ECR Retail Loss
Enabling the Retail Sector to Sell More and Lose Less
Overview Retail crime and risk is not evenly distributed across time or by location. As such, most retailers operate with some form of risk model to identify relative risk and vulnerability across their estate. Such risk models might seek to capture and analyse shrink, incidents of violence and aggression, criminal damage, as well as other factors that could signal vulnerability e.g. under staffing / vacancies. The level of sophistication of these models, how they are used, and who has responsibility for them is currently not known. A well-designed risk model can bring multiple benefits, including: · informing investment decisions to allocate finite security resource and equipment (including NOT providing additional measures to stores that do not register as high risk) · informing operational decisions e.g., staffing, opening hours, lone worker shifts · prioritising stores for the installation of new security systems (and de-prioritising others) · as a litigation defence i.e., foreseeability Despite the range of possible benefits, a well-designed - and used - risk model can bring to a business, there does not appear to be any standardised best practice or even established knowledge about how to construct, review and use a risk model in the retail sector. There are multiple approaches that have been developed in the retail sector - from third-party designed and managed models to simple internal risk registers that are manually updated -but there is little awareness of how risk models are created and managed. This new research offers multiple benefits to the industry by shining a light on how the retail sector is using data to inform decision-making and security strategies. The project will seek to understand how risk models are constructed (identifying areas of commonality and difference between businesses), how they are used and by whom, who has operational responsibility for managing the model, and how it is reviewed and validated. At a time that crime and in-store violence appear to be increasing across many countries, the project will provide a better understanding of how risk models can contribute to data-informed security strategies and solutions. Aims and Objectives The research will be divided into two parts. Given the range of approaches taken and the lack of best practice awareness across the sector, Part A proposes to answer a wide range of questions categorised by who, why, when, what, and how? It is intended that this part of the project will commence first. The aims and objectives of Part A ‘Scoping the Retail Model Landscape’ are threefold: Objective 1: To understand how risk modelling is used across multiple industries. The objective is to establish the current state of the art in terms of risk modelling. By looking to other sectors (e.g. insurance and banking) relevant principles for risk modelling and uses will be gathered. This will be used to inform the future development and direction that risk modelling in the retail sector can take. Objective 2: To gain insight into how retailers construct and build risk models and who has responsibility for this activity. There are different approaches being taken. The project will capture this difference and seek to understand the rationale for decisions about what data is included (or not), who has responsibility for constructing the model, and what the business case is for investing in the development and maintenance of a risk model and can this be objectively and tangibly demonstrated? It will also gain insight into the review process and how frequently this is undertaken (e.g., quarterly, annually, or more frequently / ad hoc) Objective 3: to understand how risk models are being used, by which business functions and with what impact. There are different approaches to using risk models. The project will collate information on the range of operational decisions that risk models are used to inform in the retail sector e.g., guarding allocation, opening hours, acquisitions and new sites, and staffing. We will also attempt to gain information on the impact of the model and the degree of confidence with which it is used. We will gather examples of any real life ‘tests’ of its robustness e.g., legal challenges. Part B will seek to explore the risk model of up to three retailers to try to better understand which variables have the strongest explanatory and predictive power. Part B is entirely contingent on being able to access the data – and of it being of a useable quality to run the required analysis. Methodology As is always the case with an exploratory project of this nature, multiple methods yield the best results. To generate as full a picture as possible in relation to the current landscape of risk modelling, the project has four main elements: 1. Focus group with retailers The project will start with an online ‘focus group’ discussion with invited retail representatives. This initial meeting scheduled for May 1st 2024 will be used to shape the project i.e. understanding levels of maturity in risk modelling, gaps in knowledge and understanding, and to provide input on key literature to explore. 2. Literature review of risk modelling in different sectors. The project will be supported by a comprehensive search of relevant literature and publications from multiple sectors. This will provide background to how different industries approach risk analysis and identify any key learnings for the retail sector. The review will also inform the development of the survey tool. 3. Industry Survey A survey of retailers will be conducted to answer the who, why, what, when, and how questions (see Appendix A). The survey will be hosted online using Qualtrics software and distributed to ECR’s membership. Questions will explore which companies are using a risk model, whether it is internally built or third-party, the variables that it incorporates and to what degree it has been tested for accuracy and reliability (either validated in house or scrutinised through litigation proceedings). It will also explore whether multiple different models exist in the same business for different functions, the ways in which the model is used – and by whom – and with what authority to act on the data. We will seek to understand what the model looks like, its functionality, cost, and ease of use. The survey will use branching and logic techniques to ensure an efficient pathway through the survey questions (i.e. only displaying relevant questions to participants). This will reduce the number of participants who do not finish completing the survey. The survey will provide an option for respondents to self-select to be interviewed to provide a fuller picture of the strengths and weaknesses of their modelling approach. 4. A) Interviews – Heads of Security / Loss Prevention Interviews with approximately 15-20 Heads of Security / Loss Prevention will be conducted to provide a more contextual and qualitative understanding of the evolution of the business’ risk model, its use/s, strengths and weaknesses. Interviews will include representation from different verticals and across multiple countries. If possible, industry events e.g. RILA will be used to arrange some in-person interviews. B) Interviews – non-retail sector representatives Interviews will also be sought with representatives from other sectors that are potentially more advanced in the development and use of risk modelling. The initial focus group will request suggestions for relevant people / industries. Interviews will be conducted using online meeting platforms (e.g. Zoom or MS Teams). Interviews will be recorded (with permission) and fully transcribed using the automated transcribing function (manually checked and cleaned). This will provide for full but anonymised verbatim quotes to be included in the report. Outputs The outputs will take the form of a report and a maturity benchmarking tool. Report: The report will provide a detailed analysis of findings from the survey and interviews. While sophisticated in its analysis, it will be accessible in “plain English” and engaging to read with visual charts and tables for numerical data generated from the survey and insightful quotes from the interviews with LP/AP Leads. Maturity benchmarking tool: Drawing upon the findings from the study, the tool will be developed to enable businesses to assess the level of maturity that their risk modelling has achieved. It will incorporate, as far as is possible from the findings, industry good practice and level of sophistication. The dissemination strategy will include an ECR meeting (invitees only) and webinar (open). Opportunities will also be taken to disseminate more broadly via trade publications (e.g. LPM and The Grocer), industry conferences and trade shows. Timescale The research will take approximately 8 months to complete. Anticipating a early May start date, the project will be completed by the end of November 2024. Research Leader The research will be undertaken by Emmeline Taylor, who is Professor in Criminology at City, University of London. She has more than 15 years of experience in research across the private, public and academic sectors. Professor Taylor has worked at world-leading institutions on three continents (in England, Singapore and Australia) and is now London based. She has retained strong global links and continues to work with international clients. She has worked with the police, probation, prisons, national government and private business to develop projects to address some of the most challenging societal issues including violent crime, antisocial behaviour, retail security, burglary, and the responsible use of surveillance technologies. Next Steps The research will kick off on May 1st, if you are interested in learning more and possibly participating, please register for the meeting. If you have other questions or want to discuss this research, please email Colin Peacock at email@example.com
The working group met in February to discuss the problem of unknown items presented at the self-checkout. Over sixty retailers joined the discussion, sharing their perspectives on the problem itself and their responses. Here are three things we learnt: 1) We really have two problems [for the price of one!] :) The first problem occurs when the barcode is scanned / read but when the system "looks it up" there is no item or item & price in the system. This problem is also called item "Not on File" Reasons that the item might not be on file could be that the item is new to the assortment, or it could have been discontinued, or it is not intended to be sold in this store, or is a barcode of a local supplier not associated with a master product. This list of reasons is not meant to be exhaustive. The second problem is more simply that the barcode cannot be read. Poor packaging quality distorts the bar code quality, curved edges, and condensation are possible causes. Other causes might be that the scanner is trying to, and failing to read another code, this could be the code used by supply chain for tracking or more commonly as we learnt on the call, trying to read a QR code. Again, this list of reasons is not meant to be exhaustive. 2) Retailers are building tools, systems and feedback loops to monitor and correct problems. We heard in the meeting that retailers were creating regular reports and tracking systems to share with others and to quickly correct problem SKU's, a lot of the fixes were owned by Commercial, who would be responsible for addressing the problem with the vendor. To this end, quite a few retailers had created a weekly report of problem items that was sent to the commercial teams. One retailer shared that they had created a "most manually keyed in" report for the commercial team. Another retailer shared that they had built in code to link the alternative codes to EAN codes. Other retailers had found ways to work around the reading of QR codes but often retrospectively. 3) Unknown items create friction and the opportunity for loss When an item cannot be scanned / read or is "Not on File" retailers would either stop the transaction, or ask shoppers to put the items to one side. Both intervention approaches add time to the shopper journey, slowing down throughput. Most retailers were able to quantify the time for SCO hosts to resolve, which was between 12 and 24 seconds. While few retailers in the meeting shared the scale of the problem, prior to the meeting, some data was shared with ECR on the number of unknown items found as a percentage of all items scanned for ten retailers. This small benchmark study of ten retailers suggested that unknown items represented 0.4% of all items presented at the self checkout (i.e. 4 items per 1000 scans) If all these unknown item problems could be resolved by the SCO host, the problem would remain one of productivity and possible lost margin. However, what if the SCO host did not resolve? Would the items stay with the store or would the shopper walk away with them and cause a loss to the business? One retailer shared that they found that 10 - 14% of unknown items were not being added to the transaction. Next Step It was clear from the number of registrations and interest in the meeting, that this problem is a cause of concern, adding friction to the shopping experience, draining productivity and potentially creating loss. We will "anniversary" and check in on progress made on this problem. Click below to register. In the meantime, we will investigate undertaking some new research to identify a top 25 list of items that are not scanning. Finally, for some extra context, click below to see the recap of the meeting and the takeaways from Professor Beck.
Theft of fuel from petrol stations, or drivers with no means of payments [NMOP] are both seen as too big a risk for many forecourt retailers around the world, for example, in USA, most retailers require payment before the customer puts fuel in their vehicle. However, an alternative shopper journey, that would be more commonly found in the UK and Australia, is that the shopper fills the car with fuel first and pays for the fuel inside, after they may have made some additional purchases inside the store. To address the increased risk of "drive offs" and NMOP, retailers in the working group shared their plans to invest in ANPR & Video Analytics technology, the adoption of simpler digital reporting systems and increased staff training & engagement. Summarised below are the five practices or habits they were each adopting to help them sell more inside the shop and lose less fuel at the pump. 1) They were increasing visibility to and the relevance of fuel loss to the business: Acquiring good data on the extent of the problem of fuel loss is not easy, often the data sits in multiple parts of the organisation. That said, one retailer who was able to get the data, shared that their losses of fuel equated to 0.1% of total fuel sales. A small percentage but adding up to €€€ millions for most of the retailers in the group. For any business, a clearly articulated size of the prize is the key to engaging top management and securing the investment needed to support any loss prevention programme. 2) They were adopting simpler, faster digital reporting systems, to support civil recovery efforts. Store associates and managers are busy, acquiring accurate incident reporting data has forever been problematic. However, all retailers in this meeting shared strong progress in moving to digital reporting, one retailer who had built their own system, shared how they had increased reporting rates through the use of an app on a mobile device that had drastically reduced the time it takes to report "drive off" or NMOP incidents. They did this by making it easy to import video images, through the use of drop down menus and "look up" functionalities. Other retailers had invested in third party systems and again reported higher levels of reporting compliance. With robust digital reporting, the true scale of the problem becomes more evident and civil recovery programmes and their success rates can improve significantly. All retailers reported strong improvements in recovery rates, most were over 50%. Further improvements in reporting can be expected in the UK if government agencies became more supportive of digital, real time access to vehicle ownership details and the make / model of cars associated with licence plates [to help identify clone / fake plates] 3) They were investing in ANPR / LPR technology to block repeat offenders. ANPR camera technology can help support the loss prevention strategy in two ways. Firstly, it can help with reporting, with the licence plate data directly integrated into the reporting systems. Secondly, the licence plates of those vehicles who "drive off "or who have NMOP can be added to a data base of "watch list" licence plates. Access to this list can be for just one location or can be shared across the retailers network, When any licence plate on the "watch list" appears on the forecourt, an alert can be sent to the store associate to not authorise the pump and thus deny repeat offenders access to fuel. Retailers shared the number of vehicles being blocked on a daily and weekly basis. One retailer for example reported blocking 24 vehicles per site per week. With the average drive off value of £50, this represented a potential saving per site of £1200 per week. One final point, ANPR also helps improve the integrity and reliability of reporting, With ANPR technology, the records of any licence plate in the system can no longer be subject to error or mistaken entries. This can help prevent collusion. By way of example, one forecourt retailer reported that the installation of ANPR technology had reduced their drive offs by 60% in the first month of installation and yet, the system was covert and only the retailers staff knew about its installation. 4) They recognised the need to educate and engage store leaders and associates The bedrock to any robust loss prevention programme is the engagement of those in stores, the leaders and the store associates. To them, loss has to matter and be a problem that they feel they can control. To help them win, the business needs to give to stores the right tools, technology and the recognition when improvements are delivered. These higher levels of engagement ensure that stores become much harder targets for the bad actors. This point was bought to life by one retailer who shared that they have a new type of thief operating in the forecourt stores where self checkouts had been installed. This so called "dream off" thief will fill their vehicle with fuel, spend some time browsing in the store and then buy something at the self-checkout and then walk out of the store without paying for the fuel. If caught, the bad actor would claim they had "lost their mind" and forgot to pay. For the retailer to prove intent will be hard as the bad actor will simply argue that the confusion was caused by the presence of their self-checkout machines. Thus it is critical that store associates and managers in the stores are on their guard all the time and be aware of this risk, and others, looking to prevent it before it happens. 5) They partnered with law enforcement while also recognising their capacity limitations. It is no secret that law enforcement are not entirely sympathetic with forecourt retailers who encourage shoppers to fuel first and pay later. They believe that retailers are encouraging criminogenic behaviours. Law enforcement see that retailers have an easy solution to this problem, simply adopt the pay before you pump model. It was therefore not a surprise to learn in the meeting that law enforcement, in many countries, will not prioritise this crime, especially in the context of the rising crime in retail stores and other priorities that draw on their reduced and limited resources. Next Steps The group will meet again in January 2025, where we hope to learn more about the progress being delivered against these habits, to hear about new video analytics solutions and the ways in which the data can and is being shared. In the meantime, the UK retailers can expect an update on digital access to vehicle details. Click below to register for the next meeting. For further perspective on the latest trends in forecourt loss prevention, please click to see the meeting recap video with Professor Beck.
Through the systematic investigation of refund fraud profiles, consumer behaviour, and retail counter-fraud measures, this research project aims to generate new knowledge on the thorny issue of refund fraud. Importance of the project According to a recent estimate by the National Retail Federation, fraudulent returns account for ten percent of all returns in the United States, representing 1.6% of total sales, which amounts to approximately USD $85 billion annually. The recent surge in online shopping adoption, largely driven by the pandemic, has seen an accompanying rise in the trend of bracketing, where customers order multiple variations of an item with the intention of returning those that don't fit their needs. This behaviour has necessitated a more permissive returns policy from retailers. Concurrently, there is a growing commitment among these retailers to an omnichannel business model, integrating their online and offline operations to provide a seamless customer experience. This evolving retail landscape underscores the fact that returns will remain a significant aspect of the customer experience. Consequently, there is a substantial benefit for retailers in developing and implementing effective processes to detect and manage return fraud, ensuring the sustainability and profitability of their operations in this new era of retail. In March, Beck published a framework for classifying e-commerce loss. Our research project would make a valuable contribution to the 'Next Steps' identified by Beck for demonstrating that his typology has real-world application. By filling a critical gap in the existing literature on the nature and degree of retail fraud, our research will contribute to more effective action to address the abuse of returns policies. Our findings will benefit retailers, solution providers, and researchers, and provide a significant contribution to retail loss prevention. Overall aims and objectives of the research Retailers face an unenviable challenge in balancing customer needs and protecting their business and inventory from exploitation by offenders. Nowhere is this balancing act more acute than the refund process. Moreover, thanks to the growth of e-commerce, providing a seamless and stress-free refund process is now considered table stakes, if not a competitive advantage. However, this growth has accelerated the issue of refund fraud, an age-old problem that has evolved to capitalise on permissive return policies. A pivotal study by Zhang et al delved into this issue, analysing refund fraud's mechanisms and potential deterrents through expert interviews, ultimately proposing a comprehensive framework for its management. This research notably distinguished types of fraud, their underlying motives, and preventative strategies across different retail channels (online, in-store, and omnichannel). Despite these advancements, there remain critical areas unexplored. Our study aims to enhance the groundwork laid by Zhang et al. by incorporating broader perspectives and introducing novel analytical dimensions. We intend to integrate insights from both customers and fraud perpetrators to enrich the understanding of refund fraud typologies. We aim to generate a holistic understanding of refund fraud through the following objectives: 1. Investigate the operational tactics and skills employed by professional refund fraud services. 2. Examine customer attitudes towards refunds and their propensity towards fraudulent behaviours. 3. Formulate detailed narratives of various refund fraud scenarios, alongside potential retail countermeasures. 4. Quantitatively assess the impact, profitability, and practicality of different types of refund fraud, and critically evaluate the effectiveness of standard retail countermeasures. Methods, approach and activities SCOPE We aim to build on the findings of the recent Zhang et al. study, which outlines ten distinct types of return fraud across various retail formats, including physical stores, online businesses, and omnichannel operations. However, given the complexity and depth of these types, addressing all ten within the confines of a single research study may not be feasible. To ensure thoroughness and depth in our analysis, we believe it is crucial to narrow our focus. Our decision to concentrate specifically on omnichannel businesses is pragmatic, as bad actors often intentionally exploit the intricacies of such systems. Moreover, many major retailers now adopt an omnichannel model. Limiting our scope to a single channel, such as online retail, would inadvertently overlook a broad spectrum of potentially fraudulent activities spanning multiple channels. To determine the specific types of return fraud to focus on, we will adopt a "bottoms-up" approach, leveraging insights from two key research components: a customer perception survey and an ethnographic analysis. The customer perception survey will play a pivotal role in understanding public awareness and perceptions regarding the ease of executing various types of return fraud. This survey will provide invaluable data on how consumers view the vulnerability of different return processes to fraudulent activities. Simultaneously, our ethnographic analysis will shed light on the prevalence of different 'refund as a service' offerings, providing a detailed understanding of how these frauds are operationalised in real-world settings. By examining the intricacies of these practices, we can gain a comprehensive view of the current landscape of return fraud. The insights gleaned from both customer and offender perspectives will be instrumental in guiding our research focus. By synthesising these perspectives, we will prioritise a subset of return fraud types for further investigation in our expert consensus exercise. This methodical approach ensures that our research is not only grounded in empirical data but also reflects the realities of the retail environment. In essence, our project employs a process-driven approach to inform our research scope and direction. This approach is designed to maximize the relevance and impact of our findings, ensuring that our research contributes meaningfully to the understanding and mitigation of return fraud in omnichannel retail settings. Regarding market coverage, our research will primarily focus on UK, North America and Australia. This choice is largely influenced by the linguistic challenges associated with exploring customer and offender perspectives in diverse languages. While we acknowledge that some retailers may operate beyond these markets, we do not foresee this extending beyond the scope of our study to adversely affect our findings. This approach ensures a more manageable and focused investigation, allowing for a thorough analysis within these specific market contexts. APPROACH The investigation will involve a mixed-methods approach, including interviews, surveys, and qualitative analysis. This approach will enable the project to gather rich and nuanced data, providing a detailed understanding of the problem. Ethnographic appraisal of a refund fraud community. Ethnographic research is evolving to address the challenges posed by crime's migration into digital environments. 'Rapid' ethnographic methods, such as 'focused ethnography' and 'rapid appraisals', are increasingly used in fields like health emergencies to gather data quickly and cost-effectively. These methodologies are particularly pertinent for studying online illicit markets, known for their rapid innovation, and changing practices. We will apply the ‘rapid’ digital ethnographic approach to study the burgeoning illicit market of online retailer fraud services. Following the development of the protocol we will apply for ethics approval via Griffith University’s Human Research Ethics. The ‘rapid’ ethnography will generate key insights into the online markets for online retailer fraud services. This will help identify the characteristics of distributors, the types of products distributed (e.g. refunding services), and the archival and documenting of new cybercrime communities. These insights will ultimately be instrumental in informing the prevention of activities. Customer perception and willingness to defraud. We will design an online survey to gather insights from retail customers regarding their experiences with returning goods and their understanding and attitude toward refund fraud. The survey will explore customers' perspectives on various factors related to refund fraud, including their perception of the risks involved, potential rewards, and the likely sanctions they anticipate from engaging in such fraudulent activities. We will also seek to understand the effectiveness of interventions that could deter individuals from abusing refund policies. We intend to employ the services of Prolific, a survey firm renowned for its exclusive focus on academic research surveys for participant recruitment. Our team has previously collaborated with Prolific, yielding exceptional results in survey-based research. Prolific's unique positioning in the market stems from its commitment to hosting solely academic research surveys, which fosters a panel of participants who are genuinely engaged and interested in the surveys they complete. Furthermore, Prolific ensures the reliability and quality of its participants through rigorous vetting processes, including bank-grade Know Your Customer (KYC) checks. This meticulous approach to participant selection is crucial in maintaining the integrity of our research. Additionally, Prolific's capability to apply over 300 different filters for panel creation allows for precise and targeted recruitment of survey participants. This level of specificity is instrumental in ensuring that our survey reaches the most relevant and appropriate audience, thereby enhancing the validity and applicability of our research findings. Literature review of refund fraud in retail. The first phase will involve a comprehensive literature review strategy, guided by the methodology outlined in Zhang et al. Our primary objective is to delve into both academic and grey literature to identify and analyse additional studies focusing on refund fraud. This approach is designed to augment and extend the insights revealed by Zhang et al, rather than merely replicating previous work. Our literature review will encompass a range of perspectives to ensure a holistic understanding of refund fraud. This includes examining literature from the viewpoints of both customers and offenders, providing a dual perspective that is often lacking in traditional analyses. By incorporating these diverse viewpoints, we aim to gain a more nuanced understanding of the motivations, methods, and impacts associated with refund fraud. Furthermore, a significant focus of our literature search will be on the commission process of refund fraud. Understanding the mechanics and methodologies of how refund fraud is perpetrated is essential for the subsequent phases of our project. This detailed exploration will enable us to identify gaps in current knowledge and practice and inform the development of more effective prevention and intervention strategies. Expert consensus exercise. The consensus exercise proposed in this phase of our research is a modified type of "red teaming", a strategy used to improve insight by assuming an adversarial role or perspective. It involves critically examining a problem or situation from the viewpoint of a competitor or adversary, to identify potential weaknesses, threats, or unforeseen challenges. This approach is particularly beneficial in security and risk management contexts, as it allows for a more comprehensive understanding of vulnerabilities and the development of robust countermeasures. Our project's consensus exercise will be conducted in two distinct parts: 1. Development of Prevention Strategies: In the first part of the exercise, we will present our experts with a series of crime scripts that detail various refund fraud scenarios. These scripts will be primarily sourced from the Zhang et al. study, supplemented with insights from both the customer perception survey and ethnographic analysis of the refund fraud community. The experts, working in groups, will be tasked with developing prevention strategies to counteract these refund fraud scenarios. This collaborative approach not only leverages the collective expertise of the participants but also encourages innovative and diverse solutions. 2. Evaluation of Prevention Strategies: The second part of the exercise involves the experts assessing the prevention strategies developed in the first part. They will rate these strategies across four dimensions: (i) potential harm to a business if the fraud is successful; (ii) anticipated profit for the offender if the fraud is successful; (iii) feasibility of the prevention strategy; and (iv) the defeatability of the prevention strategy. To ensure a comprehensive evaluation, groups will be assigned different scripts from those they worked on in the first part. This multi-dimensional rating system is designed to provide nuanced insights into the effectiveness and practicality of each strategy, highlighting potential areas for further research and development. We plan to hold a one-day workshop in an Australian city and an online workshop series for UK experts. The online format for UK participants will likely span several days to accommodate different time zones and to avoid screen fatigue. The experts will comprise retailers, researchers, and technology experts. Additionally, we anticipate conducting a series of one-on-one interviews with solution providers. These interviews will be crucial in capturing the technical aspects of refund fraud and its detection, providing valuable insights into the current state of practice and potential areas for technological innovation. Through this consensus exercise, we aim to gain a deeper understanding of the perspectives of retailers, the realities, and constraints they face in thwarting refund fraud, and the practicality of various prevention strategies. This comprehensive approach will significantly contribute to our research objectives, providing a well-rounded understanding of the complexities involved in combating refund fraud. Outcomes, outputs and dissemination Project Report: A substantive report, outlining the size of the problem, methodology, sources used, participant recruitment and analysis. We would provide guidance and practical strategies to address refund fraud. The writing style will be accessible to professionals working in retail but devoid of academic jargon. Academic article: To increase exposure to the issues and feasible solutions, manuscripts targeted at an academic audience will be prepared. Workshop presentation: The researcher would participate in a recorded online workshop to present the research and discuss significant findings and mitigation strategies. Staffing Griffith University’s Professor Michael Townsley is a highly respected and accomplished criminology researcher with over 20 years of experience in the discipline. He is a quantitative researcher focusing on spatial criminology and has published extensively in international journals on a diverse range of topics. He is the only academic on the Profit Protection Future Forum steering committee, Australia and New Zealand’s peak body for loss prevention professionals. Associate Professor Joe Clare is based in the Law School at The University of Western Australia and is the author of 52 journal articles and book chapters. His research focuses on policing, applied evaluations, offender decision-making, and academic integrity and he is a member of the ANZ Society of Criminology Policing executive committee. Dr Andrew Childs is a Lecturer in the School of Criminology and Criminal Justice at Griffith University. His research primarily focuses on the evolution of illicit online marketplaces, the nature of trust and risk in online spaces, and innovative digital research methods for studying offenders. More broadly his work on digital culture has been featured in various outlets such as The Conversation and The Interpreter. Next Steps The research was officially launched in February, with deliverables due at the end at the end of 2024. If you would like to participate, please email Colin Peacock at firstname.lastname@example.org References.  National Retail Federation (2022). 2022 Consumer Returns in the Retail Industry. National Retail Federation and Appriss, December 2022.  Beck. A (2023) Developing a Framework for Understanding and Measuring E-commerce Losses in Retailing. ECR Retail Loss  Zhang, D., Frei, R., Senyo, P., Bayer, S., Gerding, E., Wills, G., and Beck, A. (2023). “Understanding Fraudulent Returns and Mitigation strategies in Multichannel Retailing”. Journal of Retailing and Consumer Services, 70:103-145.  Johnson, G, A, & Vindrola-Padros, C. (2017). Rapid qualitative research methods during complex health emergencies: A systematic review of the literature. Social Science & Medicine, 189, 63-75.  Pink, S. & Morgan, J. (2013). Short-term ethnography: intense routes to knowing. Symbolic Interaction, 36(3), 351-361  Sangaramoorthy, T. & Kroeger, K. A. (2020). Rapid ethnographic assessments: a practical approach and toolkit for collaborative community research. Routledge.  Quinn, L., Clare, J., Lindley, J., and Morgan, F. (2022). Demand for and disposal of stolen goods in legitimate second-hand online markets: an explorative online survey. Global Crime, 24(1):19–48. DOI: 10.1080/17440572.2022.2142781.
The research priorities are determined by its members – they drive the agenda to ensure ECR delivers research that meets the need of the industry bringing new insights, tools and techniques that enables retailers to sell more and lose less.
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