Toward customer hyper-personalization experience — A data-driven approach

Mendia, J. M. Valdez; Flores-Cuautle, J. J. A. Article Toward customer hyper-personalization experience — A data-driven approach Cogent Business & Management Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Mendia, J. M. Valdez; Flores-Cuautle, J. J. A. (2022) : Toward customer hyperpersonalization experience — A data-driven approach, Cogent Business & Management, ISSN 2331-1975, Taylor & Francis, Abingdon, Vol. 9, Iss. 1, pp. 1-16, https://doi.org/10.1080/23311975.2022.2041384 This Version is available at: https://hdl.handle.net/10419/288518 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Published online: 23 Feb 2022. Submit your article to this journal Article views: 9639 View related articles View Crossmark data Citing articles: 6 View citing articles OPERATIONS, INFORMATION & TECHNOLOGY | RESEARCH ARTICLE Toward customer hyper-personalization experience — A data-driven approach J. M. Valdez Mendia 1 * and J. J. A. Flores-Cuautle 2 Abstract: Today’s omnichannel business models incorporate physical and digital touchpoints interacting with customers. A hyper-personalization strategy relies on the organization’s capability to gather and transform customer data into personalized experiences; therefore, when a hyper-personalization organizational plan is put in place, it serves two main functions: to deliver personalized experiences and increase the number of customers receiving such experiences. For this to happen, four elements are required for a hyper-personalization strategy: data foundation, decisions, design, and distribution. While customer master data management relies on the correct identification of a customer, a real customer insight can only be achieved when three types of customer data are gathered: Identity, Contactability, and Traceability (I, C, T)- fulfilling the first element of a hyper-strategy. This article aims to identify the benefits in the total number of customers that can receive a hyper-personalization strategy when real-time touchpoints are linked to a customer Master Data Management that integrates the three types of customer data. Subjects: Consumer Behaviour; Relationship Marketing; Retail Marketing Keywords: Big Data; Customer interaction; touchpoints; personalization J. M. Valdez Mendia ABOUT THE AUTHORS J. M. Valdez Mendia is a digital transformation executive that centers his research in customer centric data driven initiatives. J. J. A. Flores-Cuautle is a Research fellow of the Mexican National Science and Research Council; his research interests include data analysis and processing. PUBLIC INTEREST STATEMENT The growing volumes of consumer data have turned its analysis into a powerful tool to increase productivity and develop focused strategies for the target consumer. Consumer data is crucial for decision-making and strengthening the companies-customers relationship from the companies’ perspective. Although big data plays a more critical role in decision-making, there is still necessary to analyze data with the appropriate techniques depending on the final purpose.This paper focuses on identifying the benefit of customer data integration from digital and physical touchpoints by filling incomplete customer information in databases. The effectiveness of customer database migration and strategies transformation of anonymized records into complete customer records are analyzed to identify the benefits in the total number of customers that can receive a hyper-personalization strategy in real-time. Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 1 of 16 Received: 01 October 2021 Accepted: 04 February 2022 *Corresponding author: J. M. Valdez Mendia, Customer experience & Data Analytics, 360 Centricity, Founding Partner, Mexico Email: mau_valdezmendia@sloan. mit.edu Reviewing editor: Albert W. K. Tan, Asia Pacific Graduate Institute, Shanghai Jiao Tong University, Singapore Additional information is available at the end of the article © 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. 1. Introduction Today, organizations must aspire to an institutional and comprehensive knowledge of all interactions between them and their customers (Jain et al., 2018; Kalia & Paul, 2021; Low, 2000). In addition, the need to deliver personalized communication and services is a challenge that organizations face due to the introduction of digital channels while expanding traditional ones (Andreassen et al., 2018; Bleier & Eisenbeiss, 2015; Wolny & Charoensuksai, 2014). Each interaction between the organization’s touchpoints and its customers generates data, and those touchpoints offer companies the opportunity to record the customers’ data that can generate value for them (Erevelles et al., 2016; Rekettye & Rekettye, 2019). Each touchpoint is supported by platforms or systems, which ingest that data into their databases (Ducange et al., 2018). Customer information must be available for all touchpoints and source systems to deliver hyperpersonalized experiences through an omnichannel business model (Kalia & Paul, 2021). This work uses the proposed definition of personalization established by Imhoff to achieve personalization goals (Imhoff et al., 2001), “Personalization is the ability of a company to recognize and treat its customers as individuals through personal messaging, targeted banner ads, special offers on bills, or other personal transactions.” Previous works identified two personalization aims: delivering information relevant to specific individuals or groups of particular individuals in the format and layout specified at appointed time intervals. The second is the increase of the revenue and decrease of the business by applying a one-to -one marketing understanding of its customers’ needs, habits, lifestyle, preferences, likes, and dislikes. In the end, the combination of both aims is addressed or at least given the illusion of satisfying the customers’ individual needs and preferences (Hess et al., 2020; Jain et al., 2021, 2018; Won, 2002). A hyper-personalization strategy has four elements: data foundation, decisions, design, and distribution (Boudet et al., 2017; Jain et al., 2021). All these elements are necessary; however, data foundation is the starting point because a hyper-personalization strategy needs a customer’s feedback to deliver the experiences. For example, if all customers are unknown, a hyperpersonalization approach will help build the customer database to receive hyper-personalized experiences later on. Digital clienteling is defined as improving customer engagement by providing a unified customer information resource (Jain et al., 2021); the increasing use of smartphone applications makes this device the preferred tool for customization (Mallya & Nair, 2016). As the current literature sets, implementing hyper-personalization strategies can improve the clients’ contactability and customer engagement as needed (Jain et al., 2021; Micu et al., 2022; Ngoc Thang et al., 2020). However, it is necessary to study the impact, peak, and limit of these strategies and the best possible moment to implement a new approach to continue increasing the number of customers that can receive hyper-personalized engagements. Additionally, most studies rely on studying either e-commerce or physical stores but not a combination of both (Micu et al., 2022; Ngoc Thang et al., 2020). This study intends to demonstrate the added benefits of establishing a hyper-personalization strategy through physical and digital touchpoints concerning the number of customers receiving such a strategy. An example of the previously mentioned occurred at a retail company selected to run hyperpersonalization strategies. This trial was necessary to demonstrate the impact of implementing digitalization strategies on the number of customers who could receive Hyper-personalization strategies. Such a company made it possible to reach 15 million clients that could be considered during the research to demonstrate the effects of the aims of the project while being monitored for 15 months. In the end, all the work resulted in the increment of the number of customers. The collected data shows that more significant benefits can be obtained when customers receive Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 2 of 16 hyper-personalized offers through such strategies, real-time data input through an architecture that links a customer data platform (real-time) and an MDM (near real-time). 2. Literature review Data personalization strategy, data gathering, and master data management are the sections that conform to the theoretical framework. The literature review was performed to support the proposed architecture theoretically for the hyper-personalization experience. 2.1. Data for a personalization strategy The necessity for integrating customer data across the organization has been an old problem that has been addressed by various technologies and governance methodologies (White et al., 2006). Three information types can be gathered from customers’ Identity, Contactability, and Traceability; despite customers providing information without being awarehidden information, like cookies or IP addresses, this type of information can also be grouped into one or all three of those categories when handled correctly. The customer information can be defined as identity (I): Personal Information from the customer such as their name, last name, date of birth, gender, social security number, tax ID, and all other types of information is used for authenticating an individual excluding contact information (Narayanan & Shmatikov, 2010). Contactability (C): Contact information used in campaign management direct and online channels (Anderson, 2009), like address, telephone, cell phone, App, WhatsApp, social networks. Traceability (T): identifies that a customer is interacting with the company and is divided into two types. (1) Transaction or receipt: the interaction where a transaction is carried out (Kurniawan et al., 2017), and there is a record of what occurred (or there should be a record), for example, a purchase, first and second-party data (Paulina, 2017), a complaint, balance inquiry. (2) Visit: interaction without creating a record, like a store customer’s visits without purchase. Personalization is defined as individualization reached by considering specific customer preferences (Nobile and Kalbaska 2020). Therefore, the path toward hyper-personalization is based on customer information: Identity, Contactability, and Traceability. Figure 1. Relationship among the three pillars of the hyperpersonalization. Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 3 of 16 Figure 1 shows the relationships among a customer’s knowledge elements. Relationship among information gathered from customers enables organizations to map their customer information and allocate it into each of the seven states. The initial result of mapping customer information in states A to Z will vary across industries and business models. Some industries have a more significant customer concentration in state A because of their innate customer relation, for example, e-commerce, banking, retail by membership, among others. However, some strategies help complete data profiles and upgrade towards an A state regardless of industry and business models. The initial result shall reflect its business model and its current process of customer data gathering. The initial result can be improved by migrating a customer record by completing the missing information (I, C, T). The desired state is A, where all three Customer Information types are present. Personalization can be delivered to customers in conditions A and D; the main difference is that state A allows to address them personally. Therefore, A state represents hyperpersonalization. In addition, both states (A and D) will enable us to analyze and connect with them using historical omnichannel information. 2.2. Data gathering across touchpoints Data gathering has been facilitated by digital technologies which interface through digital touchpoints (Erevelles et al., 2016; Straker & Wrigley, 2018). For pure digital business models, where most of its touchpoints are digital, customer data is easy to find (Wirtz, 2019). However, business models that rely on physical touchpoints must implement extra efforts to collect customer data. Table 1 compares a customer experience between purchasing online and through a physical store. The customer’s knowledge and identity are facilitated if digital touchpoints are being created or enhanced through the organization (López García et al., 2019). Depending on the business model, the customer information comprises different personalization levels; it is easy to imagine the knowledge that a Shared-ride app has about the customers: in addition to contact information, identity, and form of payment, it has collected all geographical coordinates from origins and destinations, AC preference (Gilibert & Ribas, 2019; Shaheen & Cohen, 2019). 2.3. Master data management Master Data Management (MDM) enables organizations to have a customer core entity solution for data consistency, simplification, and uniformity of process, analysis, and communication across the enterprise (Erevelles et al., 2016; White et al., 2006). MDM has proved efficient in breaking data silos of a defined entity across an organization and delivering a unified customer record. However, Table 1. Customer’s knowledge comparison between the online and physical store (López García et al., 2019) Action online store Physical store Awareness Knowledge of customer origin (search engine, social network, announcement, or email advertising) NA. Search and Selection It is known that each element selected, search made, selection, and movement made to the cart NA. Check out We know the products, place of delivery, payment method, and contact with the customer, all the elements (I, C, T) are present The products and payment methods are known (T). Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 4 of 16 Figure 2. Proposed architecture to fulfill a complete customer information database (Karanam et al., 2021; Richter & Wood, 2015). Figure 3. Real-time algorithm for creating new customer records and avoiding data duplication. Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 5 of 16 its primary purposes have been described as Deduplicate and Regular Batch Matching for Duplicate Removal (Haneem et al., 2017; Oracle, 2015). 3. Proposed architecture for hyper-personalization experience The architecture shown in Figure 2 is proposed to fulfill a complete customer information database. The proposed architecture comprises five blocks named Real-time feedback source systems, Other Source Systems, Master Data Management (MDM) Repository, Customer Data Platform (CDP), and Machine Learning Repository (Karanam et al., 2021; Richter & Wood, 2015), and each block is described as follows: (1) Real-time feedback Source Systems: three source systems selected to have real-time feedback for customer interaction- 1) Point of Sale, 2) E-commerce, and 3) Delivery. All three source systems would integrate contactability information for real-time matching- Mobile phone number and email. The flow of customer information from these source systems passes through the Customer Data Platform and then to the MDM, allowing the CDP to feed the real-time system required data. (2) Other Source Systems like customer data from all other source systems follow the traditional path and are connected directly to the Master Data Management system. (3) Master Data Management Repository contains the Customer Golden record after performing the following processes: Data Normalization, Data Cleaning, Matching, and De-duplicating processes. (4) Customer Data Platform: a historical version of MDM that includes all Sources Systems and is the real-time enabler between real-time source systems: 1) Point of Sale, 2) E-commerce, and 3) Delivery and the traditional MDM flow information. Through its connection with MDM, any interaction with a known customer triggers a real-time response that includes a Best Next Offer, or Best Next Action prepared for that customer in the Machine Learning Repository. (5) Machine Learning Repository: a repository that enables advanced matching through Customer Traceability or Contactability Information. 3.1. Realtime matching CDP An algorithm to trigger a real-time response to customer interaction is implemented in the customer data platform. This algorithm works with a machine learning repository to successfully Table 2. Personalization strategies based on the customer data state (Fabrizi & Banoub, 2014) Identity Contactability Traceability Communication efforts A O O O Personalize Direct Offers based on BNO B O O - Institutional communication with touches of personalization C O - O D - O O Direct Offers based on BNO X - - O Y - O - Institutional Communication Z O - - Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 6 of 16 integrate master data management by creating new customer records and avoiding duplications (ActionIQ, 2021; Oracle, 2021). Figure 3 shows the proposed algorithm. 4. Architecture methodologies Without regard to the applied business model or touchpoints, it is possible to have more significant customer participation in state A. However, the path for personalization may begin in the customer’s present status. Table 2 shows the different communication efforts that can be performed in each state. When a customer is in the desired state, this does not mean to stop communicating with the customer; therefore, communication should be based on offering the best possible offer for each customer based on data miningbased on the specific preferences of each customer, in what is known as the Best Next Offer (or action) (BNO/BNA; Fabrizi & Banoub, 2014). States A and D enable personalized communication based on previous interactions where BNO and BNA can be achieved (Liermann & Stegmann, 2019; Valtonen, 2020). Best Next Offer and Best Next Action mean that different customers receive different content depending on their previous behavior. State B enables communication with minimal personalization derived from Identity Data. State Y promotes the simplest form of communication where no data but the contact information is known; therefore, almost all customers receive the same content. It is crucial to have an omnichannel communication strategy that respects the number of touches for the customer at the company level. It is also fundamental to have a communication strategy that includes direct channels: email, SMS, push notification, www, app, and contact center Table 3. Customer migration strategies (Bolton et al., 2008; Wang & Hong, 2006) Identity Conta ctability Traceability Strategy to Migrate to a Better Status Action AO O O Loyalty Program and Data Quality Program Active BO O Receipt Registration, Digital Sweepstake or Activation, Digital Receipt, purchase with delivery and Digital Record (include A) Active CO O Subscription (Include A and B) Passive DO O A, B, and C Active XO At the next opportunity A, B, and C Passive YO Launch communication A, B, and C Active ZO At the next opportunity A, B, and C Passive Valdez Mendia & Flores-Cuautle, Cogent Business & Management (2022), 9: 2041384 https://doi.org/10.1080/23311975.2022.2041384 Page 7 of 16 and robust its personalization strategies and establish means to migrate customers from poor information states to complete information states. All migration strategies are intended to complete and upgrade to the original State of Information that an organization already has. Currently, most organizations plan to migrate to State A, which contains the intersection of Identity, Contactability, and Traceability (I, C, T). However, let it be noted that certain conditions are not attributed to the migration strategies that impede certain records from upgrading (invalid data, Identity data error, homonyms, among others). State X, which contains anonymous transactions, contributed more records to the complete State A’s transition. Real-time matching helps deliver personalized experiences and adds prospects to the customer database with an initial state D, which is why the D state increases from 16.96% to 26.40%. It is imperative to note that the main migration path is X state to D state to A state. The mobile or email request in physical touchpoints increased states Y and D; one of the data issues faced when managing mobile as customer identifier is that sometimes various customers can share such information within their accounts. In our experiment, 0.58% of customers shared a mobile number. A similar issue was found with email as a customer identifier, and it was found that 8.7% of all customers had more than one account, each with a different email. While conducting this study, all digitalization strategies were implemented simultaneously. This study fails to address the impact, peak, and limit of each digitalization strategy running by itself. This might be because the corporation implemented a digitalization program that contained all eight digitalization strategies for a unified implementation to reduce costs and resource allocation. Therefore, the implementation of each digitalization strategy should run independently in future studies. Funding The authors received no direct funding for this research. Author details J. M. Valdez Mendia 1 E-mail: [email protected] ORCID ID: http://orcid.org/0000-0002-7814-5256 J. J. A. 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