Big Data Analysis. coffee shop chain Second Cup

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Contents

Introduction...2

Coffee Shop Chains as distributed by Regions of Toronto (WARD, BIA)...2

Analysis of the Coffee Shop Chains configuration...5

Fractal Analysis of Spatial Café Distribution. ... 10

Common knowledge about Second Cup...11

One-By-One Café Analysis ... 14

Elements of Correlation Analysis ...22

Where the money is ...25

(Estimates of the potential customers number based on the WalkersBy Statistics). ...25

Big Data Analysis

visitors and walkers by

;

coffee shop chain Second Cup

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Introduction

For that report and for the corresponding research all visible in Toronto chains of coffee shops have been selected. There do exist some differences in the character and the service parameters of the nets mentioned above; some of them are presented by very limited number of cafes but in that case all-Canadian size of a network has been taken into consideration. Some of café nets have chosen strategy not to take part in competition in Toronto (GTA), preferred to develop in other cities in Ontario (for instance, Neighbours; Good Earth Coffee House and Bakery; Blentz), also according to the reasons above Country Style and Nestle Toll House Café.

As a result at list under consideration for better understanding of the Second Cup net performance has been included:

 74 cafes of Second Cup;

 47 cafes of Starbucks;

 25 cafes of Tim Hortons;

 22 cafes of Timothy’s World Coffee;

 5 cafes of Coffee Time Donuts;

 2 cafes of Presse Café.

The number and choice of cafes have been determined by the opinion of the every net itself what the limits of the city and the net are.

Period of time between January 2nd, 2014 and April 22, 2014 is taken in consideration for getting figures from measurements on Second Cup sites.

Coffee Shop Chains as distributed by Regions of Toronto (WARD,

BIA).

Distribution of cafes over city regions could be considered as one of the forms of visual presentation of the Second Cup cafes in downtown Toronto and outskirts. There do exist two main adopted forms of Toronto’s subdivision: WARD system and BIA areas. Information about WARDs (Picture 1) could be found at URL

http://app.toronto.ca/wards/jsp/wards.jsp .

Those pages contain the very detailed information about the structure of households and age groups of people as they distribute among WARD regions. As one can see from the Table 1 (WARDs), the most number of cafes from different nets concentrate in three WARD regions: namely 20, 27 and 28. Together, these three regions accommodate 43% of all cafes under consideration.

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Picture 1. Map of Toronto WARD regions.

It must be mentioned hat in these three WARD regions live as many as 222 thousand inhabitants, which is less than 1/10 of the Toronto population (2.6 - 2.8 million of people). The Torontonians who lives in these regions are mostly 25-44 years old and more than a half live by themselves. Here one can conclude that the evident discrepancy in spatial café distribution follows from the principle that all café nets have been developed relying upon the white collars moving to their job places from the city outskirts to the Old Downtown. However, there are two nets with much more even distribution of cafes over the WARD regions – Second Cup and Timothy’s World Coffee.

Table 1. Café networks as distributed by the WARD regions (Toronto) WARD Coffee Shop Chain 4 5 6 8 11 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28 29 30 32 37 38 Total Second Cup 2 7 1 1 0 1 0 1 2 1 1 11 0 9 5 1 5 2 10 11 0 1 0 1 1 74 Starbucks 11 19 17 47 Tim Hortons 4 11 10 25 Timothys World Coffee 2 1 1 1 1 2 1 7 5 1 22 Coffee Time Donuts 1 2 1 1 5 Press Café 1 1 2

From our point of view, the distribution of cafes of Second Cup among the Ward regions much more corresponds with the basic idea of “the third place” in comparison with the Starbucks one. Cafes of Second Cup are so evenly distributed over the city that the slogan “You have not and you should not go to the downtown for

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Table 2. Café networks as distributed by the BIA regions (Toronto) BIA Coffee Net 4 7 10 13 20 22 27 28 30 31 40 41 46 53 57 60 61 65 66 68 69 70 71 76 Total Second Cup 2 1 3 1 1 4 1 1 7 1 1 1 0 2 0 0 0 2 4 0 1 1 1 1 35 Starbucks 11 15 3 2 8 38 Tim Hortons 6 7 4 1 18 Thimothy’s World Coffee 2 1 1 4 1 1 1 1 2 1 1 1 17 Coffee Time Donuts 0 Press Café 1 1

Division of Toronto in the BIA regions - the so-called Business Improvement Areas – is being created in cooperation between Toronto City Council and local property owners and tenants to enhance the look, feel and

safety of neighbourhoods

(http://www1.toronto.ca/wps/portal/contentonly?vgnextoid=673032d0b6d1e310VgnVCM10000071d60f89RCR D).

BIAs are smaller in size in comparison with the WARD regions and start because of local business initiative. That is why the distribution of cafes among the BIA regions looks a little bit more important: BIA regions are less formal and denote some real processes of developing and reengineering of territories. The developed regions become more attractive to businesses and, therefore, people. And so more people bring their money to the coffeemakers.

We may conclude with caution: to be in the BIA region is more profitable than not to be. One can conclude from Table 2 that in a parallel way with the WARD regions there are three BIA regions with obvious concentration of cafes of different nets. They are BIA regions 22 (Downtown-Yonge), 30 (Financial District) and 66 (Toronto Entertainment District). They all together accommodate 63% of all cafes (110 ones) in BIA.

Again, the most wide-spread spatial distribution is in Second Cup and Timothy’s World Coffee shop chains; however the best ratio of cafes in BIA regions belongs to Starbucks. Also should be noted that Coffee Time Donuts do not have any cafes in BIA at all.

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Picture 2. Map of BIA regions

Analysis of the Coffee Shop Chains configuration

Analysis of spatial distribution is helpful to determine how the café network is being developed in time and space under the pressure of rivals and according to the changes in the city infrastructure. One of the most practical ways to travel around Toronto is the (mostly) underground railway – Metro. Many people use the Metro to avoid traffic jams, especially in the rush hours. One could consider a number of models of social behavior of an individual but some of them are evident and lay on the surface: the morning and evening travel of a person (“white” or “blue collar”) to his place of work. The person could possibly be using a complex route with transfers (trams and buses) but what he/she will definitely use are two metro stations twice a day. That makes Metro be a very attractive place for settlement of any kind of an impulse-driven business.

One has to also keep in mind that the person described previously also typically has his/her lunchtime near the job place. That is the second attractive point in space for opening a bistro/café/lunchbox kiosk nearby or in the business center area.

We have placed cafes of several visible café chains (nets) onto a map with the metro stops marked (Picture 3). The Cartesian coordinates in NAD27 system have been used so all the measurements on this relative map are in one internal system and are precise with accuracy up to centimeters.

Firstly let us evaluate the distribution of cafes of different chains with respect to the metro stops. The map on picture 3 at this scale makes us notice that

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1. Second Cup and Timothy’s World Coffee have a tendency to be closely located and these pairs are distributed over the GTA more or less evenly;

2. Second Cup cafes have a tendency to be located near the metro stops even outside of the Downtown limits;

3. Starbucks cafes are located only in the Downtown and encircle metro stops; one could see that up to six cafes surround a chosen metro stop, so a person getting out of Metro will definitely face with a Starbucks café wherever he plans to go.

Considering the same map (Picture 4) under magnification only improves the first impression. The Second Cup chain, besides the tendency to be located along the metro lines, demonstrates a couple of other spatial peculiarities:

1. Many of the cafes in the outskirts are located in the shopping/mall areas – East Mall region, North York;

2. Some cafes locate on/near the Shore with evident tendency to recreational/weekend activities. Unfortunately, by no scale could be noticed a parallel with encircling of metro stops as in case of Starbucks.

Conclusion: All the coffée shop chains have had different marketing tactics (if any) in their geographic placement. To be considered at the angle of spatial distribution, the Timothy’s World Coffee ShopChain should be considered as the closest Second Cup’s competitor.

Starbucks is aggressively encircling several downtown metro stops and has a relatively dense net in the Downtown.

Nothing to say about Tim Hortons. The absence of tendency of distribution in that case may (or may not) be connected with some special requirements or limitations of the net: they could be limited by the footage available for rent from beyond, so they get every property they can.

Some confirmations to the thesis above could be found in the table with distances to the nearest stop/friendly café/rival (Table 3). For instance, café Second Cup (1136, 30 Bond Street) has 5 cafes Starbucks on the same distance from it: 170-180 meters. The 5 closest Second Cup cafes are located in 200-500 meters interval from it. So, who is circled?

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M

Red – Toronto Metro stations Black – Second Cup Café

Green – Starbucks Cafe Blue – Tim Hortons Cafe

+

Pink – Timothy’s World Coffee

Picture 3. Map of café networks and metro lines

Toronto metro map

M

Red – Toronto Metro stations Black – Second Cup Cafe

Green – Starbucks Cafe Blue – Tim Hortons Cafe

+

Pink – Timothy’s World Cafe

Picture 4. Map of café networks and metro lines (Downtown, magnification)

Some results of calculation of distances between cafes and metro stations are presented in Table 3. Those figures are valuable for further calculations of correlations, where distances to rivals play an essential role.

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Table 3. Minimal distances (meters) to the metro stops, rivals and friendly cafes for 5 chosen SC cafes

METRO STURBUCKS TIM

HORTONS

TIMOTHY WORLD COFFEE SECOND CUP

Second Cup coffee shop 1136

135,80 Queen 3 Queen St E 163,32 167 Church Street Toronto M5B 1Y6 3,76 14 352,47 483 Bay Street Unit #170S Toronto M5G 2C9 195,81 1231 220 Yonge Street P 367,32 Dundas 3 Dundas Street East 163,78 209 Victoria St Toronto M5B 1T8 141,06 9 535,83 595 Bay Street Toronto M5G 2C2 385,20 1023 1 Dundas Street We 495,31 St George 139 St George Street 172,18 1 Queen Street East Toronto 193,02 3 632,02 100 King Street West Toronto M5X 1A3 423,22 1328 333 Bay Street 505,63 King 3 King Street East 189,82 218 Yonge Street Toronto M5B 2H6 194,32 4 710,87 150 York Street Toronto M5H 3S5 488,27 1095 40 King Street Wes 805,77 Ossington 746 Ossington Ave 195,45 220 Yonge St Toronto M5B 2H1 284,75 7 800,12 425 University Avenue Toronto M5G 1T6 533,24 1228 119 St. George Stre

482,77 St Clair West 10 Tichester Road 3632,65 37 Grosvenor Street Toronto M4Y 3G5 4040,79 18 1950,45 1070 Eglinton Avenue West Toronto M6C 2C5 1400,81 1299 1560 Yonge Street 1570,01 St Clair 15 St Clair Avenue East 3748,85 661 University Avenue Toronto M5G 1M2 4233,61 11 1977,62 10 Scrivener Square Toronto M4W 3Y9 1499,69 1047 2 St. Clair Avenue 1592,79 Davisville 1900 Yonge Street 3824,24 450 Yonge St. Toronto M4Y 1X1 4276,36 12 2477,94 90 Eglinton Avenue East Toronto M4P 2Y3 1649,78 1121 1881 Yonge Street 1622,91 Dupont 263 Dupont Street 3892,88 585 University Avenue Toronto M5G 2N2 4312,84 20 2665,01 700 Mount Pleasant Road Toronto M4S 2N7 1657,00 1161 518 Eglinton Ave. 1889,59 Summerhill 16 Shaftesbury Avenue 3931,58 30 Carlton Street Toronto M5B 2E9 4354,11 6 2864,93 55 Bloor Street West Toronto M4W 1E4 2216,41 1031 2200 Yonge Street

669,79 Museum 75 Queen’s Park 807,44 661 University Avenue Toronto M5G 1M2 1074,30 18 1065,20 801 Bay Street, Unit #3, #4 Toronto M5S 1Y9 263,13 1263 66 Harbord Street 697,18 Spadina 6 Spadina Avenue 913,14 585 University Avenue Toronto M5G 2N2 1195,46 11 1132,45 55 Bloor Street West Toronto M4W 1E4 544,38 1163 179 College Street

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707,46 Queen’s Park 129 College St 1030,18 37 Grosvenor Street Toronto M4Y 3G5 1201,81 12 1198,09 444 Yonge Street Toronto M5B 2H4 603,22 1067 750 Spadina Avenue 1099,97 Bathurst 819 Bathurst Street 1067,45 555 University Avenue Toronto M5G 1X8 1262,59 6 1278,22 425 University Avenue Toronto M5G 1T6 604,89 1029 324 Bloor Street We 1112,92 Bay 240 Bay St. 1089,32 525 University Avenue Toronto M5G 2L3 1460,63 2 1419,58 595 Bay Street Toronto M5G 2C2 667,36 1182 340 College Street

269,65 King 3 King Street East 119,40 40 King Street West Toronto M5H 4A9 16,30 8 208,77 100 King Street West Toronto M5X 1A3 118,37 1095 40 King Street Wes 294,75 Queen 3 Queen St E 186,47 1 Adelaide St E Toronto M5C 2V9 93,62 5 350,45 150 York Street Toronto M5H 3S5 224,61 1070 25 King Street Wes 505,04 St Andrew 173 King Street West 189,09 394 Bay Street Toronto M5H 2Y3 121,87 17 400,31 483 Bay Street Unit #170S Toronto M5G 2C9 366,99 1231 220 Yonge Street P 520,90 Union 55 Front Street West 197,19 4 King Street West Toronto M5H 1B6 141,03 7 691,88 595 Bay Street Toronto M5G 2C2 411,77 1288 165 University Ave 522,28 Ossington 746 Ossington Ave 202,05 22 Bay st and Queen st 145,46 10 723,70 425 University Avenue Toronto M5G 1T6 423,35 1136 30 Bond Street

473,33 Spadina 6 Spadina Avenue 1070,42 661 University Avenue Toronto M5G 1M2 1334,21 18 1259,53 55 Bloor Street West Toronto M4W 1E4 263,16 1284 100 St. George Stree 808,59 Museum 75 Queen’s Park 1173,64 585 University Avenue Toronto M5G 2N2 1442,44 12 1322,10 801 Bay Street, Unit #3, #4 Toronto M5S 1Y9 371,41 1067 750 Spadina Avenue 836,81 Bathurst 819 Bathurst Street 1278,41 37 Grosvenor Street Toronto M4Y 3G5 1445,03 11 1456,41 444 Yonge Street Toronto M5B 2H4 401,71 1029 324 Bloor Street We 970,15 Queen’s Park 129 College St 1327,43 555 University Avenue Toronto M5G 1X8 1496,32 6 1523,13 425 University Avenue Toronto M5G 1T6 657,95 1182 340 College Street

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1212,45 Bay 240 Bay St. 1342,89 525 University Avenue Toronto M5G 2L3 1720,57 2 1680,99 595 Bay Street Toronto M5G 2C2 673,92 1073 537 Bloor Street We

Fractal Analysis of Spatial Café Distribution.

It is very useful to get some quantitative estimates for or against the thesis described above and replace “ it is visible that” with calculations.

We use fractal analysis for analysis of the spatially distributed data. Fractals are well-known in many science applications and their definitions are very abstract and complex. Nevertheless, there do exist a number of simplifying approaches to what is called as a fractal dimension and its measurements. Fractal (or mass, or density, or cell) dimension is not an integer number in most cases and it shows how far the object under consideration is from a standard geometrical object: straight line (dimension 1), plane (dimension 2) and so forth. The more the object differs from the regular pattern the more it looks like dotted or corrupted, the more chaotic it is. The algorithm of calculation of the fractal (cell) dimension is as follows: the plane is filled with the rectangular grid with respectively small size of the side of a rectangle; circles of a growing radius are constructed from the every vertex of the grid; the number of dots of a set under analysis is calculated in every circle. After averaging over all vertices the cell dimension is determined from the asymptotic to the graph of function of Log(number of dots) to Log(radius of circle) for a relatively large radius.

The calculations were performed for each object under consideration. Results are shown on Picture 5.

7 8 9 10 5 6 7 8 9 7 8 9 10 5 6 7 8 9 7 8 9 10 5 6 7 8 9 7 8 9 10 5 6 7 8 9 7 8 9 10 5 6 7 8 9 log(r_sph) log(n)

______ Toronto Metro Stations ______ Second Cup

______ Starbucks

______ Tim Hortons

______ Timothy World Caffee

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The following dimensions were obtained: Dmetro=0.89; DSecondCup=0.90; DStarbucks=0.84; DTimHortons=0.77;

DTimothy’s=0.78. Results are at least confusing: calculations show that the Toronto Metro scheme is not very far

from straight line; a more surprising that the Second Cup is very close to the Toronto Metro configuration. That fact supports our visual resume in the chapter above. But, moreover, the graph of the Second Cup lies above the graph of the Metro. This simply means that the Second Cup cafes are not only distributed along the Metro lines, but they also exceed the Metro stations in the density of spatial distribution. The largest part of Second Cup cafes lies on or near a few straight lines; some of them are metro lines. Starbucks, also in support of the visual results far above, stays apart from all rivals and is not a line. It is a very sparse geometrical object. But not as diluted as the remaining two companies on the Picture 5: Tim Hortons and Timothy’s World Coffee. Their graphs intersect and they are even more randomly distributed on the plane than Starbucks is. There seems to be no pattern in spatial distribution of those “rivals in space”. On the contrary, it seems like that the spatial distribution of Second Cup Cafes is not random.

Resume: Metro of Toronto stop chain and the Second Cup coffee shop chain are so close that it seems to be very natural to use the Metro as a part of some kind of an advertising campaign.

Common knowledge about Second Cup

For that report and for corresponding research all major in Toronto chains of coffee shops have been chosen. There do exist some differences in the character and service parameters of the nets mentioned above; some of them are presented by a very limited number of cafes, but in that case the all-Canadian size of a

network has been taken into consideration. Some coffée shop chains have chosen a strategy not to take part in a competition in Toronto (GTA) instead preferring to develop in other cities in Ontario (for instance, Neighbours; Good Earth Coffee House and Bakery; Blentz), also according to the reasons above Country Style and Nestle Toll House Café.

As a result at list under consideration for better understanding of the Second Cup net performance has been included:

- 74 cafes of Second Cup;

- 47 cafes of Starbucks;

- 25 cafes of Tim Hortons;

- 22 cafes of Timothy’s World Coffee;

- 5 cafes of Coffee Time Donuts;

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The number and choice of cafes have been determined by the opinion of every chain itself what the limits of the city and the chain are.

Period of time taken in consideration for getting figures from measurements on Second Cup sites is between January 2nd, 2014 and April 22, 2014.

During this period of time there have been registered a total of 3704681 visitors in all coffee shops of Second Cup chain.

If one consider the amount of days under consideration to be 112, than for average number of visitors for every day we get 3704681/112 = 33077.

If translate this figure to a single café we would have 33077/74 = 446 visitors in a café on average

One must take into account that the most cafes have different working days and times so the figures projected as a result on the whole net can have visible mistakes. Nevertheless those figures gave some impression about the Second Cup overall performance. Please find the pictures below.

0 2 4 6 8 10 12 14 16 18 20 22 24 0 500 1000 1500 2000 2500 3000 n t, hour WalkBy

Average number of customers as a function of the day hour

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0 60 120 180 240 0 100000 200000 300000 n t,min WalkBy Distribution of customers

as a function of time spent inside all cafes

Picture 7. Averaged by the whole net number of customers as a function of time spent inside

0 30 60 90 120 0 20000 40000 60000 80000

January February March April

WalkBy Daily customers

n

t, day

Picture 8. Daily customers of Second Cup as a function of a day

0 10000 20000 30000 40000 n

Sunday Tuesday Thursday Saturday Monday Wednesday Friday

WalkBy

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One-By-One Café Analysis CAFÉ SECOND CUP REF.No. 1136

Address 30 Bond Street

WARD region Toronto Centre-Rosedale

BIA region -

Metro station (closest), distance Queen St E, 136 m

Working hours M—Su 12:00-23:59

Starbucks (closest), distance, address 167 Church St., 167 m Tim Hortons (closest), distance 4 m

Timothy’s World Coffee (closest), distance 353 m

Another Second Cup, distance 196 m

Total number of customers, period 12917 Daily number of customers, period 63

0 30 60 90 120 0 50 100 150 200 250

January February March April n

t, day

CAFE 1136 Daily customers

Picture 10. Daily customers of the Second Cup Café 1136 as a function of a day

0 60 120 180 240 0 200 400 600 800 CAFE 1136 Distribution of customers

as a function of time spent inside Cafe

t n

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12 16 20 24 0 2 4 6 8 10 12 CAFE 1136

Average number of customers as a function of the day hour n

t, hour

Picture 12. Average number of customers as a function of the day hour for SC Café 1136

0 40 80 120

CAFE 1136 n

Picture 13. Average number of customers as a function of the day of a week for SC Café 1136

CAFÉ SECOND CUP REF.No. 1138

Address 415 Spadina Road

WARD region St.Paul’s

BIA region Forrest Hill Village

Metro station (closest), distance St Clair West, 483 m

Working hours M-Su 7:00-23:00

Starbucks (closest), distance, address 37 Grosvenor St., 3632 m Tim Hortons (closest), distance 4041 m

Timothy’s World Coffee (closest), distance 1950 m Another Second Cup café, distance 1400 m Total number of customers, period 26318 Daily number of customers, period 280 +/- 77

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0 30 60 90 120 0 100 200 300 400 500 CAFE 1138 Daily customers n t, day

Picture 15. Distribution of customers as a function of time spent inside SC Café 1138

8 12 16 20 24 0 10 20 30 CAFE 1138

t, hour n

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0 50 100 150 200 250

n CAFE 1138

Picture 17. Average number of customers as a function of the day of a week for SC Café 1138 CAFÉ SECOND CUP REF.No. 1263

Address 66 Harbord street

WARD region Willowdale

BIA region -

Metro station (closest), distance Spadina, 473 m

Working hours M-F 7:00-20:00, Sa/Su 9:00-19:00

Starbucks (closest), distance, address 661 University Ave, 1070 m Tim Hortons (closest), distance 1334 m

0 30 60 90 120 0 50 100 150 200 250 300 CAFE 1263 Daily customers t, day

n

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0 60 120 180 240 0 200 400 600 800 1000 1200 CAFE 1263 Distribution of customers

t, min n

8 12 16 20 0 2 4 6 8 10 12 14 16 18 CAFE 1263

Average number of customers as a function of the day hour n

t, hour

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0 40 80 120 160

CAFE 1263

n

Address 100 St.George Street

WARD region Toronto-Centre Rosedale

BIA region St.Lawrence market neighbourhood

Metro station (closest), distance Museum 75 Spadina Avenue, 669 m

Working hours M-Th 7:00-21:00, Fr 7:00-18:00, Sat 8:30-16:00, Sun Closed Starbucks (closest), distance, address 661 university Ave, 807 m

Tim Hortons (closest), distance 1074 m Timothy’s World Coffee (closest), distance 1065 m Another Second Cup cafe, distance 263 m Total number of customers, period 24298 Daily number of customers, period 290 +/- 202

0 30 60 90 120 0 200 400 600 CAFE 1284 Daily customers t, day

n

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0 60 120 180 240 0 100 200 300 400 500 600 700 CAFE 1284 Distribution of customers

t,min n

8 12 16 20 0 5 10 15 20 25 30 _{CAFE 1284}

t, hour n

0 100 200 300

CAFE 1284

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Address 333 Bay Street

WARD region Toronto-Centre Rosedale

BIA region Financial District

Metro station (closest), distance King, 270 m

Working hours M-F 6:00-17:00 Sa/Su Closed

Starbucks (closest), distance, address 40 King St. west, 119 m Tim Hortons (closest), distance 16 m

0 30 60 90 120 0 100 200 300 400 CAFE 1328 Daily customers

t, day n

0 60 120 180 240 0 200 400 600 800 CAFE 1328 Distribution of customers

t n

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4 8 12 16 0 10 20 30 CAFE 1328

t, hour n

0 40 80 120 160 CAFE 1328

n

Elements of Correlation Analysis

Table 5. Averaged number of customers by the week day at chosen cafes

1136 1263 1284 1138 1328 Sunday 27 109 0 145 0 Monday 108 129 289 171 132 Tuesday 104 137 270 208 139 Wednesd 111 139 293 221 147 Thursday 107 118 243 205 139 Friday 98 94 105 216 116 Saturday 37 90 41 174 0

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Cross-correlations between corresponding cafes could be of help in determining the cafes with similar curve of distribution of customers through a week. It helps not only in classification of the apparently close models of cafes themselves, but also helps to determine human background of cafes, which is connected naturally with the place, style and marketing of a separate café.

On the basis of the above figures coefficient of correlation has been calculated for each pair of cafes in the table 5. The result is depicted below in the Table 6.

Table 6. Coefficients of correlation of the averaged weekly customer’s distribution for pairs of cafes

1136 1263 1284 1138 1328 1136 0.63 0.91 0.77 0.99 1263 0.63 0.85 0.31 0.68 1284 0.91 0.85 0.58 0.91 1138 0.77 0.31 0.58 0.77 1328 0.99 0.68 0.91 0.77

One could see from the Table 6, that there does exist a visible correlation among the café visiting models. We can underline two groups for further consideration: I group (1284, 1136, 1328) and less likely group II (1263, 1284). We can expect a number of similarities in financial results of those cafes in groups and also we can predict the similarity of the café’s target audience.

Another possible scale of measurements goes down to distribution of customers as a function of the hour of the day. This scale presumes the similarity of customers behavior through a day so one could be able to suppose and classify type of the spatial placement of a chosen café. Even it may not be visible from the city map, similar distributions suppose similarity of the local social distribution curve.

Input data for cafes is shown in Table 7.

Table7. Averaged number of customers as a function of the working hour of a café

1328 1138 1284 1263 1136 7 0 1 0 2 8 0 1 0 1 9 1 3 0 1 10 4 6 0 3 11 10 11 1 6 12 20 18 5 11 11 13 22 22 15 14 11 14 13 20 25 13 10 15 10 17 26 10 8 16 15 18 25 12 8

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18 14 20 9 7 19 12 16 8 5 20 12 11 8 3 21 11 3 22 8 2 23 13 3

On the base of the above figures coefficient of correlation has been calculated for each pair of cafes in the table 7. The result is depictured below in the Table 8.

Table 8. Coefficients of correlation of the averaged daily customer’s distribution for pairs of cafes

1136 1263 1284 1138 1328 1136 0.85 0.04 0.94 0.80 1263 0.85 0.80 0.99 0.93 1284 0.04 0.80 0.78 0.56 1138 0.94 0.99 0.78 0.94 1328 0.80 0.93 0.56 0.94

One could see a very strong dependence of distribution of visitors per day formed by probably the same list of factors for 1138, 1328 and 1263 cafes.

Let us consider possible connections or interdependence of audience at a café at chosen day of the week and its distance from metro. The hypothesis is that for cafes in business regions there should be some difference in number of people especially during weekends; customers are away. Please take a look at the table below.

Table 8. Averaged audience of the chosen cafes at days of the week, distance to metro and correlation Monday Tuesday Wednesday Thursday Friday Saturday Sunday Distance,

metro 1136 110 105 113 107 98 36 27 136 1138 170 205 218 201 215 170 140 483 1263 127 135 135 120 100 91 110 473 1328 132 140 147 140 112 0 0 270 1284 285 267 290 250 102 40 0 670 Correlation 0.83 0.87 0.86 0.83 0.2 0.36 0.2

The table above is surprising a little bit, however, the hypothesis that there do exist connection between some working days and distance to metro gets some grounds for further investigation.

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Where the money is

(Estimates of the potential customers number based on the WalkersBy

Statistics).

If I were you I would definitely consider as the most threatening the fact that in last two years over 19% of Canadians who drink coffee every day (that is 64% of the total population) state that they are going to invest some money in coffee-machines to lower the spendings for coffee. On the other side, Canadian Living declared that consumers of coffee get younger and average Canadian consumes 2.6 cup a day.

Health Canada clarify the role of caffeine in human life (“for an adult, a moderate intake of caffeine 400-450 milligrams per day, or the equivalent of three of four 8-ounce cups of brewed coffee is not associated with any adverse effects”). Wired.ca (Pete Blackshaw) underlined that coffee and coffee chains are not empty symbol for a Canadian if spoken about Tim Hortons: “Admit it, Timmies… is our home away from home… As Canadians we are a little bit unsecure culturally, given US media and cultural domination. So… we cling to our remaining cultural icons. Tim’s is one of them.”

The whole report on previous pages is devoted to the analysis of current customers behavior. But the data is mmuch more wider and simple evaluations could be of help in estimation of underperformance of cafes and therfore the whole net too.

Technically is being registered all devices that are for some time in the visibility area of the registering devices. However, those people in their volume could not be considered as a customers of Second Cups. Special algorythms are applied to exclude from consideration devices (e.g. people) with weak and short signal etc. And the final analysis is being performed over the cleansed data. Here is only one thing important to understand: all the registered people in the limits of visibility of a device are potential customers of Second Cup. In other words that is the money lost. Let us get an estimate of such a potential market niche.

Our assumptions are as follows. Let us consider the number of Canadians who drink coffee every day. That is 64% of population (66% in Vancouver, 63% in Quebec). We can assume bassing on the universal Law of big numbers that the proprtion of people drinking coffee every day is the same around every café of Second Cup net.

If we have from measurement the number of all people walked by the café that day, let it be signed as “T”, by multiplying 0.64 by that number we will get the number of walkersBy, who potentially drink coffee every day. Among those people of course exist fans of different city coffee nets – Starbucks, Tim Hortons, etc. Let us think that they are distributed among the trademarks of nets according to the rate of cafes. In our consideration, we think that the total number of coffee shops in GTA is near 175 cafes. Second Cups is about 74

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Therefore 74/175 = 0.42 – that is the rate of presence of Second Cup on Toronto coffee shop market by number of cafes and that is the number of customers who will not be potentially against a cup of espresso from Second Cup (of course, the number her is much bigger because people are tolerant and not as devoted to their cafes as a hockey fans are. But the worse is the estimate the better is.). If the number “N” of customers has visited the chosen Second Cup café on that day, we can easily estimate the total target nuber of customers for that café:

T*0.64*0.42 – N = target estimate (TE)

Please find below the Table and the pictures for one day for chosen cafes containing the informatin about the nubers of real visitors and about the all people registered in the working hours walking around a café.

8 12 16 20 24 0 50 100 150 200 250 8 12 16 20 24 0 50 100 150 200 250 CAFE 1284 t, hour n 12 16 20 24 0 200 400 600 800 12 16 20 24 0 200 400 600 800 CAFE 1136 t, hour n 8 12 16 20 0 200 400 600 800 8 12 16 20 0 200 400 600 800 CAFE 1263 t, hour n 8 12 16 20 24 0 100 200 300 400 8 12 16 20 24 0 100 200 300 400 CAFE 1138 t, hour

(27)

4 8 12 16 0 400 800 1200 4 8 12 16 0 400 800 1200 CAFE 1328 n t,hour

Picture 30. Graphs of real customers (lower curve) and walkedBy people (upper curve) at selected cafes as a function of day time (Date: April 22, 2014).

Table 9. Figures of the real customers and walkers by at selected cafes.

Café Customers (visitors), N Walkers By (registered

around), T Potential customers, TE 1284 92 1411 289 1138 350 2988 456 1136 154 6328 1554 1328 178 6533 1586 1263 247 6808 1591

On the base of the figures above one could suggest that there do exist a potential for growth and development of cafes even if take into account the simplified process of getting estimates. Even so it is very interesting to understand why cafes 1136 and 1328 are so underperforming in comparison with the 1138 one; the last attracts near 50% of possible clients (N/(N+TE)=43%); at the same time for the 1136: 9%.

Even if not speaking about rivals the table above makes to think about the existence of internal reserves at the Second Cup net.

In some sense the coefficient we derived N/(N+TE) describe efficiency of café’s performance. It is interesting to understand is there any dependence or interconnection of this coefficient with the distance of café from metro or rivals. Consider the following table.

Table 10. Correlations between “efficiency” coefficient and distances to the nearest metro and rival

Café N/(N+TE) Distance to metro Distance to Starbucks

1136 0.09 136 163

1138 0.43 483 3632

1263 0.14 473 1070

1328 0.10 270 119

(28)

As it could be seen from the Table 10, there do exist strong connection between the distance to rival and performance of the café, measured as a part of people who entered the café from all possible visitors that day. Metro is not influencing as much as proximity to Starbucks. That important hyposesis deserves serious consideration and research.