and helpful content (Halligan, B., & Shah, D. 2014, p. 3).
Pull vs. Push Media Strategies
The terminologies of pull and push strategies are not new in the field of strategic communication but these terminologies are differently perceived by strategic communicators from the way marketers are using Pull and Push strategies with inbound and outbound marketing techniques. Traditional way of communication (especially in marketing and strategic communication) is more like pushing messages towards a general audience on media such as TV, newspapers, radio, internet, and magazines, mail campaigns, and face-to-face on site. This strategy is known as Push media as stakeholders/ audience are not looking for the communication content in advance and exposure to such information might annoy them being uninterested and pushed towards something irrelevant to them whereas in Pull media strategy audience/stakeholders are already knowledgeable and seeking for the relevant information communicated through the campaign (Hagel and Brown, 2011).
Earlier researcher, Corniani, M. (2006) presented these marketing concepts in the same manner as pull and push media concepts of strategic communication. She defined outbound marketing as push communication and inbound marketing as pull communication.
“In push marketing4 the company promotes a message and communicates it by ‘pushing’ it along a channel to an audience that is usually not directly interested in it (passive interest), whereas in the case of pull marketing5, the communication flow is actually requested by the market. So the market takes action to acquire the information flow (business communication), and thus has a precise interest in it (active interest).” (Corniani, M., 2006, p. 52)
Pull media strategy vs. Inbound marketing
Researchers in strategic communication has used the terms of push and pull media strategies with a more focus on the media as a platform and prior knowledge of stakeholders to choose the media for display without giving required importance to online platforms and communication content. In marketing some advanced terms are used to define the push and pull strategies which are Outbound marketing and Inbound marketing respectively but marketers are using these strategies in a different way.
Opreana and Vinerean (2015) said that outbound marketing has lost its effectiveness due to being costly and more general approach as compared to inbound marketing which they called more targeted, engaged and interactive for a customer plus less costly for the company. They further explained the process of digital inbound marketing as a procedure of creating organic and search engine optimized content to reach and to convert qualified consumer into a long term loyalty.
The focus area in this study are inbound marketing and pull media strategy. The understanding about pull media strategy in strategic communication is to target the specific sub-groups of stakeholders with prior knowledge about the product/ service with a media mix which includes newspapers, social media, radio, personalized emails or face-to-face briefing session (Gulbrandsen, I. T., & Just, S. N., 2016, p. 220). On the other hand, inbound marketing concept is the next level of pull media strategy. In marketing, first they pull the customer through Search engine optimization (SEO) towards the company’s website, product or service through pull marketing and inbound marketing is the second step after pulling the customer. Inbound marketing is basically content marketing in the form of blogs, videos, e-books, whitepapers, social media marketing and newsletters to attract and engage the already interested and knowledgeable audience in buying the product or service. Z Lin C O Y, and Yazdanifard R (2014) defined Inbound marketing as a marketing methodology of creating and sharing content on online and digital platforms with an aim to get discovered by the targeted audience through the shared content. In inbound marketing message approach is more audience/receiver centric. So instead of producing general content, targeted content is produced for specific audience, on specific online channels, and published according to the audience’ online behavior. Inbound marketing focuses mainly on the audience centric content and digital platforms.
“The practice of promoting products and services in an innovative way, using primarily database-driven distribution channels to reach consumers and customers in a timely, relevant personal and cost-effective manner is known in the theory and practice as digital marketing”. (Wsi, 2013, p. 7) Inbound marketing is a very effective way to reach the desired audience by getting found on different online platforms through search engines and sites like Facebook, YouTube, Twitter-sites that hundreds of millions of people used to find their answers each day. (Halligan, B., & Shah, D. 2014, p. xiv).
Concept of inbound marketing is majorly used in sales and owned by marketing but the base of this concept is closely connected with strategic communications (Pull media) as many of the strategic communication processes follows the same basic principles as marketing. There is a lack in the scholarly strategic communication research that inbound marketing can fill by adopting and calling it inbound communication to focus on the content, digital platforms and data driven decision making. The concept of audience oriented and targeted communication was derived from strategic communication. Term of “strategic” was initially used in 1950’s in Organizational theory to explain the organizations’ communication strategies to increase market share and their profits. (Hatch 1997; Argenti 2005; Bütschi 2006; Hallahan et. al., 2007).
Micro-segmentation of desired audience
Offsey (2014) shared that big data analysis enables companies to target each and every customer individually based on their preferences and buying habit by collecting users personal information such as online behaviors, browsing data, purchase histories, physical location, demographics (memberships, work history) and psychographics (social influence and sentiment data). Big data analysis allows companies to observe past, real-time and step by step behaviors of desired audience to target them with customized and personalized communication content. Such micro-segmentation leads towards fine targeting with audience oriented content (inbound marketing) and increase the success rate of a campaign.
In academic research on marketing, the terminology of customer journey is used to link customers’ experience with a company and its products and services. Kankainen et al. (2012) defined customer journey as “the process of experiencing through different touch points from the customer’s point of view” (p. 221). Asbjørn et al. (2018) in their research paper reviewed the terminology of customers journey in the academic literature and explained that this term is used as a path, process, and a set of sequence through which a customer use a product or service. Lee (2010) called it as online decision journey which starts with customers first interact with the company’s online media and takes it towards the online purchase. It’s a journey from a potential curious customer who is searching online platforms to get the best offers towards selecting and buying the product/ service. Stone and Liyanearachchi (2007) called different phases of customer journey as life -cycle stages of pre-acquisition, welcoming, maturity, and renewal.
Micro-segmentation vs. customer journey
Big data driven micro-segmentation of desired audience allows the communicators to target users at individual level (as mentioned above in section 2.4). So in a big data driven campaign, communicators can micro-target individuals with personalized communication content which is more targeted communication as compared to dividing and targeting the audience into the categories of customer journey.
Big data ethics and privacy issues
There is a huge debate around ethical and privacy issues related to big data. Some practitioners and scholars called it Big data era, as selling big data of online users after collecting it from different sources is a big market now. Generally, before selling big data to other companies, data collectors or sellers uncouple the surnames, first name, sometimes remove age and addresses but some critiques such as Buhl, et. al, (2013) work on privacy and ethical issues of Big data said that “In a Big Data era with many different data from different sources, privacy and anonymity means more than just uncoupling surname, first name, age, and address from a dataset. Location-based data and other sources still allow for easy and clear identification and tracking.” (p. 67) But it is a separate debate and can be considered as a topic for future research as this debate is still ongoing.
Firstly the literature reviewed showed that there is a need for more academic work around big data analysis and inbound marketing in the realm of strategic communication. And this is not possible without understanding the role of big data analysis in marketing and sales oriented communication as most research and practical work is done in those fields. By creating an understanding about the role of big data analysis and inbound marketing in the selection of desired target audience and communication content in marketing campaigns, one can think about using the same procedures in strategic communication. So we cannot ignore and by-pass sales and marketing communication research from strategic communication research to develop a better ground for understanding big data analytics.
Table of contents :
1.1 Purpose of the Study
1.2 Research question
1.3 Rationale to select Vattenfall
1.4 Limitations of the study
1.5 Definitions of Key concepts
1.6 Thesis Disposition
2. Literature Review
2.1 Strategic communication
2.2 Big data analysis
2.3 Inbound Marketing
2.3.1 Pull vs. Push Media Strategies
2.3.2 Pull media strategy vs. Inbound marketing
2.4 Micro-segmentation of desired audience
2.5 Customer journey
2.6 Micro-segmentation vs. customer journey
2.7 Big data ethics and privacy issues
3. Theoretical framework
4.1 Description of the research method(s)
4.2 Selected Campaigns
4.3 Google Analytics 360
4.4 Communication content of both campaigns
4.5 Big data analysis
4.6 Data sets
4.7 Big data feedback loop
4.8 Data Mining
4.9 Big data analysis of Vattenfall’s selected campaigns
4.9.1 Data Sets of both selected campaigns
4.9.2 Selected campaigns and Big data feedback loop
4.9.3 Real time evaluation and content tweaking
4.9.4 Data Mining and audiences’ micro-segmentation
4.10 Data Collection
4.11 Research Ethics
5. Results and Data Analysis
5.1 Difference in Big Data driven campaigns’ performance overtime with the help of evaluation
5.1.1 Website Traffic
5.1.2 Conversion rate
5.1.3 Session duration
5.1.4 Search Engine Marketing (SEM) Campaign
Target audience size and budget
Number of impressions
5.1.6 Social Media data
Impressions on Facebook and Instagram content
Click through rate (CTR)
Cost per lead (CPL)
5.1.7 Programmatic Display
Impressions on Programmatic display
Clicks on Programmatic display
Orders on Programmatic display
5.1.8 Goal Completion
5.1.9 Analysis of part one
5.2 Big data driven micro-segmentation and difference in the performance of target audience categories
5.2.1 Big Data and targeted audience groups
5.2.2 Analysis of second part
6.1 Expansion of three staged strategic communication Plan
a. Potential benefits of micro-segmentation and inbound marketing supported by big data analysis
7.2 Future agenda