How Portsmouth shopping area marketing managers can benefit conducting data mining

activities as a way in which to support their decision-making process

Module: Metrics for Marketing Performance

Seminar Tutor: Singh Pallavi

Student number: B4031203

Word count: 2,500

Katy Johnson

1 | P a g e

Contents

1.1 Introduction Page 3

1.2 Data mining activity quantitative Page 3

1.3 Data mining activity qualitative Page 8

1.4 Quantitative analytical method Page 10

1.5 References Page 14

1.6 Appendix Page 15

Appendix 3.0 Qualitative analytical method Page 24

Appendix 3.1 Qualitative conclusion Page 28

Appendix 3.2 Quantitative conclusion Page 30

2 | P a g e

1.1 Introduction

Within this report, will be critically analyse Portsmouth area shopping. Their problems are an implication of growth concentration in food retailing in the UK this is a result of small store decline over the long term. Therefore, this will be impacting customer choices. The large quantitative behaviour was combined with qualitative richer understanding of different household use and experiences of local retail provision. Justified will be how marketing managers might gain from conducting data mining activities as a way in which to support their decision-making process by using qualitative and quantitative analytical methods. The topics analysed will be; whether people mind going out of their way to do their shopping, whether shopping is enjoyable, price comparison, age, too many own brands in large stores and whether convenience matters. Further to this analysis I will decide how valuable the analysis is for the marketing managers.

According to Katrin Niglas (2000) “although quantitative and qualitative research use different methods, they are also similar in some ways and the combined use of them will be beneficial in understanding Marketing performance.” I will therefore be using qualitative and quantitative methods.

1.2 Data mining activity quantitative

According to Foster, Diamond and Jefferies (2014), there are four different types of variables. The key type is categorical variables; this variable represents the measures that are discrete values which are not necessarily the same distance apart on the variable. The second variable is nominal variable; this is where the values are not even in any particular order such as a measure of people’s favourite newspaper. The third type of variable is ordinal variables the values are in order but we cannot assume them to be equidistant, e.g. people’s agreement with a proposition. This type of variable identifies people’s degree of agreement with that statement. The final type of variable is continuous variable; this variable represents things we want to measure which are not formed into discrete values such people’s height or weight in which case the distance between each value is the same. Whereas zero in a continuous variable indicates, an absolute point the variable is said to be a ratio variable. Where zero in a continuous variable is arbitrary the variable is said to be an interval variable. Continuous variables might be constructed as a composite through the combination of several categorical variables. This should be done with argument and an underpinning theory.

Within this section I aim to choose the right variables to conduct data mining activities to collect a range of data that will support a comparison of qualitative and quantitative analytical methods which might be useful for marketing managers.

These variables are ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

The first variable is; Avoid walking more than 5 mins. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would

3 | P a g e

measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

Univariate of avoid walking more than 5 minutes

The second variable is; don’t mind going out of my way for better shops. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

This univariate chart illustrates that 38% of people agree that they would avoid walking more than 5 minutes to do their grocery shopping. Therefore, this is partial correlation. By referring to appendix 1.1 the bivariate chart illustrates a comparison of people who avoid walking more than 5 minutes and people who don't mind going out of their way for better shops. The trend in the bivariate chart is 18.87% of people agree that they don’t mind going out of their way for better shops in comparison to avoid walking more than 5 minutes.

The third variable is; Shopping for groceries is usually enjoyable. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey

4 | P a g e

within the store. These values offered for this would include the strength on how much they agree with the suggestion.

Univariate for grocery shopping is usually enjoyable

The fourth variable is; food shopping very tiring. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

This univariate chart illustrates that 42% of people agree that they enjoy grocery shopping; therefore, this has partial correlation. By referring to appendix 1.2 the bivariate chart illustrates a comparison of grocery shopping is usually enjoyable and food shopping very tiring. The bivariate has a trend that is 21.34% of people disagree with food shopping is tiring in comparison to grocery shopping is usually enjoyable.

The fifth variable is; what age group are you in? The type of variable for this question would be categorical continuous. This is meaning numbers go up and are within categories. I plan to measure this information through a questionnaire given to customers in the store. This way the information would be directly from the

5 | P a g e

customers and be able to identify what the trend age range is for Portsmouth area shoppers.

Univariate for age

The sixth variable is survey; large store has too many own labels? This type of This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

This univariate chart illustrates that 38% of people are over 60; therefore, this is partial correlation. By referring to appendix 1.3 the bivariate chart illustrates a comparison of what age group are you in and large stores have too many own labels. The bivariate trend is 15.44% of people disagree with large stores have too many own labels in comparison to their age group.

The seventh variable is; too many brands in large stores. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion

6 | P a g e

Univariate for too many brands in large stores

The eighth variable is; I usually do a lot of comparing of price for ordinary food purchases. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

This univariate chart illustrates that 38% of people disagree that there are too many brands within a large store; therefore, this is a partial correlation. By referring to appendix 1.4 the bivariate chart illustrates a comparison of too many brands in large stores and usually does a lot of comparing of price for ordinary food. The bivariate trend is 15.37% of people agree they usually compare food prices in comparison to food too many brands in large.

The ninth variable is; can easily get to any shop in area. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store.

7 | P a g e

These values offered for this would include the strength on how much they agree with the suggestion.

Univariate for can easily get to any shop in area

The tenth variable is; convenience worth extra cost. This variable is ordinal. This means the values are in order but cannot assume them to be equidistant. I would measure this data through getting customers to answer a survey within the store. These values offered for this would include the strength on how much they agree with the suggestion.

This univariate chart illustrates that 58% of people agree that they can easily get to any shop in area; therefore, this is a partial correlation. By referring to appendix 1.5 the bivariate chart illustrates a comparison of can easily get to any shop in area and convenience worth extra cost. The trend in the bivariate is 24.05% of people agree convenience is worth extra cost in comparison to can easily get to any shop in area.

1.3 Data mining activity qualitative

8 | P a g e

Within this section will be reviewed three different pieces of qualitative data; focus groups to collect a range of data which will support a comparison of some of the quantitate and qualitative analytical methods for marketing managers.

1 st focus group

The first focus group is based in London. The environmental psychologist, Michelle is from University of Sheffield. Her colleague works within Sheffield, she's from London College of Fashion and she's also a famous fashion designer. The first volunteer within the focus group is called Tom, he was interested and curious as he has never done a focus group before then wanted to discuss the shopping area. The second volunteer within the focus group is called Tina, she is a passionate shopper. The importance of the beginning of the study is that the environmental psychologist asked them multiple question getting the conversation going to gain a basic understanding of who they are and their initial opinions on the shopping area without any influence. They queried about their background, age, purchasing habits, quantity of spending, ethical considerations, how they are influenced, whether shopping is a social activity, spending on electric/water and does the customer choice factors differ. After this stage, Michelle and her colleague go into more detail about the project. They stated that they came together to bring energy focused and sustainable fashion. They are related by manufacturer, therefore when looking at the sustainability in the fashion industry then identifying how they could link this to the energy industry. They are working with ASOS, Lush and M&S. They have E.ON, ecotricity and an independent fashion designer involved. Their first stage of research was at Westfield. They got the fast fashion; couture designers, fashion journalists, Lush and energy staff together and came up with some solutions of how energy and fashion can work together. The second stage was the focus group and came up with solutions of how energy and fashion can work together in Sheffield. The third stage is looking over all the feedback and providing the information to the partners. They then played a video of the workshop to the focus group. The workshop was divided into three groups a mix of fashion and energy. The partners tend to disagree a lot. They queried the focus groups opinion on QR reader/ bar code on wash label then a link to where it was made. The final discussion was to ask the focus group whether they thought a pass on savings would be effective; this is where they would actually see an image of where they would be saving money. Finally, when the focus group was done she handed out a survey to be filled out.

2 nd focus group

The second focus group is based in Sheffield. There are two moderators hosting the focus group to 6 people. Again, colleague Mary was moderating this focus group. There is a student called Emily in her second year of psychology. The second volunteer is a man named Matthew; he works at the University of Sheffield. The third volunteer is his wife Nikki; she has done some other projects in the past. The fourth volunteer is Davina; she found out about the focus group through a blog and thought

9 | P a g e

it would be interesting. The fifth volunteer is Anna; she works in health care and heard about the focus group through a friend. Finally, there's Laura; she heard about it through her daughter and was interested. The moderator starts off discussing clothing, they ask about their feeling when purchasing, why they shop for clothing and the considerations they think about. The moderator then asks where they shop for energy and water; asked about feelings and considerations. Then asked about the similarities between purchasing energy and clothing; what was their considerations and feelings when purchasing. Mary had been to a conference on sustainable clothing, although it wasn't very worthwhile as nothing stood out to her. The moderator then asked the focus group's thoughts on companies presenting green ethos. What challenges and opportunities do they think the company will have. Does culture effect how green ethos is promoted? Then they played a clip on an energy company and asked would passing on the savings change perspective on whether to go with that company and support ethos green. The moderators take a more qualitative approach and report some of the focus groups comments as themes as they work in partners. The key discussions within this focus group were identified as price; the idea of looking for bargaining with energy and fashion. The idea concludes that you can only be sustainable if you are from a higher income or make a continuous effort to support green ethos.

1.4 Quantitative analytical method

Figure 1 correlationAvoid walking more than 5 minutes in correlation to don’t mind going out of way for better shops. - 0.92 figure means that this is a negative correlation. This also represents the small effect (Field, 2013).

Correlationsavoid walking

more than 5

mins

don't mind going

out of way for

better shops

Spearman's rho avoid walking more than 5

mins

Correlation Coefficient 1.000 -.092

Sig. (2-tailed) . .059

N 427 424

don't mind going out of way

for better shops

Correlation Coefficient -.092 1.000

Sig. (2-tailed) .059 .

N 424 426

Chi - Square testsThe Chi - square test represents a relationship between avoid walking more than 5 minutes and don’t mind going out of way for better shops. The 8 cells (32.0%) have expected count less than 5. The minimum expected count is 77. The distribution compared to the expectation is 16. Therefore, this distribution is low compared to expectation (Foster et al, 2014).

Chi-Square Tests

10 | P a g e

Value Df

Asymptotic

Significance (2-

sided)

Pearson Chi-Square 35.456a 16 .003

Likelihood Ratio 35.196 16 .004

Linear-by-Linear Association 3.166 1 .075

N of Valid Cases 424

a. 8 cells (32.0%) have expected count less than 5. The minimum

expected count is .77.Figure 2 correlation

Grocery shopping is usually enjoyable in correlation to food shopping is very tiring. -.315 this is a negative correlation. It also represents a medium effect (Field, 2013).

Correlationsgrocery

shopping usually

enjoyable

food shopping

very tiring

Spearman's rho grocery shopping usually

enjoyable

Correlation Coefficient 1.000 -.315**

Sig. (2-tailed) . .000

N 423 417

food shopping very tiring Correlation Coefficient -.315** 1.000

Sig. (2-tailed) .000 .

N 417 422

**. Correlation is significant at the 0.01 level (2-tailed).

Chi - square testsThe Chi - square test represents a relationship between Grocery shopping is usually enjoyable and food shopping is very tiring. The 6 cells (24%) have expected count less than 5. the minimum expected count is 1.23. The distribution compared to the expectation is 16. Therefore, this distribution is high compared to expectation (Foster et al, 2014).

Chi-Square Tests

Value Df

Asymptotic

Significance (2-

sided)

Pearson Chi-Square 114.905a 16 .000

Likelihood Ratio 95.581 16 .000

Linear-by-Linear Association 45.006 1 .000

N of Valid Cases 417

a. 6 cells (24.0%) have expected count less than 5. The minimum

expected count is 1.23.

Figure 3 correlationWhat age group are you, in correlation to usually compare food prices. -.013 this has no correlation. It represents a small effect (Field, 2013).

11 | P a g e

Correlationsusually compare

food prices

what age group

are you in

Spearman's rho usually compare food prices Correlation Coefficient 1.000 -.013

Sig. (2-tailed) . .797

N 425 418

what age group are you in Correlation Coefficient -.013 1.000

Sig. (2-tailed) .797 .

N 418 421

Chi - square testsThe Chi - square test represents a relationship between what age group are you in and usually compares food prices. The 13 cells (43.3%) have expected count less than 5. the minimum expected count is 0.9. The distribution compared to the expectation is 20. Therefore, this distribution is high compared to expectation. (Foster et al, 2014)

Chi-Square Tests

Value Df

Asymptotic

Significance (2-

sided)

Pearson Chi-Square 26.284a 20 .157

Likelihood Ratio 26.623 20 .146

Linear-by-Linear Association .006 1 .937

N of Valid Cases 418

a. 13 cells (43.3%) have expected count less than 5. The minimum

expected count is .09.

Figure 4 correlationCan easily get to any shop in area in correlation to convenience worth extra cost. .084 This has no correlation. It represents a small effect (Field, 2013).

Correlationscan easily get to

any shop in area

convenience

worth extra cost

Spearman's rho can easily get to any shop in

area

Correlation Coefficient 1.000 .084

Sig. (2-tailed) . .085

N 427 420

convenience worth extra cost Correlation Coefficient .084 1.000

Sig. (2-tailed) .085 .

N 420 421

Chi - square testsThe Chi - square test represents a relationship between can easily get to any shop in area and convenience worth extra cost. The 8 cells (32%) have expected count less than 5. the minimum expected count is 1.50. The distribution compared to the

12 | P a g e

expectation is 16. Therefore, this distribution is high compared to expectation (Foster et al, 2014).

Chi-Square Tests

Value Df

Asymptotic

Significance (2-

sided)

Pearson Chi-Square 73.099a 16 .000

Likelihood Ratio 67.828 16 .000

Linear-by-Linear Association 2.542 1 .111

N of Valid Cases 420

a. 8 cells (32.0%) have expected count less than 5. The minimum

expected count is 1.50.

Figure 5 correlation

Large store has too many own labels in correlation to too many brands in large

stores. .349 This has a positive correlation. It has a medium effect (Field, 2013).Correlations

large store have

too many own

labels

too many brands

in large stores

Spearman's rho large store have too many

own labels

Correlation Coefficient 1.000 .349**

Sig. (2-tailed) . .000

N 429 427

too many brands in large

stores

Correlation Coefficient .349** 1.000

Sig. (2-tailed) .000 .

N 427 427

**. Correlation is significant at the 0.01 level (2-tailed).

Chi - square testsThe Chi - square test represents a relationship between large stores has too many own labels and too many brands in large stores. The 6 cells (24%) have expected count less than 5. the minimum expected count is 1.31. The distribution compared to the expectation is 16. Therefore, this distribution is high compared to expectation (Foster et al, 2014).

Chi-Square Tests

13 | P a g e

Value Df

Asymptotic

Significance (2-

sided)

Pearson Chi-Square 221.234a 16 .000

Likelihood Ratio 179.875 16 .000

Linear-by-Linear Association 46.700 1 .000

N of Valid Cases 427

a. 6 cells (24.0%) have expected count less than 5. The minimum

expected count is 1.31.

1.5 References

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th Ed). Sage.

Foster, L., Diamond, I., & Jefferies, J. (2014). Beginning statistics: an introduction for social scientists (2nd Ed). Sage.

Niglas, K. (2000). Combining quantitative and qualitative approaches. In European Conference on Educational Research, Retrieved from http://www.leeds.ac.uk/educol/documents/00001544.htm.

Trip Advisor. (2016). Gun wharf Quays. Retrieved from https://www.tripadvisor.co.uk/Attraction_Review-g186298-d1908667-Reviews-Gunwharf_Quays-Portsmouth_Hampshire_England.html.

14 | P a g e

1.6 Appendix

Appendix 1.1

Bivariate of ‘avoid walking more than 5 minutes’ and ‘don’t mind going out of way for better shops’

15 | P a g e

Appendix 1.2

Bivariate for grocery shopping is usually enjoyable and food shopping very tiring

16 | P a g e

Appendix 1.3

Bivariate for what age group are you in and large store have too many labels

17 | P a g e

Appendix 1.4

Bivariate for too many brands in large stores and usually compare food prices

18 | P a g e

Appendix 1.5

Bivariate of can easily get to any shop in area and convenience worth extra co

19 | P a g e

Appendix 2.1

Figure 1 Chi - square tests

20 | P a g e

Case Processing SummaryCases

Valid Missing Total

N Percent N Percent N Percent

avoid walking more than 5

mins * don't mind going out

of way for better shops

424 98.6% 6 1.4% 430 100.0%

Symmetric Measures

Value

Approximate

Significance

Nominal by Nominal Phi .289 .003

Cramer's V .145 .003

N of Valid Cases 424

Appendix 2.2Figure 2 Chi - square tests

Case Processing SummaryCases

Valid Missing Total

N Percent N Percent N Percent

grocery shopping usually

enjoyable * food shopping

very tiring

417 97.0% 13 3.0% 430 100.0%

Symmetric Measures

21 | P a g e

Value

Approximate

Significance

Nominal by Nominal Phi .525 .000

Cramer's V .262 .000

N of Valid Cases 417

Appendix 2.3

Figure 3 Chi - square tests

Case Processing SummaryCases

Valid Missing Total

N Percent N Percent N Percent

usually compare food prices

* what age group are you in

418 97.2% 12 2.8% 430 100.0%

Symmetric Measures

Value

Approximate

Significance

Nominal by Nominal Phi .251 .157

Cramer's V .125 .157

N of Valid Cases 418

22 | P a g e

Appendix 2.4

Figure 4 Chi - square testsSymmetric Measures

Value

Approximate

Significance

Nominal by Nominal Phi .417 .000

Cramer's V .209 .000

N of Valid Cases 420

Case Processing SummaryCases

Valid Missing Total

N Percent N Percent N Percent

can easily get to any shop in

area * convenience worth

extra cost

420 97.7% 10 2.3% 430 100.0%

Appendix 2.5

Figure 5 Chi - square tests

Case Processing SummaryCases

Valid Missing Total

23 | P a g e

N Percent N Percent N Percent

can easily get to any shop in

area * convenience worth

extra cost

420 97.7% 10 2.3% 430 100.0%

Symmetric Measures

Value

Approximate

Significance

Nominal by Nominal Phi .417 .000

Cramer's V .209 .000

N of Valid Cases 420

Appendix 3.0

Critically analysed is review by the public of Portsmouth shopping centre; Gunwharf Quays (Trip advisor, 2016) This data will gather information of the general public’s opinions on the Portsmouth shopping centre after their visit. This will allow the marketing managers to gain a better understanding of their customers and how they can improve their stores to meet customer satisfaction.

24 | P a g e

Tim121homeSouthampton, United Kingdom“Use park & Ride”

3/5 stars

Perhaps because all stores are holding back for January sales, but prices seem to be creeping up lately. Use the Park & Ride service, very efficient and cheaper than the car park.

andywong1138Hong Kong, China“A nice place for shopping”

5/5 stars

Travel to London once very year, Gunwharf Shopping Centre is A MUST go place buying heavily discounted clothes. You can find lot of well-known brands for adult and kids. A few very nice restaurants people can visit after a busy shopping!

Dawn MBasingstoke, United Kingdom“Perfect day out”

5/5 stars

We went down on the Saturday after Black Friday expecting it to be heaving but it actually wasn't that bad. There was queues to get into Kors, Nike and Fossil. All three had bouncers and were cordoned off so it was very well organised. Lots of lovely girls from Cadburys handing out sweets, full of smiles and stopped to chat and make sure everyone was ok. The staff in every shop we went to were lovely and friendly and couldn't be better. Would definitely go back again

DonmeaLondon, United Kingdom“Was ok, not what I expected”

5/5 stars

So, after hearing rave reviews about this place from my friend, my partner and I decided to check it out. The outlet is super clean, very helpful staff. A good selection of shops but was expecting it to be much bigger than it was. Having said that definitely worth a visit especially because of the views of the quays.

George KBournemouth, United Kingdom“The perfect place for shopping and dining”

5/5 stars

There is not much to say...actually, I could go on forever, but I won't :-DThe best makes, at the best prices you will ever find, short of online (but what's the

25 | P a g e

fun in clothes shopping like that?!), and there are so many bargains to be had.The range of restaurants, and the parking availability, make this a definite treat for any occasion.

maddiePortsmouthTitchfield, United Kingdom“Way too many designer shops”

4/5 stars

Used to come here on a regular basis but although there's still an extensive choice of shops they are all pitched at the high end of the market. As such we miss the fat Face and normal middle of the range items. We went early on a Sunday morning and it was really clean too.

Chris AWinchester, United Kingdom“Good Shopping”

4/5 stars

A little retail therapy does you good! Gunwharf Quays is a nice, clean and easy to get to set of factory outlet stores. There is plenty of underground parking. There are many stores and plenty of restaurants, all with the backdrop of Portsmouth harbour. After the shopping, it is an easy walk to either Old Portsmouth or the other way to the old dockyards to visit the maritime museum with HMS Victory and Mary Rose ships. A really great day out for couples or with the family! The shops are open 7 days a week and information on opening times etc. Are easy to find online!Have a great time at Gunwharf Quays!

26 | P a g e

27 | P a g e

28 | P a g e

3.1 Qualitative Conclusion

Reviewing, the two group interviews based in London and Sheffield of value for the marketing managers. This is a very good illustration of how the buying process can differ depending on the individual. The shoppers who are based within London would care more about the quality of a purchase. They would consider some influence by the price of product although their main influence would be quality. The customers would be more willing to go further locations for their products. Whereas, the shopper based in Sheffield are very price driven. Their preference is to have a lower quality item and a better price. The customer is less likely to go further for their product. They would be a convenience buyer. This is also a good illustration on how brand driven a person is. This will be of value for the marketing managers as this can help them have a better understanding of competitor customers. Then they can use knowledge of the customer’s needs and wants to implement into Portsmouth shopping centre. Therefore, gaining greater competitor advantage and hold a better positon amongst the market.

29 | P a g e

Overviewing, the analysis on customer reviews of the Portsmouth shopping centre. There have been positives and negatives throughout about the Portsmouth shopping centre. There have been predominately positives. One customer stated they loved the quality designer brands. Customer satisfaction has been met as the shopping centre provides clothes for adults and kids. Multiple customers praised the variety of restaurants within the shopping centre; suitable place for families and couples. Long opening hours provided. One customer praised the organisation for the Black Friday event. How the staff maintained its friendly atmosphere and bouncers provided a good service. Some customers praised the parking availability as they were coming from a far.

An Improvement would be to provide a similar service to Black Friday where they would meet customer satisfaction. The marketing managers should ensure this service is maintained in order to keep customer satisfaction high during events. One particular customer was dissatisfied as the prices for parking had increased during the Christmas period. However, the marketing managers should not take any action as this is an increase in revenue for the peak time of year. If they were to alter this, it would become a disadvantage for the Portsmouth shopping centre. A customer had been recommended this shopping centre by numerous people and enjoyed their experience. However, they were expecting a larger shopping centre. The marketing managers could take into consideration for an expansion to meet the customer satisfaction as this is a well-known, regularly used shopping centre. Some customers preferred the previous stores within the shopping centre as the stores where lower quality/cheaper. Some standard stores could be implemented into the shopping centre.

It would seem that customers do not come to shop at Portsmouth shopping centre for food retailing. The implication of growth of concentration in food retailing amongst the UK is as a result of small store decline over long term. Through market research it is clear that customers come to Portsmouth shopping centre for clothes retailing. Also, a quantity go to restaurants whilst on their visit. This has influenced customer behaviour patterns resulting in a decline of food retailing. The upper class tend to come to this area since the alterations was made to the shops. The shops now are high quality and cost which would meet the upper class’ needs. Therefore, some lower-class families would not come to visit the Portsmouth shopping centre. Another impact could be whether the customer is a convenience shopper or would be willing to go further locations to do their shopping. This is of value for the marketing managers as the target market does not seem to visiting for food retail. The customer’s preference is to go for clothes retail and visit a restaurant. This is the reasoning for the decline on food shopping. If the marketing managers wish to increase their food retail, they should re-evaluate their target market. It tends to be customers from varied locations visit the shopping centre. If more visitors from local areas shopped within this shopping centre than they would is more willing to spend on food retail.

30 | P a g e

3.2 Quantitative conclusion

Reviewing, the quantitative data mining there was a number of univariate, bivariate and Chi-square charts comparing two variables. This is to measure customer preferences in order to improve the Portsmouth shopping centre. The first set of data compares people who avoid walking more than 5 minutes to do their grocery shopping and people who don't mind going out of their way for better shops. The univariate chart illustrates that 38% of people agree that they would avoid walking more than 5 minutes to do their grocery shopping. Therefore, this is partial correlation. The bivariate chart illustrates a comparison of people who avoid walking more than 5 minutes and people who don't mind going out of their way for better shops. The trend in the bivariate chart is 18.87% of people agree that they don’t mind going out of their way for better shops in comparison to avoid walking more than 5 minutes. The Chi-square chart illustrates the figure - 0.92 which means that this is a negative correlation. This also represents the small effect. It is also found that the distribution is low compared to expectation.

The second set of data compares grocery shopping is usually enjoyable and food shopping is very tiring. The univariate chart illustrates that 42% of people agree that they enjoy grocery shopping; therefore, this has partial correlation. The bivariate has a trend that is 21.34% of people disagree with food shopping is tiring in comparison to grocery shopping is usually enjoyable. The Chi-square chart illustrates the figure -.315 this is a negative correlation. It also represents a medium effect. The distribution compared to the expectation is 16. Therefore, this distribution is high compared to expectation

The third set of data compares the age group of the customer and large stores have too many own labels. The univariate chart illustrates that 38% of people are over 60; therefore, this is partial correlation. The bivariate trend is 15.44% of people disagree with large stores have too many own labels in comparison to their age group. The Chi-square chart illustrates the figure -.013 this has no correlation. It represents a small effect. It is also found that the distribution is high compared to expectation.

The fourth set of data compares too many brands in large stores and usually does a lot of comparing of price for ordinary food. The univariate chart illustrates that 38% of people disagree that there are too many brands within a large store; therefore, this is a partial correlation. The bivariate trend is 15.37% of people agree they usually compare food prices in comparison to food too many brands in large. The figure .084 has no correlation. It represents a small effect. The distribution compared to the expectation is 16. Therefore, this distribution is high compared to expectation.

The final set of data compares can easily get to any shop in area and convenience worth extra cost. This univariate chart illustrates that 58% of people agree that they can easily get to any shop in area; therefore, this is a partial correlation. The trend in the bivariate is 24.05% of people agree convenience is worth extra cost in comparison to can easily get to any shop in area. The figure .349 has a positive correlation. It has a medium effect. This distribution is high compared to expectation.

31 | P a g e

32 | P a g e