A theoretical model for testing new product sales velocity at small format retail stores

The present study proposes a theoretical model to test sales velocity for new products introduced in small format retail stores. The model is designed to distinguish fast moving products within a relatively short period. Under the proposed model, the sales of a newly introduced product are monitored for a prespecified period T, e.g., one week, and if the number of items sold over T is equal to a prespecified integer k or more, the product is considered a fast moving product and is carried over to the following sales periods. A slow moving product could be quickly replaced with alternative merchandise in order to make best use of shelf space. The paper first presents definitions of fast and slow moving products, and then a proposed sales test policy based on the model is formulated, where the expected loss is to be minimized with respect to the integer k. Numerical examples based on actual data collected from a convenience store in Japan are also presented to illustrate the theoretical underpinnings of the proposed sales test model

Franchisor-Franchisee Relationship And Franchisee Competencies As The Predictors Of Franchisees’ Business Performance In Malaysia Retail Sectors

Industri francais dilihat semakin penting di Malaysia dan ia menyumbang sebanyak sepuluh bilion jualan tahunan runcit di Malaysia(Ismail, 2004). The franchising industry has increased in importance and accounted for more than 10 billion annual sales in Malaysia (Ismail, 2004)

The impact of product, service and in-store environment perceptions on customer satisfaction and behaviour

Much previous research concerning the effects of the in-store experience on customers’ decision-making has been laboratory-based. There is a need for empirical research in a real store context to determine the impact of product, service and in-store environment perceptions on customer satisfaction and behaviour. This study is based on a literature review (Project 1) and a large scale empirical study (Projects 2/3) combining two sources of secondary data from the largest retailer in the UK, Tesco, and their loyalty ‘Clubcard’ provider, Dunnhumby. Data includes customer responses to an online self-completion survey of the customers’ shopping experience combined with customer demographic and behavioural data from a loyalty card programme for the same individual. The total sample comprised n=30,696 Tesco shoppers. The online survey measured aspects of the in-store experience. These items were subjected to factor analysis to identify the influences on the in-store experience with four factors emerging: assortment, retail atmosphere, personalised customer service and checkout customer service. These factors were then matched for each individual with behavioural and demographic data collected via the Tesco Clubcard loyalty program. Regression and sensitivity analyses were then conducted to determine the relative impact of the in-store customer experience dimensions on customer behaviour. Findings include that perceptions of customer service have a strong positive impact on customers’ overall shopping satisfaction and spending behaviour. Perceptions of the in-store environment and product quality/ availability positively influence customer satisfaction but negatively influence the amount of money spent during their shopping trip. Furthermore, personalised customer service has a strong positive impact on spend and overall shopping satisfaction, which also positively influences the number of store visits the week after. However, an increase in shopping satisfaction coming from positive perceptions of the in-store environment and product quality/ availability factors helps to reduce their negative impact on spend week after. A key contribution of this study is to suggest a priority order for investment; retailers should prioritise personalised customer service and checkout customer service, followed by the in-store environment together with product quality and availability. These findings are very important in the context of the many initiatives the majority of retail operators undertake. Many retailers focus on cost-optimisation plans like implementing self-service check outs or easy to operate and clinical in-store environment. This research clearly and solidly shows which approach should be followed and what really matters for customers. That is why the findings are important for both retailers and academics, contributing to and expanding knowledge and practice on the impact of the in-store environment on the customer experience

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making

This dissertation examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision-making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi-method research process. First, an online survey of retail location decision makers across a range of sizes and sub-sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in-depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

Anatomy of an Acquisition: The Challenges of Selling a Privately Held Electronics Manufacturing Company

Sweeny Electronics is a family-owned S Corporation based in St. Paul, Minnesota. The company was started in 1946 by a returning army veteran, Mike Sweeney, and focused on the heating, air quality and cooling markets. The company has survived numerous recessions, market consolidation, and manufacturing challenges in China, and currently is run by the third generation Sweeney family. The current owner and CEO, George Sweeney, is the grandson of the founder and is approaching retirement age. The board of directors has seven members: George Sweeney, his wife Jane and five members of the business community. Under the direction of the CEO, the board has determined that there is no “heir apparent” in the family or in the current management team. They have elected to hire an investment banking firm to position the company for an asset-based sale. Sweeney would like to sell the company for estate planning purposes and allow him to transition to a consulting role with the new owner

Anatomy Of An Acquisition: The Challenges Of Selling A Privately Held Electronics Manufacturing Company

Sweeny Electronics is a family-owned S Corporation based in St. Paul, Minnesota. The company was started in 1946 by a returning army veteran, Frank Sweeney, and focused on the heating, air quality and cooling markets. The company has survived numerous recessions, market consolidations, and manufacturing challenges. The company is currently run by the third generation of the Sweeney family, George Sweeney, who is the current owner and CEO, is approaching retirement age. The board of directors has seven members: George Sweeney, his wife Jane and five members of the business community. Under the direction of the CEO, the board has determined that there is no “heir apparent” in the family or in the current management team. They have elected to hire an investment banking firm to position the company for an asset-based sale. Sweeney would like to sell the company for estate planning purposes and allow him to transition to a consulting role with the new owner