Are retailers leveraging in-store analytics? An exploratory study

PurposeThe purpose of this study is to analyse the level of adoption of in-store analytics by brick-and-mortar retailers. Web analytics technology has been widely adopted by online retailers, and the technology to gather similar information in physical stores is already available. This study explores how such technology is valued and adopted by retailers.Design/methodology/approachThis study is based on interviews and a focus group of 21 retail executives using a semi-structured interview methodology. An in-store analytics service was defined, along with specific key performance indicators (KPIs) and use cases to structure respondents' feedback.FindingsAlthough noteworthy differences have been found in the value of KPIs and use cases by type of business, the main finding is that none of the respondents reached the stage of a brick-and-mortar data-driven company. In-store analytics services are in the early stages of Rogers' (1983) model of diffusion of innovations. Three main reasons are presented: lack of technology knowledge, budget priority and a data culture inside the companies.Practical implicationsThe results should encourage scholars to further investigate the drivers accelerating the adoption of these technologies. Practitioners and solution providers should strive for improvement in the simplicity of their solutions.Originality/valueThis study is the first to analyse the level of adoption of in-store analytics from the perspective of retailers.

[1]  Zheng Li,et al.  Business Value of Telecom Operators’ Big Data , 2020, Journal of Physics: Conference Series.

[2]  Kenneth L. Kraemer,et al.  Information Technology Payoff in E-Business Environments: An International Perspective on Value Creation of E-Business in the Financial Services Industry , 2004, J. Manag. Inf. Syst..

[3]  Jan Hendrik Betzing Beacon-based Customer Tracking across the High Street: Perspectives for Location-based Smart Services in Retail , 2018, AMCIS.

[4]  Chandan Dasgupta,et al.  Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM) , 2017, J. Enterp. Inf. Manag..

[5]  S. Noble,et al.  Retail Apocalypse or Golden Opportunity for Retail Frontline Management? , 2019, Journal of Retailing.

[6]  H. Saleem,et al.  Strategic Data Driven Approach to Improve Conversion Rates and Sales Performance of E-Commerce Websites , 2019 .

[7]  Ignac Lovrek,et al.  Discovering shoppers' journey in retail environment by using RFID , 2012, KES.

[8]  Michael Groß Mobile shopping: a classification framework and literature review , 2015 .

[9]  B. Kamaladevi Customer Experience Management in Retailing , 2009 .

[10]  Jacek M. Zurada,et al.  Big Data-driven Value Creation for Organizations , 2019, HICSS.

[11]  Takao Terano,et al.  AnalyzingIn-store Shopping Paths from Indirect Observation with RFIDTags Communication Data , 2014 .

[12]  J. Hagberg,et al.  The digitalization of retailing: an exploratory framework , 2016 .

[13]  Shih-Chun Chou,et al.  Customer's Flow Analysis in Physical Retail Store , 2015 .

[14]  Milan Jocevski Blurring the Lines between Physical and Digital Spaces: Business Model Innovation in Retailing , 2020, California Management Review.

[15]  L. Lim,et al.  Relative effects of store traffic and customer traffic flow on shopper spending , 2010 .

[16]  K. Basso,et al.  Remodelling the retail store for better sales performance , 2018, International Journal of Retail & Distribution Management.

[17]  Yongli Ren,et al.  A new approach for indoor customer tracking based on a single Wi-Fi connection , 2014, 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN).

[18]  Bikrant Kesari,et al.  Satisfaction of mall shoppers: A study on perceived utilitarian and hedonic shopping values , 2016 .

[19]  Ufuk Celikkan,et al.  Capturing Supermarket Shopper Behavior Using SmartBasket , 2011, ICDIPC.

[20]  John Mingers,et al.  The paucity of multimethod research: a review of the information systems literature , 2003, Inf. Syst. J..

[21]  Mithu Bhattacharya,et al.  A conceptual framework of RFID adoption in retail using Rogers stage model , 2015, Bus. Process. Manag. J..

[22]  Yanqing Duan,et al.  E-commerce and Business Analytics: A Literature Review , 2019, ICDEc.

[23]  Bernard Fertil,et al.  Tracking multiple persons under partial and global occlusions: Application to customers' behavior analysis , 2016, Pattern Recognit. Lett..

[24]  Ju-Young M. Kang,et al.  In-store mobile usage: Downloading and usage intention toward mobile location-based retail apps , 2015, Comput. Hum. Behav..

[25]  A. Minin,et al.  Digital transformation in the banking industry , 2018 .

[26]  Parminder Singh,et al.  Web Analytics: Increasing Website's Usability and Conversion Rate , 2013 .

[27]  Parth H. Pathak,et al.  Analyzing Shopper's Behavior through WiFi Signals , 2015, WPA@MobiSys.

[28]  Peter Jones,et al.  Improving Service: Managing Response Time in Hospitality Operations , 1994 .

[29]  Andreas D. Landmark,et al.  Tracking customer behaviour in fashion retail using RFID , 2017 .

[30]  Basar Oztaysi,et al.  Analysis of Frequent Visitor Patterns in a Shopping Mall , 2019, Lecture Notes in Management and Industrial Engineering.

[31]  S. Kopp,et al.  Privacy and RFID Technology: A Review of Regulatory Efforts , 2017 .

[32]  Jayashankar M. Swaminathan,et al.  Effect of Traffic on Sales and Conversion Rates of Retail Stores , 2012, Manuf. Serv. Oper. Manag..

[33]  Eric T. Bradlow,et al.  Testing Behavioral Hypotheses Using an Integrated Model of Grocery Store Shopping Path and Purchase Behavior , 2009 .

[34]  Roberto Pierdicca,et al.  Robust and affordable retail customer profiling by vision and radio beacon sensor fusion , 2016, Pattern Recognit. Lett..

[35]  Johannes Schöning,et al.  The path-to-purchase is paved with digital opportunities: An inventory of shopper-oriented retail technologies , 2017 .

[36]  Ville Huotari Depth camera based customer behaviour analysis for retail , 2015 .

[37]  Stephen Dovers,et al.  Integrative research in the university context: Centre for Resource and Environmental Studies, the Australian National University , 2005 .

[38]  Ingrid Poncin,et al.  The impact of "e-atmospherics" on physical stores , 2014 .

[39]  G. Lilien,et al.  Do Retailers Benefit from Deploying Customer Analytics , 2014 .

[40]  E. Pantano,et al.  Who is innovating? An exploratory research of digital technologies diffusion in retail industry , 2019, Journal of Retailing and Consumer Services.

[41]  D. Chaffey,et al.  From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics , 2012 .

[42]  K. Barriball,et al.  Collecting data using a semi-structured interview: a discussion paper. , 1994, Journal of advanced nursing.

[43]  Katsutoshi Yada,et al.  Relation between Stay-Time and Purchase Probability Based on RFID Data in a Japanese Supermarket , 2010, KES.

[44]  S. Deshmukh,et al.  Vendor Selection Using Interpretive Structural Modelling (ISM) , 1994 .

[45]  M. Ratchford,et al.  Development and Validation of the Technology Adoption Propensity (TAP) Index , 2011 .

[46]  Anna Nagyova,et al.  How to Build Manual for Key Performance Indicators – KPI , 2009 .

[47]  Zan Li,et al.  A real-time robust indoor tracking system in smartphones , 2018, Comput. Commun..

[48]  Nobuhiko Nishio,et al.  Statistical analysis of actual number of pedestrians for Wi-Fi packet-based pedestrian flow sensing , 2015, UbiComp/ISWC Adjunct.

[49]  David P. Oulton,et al.  New Insights into Retail Space and Format Planning from Customer Tracking Data , 2002 .

[50]  H. Timmermans,et al.  What is smart for retailing , 2014 .

[52]  Howard Hao-Chun Chuang,et al.  Traffic-Based Labor Planning in Retail Stores , 2015 .

[53]  Songyee Hur,et al.  Non-traditional marketplaces in the retail apocalypse: investigating consumers' buying behaviours , 2020 .

[54]  Yulia Vakulenko,et al.  Customer value in self-service kiosks: a systematic literature review , 2018 .

[55]  J. Rowley Conducting research interviews , 2012 .

[56]  Gopal Das,et al.  Consumer emotions: Determinants and outcomes in a shopping mall , 2017 .

[57]  Chung Yim Edward Yiu,et al.  Buyers-to-shoppers ratio of shopping malls: A probit study in Hong Kong , 2010 .

[58]  Luis Miguel Bergasa,et al.  Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls , 2015, Expert Syst. Appl..

[59]  Udo Wagner,et al.  Happy grocery shopper: The creation of positive emotions through affective digital signage content , 2017 .

[60]  Yogesh Kumar Dwivedi,et al.  Investigating the Research Approaches for Examining Technology Adoption Issues , 2005 .

[61]  Jürgen Anke,et al.  Towards the Omni-Channel: Beacon-Based Services in Retail , 2016, BIS.

[62]  N. Omar,et al.  Exploring the influence of store attributes on customer experience and customer engagement , 2017 .

[63]  Michael Veale,et al.  Eavesdropping Whilst You're Shopping: Balancing Personalisation and Privacy in Connected Retail Spaces , 2018, IoT 2018.

[64]  Francesco Ciampi,et al.  Big data for business management in the retail industry , 2019, Management Decision.

[65]  Steven Furnell,et al.  A practical evaluation of Web analytics , 2004, Internet Res..

[66]  Katri Kerem,et al.  Perceived Intrusiveness of Personalized Marketing , 2018, Bled eConference.

[67]  Martin Meißner,et al.  Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments , 2020, Inf. Syst. Res..

[68]  Ava Farshidi The New Retail Experience and Its Unaddressed Privacy Concerns: How Rfid and Mobile Location Analytics Are Collecting Customer Information , 2016 .

[69]  Angela Roth,et al.  Development of a classification framework for technology based retail services: a retailers’ perspective , 2020 .

[70]  Orlando Troisi,et al.  Growth hacking: Insights on data-driven decision-making from three firms , 2019 .

[71]  Kelin Li,et al.  Visual analysis of retailing store location selection , 2019, Journal of Visualization.

[72]  Rafaqut Kazmi,et al.  An Intelligent Data Analytics based Model Driven Recommendation System , 2019, J. Univers. Comput. Sci..

[73]  Nico Van de Weghe,et al.  Bluetooth tracking of humans in an indoor environment: An application to shopping mall visits , 2017 .

[74]  R. Dholakia,et al.  Factors Driving Consumer Intention to Shop Online: An Empirical Investigation , 2003 .

[75]  Ashish Bhaskar,et al.  Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses , 2015 .

[76]  Federico Alvarez,et al.  Improving retail efficiency through sensing technologies: A survey , 2016, Pattern Recognit. Lett..

[77]  Roberto Pierdicca,et al.  Low cost embedded system for increasing retail environment intelligence , 2015, 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW).

[78]  Hsin-Chieh Wu,et al.  Determinants of RFID adoption intention: Evidence from Taiwanese retail chains , 2010, Inf. Manag..

[79]  Ayan Kumar Das,et al.  A Study on Routing Protocols in Wireless Sensor Network , 2013 .

[80]  Cristina Calvo-Porral,et al.  Pull factors of the shopping malls: an empirical study , 2018 .

[81]  Peter S. Fader,et al.  An Exploratory Look at Supermarket Shopping Paths , 2005 .

[82]  Jonghyuk Kim,et al.  Store layout optimization using indoor positioning system , 2017, Int. J. Distributed Sens. Networks.