Applications of store loyalty card big data in the location planning process

2021 ◽  
pp. 107-120
Author(s):  
Nick Hood ◽  
Graham Clarke ◽  
Andy Newing ◽  
Tim Rains
Keyword(s):  
Big Data ◽  
Planning Process ◽  
Store Loyalty ◽  
Location Planning ◽  
Loyalty Card

scholarly journals Understanding an Urban Park through Big Data

2019 ◽  
Vol 16 (20) ◽  
pp. 3816 ◽  
Author(s):  
Jisoo Sim ◽  
Patrick Miller
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Planning Process ◽  
Big Data Analytics ◽  
Urban Park ◽  
Social Media Analytics ◽  
Forest Park ◽  
Planning And Design ◽  
Data Source

To meet the needs of park users, planners and designers must know what park users want to do and how they want the park to offer different activities. Big data may help planners and designers gain this knowledge. This study examines how big data collected in an urban park could be used to identify meaningful implications for planning and design. While big data have emerged as a new data source, big data have not become an accepted source of data due to a lack of understanding of big data analytics. By comparing a survey as a traditional data source with big data, this study identifies the strengths and weaknesses of using big data analytics in park planning and design. There are two research questions: (1) what activities do park users want; and (2) how satisfied are users with different activities. The Gyeongui Line Forest Park, which was built on an abandoned railway, was selected as the study site. A total of 177 responses were collected through the onsite survey, and 3703 tweets mentioning the park were collected from Twitter. Results from the survey show that ordinary activities such as walking and taking a rest in the park were the most common. These findings also support existing studies. The results from social media analytics found notable things such as positive tweets about how the railway was turned into a park, and negative tweets about diseases that may occur in the park. Therefore, a survey as traditional data and social media analytics as big data can be complementary methods for the design and planning process.

scholarly journals Cancer Loyalty Card Study (CLOCS): protocol for an observational case–control study focusing on the patient interval in ovarian cancer diagnosis

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e037459
Author(s):  
Hannah R Brewer ◽  
Yasemin Hirst ◽  
Sudha Sundar ◽  
Marc Chadeau-Hyam ◽  
James M Flanagan
Keyword(s):  
Ovarian Cancer ◽  
Cancer Risk ◽  
Cancer Diagnosis ◽  
Case Control Study ◽  
Case Control ◽  
Ovarian Cancer Risk ◽  
Store Loyalty ◽  
Purchase Patterns ◽  
Loyalty Card ◽  
Control Study

IntroductionOvarian cancer is the eighth most common cancer in women worldwide, and about 1 in 5 women with ovarian cancer do not receive treatment, because they are too unwell by the time they are diagnosed. Symptoms of ovarian cancer are non-specific or can be associated with other common conditions, and women experiencing these symptoms have been shown to self-manage them using over-the-counter medication. Results from a recent proof-of-concept study suggest there may be an increase in the purchases of painkillers and indigestion medication 10–12 months before ovarian cancer diagnosis. We propose a case–control study, as part of a larger project called the Cancer Loyalty Card Study (CLOCS), to investigate whether a significant change in medication purchases could be an indication for early signs of ovarian cancer, using data already collected through store loyalty cards.Methods and analysisUsing a retrospective case–control design, we aim to recruit 500 women diagnosed with ovarian cancer (cases) and 500 women without ovarian cancer (controls) in the UK who hold a loyalty card with at least one participating high street retailer. We will use pre-existing loyalty card data to compare past purchase patterns of cases with those of controls. In order to assess ovarian cancer risk in participants and their purchase patterns, we will collect information from participants on ovarian cancer risk factors and clinical data including symptoms experienced before diagnosis from recruited women with ovarian cancer.Ethics and disseminationCLOCS was reviewed and approved by the North West-Greater Manchester South Research Ethics Committee (19/NW/0427). Study outcomes will be disseminated through academic publications, the study website, social media and a report to the research sites that support the study once results are published.Trial registration numberISRCTN 14897082, CPMS 43323, NCT03994653.

scholarly journals Big Data-driven Budgeting and Business Planning

2020 ◽  
Author(s):  
Alessio Faccia
Keyword(s):  
Big Data ◽  
Planning Process ◽  
Business Plan ◽  
Financial Accounting ◽  
Data Driven ◽  
Business Planning ◽  
Standard Process ◽  
Proposed Model ◽  
Almost All

The business planning process can be considered as a strategic phase of any business. Given that the business plan is a management accounting tool, there are countless approaches that can be adopted to prepare it since there is no legal requirement, as opposed to obligations relating to financial accounting. However, in general, every business plan consists of a numerical part (budget) and a narrative part. In this research, the author highlights, on the basis of experiences and commonly used theories, a standard process that can be adaptable to the business plan of any type of activity. The use of big data is highlighted as an essential part of feeding the data of almost all the steps of the budget. The author then manages to determine a generally applicable standard process, indicating all the data necessary to prepare an accurate and reliable business plan. A case study will provide adequate support to the demonstration of the immediate applicability of the proposed model.

scholarly journals Integrated Natural Language Processing and Machine Learning Models for Standardizing Radiotherapy Structure Names

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 120
Author(s):  
Khajamoinuddin Syed ◽  
William Sleeman IV ◽  
Kevin Ivey ◽  
Michael Hagan ◽  
Jatinder Palta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Radiation Therapy ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Planning Process ◽  
Treatment Quality ◽  
Health Administration ◽  
Improvement Programs

The lack of standardized structure names in radiotherapy (RT) data limits interoperability, data sharing, and the ability to perform big data analysis. To standardize radiotherapy structure names, we developed an integrated natural language processing (NLP) and machine learning (ML) based system that can map the physician-given structure names to American Association of Physicists in Medicine (AAPM) Task Group 263 (TG-263) standard names. The dataset consist of 794 prostate and 754 lung cancer patients across the 40 different radiation therapy centers managed by the Veterans Health Administration (VA). Additionally, data from the Radiation Oncology department at Virginia Commonwealth University (VCU) was collected to serve as a test set. Domain experts identified as anatomically significant nine prostate and ten lung organs-at-risk (OAR) structures and manually labeled them according to the TG-263 standards, and remaining structures were labeled as Non_OAR. We experimented with six different classification algorithms and three feature vector methods, and the final model was built with fastText algorithm. Multiple validation techniques are used to assess the robustness of the proposed methodology. The macro-averaged F1 score was used as the main evaluation metric. The model achieved an F1 score of 0.97 on prostate structures and 0.99 for lung structures from the VA dataset. The model also performed well on the test (VCU) dataset, achieving an F1 score of 0.93 for prostate structures and 0.95 on lung structures. In this work, we demonstrate that NLP and ML based approaches can used to standardize the physician-given RT structure names with high fidelity. This standardization can help with big data analytics in the radiation therapy domain using population-derived datasets, including standardization of the treatment planning process, clinical decision support systems, treatment quality improvement programs, and hypothesis-driven clinical research.

scholarly journals Digital loyalty card ‘big data’ and small business marketing: Formal versus informal or complementary?

2013 ◽  
Vol 33 (4) ◽  
pp. 422-442 ◽  
Author(s):  
Christina Donnelly ◽  
Geoff Simmons ◽  
Gillian Armstrong ◽  
Andrew Fearne
Keyword(s):  
Big Data ◽  
Small Business ◽  
Loyalty Card

scholarly journals PENGARUH CUSTOMER SATISFACTION DAN MEMBERSHIP CARD LOYALTY TERHADAP STORE LOYALTY PADA INDUSTRI RITEL KATEGORI MINIMARKET DI JAKARTA

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Januar Robin Stanley ◽  
Pepey Riawati Kurnia
Keyword(s):  
Customer Satisfaction ◽  
Perceived Value ◽  
Store Loyalty ◽  
Loyalty Program ◽  
Loyalty Card

Industri ritel kategori minimarket di Indonesia saat ini sedang berkembang dalam persaingan yang ketat. Peritel minimarket di Indonesia memanfaatkan loyalty program berupa membership card untuk menjaga loyalitas konsumennya. Konsep loyalitas saat ini tidak hanya terbatas pada kesetiaan konsumen hanya pada salah satu gerai peritel, namun sudah berkembang hingga multi-loyalty. Dalam penelitian ini dilibatkan 115 responden di area Jakarta sebagai sample untuk mengukur pengaruh antara card perceived value dan card satisfaction terhadap card loyalty. Serta pengaruh antara card loyalty dan store satisfaction terhadap store loyalty. Selain meneliti tentang pengaruh antar variabel, penelitian ini juga menunjukkan harapan yang diinginkan terhadap program membership card, serta dampak membership card terhadap tindakan konsumen.Hasil penelitian menunjukkan bahwa card perceived value dan card satisfaction berpengaruh positif terhadap card loyalty. Card card perceived value memiliki pengaruh yang lebih besar dibandingkan dengan card satisfaction. Dalam pengukuran store loyalty, card loyalty dan store satisfaction berpengaruh positif terhadap store loyalty. Card loyalty memiliki pengaruh yang lebih besar dibandingkan store satisfaction.Hasil dari penelitian ini mengungkapkan bahwa konsumen banyak mengharapkan membership card yang juga dapat digunakan sebagai alat pembayaran. Serta memperoleh privilege dalam antrian dan informasi promosi. Selain itu, 25% dari responden memiliki lebih dari 1 kartu keanggotaan. Kondisi ini menunjukkan keberadaan multi-loyalty pada konsumen minimarket di Jakarta.

Generative Computational Urban Planning Through Big Data Analysis

2021 ◽  
pp. 16-47
Author(s):  
Luca Saverio Valzano ◽  
Carlo Caldera ◽  
Carlo Luigi Ostorero ◽  
Valentino Manni ◽  
Andrea Galli
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Planning Process ◽  
Urban Metabolism ◽  
Social Phenomena ◽  
Planning Approach ◽  
Future Challenges ◽  
Near Future ◽  
The City

For the near future, forecasts predict an uncontrolled growth of urbanization in the world, in which cities are fragmented and uneven systems in relation to fast evolving environmental, economic, and social phenomena. The traditional urban planning approach, essentially theoretical-predictive, adapts poorly to face future challenges. Hence, the need to rethink how to govern the transformations of cities, which can be described by models of urban metabolism. The city sensing has changed the way cities are explored and used. With the transition from digitalization to datafication, through the computational approach, georeferenced big data can be analysed and exploited by algorithms. They originate a generative computational urban planning process, which can achieve a higher quality of the project and provide cities with adaptive capability. This process exploits data provided by public administrations, companies, and citizens who take part in an inclusive and adaptive urban planning.

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