Modern technological advances are enabling marketers to catch rich customer data with greater volume, velocity and variety than ever before (Firestein, 2012).
With the help of big data, consumer behavior can be proactively predicted, and as a result, organizational decision-making can be improved (Erevelles, Fukawa
& Swayne, 2016). Various personalization techniques including the recently pop-ular recommendation models have brought relief to the continuous battle of con-sumer attention (Adomavicius & Kwon, 2007). The developing technologies help organizations predict what to offer for whom and when (Jugovac, Jannach & Ler-che, 2017). At the same time, the recommendations assist consumers in decision-making and decrease the consumers’ information overload (Ginovsky, 2010; Said
& Bellogín 2014).
Leeflang et al. (2014) state that efficient tracking of customer journeys is a consequential success factor for organizations in terms of optimizing marketing activities and budget. Recommendation models help organizations identify which products or services could match their customers’ wishes and needs. An in-depth understanding of the decision-making strategies of the customers’
needs to be developed to create successful recommendations. (van Capelleveen et al., 2019; Amrit, Yazan, & Zijm, 2019.)
All customers have several touchpoints with organizations through trans-actions, social media and various other online activities, which continuously gen-erate customer behaviour data (Sleep & Hulland, 2019). The gathered product, transaction, enquiry and web-data can then be analysed to predict the needs of the customers and to target offers in all channels based on customer behaviour (Woodcock & Stone, 2012). Analysing customer behaviour patterns and building scoring models to predict future purchase patterns and customer preferences can be further used to optimize customer-centric marketing communication through all channels. As a result, organizations can increase the financial outcomes (Teer-link & Haydock, 2012). Based on the analysis of Teer(Teer-link and Haydock (2012), the organizations using predictive analytics and executing multi-channel marketing strategy can drive top-line growth even five times more than less-advanced busi-nesses. Additionally, predictive modelling such as next best offer can improve the organization’s cross-selling rates (Woodcock and Stone 2012). The appropri-ateness and timeliness in the next best offer models achieved through deep cus-tomer understanding are the keys to successfulness also in building cuscus-tomer re-lationships (Ginovsky, 2010).
NBO is a predictive analytics recommendation model for tailoring prod-uct or service offers for individual customers across all communication channels to increase customer value (Deloitte MSC Limited, 2013). NBO model addition-ally enables generating real-time recommendations for customers and allows multi-channel customer monitoring (Teerlink & Haydock, 2012). Robbins, Palan, Mui and Tao (2019) determine NBO as a follow-up offer to an identified or po-tential customer, based on customer data collected from various sources, includ-ing marketinclud-ing database and third-party data. NBO is commonly used for mar-keting purposes, such as targeting marmar-keting messages based on known or iden-tified information and customer identity (Robbins et al., 2014). The purpose of NBO is to find solutions to customers’ needs rather than find a target group for
a campaign. Accordingly, NBO enables targeting personalized offers for individ-ual customers instead of costly mass-media advertising campaigns. (Deloitte MSC Limited, 2013.)
In addition to marketing, NBO is often used also in customer service as a sales tool. Ginovsky (2010) states, that the customer service representatives (CSRs) should have the information and intelligence of the NBO model at dis-posal to make relevant product or service recommendations for the customers.
This capability should be automated in the sales system the CSRs use, so that they do not have to analyse the customer information themselves and deduce what product or service should be offered to each customer. This enables the CSRs to focus on interacting with the customer and building the customer rela-tionship, rather than working with the computer.
Individual customer’s NBO can be predicted by analysing trends and pat-terns in data and using various modelling techniques to predict and anticipate the individual customer’s needs and preferences (Kiron et al., 2012). The data are collected from various sources, including internal sources such as transaction data and external sources (van Capelleveen et al., 2019). More accurate NBO pre-dictions allow more precise targeting, a bigger increase in sales and larger cost reduction, as marketing investments are not used for activities that get ignored by the target group. In addition, understanding the causes behind customer churn and analysing which customers are of the highest value and most im-portant to retain and at what cost, has become highly imim-portant, especially for banks. (Deloitte MCS Limited, 2013.)
NBO models also encompass challenges and limitations. Ginovsky (2010) states, that catching the remarkably large amount of customer data at the precise moment when the customers are likely to acquire their next product or service can be challenging. Additionally, aging of the data and challenges in structuring the data generate additional challenges to predict the NBO reliably. Bringing the data and knowledge derived from data together on a real-time basis to engage with the customer requires a set of infrastructures that enable collecting infor-mation on a real-time basis and only when it is needed. Further, calculating the profitability of a single customer is an identified challenge regarding the NBO models.