Business_Research_Methods_Th
e_ShoppingPro_Exploratory_Des
criptive_The_Online_Shopper_Pr
Business Research Methods Projec...
Table of Contents
Chapter
Number

Title

Page
Number

1

Background………………………………………………………………………….

1

2

Defining the Probl...
Chapter One
Background
The company is an entrepreneurial start up born out of a vision to
transform the complicated world ...
Chapter Two
Defining the Problem
This chapter will define the problem, and break into more specific
sub problems. This wil...
Sub problem 3: To determine the reason for low reuse of the product
Research Question 7: What are the major usage patterns...
Chapter Three
Research Methodology

Part 1: Sources of Data

Primary Source
1)
For Market Research Problem 1 we will have ...
Data collection method includes survey from online shoppers. The
respondents were asked perception about such products (on...
Questionnaire Design
Questionnaire 1
Question Number

Use

1,2

Qualifying Questions

2, 3, 4, 5, 8, and 9

Research Quest...
13

Nominal

14

Likert Scale, Ordinal Scale

16

Ratio

17

Nominal

Questionnaire 2
Question Number

Use

1

Qualifying ...
Chapter Four
Data Analysis
We would like to mention that we could not find any variability in
demographics and hence we di...
Want_Delivary_Boy_Expertis

1.000

.687

Sad_No_Touch_Feel

1.000

.658

Love_Browsing_Online

1.000

.663

Loyal_If_Best_...
Rotated Component Matrix

a

Component
1

2

.829

-.278

.817

-.183

.796

-.372

Shop_Almost_All_My_Needs .794

-.222

...
We had decided that we recommend the product promotion if
more than 75% of the users is willing to download the plugin. To...
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.814

Bartlett's Test of Sphericity

Approx. Chi...
As we can see, based on the Eigen values there are two dominant
factors. Here we chose the selection of factors based on E...
Research Question 4: Do online shoppers regularly use price comparison
websites?
From the relevant data, we have built the...
Page 15
Research Question 5: What are top of the mind Recall for price
comparison and discount websites/plugin?
We asked the respo...
In the unaided recall question, the responses were similar. Besides,
freecharge emerged as a major source.
Research Questi...
Descriptives
Value
95% Confidence Interval for Mean
N Mean Std. Deviation Std. Error

Lower Bound

Upper Bound

Minimum Ma...
Multiple Comparisons
Value
Tukey HSD
95% Confidence Interval

(I)

(J)

Class

Class

1

2

-.271

.183

.573

-.77

.23

...
Newsletters are the least important medium of promotion, as seen from
the plot of means.

Page 20
Chapter Five
The Online Shopper’s Profile

As we had discussed in the earlier chapter, the factor analysis
yielded us the ...
So, as we can see our target segment is the price conscious
segment.

Attribute
Use of price
comparison websites
Shops for...
Chapter Six
Promoting the Plugin

As observed from the certain pie charts, close to 74% respondents
look for discount coup...
Rotated Component Matrix

a

Component
1

2

.514

.605

.823

.175

slow_my_browser

.699

.324

fill_up_my_screen_space
...
Chapter Seven
Existing Users Experiences
Since the list of users of the product was very limited we performed a
qualitativ...
•

•

•

Also the product launches itself on certain shopping portals like
Flipkart where there are no discounts available...
Chapter Eight
Conclusion

At the end of this project, we saw how business research methods
and multivariate data analysis ...
Appendix A
Questionnaire 1

Page 28
12/20/13

BRM Shopping Pro - Google Drive

Online Shopping and Plugins
*Required

1. Have you ever brought a product onlin...
12/20/13

BRM Shopping Pro - Google Drive

6. Which discount coupon websites do you recall right now? *
Type below, and se...
12/20/13

BRM Shopping Pro - Google Drive

10. If there is a plugin that will tell you about all the discount coupons avai...
12/20/13

BRM Shopping Pro - Google Drive

14. Indicate the degree of information you receive about such items from the fo...
Appendix B
Questionnaire 2

Page 33
1/9/14

BRM second - Google Drive

BRM second
*Required

1. What is the frequency of your online shopping? *
Tick all that...
1/9/14

BRM second - Google Drive

3. Enter your age *

4. What is your gender? *
Mark only one oval.
Male
Female

Pow ere...
Appendix C
In Depth Interview Questions

Q1. How has been your experience with TheShoppingPro?
Q2. Are you still using the...
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Business Research Methods: Marketing Strategy for The ShoppingPro

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This is a report that used business research methods and statistical techniques to derive a marketing plan for The ShoppingPro.

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Business Research Methods: Marketing Strategy for The ShoppingPro

  1. 1. Business_Research_Methods_Th e_ShoppingPro_Exploratory_Des criptive_The_Online_Shopper_Pr Business Research Methods Project ofile_Factor_Analysis_ANOVA_M The ShoppingPro arketing_Strategy_Promotion_B est_Mediums_Of_Promotion_Des criptive_Statistics_Z_Test_For_P roportion_InDepth_Interviews_L addering_Likert_Scale_Ordinal_ Data_Nominal_Data_Problems_O f_Existing_Consumers_Business_ Research_Methods_The_Shoppin gPro_Exploratory_Descriptive_T he_Online_Shopper_Profile_Fact or_Analysis_ANOVA_Marketing_ Strategy_Promotion_Best_Mediu Section A Group 6 Abhay Sharma 1A Manasi Jain 23A Aniruddh Srivastava 9A Sachin Gupta 38A Devansh Doshi 16A Vidooshi Joshi 55A
  2. 2. Table of Contents Chapter Number Title Page Number 1 Background…………………………………………………………………………. 1 2 Defining the Problem…………………………………………………………. 2 3 Research Methodology………………………………………………………. 4 4 Data Analysis……………………………………………………………………… 8 5 The Online Shopper’s Profile……………………………………………… 21 6 Promoting the Plugin…………………………………………………………. 23 7 Existing Users Experiences………………………………………………… 25 8 Conclusion…………………………………………………………………………. 27 Appendix A: Questionnaire 1……………………………………………… 28 Appendix B: Questionnaire 2……………………………………………… 33 Appendix C: In Depth Interview Questions………………………. 36
  3. 3. Chapter One Background The company is an entrepreneurial start up born out of a vision to transform the complicated world of ecommerce into a simple & intuitive process. The ShoppingPro is building personalized tools that adapt to each user's preference, making each one of them feel like "The Shopping Pro". The main product is a plugin for browsers which helps e-commerce customers to find out the best deals/coupons on any particularly e commerce website. With the increasing popularity of the ecommerce in India, the product was supposed to fly. As it brings convenience to the users, the product was supposed to be a hit with them. But since its launch very few people have actually adopted it. The most attractive feature of the product is that it is free for the user and needs to be downloaded once only, so it is a one-time activity which gives the user benefits. The management team has been doing promotions by the following means • • • • On social media (Facebook, Twitter) etc. By launching their own video about the benefits of the product By promotions by influencers(bloggers) Live demo to certain groups People, who actively buy from the e commerce setups are intrigued by the concept, yet when it comes to using the product it most do not. The management team wants to devise a marketing strategy to promote the product which increases hits. The team approached a team of 6 students from IIFT to analyse the problem in adoption and promoting the product. The reason for selecting this project is that it is a live problem, and we would be able to apply the concepts of BRM to this problem. Page 1
  4. 4. Chapter Two Defining the Problem This chapter will define the problem, and break into more specific sub problems. This will set the direction for further research. Management Decision problem To devise a marketing plan for The ShoppingPro Marketing Research Problem The MRP constitutes of twin problems encountered by the team: a) Developing a marketing strategy to the non-users of the product b) Increasing the reuse rate amongst existing users Sub problems Sub problem 1: To understand the market for such a product Research Question 1: What is the profile of the online shopper in terms of benefit sought from online shopping and use of discount coupons? Research Question 2: Will online shoppers download the shopping plugin? Research Question 3: What are the problems users have faced while using the online plugins in the past? Research Question 4: Do online shoppers regularly use price comparison websites? Research Question 5: What are top of the mind Recall for price comparison and discount websites/plugin? Sub problem 2: To find opinion leaders for promoting such products Research Question 6: What are the major sources where users learn about products that enhance the online experience? Page 2
  5. 5. Sub problem 3: To determine the reason for low reuse of the product Research Question 7: What are the major usage patterns of the product by the users? Research Question 8: What feature(s) of the product do users like? Research Question 9: What are the major problems faced by the users (if any)? Scope of the Research This project tries to discover the profile and attitude of the online shopper. It will determine the need and channels of promotion of the product. It also intends to determine the competition so that further study on this data can take place as required. It probes the current users to determine a list of problems that are there in the problem. This project does not undertake a quantitative survey of the problems faced by the existing users. This is not needed as the company is committed to fixing even the smallest of the bugs. Page 3
  6. 6. Chapter Three Research Methodology Part 1: Sources of Data Primary Source 1) For Market Research Problem 1 we will have a web survey of online shoppers. 2) For Market Research Problem 2 we will have in-depth interviews with currents users of the plugin. Since we don’t have many users of ShoppingPro that are accessible for the research we cannot go for quantitative research for the same. Secondary Sources 1) Minutes of Meeting from the discussion with the makers of the plugin 2) Internal discussions with the team members Part 2: Research Design 1) Market Research Problem 1: The first marketing research problem is a descriptive research. A web survey was conducted. The participants were online shoppers. The survey would be conducting using a self-administered test using closed ended questions. Some data collected in the web survey didn’t yield proper results. As a result, we floated another questionnaire and we managed to get some good results. The appendix contains both the questionnaires. Questionnaire 1 was initially floated, and Questionnaire 2 was floated later due to improper results. We received 60 responses in each of the case. Page 4
  7. 7. Data collection method includes survey from online shoppers. The respondents were asked perception about such products (online plugins), competitors, and the opinion leaders that they follow. 2) Market Research Problem 2: This would be an exploratory research. We conducted a telephonic/one-to-one interviews with the online shoppers who have used the plugin to better understand the user’s perspective about the plugin. Data collection method included telephonic/one-to-one interviews with users of the product. The laddering technique in in-depth interviews was used. The respondents would be asked pros-cons and their product experience. Part 3: Sampling Design 1) Market Research Problem 1: • Target Population: Those who indulge in online shopping (once a month at-least) • Sampling framework: Contacting friends and family • Sampling method: Non-probability (Convenience sampling) • Sample Size: Medium sample≥60 2) Market Research Problem 2: • Target Population: Those who have used the product • Sampling framework: Contacting friends and family • Sampling method: Non-Probability (Convenience sampling) • Sample Size: Small sample≥10 Page 5
  8. 8. Questionnaire Design Questionnaire 1 Question Number Use 1,2 Qualifying Questions 2, 3, 4, 5, 8, and 9 Research Question 1 10 Research Question 2 11,12 Research Question 3 8 Research Question 4 6,7 Research Question 5 13,14 Research Question 6 (Question 13 is a warm up question for question 14) Demographics 15,16 Question Number Scale 1 Nominal 2 Nominal 3 Nominal 4 Nominal 5 Nominal 6 No scale, text entry 7 Nominal 8 Nominal 9 Likert Scale, Interval Scale 10 Nominal 11 Nominal 12 Likert Scale, Interval Scale Page 6
  9. 9. 13 Nominal 14 Likert Scale, Ordinal Scale 16 Ratio 17 Nominal Questionnaire 2 Question Number Use 1 Qualifying Questions 2 Research Question 1 3,4 Demographics Question Number Scale 1 Nominal 2 Likert Scale, Interval Data 3 Ratio 4 Nominal Page 7
  10. 10. Chapter Four Data Analysis We would like to mention that we could not find any variability in demographics and hence we didn’t find it prudent to use them in our analysis. Research Question 1: What is the profile of the online shopper in terms of benefit sought from online shopping and use of discount coupons? In questionnaire 2, on a five point likert scale we captured the attitude of online shoppers towards various attributes. You can see that in the questionnaire. We then did a factor analysis to determine the major segments of online shoppers. We did a principle component analysis using SPSS. The result tables are discussed below. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .908 Bartlett's Test of Sphericity Approx. Chi-Square 393.897 df 55.000 Sig. .000 H0: The partial coefficient matrix is an identity matrix H1: The partial coefficient matrix is not an identity matrix The Barlett’s Test of Sphericity verifies this hypothesis. Using a ChiSquare test, the significance level is 0 which is less than 0.05. Hence, the null hypothesis is rejected. Hence, the overall factor analysis is significant as data reduction could take place. The KMO measure of sampling adequacy is 0.908 >0.5. This indicates that the number of samples is adequate. Communalities Initial Extraction Look_Discount_Everytime 1.000 .771 Variety 1.000 .635 Use_Price_Comparision 1.000 .730 Like_Attractive_Websites 1.000 .634 Shop_Almost_All_My_Needs 1.000 .680 Page 8
  11. 11. Want_Delivary_Boy_Expertis 1.000 .687 Sad_No_Touch_Feel 1.000 .658 Love_Browsing_Online 1.000 .663 Loyal_If_Best_Deals 1.000 .764 Want_Exclusive 1.000 .707 1.000 .701 e Feel_Unsafe_About_Online_ Payment Extraction Method: Principal Component Analysis. All communalities are more than 0.6 which satisfactory but not very good. Total Variance Explained Extraction Initial Eigenvalues % Sums of Squared Rotation Loadings of Cumulative % Sums of Squared Loadings of Cumulative % of Cumulative Component Total Variance % Total Variance % Total Variance % 1 6.268 56.986 56.986 6.268 56.986 56.986 4.194 38.126 38.126 2 1.362 12.384 69.370 1.362 12.384 69.370 3.437 31.244 69.370 3 .612 5.564 74.934 4 .546 4.963 79.896 5 .474 4.306 84.203 6 .436 3.965 88.167 7 .329 2.991 91.159 8 .308 2.799 93.958 9 .254 2.312 96.270 10 .223 2.024 98.294 11 .188 1.706 100.000 Extraction Method: Principal Component Analysis. As we can see, based on the Eigen values there are two dominant factors. Here we chose the selection of factors based on Eigen values. A total of 69.37% of variance is explained by these factors which is again satisfactory. Page 9
  12. 12. Rotated Component Matrix a Component 1 2 .829 -.278 .817 -.183 .796 -.372 Shop_Almost_All_My_Needs .794 -.222 Loyal_If_Best_Deals Feel_Unsafe_About_Online_ Payment Look_Discount_Everytime Want_Delivary_Boy_Expertis .774 -.297 Use_Price_Comparision .757 -.396 Like_Attractive_Websites -.148 .783 Want_Exclusive -.319 .778 Sad_No_Touch_Feel -.268 .765 Love_Browsing_Online -.289 .761 Variety -.353 .714 e Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations. The rotated component matrix has given us two distinct factors. Varimax rotation has been used so that variance between factors is maximized. The highlighted numbers show the variables that are associated with a certain factor. The two segments seem to the price conscious and variety seeking segment. These will be discussed in detailed in the next chapter. Research Question 2: Will online shoppers download the shopping plugin? We asked the users if they would download products with the features as that of our product. This was the response. Page 10
  13. 13. We had decided that we recommend the product promotion if more than 75% of the users is willing to download the plugin. To verify that we did a one tailed Z test for proportion. H0: π ≥ 0.75 (proportion of people demanding products is greater than 75%) H1: π < 0.75 (proportion of people demanding products is lesser than 75%) Z= ௣ିగ ഏ ሺభషഏሻ ට ೙ = ଴.଼ଷ଴ହି଴.଻ହ బ.ళఱ ሺభషబ.ళఱሻ ఱవ ට = 1.427977124 The p value at this Z is 0.0766. p>0.05, hence the null hypothesis is accepted. Hence, it makes sense to launch the product. Research Question 3: What are the problems users have faced while using the online plugins in the past? In questionnaire 1, we have asked respondents on a five point likert scale that various attributes about plugins. We then did a factor analysis to determine the major problems with plugins. We did a principle component analysis using SPSS. The result tables are discussed below. Page 11
  14. 14. KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .814 Bartlett's Test of Sphericity Approx. Chi-Square 150.298 df 21.000 Sig. .000 H0: The partial coefficient matrix is an identity matrix H1: The partial coefficient matrix is not an identity matrix The Barlett’s Test of Sphericity verifies this hypothesis. Using a ChiSquare test, the significance level is 0 which is less than 0.05. Hence, the null hypothesis is rejected. Hence, the overall factor analysis is significant as data reduction could take place. The KMO measure of sampling adequacy is 0.814 >0.5. This indicates that the number of samples is adequate. Communalities Initial Extraction 1.000 .630 1.000 .709 slow_my_browser 1.000 .594 fill_up_my_screen_space 1.000 .690 1.000 .686 1.000 .544 1.000 .854 virus_threat change_my_default_search_ engine change_my_browser_setting s security_threat_to_my_onlin e_payment good_design Extraction Method: Principal Component Analysis. All communalities are more than 0.6 which satisfactory but not very good. Page 12
  15. 15. As we can see, based on the Eigen values there are two dominant factors. Here we chose the selection of factors based on Eigen values. A total of 67.23% of variance is explained by these factors which is again satisfactory. Rotated Component Matrix a Component 1 2 .514 .605 .823 .175 slow_my_browser .699 .324 fill_up_my_screen_space .830 -.041 .828 .024 .712 .193 -.038 .923 virus_threat change_my_default_search_ engine change_my_browser_setting s security_threat_to_my_onlin e_payment good_design Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 3 iterations. Note that while analysis the data of good_design column was rotated so that halo effect is avoided. The rotated component matrix has given us two distinct factors. Varimax rotation has been used so that variance between factors is maximized. The highlighted numbers show the variables that are associated with a certain factor. We can see that there are two distinct factors. We will analyse them further in a later chapter. Page 13
  16. 16. Research Question 4: Do online shoppers regularly use price comparison websites? From the relevant data, we have built the following pie charts. Page 14
  17. 17. Page 15
  18. 18. Research Question 5: What are top of the mind Recall for price comparison and discount websites/plugin? We asked the respondents regarding the various coupon sites they are aware about. There unaided recall question followed by an aided recall question. The following table gives the results of the unaided recall results. Website Number of respondents in % that selected this Snap Deal 81 40% 5 2% Coupondunia.in 48 24% Khojguru.com 19 9% Couponzguru.com 17 8% Coupon Dekho 18 9% Other 13 6% CouponYuga.com Page 16
  19. 19. In the unaided recall question, the responses were similar. Besides, freecharge emerged as a major source. Research Question 6: What are the major sources where users learn about products that enhance the online experience? We captured ordinal data on a 4 point likert scale regarding the amount of information received from the sources. For statistical analysis, we assumed the ordinal data is interval scale data. This assumption is further corroborated by the fact that the parameters used were frequency parameters. We then did a one way ANOVA to determine if the means are equal. And we also used the Tukey-Crammer procedure to determine amongst which means there was significant distance. The relevant tables are discussed below. Class Indication 1 2 3 4 5 Friends and family Online blogs News articles Online advertising Newsletters Page 17
  20. 20. Descriptives Value 95% Confidence Interval for Mean N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum 1 59 2.46 1.119 .146 2.17 2.75 1 4 2 59 2.73 .962 .125 2.48 2.98 1 4 3 59 2.53 .935 .122 2.28 2.77 1 4 4 59 2.68 .937 .122 2.43 2.92 1 4 5 59 2.20 .996 .130 1.94 2.46 1 4 Total 295 2.52 1.003 .058 2.40 2.63 1 4 Test of Homogeneity of Variances Value Levene Statistic 1.738 df1 df2 4 Sig. 290 .142 Significance value greater than 0.05, hence the variances are equal. The ANOVA is valid as homogeneity of variances assumption is satisfied. ANOVA Value Sum of Squares Between Groups df Mean Square 10.190 4 2.547 Within Groups 285.458 290 295.647 2.588 Sig. .984 Total F .037 294 H0: µ1= µ2= µ3= µ4= µ5 H1: µ1≠ µ2≠ µ3≠ µ4≠ µ5 The significance is less than 0.05 than means the null hypothesis that all means are equal is rejected. Hence, Tukey-Crammer procedure is used to find where significant difference is observed. Page 18
  21. 21. Multiple Comparisons Value Tukey HSD 95% Confidence Interval (I) (J) Class Class 1 2 -.271 .183 .573 -.77 .23 3 -.068 .183 .996 -.57 .43 4 -.220 .183 .748 -.72 .28 5 .254 .183 .633 -.25 .76 1 .271 .183 .573 -.23 .77 3 .203 .183 .799 -.30 .70 4 .051 .183 .999 -.45 .55 5 .525 * .183 .035 .02 1.03 1 .068 .183 .996 -.43 .57 2 -.203 .183 .799 -.70 .30 4 -.153 .183 .920 -.65 .35 5 .322 .183 .397 -.18 .82 1 .220 .183 .748 -.28 .72 2 -.051 .183 .999 -.55 .45 3 .153 .183 .920 -.35 .65 5 .475 .183 .073 -.03 .98 1 -.254 .183 .633 -.76 .25 2 -.525 * .183 .035 -1.03 -.02 3 -.322 .183 .397 -.82 .18 4 -.475 .183 .073 -.98 .03 2 3 4 5 Mean Difference (I-J) Std. Error Sig. Lower Bound Upper Bound *. The mean difference is significant at the 0.05 level. Significance level of the difference between 2 and 5 is less than 0.05 that rejects the null hypothesis. The null hypothesis is that both the means are equal. Page 19
  22. 22. Newsletters are the least important medium of promotion, as seen from the plot of means. Page 20
  23. 23. Chapter Five The Online Shopper’s Profile As we had discussed in the earlier chapter, the factor analysis yielded us the following two segments of online shoppers. Rotated Component Matrix a Component 1 Loyal_If_Best_Deals 2 .829 -.278 .817 -.183 Look_Discount_Everytime .796 -.372 Shop_Almost_All_My_Needs .794 -.222 .774 -.297 Use_Price_Comparision .757 -.396 Like_Attractive_Websites -.148 .783 Want_Exclusive -.319 .778 Sad_No_Touch_Feel -.268 .765 Love_Browsing_Online -.289 .761 Variety -.353 .714 Feel_Unsafe_About_Online_ Payment Want_Delivary_Boy_Expertis e Price Conscious Variety Seeking Constantly checks for discounts Variety, and exclusive products is the motive for shopping online Compares prices before buying Shops for most of his needs online Concerned about touching and feeling a product before buying Feels unsafe about online payment Can be potentialy loyal to a site if it offers good discounts Loves browsing for new products Wants delivary boy expertise in using the product Page 21
  24. 24. So, as we can see our target segment is the price conscious segment. Attribute Use of price comparison websites Shops for most of one’s need online Feels unsafe about online payment Wants delivery boy expertise Marketing Implication Advertisement on those websites Can expect high product usage • More likely to read online about online payment safety, and ways to ensure it. Advertisement one such blogs and besides such newspaper articles. • More likely to use cash on delivery features. Try to involve the delivery sales force in promoting the plugin. Try attaching stickers on the delivery parcels about the parcel. • For sites that provide such services, the delivery boy can be used to promote our product. • Incase there are websites that do not provide such a service; the person is most likely to refer to some do-it-yourself blog for assistance. This is common in case of electronic products. Advertisement on such blogs can be done. However, we need to evaluate this more. Page 22
  25. 25. Chapter Six Promoting the Plugin As observed from the certain pie charts, close to 74% respondents look for discount coupons online, and close to 58% respondents look at multiple discount coupon websites. Close to 71% look for a discount coupon before every purchase. This indicates that there is considerable potential for our products. The Z text for proportion has already verified that there is substantial demand for our product. Close to 58% respondents search on some search engine for discount coupon websites. This justifies the spending on Search Engine Optimization, and it should be pursued aggressively. As discussed earlier, we again look at the factor analysis done for finding the major issues that users experience with plugins. Data analysis revealed Snap Deal, CouponYuga.com, Coupondunia.in, Khojguru.com, Couponzguru.com, Coupon Dekho, and Freecharge as some major discount coupon websites. Some promotion with them needs to be done. However, this needs more analysis. As seen from the ANOVA analysis, Friends and family, online blogs, News articles, and online advertising are equally important promotion media. The places for online advertising are discussed in the previous chapter depending on our target segment. The firm should try to engage in Public Relations by asking major blog writers and newspaper reporters to publish about their plugin. Word of mouth can be facilitated by retweeting the reviews of users on Twitter. Let us again refer to the factor analysis done to find out the major issues with plugins. Page 23
  26. 26. Rotated Component Matrix a Component 1 2 .514 .605 .823 .175 slow_my_browser .699 .324 fill_up_my_screen_space .830 -.041 .828 .024 .712 .193 -.038 .923 virus_threat change_my_default_search_ engine change_my_browser_setting s security_threat_to_my_onlin e_payment good_design As we can observe in the first factor, that plugins are perceived as a security threat. They are said to be elements that slow the browser, fill up the screen space, and change the default settings. Hence, all promotions should clearly address these concerns. Our product does fill up the screen space. Hence, the design should be changed in such a way that the plugin disappears after appearing once. And the user can see when whenever one clicks on the small bar. Another factor says that design is important. There should be some changes on the design front. Page 24
  27. 27. Chapter Seven Existing Users Experiences Since the list of users of the product was very limited we performed a qualitative survey/personal interview with some of the users to determine the overall experience of the users with the product and also their apprehensions about the product in general. Upon surveying 10 people who have used the product while shopping online we gathered the following trends from the same. Firstly all the existing users were excited about the product and the convenience it offered to them. Every user has had a good experience with the product and yet they do not use the product very often. This has been explained to us by the users as a tendency to cross check for a better discount deal online. Moreover most of the existing users used the product for availing the discounts for fashion and accessories and the most visited website/online shopping portal is jabong and myntra. The users gave the rationale that since there is a lot of variety in this segment, thus various deals are available which may not be easy to search. But with the product they can avail such limited scope deals. The major benefits of the product as highlighted by the users are as follows: • • • • Convenience to get all the best deals at one place Saves time people devote to searching for discount Activates automatically for every online shopping site One time plugin download activity However, users also complaint of the following problems they faced while using the product: • • • Cash back feature doesn’t work at times. Some of the customers who used cash back deals didn’t get any cash back. The product doesn’t give a product specific deal across the shopping portals, thus there is a need to open various portals to check the best deals individually. The registered users were supposed to get points when they shop online using the product; however this has not been implemented. Page 25
  28. 28. • • • Also the product launches itself on certain shopping portals like Flipkart where there are no discounts available by coupons, thus there is no need for the product to launch itself for these sites. Sometimes the discount is on the MRP of a product, but these details are available only at the payment page, thus the product doesn’t solve the budget problem of a customer till the very last step. User has to spend a high time to get the best deal for a basket of products a customer is buying. The major competitors for the products are price comparison sites which can get the prices of a specific product across platforms and thus give the customer a better idea of ‘where to buy from’. The product is innovative for the customers to try out but it loses sheen due to failed cash backs. There were also some suggestions from the users to the makers of the product: • • • • • Avoid unnecessary launch of product on every site Make avail the best deal product wise on every shopping portal Implement the cash back and reward point scheme Reduce bars from either end of the window to just one end Faster search for desired coupon Page 26
  29. 29. Chapter Eight Conclusion At the end of this project, we saw how business research methods and multivariate data analysis techniques can be used to garner business intelligence. It helped us to understand how data can be used to make real time marketing decisions. This project required us to combine our analytical and creative abilities and hence it was a good learning experience. Page 27
  30. 30. Appendix A Questionnaire 1 Page 28
  31. 31. 12/20/13 BRM Shopping Pro - Google Drive Online Shopping and Plugins *Required 1. Have you ever brought a product online? * Mark only one oval. Yes No 2. If yes, what is the frequency of your online shopping? * Mark only one oval. At least once a week At least once in two weeks At least once a month Rarely 3. Do you look for discount coupons while shopping online? * Mark only one oval. Never Sometimes Everytime 4. Where do you look for discount coupons online? * Mark only one oval. I do a Google Search I search via some search engine other than Google I am loyal to certain discount coupon websites Other: 5. Do you search on multiple discount coupon websites? * Mark only one oval. Never Sometimes Everytime https://docs.google.com/forms/d/1CBkYdAyYas3sOAZuuX0VF9EkkjTgwtM60hwrxxJqDNE/edit 1/4
  32. 32. 12/20/13 BRM Shopping Pro - Google Drive 6. Which discount coupon websites do you recall right now? * Type below, and separate each by a comma. 7. Which discount coupon websites do you recall? * Select all that apply Tick all that apply. Snap Deal CouponYuga.com Coupondunia.in Khojguru.com Couponzguru.com Coupon Dekho Other: 8. How frequently do you use price comparison websites? * Mark only one oval. I don't use them at all I use them sometimes I use them before every purchase 9. Indicate your degree of agreement or disagreement with the following statements about online shopping. * Mark only one oval per row. Strongly Disagree Disagree Neutral Agree Strongly Agree It is convenient It is safe It is cheaper There are more discounts available There is more variety https://docs.google.com/forms/d/1CBkYdAyYas3sOAZuuX0VF9EkkjTgwtM60hwrxxJqDNE/edit 2/4
  33. 33. 12/20/13 BRM Shopping Pro - Google Drive 10. If there is a plugin that will tell you about all the discount coupons available for the product you are buying online, would you like to use it? * Mark only one oval. Yes No 11. Have you ever installed an online plugin? * Mark only one oval. Yes No Most of the times it is a software installation that leads to a plugin installation 12. Indicate your degree of agreement or disagreement with the following statements about online plugins. * Mark only one oval per row. Strongly Disagree Disagree Neutral Agree Strongly Agree Plugins have a virus threat Plugins change my default search engine Plugins slow my browser Plugins fill up my screen space Plugins change my browser settings Plugins are a security threat to my online payment Plugins have a good design 13. Which items enhance your computer and internet experience? * Tick all that apply. Softwares Widgets Browser addons Browser extensions Browser widget Mobile Applications Other: https://docs.google.com/forms/d/1CBkYdAyYas3sOAZuuX0VF9EkkjTgwtM60hwrxxJqDNE/edit 3/4
  34. 34. 12/20/13 BRM Shopping Pro - Google Drive 14. Indicate the degree of information you receive about such items from the following sources * Mark only one oval per row. No information Little information Some Information Most information Friends and Family Online Blogs News Articles Online Advertisement Newsletters 15. Age * in years 16. Gender * Mark only one oval. Male Female Pow ered by https://docs.google.com/forms/d/1CBkYdAyYas3sOAZuuX0VF9EkkjTgwtM60hwrxxJqDNE/edit 4/4
  35. 35. Appendix B Questionnaire 2 Page 33
  36. 36. 1/9/14 BRM second - Google Drive BRM second *Required 1. What is the frequency of your online shopping? * Tick all that apply. At least once a week At least once in two weeks At least once a month Rarely 2. Indicate your degree of agreement or disagreement with the following statements about online shopping. * Mark only one oval per row. Strongly Disagree Disagree Neutral Agree Strongly Agree I look for discount coupons everytime before I make a purchase I like shopping online because it offers me a variety I use price comparison websites before any purchase I tend to shop online for almost all my needs I like websites that are attractive I feel sad that I don't like to touch and feel before buying If I buy a gadget online, I want the delivary boy to help me using it I feel online payment systems are insecure I love browsing products online I like online shopping since it provides me with exclusive products I will be loyal with a website if it continues offering me the best deals https://docs.google.com/forms/d/1JPKTppRmCwWd6DVh2gI5YnMz5XlvJnHIAGEnOnkTGIA/edit 1/2
  37. 37. 1/9/14 BRM second - Google Drive 3. Enter your age * 4. What is your gender? * Mark only one oval. Male Female Pow ered by https://docs.google.com/forms/d/1JPKTppRmCwWd6DVh2gI5YnMz5XlvJnHIAGEnOnkTGIA/edit 2/2
  38. 38. Appendix C In Depth Interview Questions Q1. How has been your experience with TheShoppingPro? Q2. Are you still using the product? Q3. What are the major products/sites for which you are/have used after using the product TheShoppingPro? Q4. What are the major benefits of the product according to you? Q5. What are the problems faced by you while using the product? Q6. Since the installation of the product have you used any other price comparison/coupon search website? Q7. Any suggestions for the improvement of the product Page 36

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