- A marketing research project was conducted to analyze factors affecting stagnant sales at Retail House, a 50-year old hosiery store in IIT Kanpur.
- Data was collected through an online/printed questionnaire and analyzed using chi-square tests, t-tests, and discriminant analysis.
- Results showed that footfall at the shopping complex was associated with footfall at Retail House, and advertisement was associated with increased visitation. However, parking availability and distance of residence were not associated with shopping preferences. Customer turnaround time was also found to be less than 5 minutes, indicating efficient staff.
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It also comprises more than 560 reliance fresh stores all over the country. The outlet sells fresh fruits, staples, dairy products, fresh juice bars, groceries and vegetables. A distinctive Reliance Fresh outlet is around 3000 to 4000 sq. feet and accommodates catchment area of one to three Kilometers.
The first ever a Reliance Fresh store was established in Hyderabad, wherein the company, mainly focused on the fresh produced vegetables and fruits at comparatively low price along with an introduction of farm to fork theory.
This was the idea, which was anticipated by the company was to take the supply direct from the farmers and then sell straightaway to the consumers removing the middle-men off the beaten track.
The product details are listed below:
• Fresh fruits
• Vegetables
• Chocolates
• Confectionaries
• Cold drinks
• Freeze items like butter, ice-cream etc.
• House hold products
• Toilet items
• Spices and dry food
• Office stationeries
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.
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Reliance Fresh a convenient store format, is governed by the Mukesh Ambani and is the most important part of Reliance Industries retail Business. Reliance Ltd. has planned to invest more than Rs. 25000 crores in the retail division.
It also comprises more than 560 reliance fresh stores all over the country. The outlet sells fresh fruits, staples, dairy products, fresh juice bars, groceries and vegetables. A distinctive Reliance Fresh outlet is around 3000 to 4000 sq. feet and accommodates catchment area of one to three Kilometers.
The first ever a Reliance Fresh store was established in Hyderabad, wherein the company, mainly focused on the fresh produced vegetables and fruits at comparatively low price along with an introduction of farm to fork theory.
This was the idea, which was anticipated by the company was to take the supply direct from the farmers and then sell straightaway to the consumers removing the middle-men off the beaten track.
The product details are listed below:
• Fresh fruits
• Vegetables
• Chocolates
• Confectionaries
• Cold drinks
• Freeze items like butter, ice-cream etc.
• House hold products
• Toilet items
• Spices and dry food
• Office stationeries
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.
Arvind Internet a division of Arvind Limited Internship Project Reportkunal mittal
Omni Channel for better customer experience and retail Profitability, Arvind Internet, IIP Report, Industrial Internship, Retail Report, Internship Project, Brief Introduction to Omni channel
Validate Your Redefined Customer Journeys QuicklyApplause
COVID-19 has accelerated the need for new customer journeys like curbside pickup. Now is the time for businesses to account for contactless services and ensure customer safety.
Sample assignments of two year mba programsmumbahelp
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Having taken their first step into digital with Sitecore Gold Partner, Mando two years ago, there was a desire to understand and overhaul their customer journeys, and build them with automated customer engagement across multi-channels.
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A practical guide on how to do funnel analyses & use them for decision making
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In the presentation you will learn the following things:
What are funnels?
1. How to analyze funnels in Excel
2. How funnels look like for e-commerce, marketplace, SaaS firms and other business models
3. How to use funnels and funnel analysis to increase sales and profits
4. What are cohorts and how to use them to analyze funnels?
5. How to use funnels to manage tests, projects, or tasks
For more check the following course:
https://bit.ly/FunnelsConsulting
Functions required for building a deep technology company in robotics, computer vision and AI. Learning from my experience of managing a company at leadership level.
Data Strategy for Digital Sales : Case Study & Best PracticeBarry Magee
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I'm an experienced senior business leader focused on how data-driven transformation creates organisational value with deep experience in sales, marketing, strategy, operations, and change management. I’m a recognized industry-leading specialist and academic on effective and systemic innovation using data and analytics to build competitive advantage and tangible results.
https://www.linkedin.com/in/barrymagee/
• Identifying the reasons for temperature rise in primary movement from technical documents.
• Identifying the reasons for temperature rise by observing loading/unloading of vehicles, monitoring vehicle movement and source of heat leakages in vehicle.
• Designing questionnaires to understand standards and practices followed by Third party logistics, Insulated reefer box manufacturers and at loading/unloading points.
• Conducting telephonic survey of Cold Chain Logistics and Insulated reefer box manufacturers.
• Meeting industrial experts to discuss factors recognized during the survey.
• Mitigation plan to resolve temperature issues.
• Feasibility study of recommendations.
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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.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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
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
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Sum with different modes (reduce)
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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).
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. PROJECT REPORT - MBA633A
MARKETING RESEARCH
“RETAIL HOUSE”
Submitted to:
Prof. Shankar Prawesh | IME, IIT K
Submitted by:
Ankit Panwar (16125010)
Gughapriyan M (16125017)
Sai Barath Sundar (16125035)
Shivam Gupta (16125039)
Department of Industrial and Management Engineering
INDIAN INSTITUTE OF TECHNOLOGY KANPUR
2. PAGE 1
Contents
1. Problem Definition....................................................................................................2
2. Approach to the Problem...........................................................................................3
3. Research Design.......................................................................................................3
4. Data Analysis............................................................................................................5
5. Results......................................................................................................................6
6. Recommendations ....................................................................................................8
7. Limitations................................................................................................................8
8. Appendix...................................................................................................................9
Questionnaire......................................................................................................9
Statistics From Questionnaire ..........................................................................13
3. PAGE 2
ProblemDefinition
Retail Shop is a hosiery store located in old shopping complex. The store is owned by a family doing
business for the third continuous generation. The shop has been in place for more than 50 years
which time it was just called retail store and is being called the same since then. At present, the store
is being run by a store manager and an attendant hired by the owner.
The products available in retail shop ranges from pillow, pillow covers, bed sheets, blankets curtains
and under garments. Recently they have added jackets, ready-made clothing and ladies wear to their
product portfolio.
Over the years, store is facing stagnant sales with most of it coming during admission season while
the cost of the running the shop has been increasing such as salary and shop rent. They are also
facing competition from new general stores opened in each hall and E shop opened in new shopping
complex which provides similar products at competitive prices and is closer by for customers.
Discussion with Decision Makers/Industry Experts
Over the past 6 to 7 years, the sales have been stagnant peaking only during admission phase and
starting of winters. The shop manager claimed following reasons for stagnant sales:
1. IIT Kanpur authorities allowing Hall Shops which were selling similar products during peak
time.
2. IIT Kanpur authorities also disallowed promotionalhoardingsin IIT Kanpurwhich prevented
shop to advertise.
3. Parking has been moved outside Old shopping complex
We also took opinion from experts from campus community about decrease in sales and we came to
know about interesting facts:
1. Opening of new shopping complex and campus-E shop
2. Poor layout of shop and front showcase lack of brands
3. Lack of motivation in Shop Staff
Management Decision Problem
How to improve stagnant sales and footfall in the Shop?
Management Research Problems
1. Does a high frequency footfall in Old Shopping Complex will lead to high frequency footfall
in “Retail House”?
2. Will advertisement lead to increased footfall in “Retail House”?
3. Will parking inside Old Shopping Complex will lead to increased footfall in Old Shopping
Complex?
4. Does distance of Customer’s Residence from Old Shopping Complex affect his decision to
travel to Old Shopping Complex?
5. Are staff efficient in locating products inside shop?
6. What factors are most important for improving sales?
4. PAGE 3
Approach to the Problem
We followed the following sequence while addressing the problem:
1. Establish Management Decision Problem
2. Find Factors affecting MDP
3. Formulate Management Research Questions for MSP
4. Select Variables for measurement
5. Formulation of Hypothesis
6. Designing of Questionnaire for measurement of variables
7. Hypothesis Testing
8. Inference
ANALYTICAL MODELS & HYPOTHESES TESTED:
Research Question Null Hypotheses Analytical Model
To check for association between
frequency of foot fall in Old Shop C
and Retail House
There is no association between
the foot falls in Old Shop C and
Retail House
Cross Tabulation – Chi Square Test
To check if advertisement leads to
increased footfall
Advertisement is not associated
with increased footfall
Cross Tabulation – Chi Square Test
To check if parking has any role in
increasing footfall
Parking is not associated with
footfall at Old Shop C
Cross Tabulation – Chi Square Test
To check if distance from residence
affects travel preference to Old Shop
C
Distance does not affect the
choice for travel to Old Shop C
Cross Tabulation – Chi Square Test
To check if staff are efficient at
work (Customer turnaround time
should be less)
Time to find product is less than
5 minutes
One Sample t test
To find most important factor to
improve sales
NA Discriminant analysis, Factor
Analysis
ResearchDesign
Type of Research:
The type of research conducted is Causal Research.
Questionnaire Development & Distribution:
Questions were asked based on the variables selected. The distribution channels selected were online
and direct intercept through printed survey forms.
Pretesting:
We conducted a short test of the questionnaire with 5 subjects. From this we determined questions
5. PAGE 4
that were difficult to understand, redundant questions and ascertained the time taken to complete the
survey. The required changes were made to the survey. The average time to fill the survey was less
than 5 minutes.
Sampling:
Our target initially was the entire population staying inside IIT Kanpur. But since distribution was
not possible to the various faculty during the time provided, the final survey was conducted by
distributing among the student community.
Since the survey was sent as an email, the technique can be considered as random sampling. This is
also applicable to the direct intercept conducted using printed surveys.
Variables, Data preparation and Scaling Techniques:
Variable Type Scale/Levels Comments
Residence Nominal 23 Place where subjectisstaying
Travel_Preference Nominal 2 Preference totravel toOldShopC –
Yes/No
Distance Scale 5 DerivedfromResidence.5categories
representingdistancesfromresidence to
OldShopC
Freq_Visit_Shop_C Nominal 4 Frequencyof visit –OldShop C
Aware_Retail_Hse Nominal 2 Awarenessof Retail House
Freq_Visit_Shop Nominal 5 Frequencyof visit –Retail House
Helpfulness Ordinal 7 Likert– Measure of staff helpfulness
Time_Find_Prod Ratio 9 Time to findproduct
Brand_Satisfaction Ordinal 7 Likert– Measure of brand satisfaction
Prod_Variety Ordinal 5 Measure of productvariety
Layoutofitems Ordinal 5 Rank - Factor
Lighting Ordinal 5 Rank – Factor
Cleanliness Ordinal 5 Rank – Factor
NewProductsBrands Ordinal 5 Rank – Factor
Staff Ordinal 5 Rank – Factor
Factor_to_improve Nominal 5 Subjectpreference
Parking_Visit_YN Nominal 2 If parkingis allowedwouldsubjectvisit
or not
Mode_of_Awareness Nominal 5 How subjectbecame aware
Advert_Others Nominal 2 Checkisother shopshave adverts
Post_Advert_Visit Nominal 2 Checkshopvisitafteradverts
Shop_Preference Nominal 4 Shoppreference forclothing
6. PAGE 5
Data Analysis
Test Analysis
Significant/Insigni
ficant
Frequency
for visit to
Shopping
Complex
vs
Frequency
for visit to
Retail
House
Chi-squared:
p-value is less than
0.05
Significant*
Advertisem
ent Vs Post
Advertisem
ent Visit to
Shop who
gave
Advertisem
ent
Chi-squared:
Significant*
p-value is less than 0.05
Parking
inside Old
Shopping
Complex
Vs
Frequency
of Visit to
Shopping
Complex
Not Rejected
7. PAGE 6
Distance of
Residence Vs
Preference of
Travelling to
Old Shopping
Complex
Not Rejected
Customer
turnaround time
should be less
than 5 minutes
Significant*
Discriminant
Analysis
-------
Results
1. On checking association of Frequency for visit to Shopping Complex and Frequency for visit to
Retail House, we find results are statistically significant Hence footfall in “Retail House” is
associated with Footfall in Shopping Complex. It can be inferred that frequent visits to Old
Shop C need not translate to frequent visits to Retail House.
2. On checking association of advertisement leads to increased footfall, we find p-value is less than
0.05 i.e. results are statistically significant and we can reject the null hypothesis.
We find that about 56% of the respondents who see an advert visit the particular shop. Hence
Advertisement is associated with increased footfall in Shops.
8. PAGE 7
3. On checking association of Parking inside Old Shopping Complex Vs Frequency of Visit to
Shopping Complex, we find p-value is more than 0.05 and we cannot reject the null hypothesis.
We can infer that parking inside old shopping complex and footfall are not significantly
associated. Thus, focus of shopkeeper should not be on wasting time on pressing authorities for
policy change regarding parking.
4. On checking association of distance of residence Vs preference of travelling to Old Shopping
Complex, we find p-value is more than 0.05 and hence we cannot reject the null hypothesis. We
can infer that distance of residence with preference of travelling are not significantly associated.
5. On checking null hypothesis, that customer turnaround time should be more than 5 minutes ft,
we find p value is less than 0.05 and hence we reject the null hypothesis
It can be inferred that at 95% CI, the time taken to find a product lies between 3.6 to 4.95
minutes. Well within 5mins. From this we can infer that staff is efficient in helping customer
locate the product. This also is an indicator of motivation level of the shop staff.
6. Discriminant Analysis
Value for function 1 is maximum for cleanliness in Table 1 & maximum for new shopping
complex in Table 2 i.e. people prefer to travel to new shopping complex due to cleanliness.
Similarly, inference from function 2 is that people visit halls shops due to lighting and function 3
tells us that people visit outside campus shops for new brands/products.
Hence from this data we can infer that
Staff is not at all significant in making a choice.
Old Shopping Center doesn’t standout on any of the factors considered. This makes old
shop c the last destination for people to go.
This decreases the footfall of Old shop C as well as the retail shop.
New Products and Brands is very important.
People are willing to travel more if they get new product/brands as it is available outside
campus.
7. Factor Analysis
Function 1 explains Lighting Function 2 explains layout of items and Function3 explains Cleanliness
Better.
9. PAGE 8
From the graphs, we infer that Lighting and New products/Brands are not at all related i.e. People
who are concerned about new products and brands don’t care about the lighting of the shop as long
as they have the products and brands they want.
From the plot of F1 and F2, It can be seen that the graph is spread out and not clustered, which
means people’s choices are very varied. But lot of people prefer New Products and Brands and
Layout of items. From the Plot of F1 and F3, we can understand that lot of respondents prefer
cleanliness in the shop. Also, people associate staff and layout of items to be related.
FAEx (2).xls
Recommendations
1. We came to know from hypothesis testing that distance from residence and parking does not affect
the footfall of “Retail House”. Focus of shopkeeper should not be on wasting time on pressing
authorities for policy change regarding parking.
2. People will be attracted to visit Retail House if they would be advertising. They can start advertising
using pamphlets with Newspaper inside IIT Kanpur during period of high sales and can give offer
during period of low sales.
3. The two most important factors are New products/brands and Layout of items. Which can be
derived from the discriminant analysis as well as from the people’s suggestions. The Retail shop has
to improve on these two fronts to attract and retain customers.
4. Almost 81.3% of sample population come to know about the shop by directly visiting the shop. The
other way people come to know about the shop is word of mouth and so the shop should improve
on overall shop experience of customer in order to improve word of mouth.
Limitations
1. Timing Constraint: - The time available for the analysis, design of survey and getting responses
from the population was limited.
2. Due to timing constraint and some other factors, we could get only students data, while the
responses from staff and faculty are still awaited.
3. We have faced population reach issue, due to official restrictions, and that’s the main reason we have
been able to collect limited data. Although we collected data manually but that remains limited only.
4. Budget Constraint: - We have faced budget constraints, due to which we were unable to conduct
Focus Group study.
5. We don’t have any secondary data sources, which entails us to rely completely on primary data
collection.
10. PAGE 9
Appendix
QUESTIONNAIRE
Retail House Survey
-Old Shopping Center
As a part of an academic study, we are looking to collect certain information on "Retail House" -
a shop located at the Old Shopping Centre of IIT-K. The information we are seeking relates to
consumer preferences,aspects of the shop and Shopping Centre as a whole.
The survey would require 5 minutes of your time. We are looking forward to your cooperation in
helping us understand your choices and coming up with a concrete analysis. Kindly start off by
telling us a bit about yourself.
Please tick ☑ the checkbox in front of option selected.
What is your current profession? *
Student
Professor
Non-teaching staff/Working inside IIT-K
Others_________________________________________________________________
What is your age? (in years) *
< 18
18 - 22
23 - 26
27 - 30
> 30
Gender *
Male Female
Where do you stay? *
Outside IIT-K
Type-I Type-2 Type-3 Type-4 Type-5 Type-6
GH1 GH2
Hall-1 Hall-2 Hall-3 Hall-4 Hall-5 Hall-6 Hall-7 Hall-8
Hall-9 Hall-10 Hall-11 Hall-12
Old/New RA
Old/New SBRA
11. PAGE 10
Howfrequently do you visit Old Shopping Complex? *
Once a Week
Once in Two Weeks
Once a Month
Rarely
Are you aware of this shop - "The Retail House"? *
Yes
No
Howoften do you visit this shop? *
Once a Week
Once in Two Weeks
Once a Month
Rarely
Never
Howhelpful are the staffin the shop? *
Howaware are the staff about the products? *
Howlong did it take to find your Product? (on your last visit) *
Did you find the shop attractive? *
12. PAGE 11
Generally: What makes ANYshop attractive? Rank the parameters from highest
importance (Rank 1) to lowest importance (Rank 5): *
Which of following does "The Retail House" need to improve most, to make it more
attractive? *
Layout of items
Lighting
Cleanliness
New Products/Brands
Staff
If you can park your vehicle just outside the shop, will you come here more often? *
Yes
No
Howdid you come to knowabout this shop? *
Word Of Mouth
Pamphlets
13. PAGE 12
Posters
Direct Visit
Others__________________________________________________________________
Have you come across advertisements from OTHER SHOPS inside the campus? *
Yes
No
If Yes, Did you visit that particular shop after seeing the advertisement? *
Yes
No
Are you satisfied with the Brands sold in the shop? *
Howmany varieties ofthe product you wanted to purchase were available? Rate on a scale
of 1 to 5 *
Where would you most prefer to buy items like Beddings, Undergarments,Readymade and
winter clothes? *
Outside Campus
Old Shopping Complex
New Shopping Complex - Campus eShop
Shops in Hall11/10/7 etc.
Do you prefer to travel from your residence to the Old Shopping Center? *
Yes
No
Maybe