Justification of the research

Machine learning and AI are currently prevailing and widely interesting themes in business and marketing discussions. As Sleep, Hulland and Gooner (2019) state, the ongoing development of marketing towards more customer-centric models emphasizing data-driven decision-making and technological advance-ments, as well as marketing practitioners’ access to fast increasing amount and variety of customer data, also known as big data, are currently shaping the cor-porate strategies. As particularly financial industry organizations have a large

amount of customer and transaction data, the organizations operating in the fi-nancial sector are in the precedence to implement data-driven decision-making strategies. Thus, the research topic is highly relevant for many businesses inno-vating in today’s competitive business environment, especially in the financial industry, where big data are continuously shaping the strategies and exponen-tially producing unstructured information about the customers.

In the research literature, machine learning, artificial intelligence (AI) and prediction models in business context are rather new themes, however widely studied themes in general. Nevertheless, the majority of the studies are made by researchers in computer science and information technology (IT). Thus, most of them focus on the technical perspective of the topics. The research concerning machine learning in marketing context from a business perspective is neverthe-less relatively scarce. However, there are a few noteworthy studies concerning the topic. In recent literature, Campbell, Sands, Ferraro, Tsao and Mavrommatis (2019) have widely studied the various possibilities of how marketeers can lever-age AI and machine learning in marketing strategy and activities. Further, imple-menting big data analytics to marketing is researched for example by Erevelles, Fukawa and Swayne (2016), Mithas et al. (2013) and Bose (2008).

Various different predictive analytics recommendation models are widely researched in literature, particularly in e-commerce business context. However, the research of recommendation models is scarce in financial industry. Particu-larly NBO or next best action (NBA) recommendation models are rather little re-searched based on searches from various scientific databases with key terms ‘next best offer’, ‘NBO model’, ‘next best action’ and ‘NBA model’. Hence why, this research provides a novel perspective on implementing and managing NBO rec-ommendation model in marketing. Further, this research provides topical insight on utilizing NBO recommendation model effectively in multichannel marketing and communications.

The concept of implementation and management of a predictive analytics recommendation model in marketing is meager in literature. Many studies con-cerning the topic focus on predictive analytics adoption and implementation drivers and impediments. For example, the research by Sleep, Hulland and Gooner (2019) studies the factors influencing the adoption and implementation of data-driven decision-making focusing on capabilities, drivers and challenges in adoption and implementation. However, the existing studies fail to form a

comprehensive view of the process from planning and adoption to implementa-tion and continuous management of predictive analytics in business. Thus, this research provides an opportunity to form a holistic picture of the implementation and management of a predictive analytics model in marketing. Further, this re-search propounds factors leading to success or failure in the implementation and management of a predictive analytics model. It is interesting to study how the identified drivers and challenges in implementation and management in existing literature are manifested in this case study research.

Hartman (2014) has recognized how the digital revolution is reshaping the field of marketing and bringing chief marketing officers (CMOs) and chief infor-mation officers (CIOs) closer together. Also, Sleep and Hulland (2019) have stud-ied how big data drive the CMO and CIO relationship and cooperation in organ-izations, and how the evolving relationship can create competitive advantage for businesses. The recently emerging number of research literature regarding the emerging relationship of marketing and IT indicates, that the topic is timely and relevant for today’s businesses who are moving towards adopting and imple-menting novel analytics solutions to their marketing and searching for best prac-tices to manage the implementation and development. This research comprises the timely topic of CMO and CIO relationship and how it manifests itself in the implementation and management of predictive analytics.

Customer-oriented business strategies is a widely researched and timely topic, which is studied extensively also in the marketing context. A substantial amount of the research studies the organizational change from a product-centric strategy to a customer-centric strategy. Further, many studies research the poten-tial implications of transforming to customer-oriented strategy. This research complements the existing literature by providing further insight into how pre-dictive analytics leverages customer-oriented business strategy and how an NBO model can be used as a tool to transform marketing communications customer-centric.

According to Malthora, Birks and Wills (2012) it is important to interact and discuss directly with the key decision-makers in the early stage of the re-search to identify a marketing problem and define the rere-search objective. Adams, Raeside and Khan (2014) further state, that it is important to also understand what is important to the stakeholders and who are the key actors regarding the topic to define the research. As the authors suggest, the research topic was first

extensively discussed with the data scientist and marketing director of the case company. The discussions concerned a brief history of the topic and the identi-fied challenges in implementation and management of the NBO model in the case company. Based on the discussions, the research topic was defined.

The case company is a Finnish retail bank which provides loan, invest-ment and daily transaction services for its customers. Thus, the scope of this re-search is in B2C business, in a retail bank. The financial industry is an interesting study subject, as retail banks generally have big amounts of customer data. Thus, employing the data to gain added value for marketing, business and customers is highly relevant for many companies. As many organizations struggle with how to utilize the enormous amount of customer data, this research provides one standpoint and proposition on how to take advantage of the data. Furthermore, especially the financial industry companies are continuously facing new chal-lenges with new market entrants constantly tightening the competition and cre-ating new demands of action for the traditional retail banks to win the customers.

Incessant innovation and increasing customer knowledge are required to suc-cessfully compete with the technology-driven start-ups entering the market. Fur-ther, the threat of ‘big giants’ as Google, Apple and Amazon, are creating addi-tional threats and challenges for local retail banks. Thereby, researching new ways to innovate and win customers is highly interesting.

The case company was selected, as the author worked there during the implementation of the NBO model. The author was responsible for product and brand marketing activities. The development of the prediction models originally began before the author started working in the case company, but the author took part in the implementation and development of the NBO model during her em-ployment.