ECR Retail Loss

Enabling the Retail Sector to Sell More and Lose Less

Call for Judges: 2024 ECR Retail Risk, Security and Innovation Challenge

March 2024. We are excited to announce the launch of our 2024 Retail Risk, Safety & Security Innovation Challenge and need the help of retail security and risk experts to act as "shark tank / dragons dens" judges. This will involve helping us short-list a longlist of innovations and then joining us at our “shark tank” like online innovation showcase finale on October 23rd, where ten of the very hottest innovations will “pitch” their ideas. AT this meeting, the judges will be expected to ask and then rate each of the ten to reveal the most promising innovations. Being a judge gives you exclusive visibility to a longlist of 100+ innovations, that in theory are all with new ideas / ways of helping you deliver your work plan / business objectives. Getting hold of such a list would normally cost a business thousands but as a judge it will be zero cost. Further, what we have learnt from previous challenges, is that the judges gain immense value from the list, stimulating new ideas, and they all tell us that even though it can be a lot to get through, they thoroughly enjoy the rating process, especially if it’s a team effort with colleagues. And yes, we cannot promise judges that all the ideas on the list will be relevant, new, or different but typically find that there will a good few that will inspire and make you go "Wow!" If you would like to be a judge, the next step would be a short interview to understand from you what’s missing and where your unmet needs might be. This meeting will be require just 30 minutes of your time. If you can commit to this short interview and be one of our judges, please email, colin@ecrloss.com

Facial Recognition: 3 Key Insights

Most retail loss prevention leaders are highly familiar with the "watch list" use case for facial recognition in retail. In previous working groups we have heard retailers such as Rite Aid, share how they implemented the technology, as well as academics, lawyers and law enforcement, each providing their perspectives on the use of this technology and the extent that the technology can be compliant with government privacy regulations, such as GDPR. In our annual update, in January 2024, we heard updates from retailers, law enforcement and academia. Below are three key insights and takeaways from this meeting. 1) Implementation in retail is growing: In the meeting, there were over ten retailers, mainly from the UK and USA, who had either deployed to all stores, a number of stores or were finalising their pilot programmes. They reported that the technology was 99% accurate, some noting that their vendors had invested in both machines and human super spotters to ensure that high level of accuracy. That the alerts were made available to a member of staff within 7-10 seconds of the entry into the store of a bad actor on the watch list. These alerts, sent to a mobile device, required trained members of staff to either ask the bad actor to leave the store and / or to smother them in positive customer service. Through the use of this technology, retailers reported a very positive trend in [reduced number of] incidents and shrink, as repeat and prolific offenders are dissuaded from visiting and stealing from stores equipped with facial recognition technology. For one retailer, it has been the single most effective intervention they have ever introduced. To be compliant, stores are required to "advertise" the presence of this technology. To date, and in this meeting, none of the retailers reported any significant levels of complaints from the public on the use of this technology nor any evidence of a reduction in sales. Based on the discussion in the meeting, we expect to see more retailers, especially in USA and UK adopting this technology. However, we are also mindful that retailer momentum on this technology can quickly be curtailed by effective lobbying by privacy groups, as was the case in Australia (click to read the story) Finally, it was made very clear in our discussions that the likelihood of the adoption of this technology in Europe would be very low due to very strong privacy rules. 2) The outsourcing of the Data Controller / the "Watch List" Data Base is a key enabler For some of the retailers deploying face recognition, their technology vendor had also become their Data Controller, responsible for who [the bad actor] gets put into the data base, how long they remain on the data base, and the number of stores that will receive notifications when the bad actor enters their store, tiered based on risk level. The process for data collection and handling was described as follows. First, an incident in a store happens and is observed, this could be the theft of a bottle of wine, a thief grabbing a whole shelf of designer jeans or a member of staff assaulted. Second, a member of staff, or in some cases, the Security Guard, would document that incident via a digital form, look up and then attach the image of the offender captured on the high res facial recognition camera at the entrance of the store. They would then confirm that this statement was a true version of the incident, and then put their signature to the incident. They would then submit the incident into the system and in turn, the vendor, aka, the data controller Third, the data controller at the third party would then review the quality of the incident report form, does it describe in an appropriate way what happened and the offence caused. If it needs to be improved, they would contact the store and edit before submitting as an incident in the system. Fourth, the data controller would then decide proportionality, and set the system up to alert at just that store, stores within 5km, stores within 25km, every store, etc, based on the seriousness of the incident. Finally, the Data Controller would monitor the response rates to alerts sent, and manage the data / images to ensure that the data base remains compliant, for example, managing retention limits. There was discussion on how data bases could be populated with data from multiple retailers but there was some nervousness with this approach. Something for the future perhaps. 3) Acceptance of the technology is growing but it is not evenly spread. For air travellers and retail associates working in distribution centres, facial recognition technology has been a huge help to their lives. Click here to read story on the use of facial recognition in airports. The same level of positive acceptance cannot be said about the use of facial recognition in retail stores. In some countries, including most of Europe, the use of the technology is now banned, with seemingly no lobbies from retailers or law enforcement to adopt. In North America and Australia, the use of the technology is in the balance, the recent Rite Aid case and their "ban" on using the technology has probably slowed down the adoption and increased the nervousness of top retail leaders who were considering its use. Click here for the Rite Aid news story. Meanwhile in the UK, there is a stronger appetite for facial recognition from law enforcement and government in support of trials and adoption, despite a strong "anti-big brother" lobby. However, the ongoing concerns around privacy are likely to slow down the deployment of this technology in retail, especially for the larger UK retailers. The working group will revisit the use of the technology and updated results from those retailers who have deployed store wide on Jan 15th 2025. To register, click on the below invitation. In the meantime, and for some additional context, please click to view the quick video recap of the key findings from the meeting with Professor Beck.

New Research: Retail Risk Models: Exploring Current Practices

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 2nd, 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 colin@ecrloss.com

Three things we have learnt about the problem of "unknown items" at the self checkout.

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 Steps 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.

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adidas
albert
asda
auchan
best buy
carrefour
coles
desiqual
dollar general
duracell
esselunga
foot locker
gap
ikea
john lewis
kroger
lidl
lowes
m&s
meijer
nike
p&g
primark
river island
sainsburys
sonae
starbucks
target
tesco
walmart
whole foods

FOCUS AREAS

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.