The document discusses a study on the impact of store design and layout on consumer purchasing behavior in a supermarket. The study was conducted over 2 months in 2018-2019 using questionnaires, interviews, and observation of 200 customers. The study aimed to analyze how the arrangement of products and store design influence what customers buy and whether they purchase unplanned items. The study hypothesized that store design would or would not have a significant relationship with customer buying behavior. Secondary data was collected from journals and magazines while primary data involved direct observation and questionnaires. The data collected related to visit frequency, commonly bought items, and spot purchases. Limitations included response interpretation differences and regional specificity.
People often talk about Market Research and i bet half of them don't know about how to do it effectively. This presentation, If you are a salesman/marketing person, will help you understand in depth about market research.
https://www.facebook.com/Grape5x
https://twitter.com/grape5x
http://pinterest.com/grape5x/
http://www.linkedin.com/company/grape5
http://www.youtube.com/user/grape5x
https://plus.google.com/u/0/115341805701046088873/posts
Using Market Build to Inform Global Product Innovation DecisionsSKIM
In today’s medical device and diagnostics industry, one of the challenges in new product development is to estimate the size of the addressable markets or market segments. Although secondary data are readily available at the national level, particularly in the US, it is still very difficult to make an informed decision on investing in an innovative concept when the solution is intended to serve user segments that are better characterized by behaviors than high level demographics.
To add to the uncertainty and complexity, practices across hospitals or even across departments within a hospital can vary significantly. Often times, the decision to pursue innovation is based on subjective interpretations of inadequate data, potentially resulting in biased outcomes.
Find out more at www.skimgroup.com/pmrg-2015.
Conjoint choice modeling in lower income countriesSKIM
When researching teenagers and those in lower social classes in developing countries, connecting with them can only be done through mobile devices. In order to get useful insights into their decision-making, we are pushing the boundaries using mobile technology to perform complex surveys with trade-off and conjoint techniques.
In this presentation, we share limitations we faced during survey setup and how we made this work in the end. Also, we show how these results with feature phone research targeting lower classes in developing countries compared to smart phones and traditional interviews.
Find out more at http://skimgroup.com/mrmw-eu-2015
Consumer Research
Introduction to Quantitative and Qualitative Research
Overview of the Consumer Decision Process
Quantitative Research
Qualitative Research
Developing Research Objectives
Types of Secondary Data
People often talk about Market Research and i bet half of them don't know about how to do it effectively. This presentation, If you are a salesman/marketing person, will help you understand in depth about market research.
https://www.facebook.com/Grape5x
https://twitter.com/grape5x
http://pinterest.com/grape5x/
http://www.linkedin.com/company/grape5
http://www.youtube.com/user/grape5x
https://plus.google.com/u/0/115341805701046088873/posts
Using Market Build to Inform Global Product Innovation DecisionsSKIM
In today’s medical device and diagnostics industry, one of the challenges in new product development is to estimate the size of the addressable markets or market segments. Although secondary data are readily available at the national level, particularly in the US, it is still very difficult to make an informed decision on investing in an innovative concept when the solution is intended to serve user segments that are better characterized by behaviors than high level demographics.
To add to the uncertainty and complexity, practices across hospitals or even across departments within a hospital can vary significantly. Often times, the decision to pursue innovation is based on subjective interpretations of inadequate data, potentially resulting in biased outcomes.
Find out more at www.skimgroup.com/pmrg-2015.
Conjoint choice modeling in lower income countriesSKIM
When researching teenagers and those in lower social classes in developing countries, connecting with them can only be done through mobile devices. In order to get useful insights into their decision-making, we are pushing the boundaries using mobile technology to perform complex surveys with trade-off and conjoint techniques.
In this presentation, we share limitations we faced during survey setup and how we made this work in the end. Also, we show how these results with feature phone research targeting lower classes in developing countries compared to smart phones and traditional interviews.
Find out more at http://skimgroup.com/mrmw-eu-2015
Consumer Research
Introduction to Quantitative and Qualitative Research
Overview of the Consumer Decision Process
Quantitative Research
Qualitative Research
Developing Research Objectives
Types of Secondary Data
Market Respect institute , Market Research Discipline,
Current trends in Market Research Institute, offline and online Tools,current Search at International Market Research level, Opportunity and risk in Market scenario, Research Outlook in Future,
How Unilever Connected with New Canadian ConsumersRobin Brown
A presentation we gave to the Canadian Market Research & Intelligence Association. We have stripped out proprietary insights but still may be interesting.
Offer Recommendation methodology for Vito's Mobile AppDipesh Patel
Require to build a recommendation engine for new and existing users. Each users should be recommended top five offers on basis of their likings and preferences
Fraser Lewis talks at Digital Henley #4 on Wednesday 4th May regarding how RB as a FMCG business utilises (or doesn't) utilise digital data to help guide their commercial decision making.
[Project] Customer experience and buying behaviour in e-commerce sitesBiswadeep Ghosh Hazra
The growing usage of internet in India provides an extremely lucrative market for many retailers and businesses. If e-retailers get to know the factors that broadly affect online behaviour, and the corresponding relationships between the type of online buyers and these factors, then they can further fine tune their marketing strategies to convert potential customers into permanent customers, while keeping the existing online ones.
This project on consumer behaviour is a part of a study, that broadly focuses on the factors which Indian online buyers keep in mind while they are shopping online. The research conducted found that Customer Service, Customer Review/Recommendations and Discount/Offers are the three dominant factors that influence online consumer perception. Consumer behaviour is an applied discipline because some decisions are significantly affected by their expected actions. The two perspectives that demand application of its knowledge are societal and micro perspectives. Internet is changing the very method consumers shop, buy goods and services, and has rapidly become a global phenomenon.
Today all companies must use the Internet with the goal of cutting marketing costs, and at the same time, received quantitative information; thereby reducing the price of the services and products, the companies offer. High competition compels companies to continuously look for cost cutting measures. Companies also use internet to communicate, convey and disseminate information, to take feedback, conduct satisfaction surveys with customers and most importantly, to sell the product.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
Market Respect institute , Market Research Discipline,
Current trends in Market Research Institute, offline and online Tools,current Search at International Market Research level, Opportunity and risk in Market scenario, Research Outlook in Future,
How Unilever Connected with New Canadian ConsumersRobin Brown
A presentation we gave to the Canadian Market Research & Intelligence Association. We have stripped out proprietary insights but still may be interesting.
Offer Recommendation methodology for Vito's Mobile AppDipesh Patel
Require to build a recommendation engine for new and existing users. Each users should be recommended top five offers on basis of their likings and preferences
Fraser Lewis talks at Digital Henley #4 on Wednesday 4th May regarding how RB as a FMCG business utilises (or doesn't) utilise digital data to help guide their commercial decision making.
[Project] Customer experience and buying behaviour in e-commerce sitesBiswadeep Ghosh Hazra
The growing usage of internet in India provides an extremely lucrative market for many retailers and businesses. If e-retailers get to know the factors that broadly affect online behaviour, and the corresponding relationships between the type of online buyers and these factors, then they can further fine tune their marketing strategies to convert potential customers into permanent customers, while keeping the existing online ones.
This project on consumer behaviour is a part of a study, that broadly focuses on the factors which Indian online buyers keep in mind while they are shopping online. The research conducted found that Customer Service, Customer Review/Recommendations and Discount/Offers are the three dominant factors that influence online consumer perception. Consumer behaviour is an applied discipline because some decisions are significantly affected by their expected actions. The two perspectives that demand application of its knowledge are societal and micro perspectives. Internet is changing the very method consumers shop, buy goods and services, and has rapidly become a global phenomenon.
Today all companies must use the Internet with the goal of cutting marketing costs, and at the same time, received quantitative information; thereby reducing the price of the services and products, the companies offer. High competition compels companies to continuously look for cost cutting measures. Companies also use internet to communicate, convey and disseminate information, to take feedback, conduct satisfaction surveys with customers and most importantly, to sell the product.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
AI in market research involves integrating Machine Learning (ML) algorithms into traditional methods, such as interviews, discussions, and surveys, to enhance the research process. These algorithms enable real-time data collection and analysis, predicting trends and extracting valuable patterns. This process results in high-quality, up-to-date insights that transparently capture even minor market changes.
This research paper considers the understanding of the customers’ satisfaction towards and perceptions towards D-mart;. Specifically this research will seek to identify which factors effect on satisfaction.
The purpose of this study is to find out overall satisfaction towards Dmart. Some people are satisfied about price, some people about product variety. Research was done through questionnaire and discus with some customers in college campus who are customers of D-mart. Retailers have recognized this trend and are of the view that customer satisfaction plays a role in the success of business strategies. Therefore it has become important for grocery retail stores to try and manage customer satisfaction. This paper was thus developed to investigate the satisfaction levels of customers in D-mart. Data was collected from D-mart in akurdi, pune. The study examined the importance of overall dimensions and specific elements of customer satisfaction towards the measurement of satisfaction levels.
leewayhertz.com-AI in market research Charting a course from raw data to stra...KristiLBurns
AI in market research involves integrating Machine Learning (ML) algorithms into traditional methods, such as interviews, discussions, and surveys, to enhance the research process.
Factors Influencing the E-Shoppers Perception towards E-Shopping (A Study wit...Dr. Amarjeet Singh
Purpose: The study focuses on identifying and exploring the various factors influencing the e-shoppers perception towards e-shopping.
Design / methodology / approach: A research model is developed based on the literature. For the purpose of study data collected from 100 e-shoppers belonged to Wardha City of Maharashtra. By using in structured questionnaire, descriptive statistical measure like mean has been used for analyzing the data.
Findings: The results reveal that the seven key factors like convenience, time saving, home delivery, price advantage, more choice, reliability and security significantly influenced the e-shoppers perception on e-shopping.
Contribution of the study: The result of this study provides a valuable reference to the e-marketers to understand the factors influencing e-shoppers perception. They can further sharpen their marketing strategies to attract and retain their customers.
A Study on Consumer Behaviour Among Retail Outlets in Chennaiijtsrd
In this research paper researchers basically focused on behaviour of consumer mainly on purchasing pattern, frequency, price, period of purchase and various factors deciding the purchase. Researchers observed that the customers prefer retail outlets because of price discount, followed by colour, quality and fitting. Researchers have also observed that generally the customer purchase the product during festive season followed by off season. It was found that there is a significant difference between the expectations of coupons for purchasing readymade garments and income level of consumers. Mrs. A Nishath Sultana | Saabhreen Nisha "A Study on Consumer Behaviour Among Retail Outlets in Chennai" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33621.pdf Paper Url: https://www.ijtsrd.com/management/consumer-behaviour/33621/a-study-on-consumer-behaviour-among-retail-outlets-in-chennai/mrs-a-nishath-sultana
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Research design sample ppt
1. Effect of Store Design of
Supermarket in Purchasing
behaviour of consumers
SUBMITTED BY:
ASWIN TR (18382008)
SARATH S NAIR (18382055)
VISHNU KM (18382070)
2. OBJECTIVES
The overall aim of the study is to investigate the impact of store
design and layout on consumer purchasing behaviour in Quick Pick
International Hypermarket.
To study the impact of arrangement of products in purchasing
behaviour of customers.
To study the impact of store design in persuading customers to
buy undesired products.
3. HYPOTHESIS
Hypothesis 1: There is no significant relation between store
design and customer buying behaviour.
Hypothesis 2: There is significant relation between store design
and customer buying behaviour.
4. SOURCE AND DATA COLLECTION
PRIMARY DATA:
Primary data was collected by direct observation, Interview
and questionnaire method.
SECONDARY DATA:
Secondary data was collected from Journals and magazines.
5. PERIOD OF STUDY
The study was conducted for a period of 2 months
from December 2018 to January 2019.
6. TOOLS USED
TOOLS FOR DATA COLLECTION:
Questionnaire
Interview
TOOLS FOR DATA ANALYSIS:
Percentage Analysis
MS Excel
Graphical Tools(Bar Charts, Pie Charts, Line Graphs and
Histograms)
7. SAMPLE SIZE
The sample size of the study is 200 customers of
Quick Pick International Hypermarket.
Convenience sampling is used to select sample.
8. DATA
Frequency of visiting Quick Pick International Hypermarket.
Items that are frequently bought by customers.
Items which the customer bought by spot purchasing
decision.
Frequency of layout change and product movement for
corresponding period.
9. LIMITATIONS
Response of the questionnaire was as per respondent understanding, which may
differ from respondent to respondent.
Data is collected from customer of specific region, finding is limited to Pondicherry.
Lack of response from customers
Fluency in language
The study confined to the age group of 18 to 45
The Study is based on sampling method. So the sampling errors bound to occur.
