SRM Report

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2011 5th Jan

2011 5th Jan

STUDY OF REASONS FOR LOW SALES OFGODREJ HOME SECURITY SOLUTION W.R.T

INDIAN MINDSET

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1. INTRODUCTION

Godr ej is a lea d ing b r an d in secu r ity systems ma rk et. It manufactu r es p rod ucts li k e safes l o ck e r s, ban k ing aut o mati o n p rod ucts etc. Off late it has a dd ed anew p rod uct t o its list o f secu r ity systems ma rk et. This is the elect ro nic secu r ity s o luti o n which can be use d in ho mes, ban k s, wa r eh o uses, r etail st or esetc.The H o me Secu r ity Systems ma rk et in In d ia is ab o ut Rs 100-125 Cror es. This inclu d es h o me safes, vi d eo door , ph o nes an d ala r m systems. Godr ej iseyeing a 7 0 pe r cent ma rk et sha r e in In d ian Ban k s an d 20 pe r cent ma rk et sha r e in r etail st or es. Afte r the campaigns o f Godr ej lik e the P ro ject Su r ak sha an d the B r ea k-In Challenge, in which f r ee t r ials o f the s o luti o n we r e p ro vid ed to the pe o ple an d the pe o ple we r e challenge d to b r ea k int o ah o me whe r e Godr ejSecu r ity s o luti o n was p ro vid ed , the H o me Secu r ity Systems ma rk et is expecte d to gro w by an o the r 30-35 pe r cent. Even if Godr ej is the ma rk et lea d e r in this

do main, the ma rk et itself is n o t ve r y lar ge. S o , it is n o w eyeing Eu ro pean ma rk ets as m o st o f the o pe r at or s in these ma rk ets have a m o no po listic c o nt ro l. So a new playe r will be welc o me d .

2. SPECIFIC OBJECTIVES

The o bjective o f the stu d y is t o assess the awa r eness an d the un d e r stan d ing o f the pe o ple o f Bhubaneswa r with r espectt o G ODREJ Ho me Secu r ity So luti o nan d d ete r mining the r eas o ns f or the l o w sales o f Elect ro nic Godr ej Lo ck in Bhubaneswa r by fact or inputs ab o ut Elect ro nic Lo ck s f ro m the view p o int o f pe o ple.

This will let us in fin d ing o ut whethe r the r e is a r elati o nship betweenp r ice, techn o lo gy use d , b r an d image, se r vice quality, c o nvenience o f use etc. o f theGodr ej e -lo ck an d its sales in the ma rk et.

3. HYPOTHESIS- The following is t he null hyp ot he sis of our stud y-

Sales of a product depends only on the awareness about the product .

If 1= Sales o f a p rod uct 2= mean p r ice o f a p rod uct 3 =mean a d ve r tising expen d itu r e etc.

Then,1= 2= 3......

I.e. sales, p r ice, a d ver tising expen d itu r e etc. o f p rod uctis in d epen d ent o f each o the r .

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4. METHODOLOGY

We co llecte d Secondar y data f ro m va r io us news an d websites. F ro m this we c o uld lea r n that the r e a r e s o me lacunae which nee d to be fille d bef or e weco uld r each a c o nclusi o n. We nee d ed to find what dr ives pe o ple t o buy an elect ro nic secu r ity s o luti o n when a numbe r o f o the r secu r ity s o luti o n a r eavailable in the ma rk et. We analyze d that pe o ple may be p r ice sensitive or techn o lo gy dr iven or co nvenience dr iven etc. M or e o ve r if a pe r so n has neve r face d a th r eat o f ro bbe r y he will neve r be able t o feel the significance o f an elect ro nic secu r ity s o luti o n an d its p o we r o ver the c o nventi o nal secu r ityso luti o n. The va r io us elements the behavi or o f the pe o ple an d the sale o f Elect ro nic Godr ej Lo ck s in Bhubaneswa r co uld be changing lifestyle, inc r ease ind ispo sable inc o me, p r ice, se r vice, c o nvenience an d techn o lo gy.

To b r id ge this lacuna o f k no wle d ge, we ma d e a st r atifie d sample su r vey o f mor e than 7 0 r esp o nd ents whe r e we ci r culate d a qu e stionnair e . (Appe ndix -1) .This questi o nnai r e was ci r culate d am o ng co llege Stu d ents,y o ung p ro fessi o nals, families, r eti r ed pe r so ns etc. The questi o nnai r e was d ist r ibute d in malls,ho useh o ld s an d was fl o ate d o nline. Afte r getting the r elevant d ata, we f or me d a Mast e r Tabl e f ro m it, an d use d it as d ata in va r io us analyzing t oo ls. Theseinclu d e Univariat e (p ie-ch arts and bar gra ph s), Bivariat e (bivariat e regre ssion ) and Multivariat e (multivariat e regre ssion -additiv e and multi p licativ e regre ssion , factor anal ysis and clust e r anal ysis).

We pe r f or me d the univa r iate analysis o f the featu r es o f o u r r esp o nd ents t o get an i d ea o f what featu r es the maj or ity o f them p o ssess. We d id biva r iateanalysis as we wante d to find the c orr elati o n or co var iance between d iffe r ent va r iables.. Multiva r iate t oo ls find a numbe r o f d iffe r ent r elati o nships am o ngstd iffe r ent va r iables. S o , we use d multiva r iate statistical t oo ls.

.

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5. FINDINGS

5.1 UNIVARIATE ANALYSIS

We pe r f or m the univa r iate analysis o f the featu r es o f o u r r esp o nd ents t o get an i d ea o f what featu r es the maj or ity o f them p o ssess. We have 7 1 r esp o nd ents an d the featu r es that we test a r e age, e d ucati o n, gen d e r , annual inc o me, pe r centage o f Godr ej b r an d use r s,pe r centage o f pe o ple wh o base d thei r pu r chases m o stly o n d iffe r ent pa r amete r s lik e p r ice, c o nvenience, se r vice an d techn o lo gy.

Fig.1 Pe rce ntag e distribution of r e spond e nts into diff e rent ag e grou ps

The ab o ve Cha r t sh o ws the pe r centage d ist r ibuti o n o f r esp o nd ents with r espect t o d iffe r ent age g ro ups. We have c o nsid e r ed age g ro ups- 10 to 20 , 20 to 30,30 to 45 , 45 to 60 , an d ab o ve 60 . Fro m the cha r t it can be clea r ly seen that the highest pe r centage o f r esp o nd ents i.e. 84% lie within 3 0 to 40 yea r s o f age.This is the y o ung gene r ati o n which is the r eflecti o n o f the p o pulati o n in In d ia whe r e the pe r centage o f the y o unge r pe o ple is highe r than the o ld e r o nes.Amo ng o ur r esp o nd ents the least pe r centage was f ro m 10 to 20 yea r s o f age an d ab o ve 6 0 yea r s o f age.

1%

84%

9%4% 2%0%20 %

40%60%80%

100 %

sample

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Fig. 2 Pe rce ntag e distribution of r e spond ents a ccording to t he ir gend e r

The ab o ve cha r t sh o ws that 7 1% o f the t o tal r esp o nd ents we r e males an d 29% o f the t o tal r esp o nd ents we r e females. This is a g ood blen d as males a r ep r ice an d techn o lo gy co nsci o us an d females a r e m or e co nce r ne d ab o ut safety, se r vice an d co nvenience o f use. S o , we can have auns k ewe d an d unbiase d r esp o nse t o o ur questi o nnai r e.

71%

29%

0%

20 %

40%

60%

80%

male female

sample

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Fig. 3 Pe rce ntag e distribution of r e spond e nts a ccording to t he ir edu cational qualifi cations

The ab o ve cha r t sh o ws the pe r centage d ist r ibuti o n o f resp o nd ents acc ord ing t o thei r qualificati o ns. The va r io us e d ucati o nal qualificati o ns that ther esp o nd ents p o ssess a r e high sch oo l, gr ad uati o n an d po st-gr ad uati o n. We can see that m o st o f the r esp o nd ents a r e g r ad uates. Next highest numbe r o f r esp o nd ents is the numbe r o f po st-gr ad uates. Rest a r e eithe r in high sch oo l or lo we r than that.

58%

37%

5%0%

20 %

40%

60%80%

grad uate p o stgr ad uate

highsch oo l

sample

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Fig. 4 Pe rce ntag e distribution of e ligible respond e nts into diff e rent in come leve ls

The abo

ve chart sh

ows the pe

rcentage

dist

ributi

on

of the

resp

on

dents wh

oa

re ea

rning int

o d

ifferent inc

ome levels. F

rom the ab

ove cha

rt, it is clea

rly

visible that m o st o f the ea r ning r esp o nd ents have an annual inc o me o f less than 5 lak hs. This is c o nd ucive with the d ist r ibuti o n o f age g ro ups whe r e thehighest pe r centage o f r esp o nd ents was within age g ro up 20 to 30 yea r s, i.e. in thei r ea r ly phase o f thei r ca r ee r s. These r esp o nd ents a r e m or e lik ely t o bep r ice sensitive an d as they a r e yo ung they a r e influence d by se r vice an d techn o lo gy als o .26% pe r centage o f the ea r ning r esp o nd ents ha d an annual inc o melevel o f 5 to 10 lak hs. Only 9% o f the r esp o nd ents ha d gr eate r than 15 lak hs as thei r annual inc o me an d these a r e the pe o ple wh o a r e least li k ely t o be p r icesensitive. They o ught t o be m or e se r vice quality an d co nvenience dr iven.

54%26%

11 % 9%0%

20 %

40%

60%

< 5 lakh

5 - 10 lak h

10 - 15 lak h

>15 lak h

% o f thesample

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Fig. 5

er e nt ge of user s and non -us er s of God rej bra nd

The ab o ve p ie -chart show s th e percentag e o f th e use rs and th e no n -u se rs o f Go dr ej Secur ity Syst e

s and o th er pr o du cts o f Go dr ej brand . From th e chart itis evident that th ere is a mo st o f th e respo nd ent s ar e aw ar e o f th e Go dr ej brand and 70% o f th e respo nd ent s ar e use rs o f Go dr ej brand pr o du cts. On ly t h e m ino rity use an y o th er pr o du ct . From th is w e ha ve to find th e cau ses that r es tr ict p eo p le f rom using G o dr ej Electr o n ic Secur i ty So lut io ns.

70%

30%

God rej Bra nd

use rs

no n-u se rs

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Fig. 6

er e nt age dist ribution of re spond e nts ha ving pr ice conv e nience ser vice a nd technolog y as pre f ere nce criter ia

The ab o ve chart show s that mo st o f th e respo nd ent s p lace techn o lo gy as th eir first pr efe rence w h ile selec t ing a brand . This is very natura l as mo st o f th e respo nd ent s ar e very yo ung and h ence tech savvy. 49% o f th e res po nd ent s lay im po rtan ce to techn o lo gy as th eir first criteria f o r selec t io n, 1 9% lay im po rtan ce to se rvice qua lity w h ich includ es se rvice pr o vided b y Go dr ej as w e ll as th e reliab ility and durab ility o f th e pr o du ct, 1 7% lay im po rtan ce to pr ice and th e m ino rity 15% lay im po rtan ce to co nven ience. This is evident f rom th e f act that 2% o f o ur r espo nd ent ar e ab o ve 60 year s o f ag e w ho wo u ld pr efe rco nven ience and ease use .

17%

15%

19%

49 %

Esse nti a l Criter ia

Priceo

nven ienceService

Tec hn o lo gy

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5.2 BIVARIATE ANALYSIS

Bivar iate analysis is do ne when we want t o find the c orr elati o n or co var iance between d iffe r ent va r iables. In o ur r esea r ch wea r e m or e co nce r ne d ab o utwhat a r e the fact or s that influence the buying behavi o u r o f cust o me r s o f the Elect ro nic Secu r ity So luti o ns. As Elect ro nic Secu r ity So luti o ns a r e expensive,highly d u r able an d limite d applicati o ns in h o mes an d wa r eh o uses, s o we a r e n o t co nce r ne d ab o ut h o w many times a pe r so n buys an elect ro nic secu r ityso luti o n. Ou r p r ima r y co nce r n is ab o ut lu r ing the cust o me r s t o buy the s o luti o n o nce an d f or all. So , imp or tance is lai d o n h o w the numbe r o f cust o me r scanbe inc r ease d . Mor eo ver ,we do no t have this insi d e r d ata ab o ut the exact sales o f the Elect ro nic Secu r ity So luti o n (th o ugh we can fin d the t o tal sales o f d iffe r ent Godr ej p rod ucts f ro m its Annual Balance Sheet) which c o uld be use d as in d epen d ent va r iable, s o we a r e n o t able t o pe r f or m the biva r iater eg r essi o n analysis.F ro m o ur d ata we can lea r n o nly whethe r o ur r esp o nd ents a r e use r s o f Godr ej b r an d or no t. This is a n o n-met r ic d ata which can t be putint o use f or biva r iate r eg r essi o n analysis as r eg r essi o n analysis inclu d es a set o f d epen d ent an d ind epen d ent va r iables an d the d ata o btaine d f or bo thsh o uld be in met r ic scale.

5.3 MULTIVARIATE ANALYSIS

All statistical techniques which simultane o usly analyze m or e than tw o var iables can be name d as multiva r iate techniques. Multiva r iate t oo ls fin d a numbe r o f d iffe r ent r elati o nships am o ngst d iffe r ent va r iables. S o me o f the t oo ls that we use he r e a r e multiva r iate r eg r essi o n analysis, fact or analysis an d cluste r analysis.

5.3.1 MULTIVARIATE REGRESSION

For multiva r iate r eg r essi o n, we nee d so me d epen d ent an d so me in d epen d ent d ata. In the c o ntext o f o u r r esea r ch, d epen d ent d ata c o uld be p r ice,techn o lo gy, se r vice an d co nvenience o f use. In d epen d ent d ata c o uld be sales o f Godr ej elect ro nic secu r ity s o luti o n within a pa r ticula r geo gr aphical a r ea. Aswe do no t have this insi d e r d ata ab o ut the exact sales o f the Elect ro nic Secu r ity So lutio n (th o ugh we can fin d the t o tal sales o f d iffe r ent Godr ej p rod uctsf ro m its Annual Balance Sheet), s o a r e n o t able t o pe r f or m the multiva r iate r eg r essi o n analysis. H o weve r we can use the t oo lslik e fact or analysis an d cluste r analysis f or find ing the app ro ximate c r ite r ia o f selecti o n o f Godr ej Elect ro nic Secu r ity So luti o n.

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5.3.2 FACTOR ANALYSIS

Fact or analysis is do ne t o chec k if the va r iability within va r iables is simila r en o ugh s o that the va r iables can be g ro upe d to gethe r int o lesse r numbe r o f fact or s that explain a pa r ticula r cha r acte r istic o f the o bject un d e r questi o n. Fact or analysis estimates h o w much o f the va r iability is d ue t o co mm o n fact or sor co mmunality. We use the P r incipal c o mp o nent meth od o f fact or analysis.

Fro m the Maste r table, the f o llo wing manif e st variabl es we r e co llecte d -

1 . Br an d o f the secu r ity s o luti o ns cu rr ently use d 2 . Numbe r o f ro bbe r ies face d 3. Value o f good s st o len4. Br an d o f the elect ro nic s o luti o n p r efe rr ed 5 . Place o f use6. Pr ice o f the p rod uct7. Techn o lo gy implemente d 8. Co nvenience o f use9. Ser vice P ro vid ed 10 . Celeb r ity Effect11 . Tr ial Effect

The fact or analysis gives the c o mmunality mat r ix, the c o mp o nent mat r ix/ ro tate d co mp o nent mat r ix , the t o tal va r iance o btaine d , Scr ee pl o t, KMO-Bar tlett

testresult, C

ova

riance mat

rix etc.

of which we select the comm unalit y matrix , the comp on ent matrix/rotat ed comp on e nt matrix , the total varian ce

obtain ed , Scree p lot f or o u r analysis.

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Comm unaliti e s

Initial Ext r acti o n

Br an d name 1 .000 .78 0

num o f ro b 1 .000 .463

valo fgood s 1 .000 .723

place o fuse 1 .000 .722

b r an d names o l 1 .000 .820

p r ice 1 .000 .78 2

co nvenience 1 .000 .637

se r vice 1 .000 .74 5

Techn o lo gy 1 .000 .725

celeb r ityeffect 1 .000 .769

t r ialeffect 1 .000 .626

Comm unalit y matrix explains the va r iability o f the va r iables explaine d by the d iffe r ent fact or s. We n o te that all the va r iables have significantva r iability.Thisco mmunality mat r ix is the o utput o f un ro tate d d ata an d when eigen value is equal t o 1 .

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Fro m the total varian ce matrix , we fin d that o nly the fi r st th r ee c o mp o nents have eigen value g r eate r than 1 , an d these explain 7 0 .836% o f the t o talvar iance.This is the t o tal va r iance explaine d f or an un ro tate d mat r ix.

Total Varian ce Explain ed w he n e igen valu e = 1

Co mp o nent

Initial Eigenvalues Ext r acti o n Sums o f Squa r ed Lo ad ings Ro tati o n Sums o f Squa r ed Lo ad ings

To tal % o f Var iance Cumulative % T o tal % o f Var iance Cumulative % T o tal % o f Va r iance Cumulative %

1 4.9 00 44. 544 44. 544 4.9 00 44. 544 44. 544 3.6 29 3 2 .99 2 32.99 2 2 1 .738 15 .799 6 0.344 1 .738 15 .799 6 0 .344 2 .198 19.986 52 .978

3 1 .15 4 10 .49 2 70.836 1 .15 4 10 .49 2 70 .836 1 .964 17.8 58 7 0.836

4 .88 5 8.047 78.883

5 .579 5 .266 84. 149

6 .47 1 4.277 88.4 26

7 .38 1 3.466 9 1.89 2 8 .3 07 2 .79 2 94.684

9 .251 2 .284 96.968

10 .212 1 .926 98.894

11 .122 1 .10 6 100 .000

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Fro m the unrotat e d comp on e nt matrix we see that m o st o f the fact or lo ad ings a r e un d e r fact or -1 an d no ne a r e the r e un d e r fact or -3. So , we fin d therotat e d comp on e nt matrix wit h e igen valu e equal to 1 which is the d efault eigen value. We use va r imax ro tati o n f or this pu r po se.

Rotat ed Comp on e nt Matrix a

Co mp o nent

1 2 3

Br an d name . 080 .102 .874num o f ro b . 194 .6 50 .050

valo fgood s .6 58 .479 -.248

place o fuse .78 2 .263 -.205

b r an d names o l .898 -.010 .11 7

p r ice .7 24 .46 2 -.212

co nvenience .397 .63 0 -.289se r vice -.00 4 .837 . 210

Techn o lo gy -.252 .032 .813

celeb r ityeffect .864 . 11 4 -.100

t r ialeffect .494 .379 -.488

Extr acti o n Meth od: Pr incipal C o mp o nentAnalysis.Ro tati o n Meth od: Va r imax with Kaise r

Nor malizati o n.

a. Ro tati o n co nve r ge d in 5 ite r ati o ns.

Unrotat ed Comp on e nt Matrix

Co mp o nent

1 2 3

Br an d name -.160 .811 .310 num o f ro b .46 0 .374 -.333

valo fgood s .847 . 047 -.05 6

place o fuse .8 29 -.01 9 .187

b r an d names o l .688 . 12 9 .575

p r ice .88 2 .072 .00 9

co nvenience .7 22 .077 -.33 2 se r vice .34 1 .597 -.521

Techn o lo gy -.44 5 .711 .145

celeb r ityeffect .79 0 .001 .38 2

t r ialeffect .74 1 -.21 7 -.172

Extr acti o n Meth od: Pr incipal C o mp o nentAnalysis.

a. 3 c o mp o nents ext r acte d .

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We see that the va r iables a r e sp r ea d ac ro ss d iffe r ent c o mp o nents. But again we have a numbe r o f cro ss-lo ad ings d ue t o which we cann o t d iffe r entiate thefact or lo ad ing o f o ne va r iable un d e r o ne fact or with that o f the same va r iable un d e r a d iffe r ent fact or .So we inc r ease the numbe r o f fact or s by d ec r easingthe eigen value. We o btain the final rotat ed comp on e nt matrix w he n e igen valu e is 0.5.The c o mmunalities mat r ix can be seen in Appe ndix -2.We fin d thatthe numbe r o f fact or s explaining the va r iability o f d iffe r ent va r iables have inc r ease d to five. These five fact or s explain 84. 149% o f the t o tal va r iance an d have eigen value g r eate r than or equal t o 0.5 .

Total Varian ce Exp lain ed w he n e igen valu e = 0.5

Co mp o

nent



1 4.9 00 44. 544 44. 544 4.9 00 44. 544 44. 544 3. 230 29.364 29.364

2 1 .738 15 .799 6 0.344 1 .738 15 .799 6 0 .344 1 .727 15 .697 4 5.061

3 1 .15 4 10 .49 2 70.836 1 .15 4 10 .49 2 70 .836 1 .654 15 .039 6 0.100

4 .88 5 8.047 78.883 .88 5 8.047 78.883 1 .353 12 .304 7 2.404

5 .579 5 .266 84. 149 . 579 5 .266 84. 149 1 .292 11 .74 5 84. 149

6 .47 1 4.277 88.4 26

7 .38 1 3.466 9 1.89 2

8 .3 07 2 .79 2 94.684

9 .251 2 .284 96.968

10 .212 1 .926 98.894

11 .122 1 .10 6 100 .000

Extr acti o n Meth od: Pr incipal C o mp o nent Analysis.

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Rotat ed Comp on e nt Matrix w he n e igen valu e = 0.5

Co mp o nent

1 2 3 4 5

Br an d name . 01 6 .9 18 -.038 . 01 7 .150

num o f ro b .137 . 080 .15 6 .12 9 .93 1

valo fgood s .67 0 -.243 . 280 .134 .4 57place o fuse .78 2 -.189 . 166 . 182 .215

b r an d names o l .920 .112 .020 .083 -.074

p r ice .73 0 -.175 .43 5 .21 6 .160

co nvenience .39 5 -.21 3 .74 2 .272 -.01 6

se r vice . 031 .240 .836 . 001 .237

Techn o lo gy -.176 .7 52 .141 -.36 2 -.15 6celeb r ityeffect .7 13 .047 -.01 8 .585 .10 7

t r ialeffect . 276 -.25 9 .250 .827 .15 8

Extr acti o n Meth od: Pr incipal C o mp o nent Analysis.Ro tati o n Meth od: Va r imax with Kaise r Nor malizati o n.


Fro m the final c o mp o nent mat r ix, we can g ro up va r iables un d e r a single fact or to find the latent va r iables. These va r iables f or ming a single g ro up sh o uld gelwith each o the r in te r ms o f simila r cha r acte r istics. Besi d es f ro m the sc r ee pl o t we can see that 3 t o 5 fact or s mo stly influence the t o tal va r iance afte r whichthe sl o pe bec o mes g r ad ient . (Appe ndix -3)

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The f o llo wing a r e the lat ent variabl e s an d thei r corr esp o nd ing fact or lo ad ings- 1 . FACTOR1 : PRICE

y Value o f good s st o len ( 0 .67 0)y Place o f use ( 0 .78 2)y Br an d o f the elect ro nic s o luti o n p r efe rr ed (0 .920 )y Pr ice o f the p rod uct ( 0 .73 0)y Celeb r ity Effect ( 0 .713)

2 . FACTOR2 : BRANDVALUE y Br an d o f the secu r ity s o luti o n cu rr ently use d (0.918)y Techn o lo gy implemente d (0 .752 )

3. FACTOR3: CONVENIENCE OF USE y Co nvenience o f use ( 0 .74 2)y Ser vice P ro vid e d (0 .836)

4. FACTOR4: PRIOR EXPERIENCE OF THE PRODUCT y

Tr ial Effect ( 0 .827)5. FACTOR5 : NUMBER OF BAD EXPERIENCES

y Numbe r o f ro bbe r ies face d (0 .93 1)

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5.3.3.CLUSTER ANALYSIS

Cluste r analysis is do ne t o find cluste r s having h o mo gene o us cha r acte r istics within it. We do a cluste r analysis t o find the natu r e o f r esp o nd ents an d withinwhat cluste r s do thei r d em o gr aphic cha r acte r istics fall. We can als o analyse within what cluste r mo st o f them fall an d whethe r it satisfies what we f o un d do ing univa r iate analysis.To find cluste r s within o u r r esp o nd ents, we c o nst r uct a d en do gr am using SPSS S o ftwa r e. (Appe ndix -4)

Analysis :

At an Eucli d ean d istance o f 10 we fin d 3 cluste r s having the f o llo wing cha r acte r istics (th o ugh the r e we r e s o me d eviati o ns)-

1 . Cluste r 1 : 45 Resp o nd ents

a. Age: 20- 30 yea r s

b. Ed ucati o n : Gr ad uati o n

c. Annual Sala r y: less than or equal t o 10 lak hs

2 . Cluste r 2 : 23 Resp o nd ents

a. Age: 30-45 yea r s

b. Ed ucati o n : Gr ad uati o n

c. Annual Sala r y: gr eate r than 10 lak hs an d less than 15 lak hs

3. Cluste r 3: 3 Resp o nd ents(Outlie r s)

a. Age: gr eate r than 4 5

b. Ed ucati o n : Po st-gr ad uati o nc. Annual Sala r y: gr eate r than 15 lak hs

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Total Varian ce Exp lain ed

Co mp o

nent

Initial Eigenvalues Ext r acti o n Sums o f Squa r ed Lo ad ings

To tal % o f Var iance Cumulative % T o tal % o f Var iance Cumulative %

1 4.874 44.3 11 44.3 11 4.874 44.3 11 44.3 11

2 1 .609 14.63 1 58.94 2 1 .609 14.63 1 58.94 2

3 1 .364 12 .405 71.347 1 .364 12 .405 71.347

4 .9 08 8. 252 79. 5995 .656 5 .964 8 5.562

6 .49 0 4.4 50 90.01 3

7 .4 21 3.8 27 93.839

8 .261 2 .37 2 96. 212

9 .21 6 1 .959 98. 171

10 .12 6 1 .142 99.3 12 11 .076 .688 100 .000

Extr acti o n Meth od: Pr incipal C o mp o nent Analysis.As eigen value c o nsid e r ed is equal t o 1 , so we have mainly th r ee va r iables with eigen value g r eate r than 1 which explain 7 1 .347% o f the t o tal va r iancewithin the g ro up.

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Comp on e nt Matrix

Co mp o nent

1 2 3

Br an d name -.234 . 569 .648

num o f ro b .48 0 .447 -.303

valo fgood s .8 58 .196 -.080

placeo

fuse .8 12 -.022 .190 b r an d names o l .688 -.097 . 55 3

p r ice .8 25 .087 . 166

co nvenience .7 12 .122 -.37 2

se r vice . 250 .814 -.255

Techn o lo gy -.624 .569 . 21 6

celebrityeffect .7 52 -.148 .4 22

t r ialeffect .7 18 -.083 -.22 3

Extr acti o n Meth od: Pr incipal C o mp o nentAnalysis.

a. 3 c o mp o nents ext r acte d .Fro m the c o mp o nent mat r ix we fin d that m o st o f the c o mp o nents c o me un d e r fact or 1, so we use a d iffe r ent ro tati o n. We fin d the ro tate d co mp o nentmat r ix using va r imax ro tati o n.

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Rotat ed Comp on e nt Matrix a

Co mp o nent

1 2 3

Br an d name . 12 3 .010 .88 5

num o f ro b .152 .700 -.090

valo fgood s .6 16 .582 -.252

placeo

fuse .764 . 269 -.199b r an d names o l .887 -.01 7 .037

p r ice .747 .368 -.149

co nvenience .336 . 597 -.43 5

se r vice -.049 .846 . 267

Techn o lo gy -.44 2 .035 .750

celebrityeffect .868 . 037 -.10 7

t r ialeffect .4 54 .376 -.474

Extr acti o n Meth od: Pr incipal C o mp o nentAnalysis.Ro tati o n Meth od: Va r imax with Kaise r

Nor malizati o n.


Fro m the ro tate d co mp o nent mat r ix, we fin d that the r e a r e a numbe r o f cro ss lo ad ings. S o , we d ec r ease the eigen value t o 0 .5. The c o mmunality mat r ixsh o ws an imp ro vement in ext r acti o ns (Appe ndix -5)an d the r e a r e five va r iables having eigen value g r eate r than 0 .5 an d these five va r iables d esc r ibe85 .562% o f the t o tal va r iance within the d ata .( Appe ndix -6) which is als o evid ent f ro m the sc r ee pl o t(Ape ndix -7).We get the f o llo wing ro tate d co mp o nentmat r ix.

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Rotat ed Comp on e nt Matrix W HEN EIGEN VALUE = 0.5

Co mp o nent

1 2 3 4 5

Br an d name . 043 -.01 9 .947 -.041 .000

num o f ro b .087 . 20 4 -.00 8 .151 .905

valo fgood s .618 .359 -.20 3 .21 4 .464

place o fuse .775 .022 -.182 .095 .417

b r an d names o l .916 .068 . 044 . 099 -.165

p r ice .775 .37 0 -.121 .197 . 068

co nvenience .366 .693 -.38 5 .333 . 001

se r vice -.00 4 .821 .245 -.02 6 .3 08

Techn o lo gy -.359 .200 .604 -.483 -.141

celeb r ityeffect .740 -.196 . 080 .46 0 .22 3

t r ialeffect . 241 .20 7 -.143 .906 .163

Extr acti o n Meth od: Pr incipal C o mp o nent Analysis.Ro tati o n Meth od: Va r imax with Kaise r Nor malizati o n.

a. Ro tati o n co nve r ge d in 6 ite r ati o ns.Fro m the ab o ve table we can see that we get the same r esult f or the cluste r as we g o t f ro m the wh o le sample. An d the latent va r iables a r e p r ice, b r an d

value, c o nvenience o f use, p r ior expe r ience o f the p rod uct an d the numbe r o f ba d expe r iences a pe r so n hasface d with the same va r iables gelling int o afact or r espectively.

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6. CONCLUSIONS

Base d o n o u r analyses we r each t o the f o llo wing c o nclusi o ns-

We r eject o u r Null Hypo thesis an d co nclu d e that sales do no t d epen d up o n the awa r eness o f the pe o ple o nly. In fact it d epen d s o n a numbe r o f var iablewhich we f o un d o ut as latent va r iable d ur ing the fact or analysis. These g ro ups o f var iables affecting sales a r e p r ice, p r ior expe r ience, b r an d value,co nvenience o f use, an d numbe r o f ba d expe r iences that a pe r so n has face d . Mor eo ve r the awa r eness o f the existence o f the b r an d Godr ej is ve r y high an d so is its ent r y int o the elect ro nic ma rk et. Still it do es n o t have high sales. F ro m the Biva r iate analysis, we can see that the pe o ple o f Ind ia r elate theElect ro nic Lo ck s as a techn o lo gically dr iven fact or .Fro m multiva r iate analysis, we can c o me t o a co nclusi o n that the maj or var iables affecting sales a r e p r ice,

techn o lo gy an d se r vice.

Since In d ian p o pulati o n mainly c o mp r ises y o ung pe o ple within the age g ro up o f 25 to 40 which is the ta r get ma rk et f or Elect ro nic Godr ej Secu r ity So luti o ns,so to imp ro ve sales bette r techn o lo gy must be use d , p r ices sh o uld be in d iffe r ent r anges f or d iffe r ent segments o f pe o ple, bette r se r vice sh o uld be p ro vid ed an d lastly m or e bu d get sh o uld be planne d f or ad ver tisements, p ro mo tio ns an d f r ee t r ials.

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7. APPENDICES

Appe ndix -1 QUESTIONNAIRE

Name :

1) Age: a) 10-20 b) 20- 30 c) 30-45 d ) 45-60 e) 60 ab o ve

2) Sex: a) Maleb) Female

3) Ed ucati o nal Qualificati o n : a) High Sch oo lb) Seco nd a r y Schoo lc) Gr ad uate Technicald ) Gr ad uate N o n-Technicale) Po st Gr ad uate

4) Sala r y(Pe r Annum)a) Less than 5 Lak hsb) 5 Lak hs -10 Lak hsc) 10- Lak hs -15 Lak hsd ) Gr eate r than 15 Lak hs

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5) What Secu r ity s o luti o n do yo u use f or secu r ing yo u r valuables?

a) Safety D oor sb) Gr ille G ates an d lo ck systemsc) Windo w Gr illesd ) Bank Lo ck e r se) Pet D o gsf) Safesg) Elect ro nic Secu r ity So luti o ns

6) Which b r an d do yo u r ely o n f or yo u r Secu r ity So luti o n?a) Godr ejb) Anch or c) Mo bajd ) D-Link e) Othe r s

7) Have y o u eve r been ro bbe d ?a) Yesb) No

8) If yes, h o w many times?a) Onceb) Twice

c) Thr iced ) Mor e than Th r ice

9) What was the value o f good s st o len?a) Less than 1 Lak hb) 1 Lak h 5 Lak h

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c) 5 Lak h 10 Lak hd ) Gr eate r than 10 Lak h

10 ) Have y o u eve r use d an elect ro nic secu r ity s o luti o n?a) Yesb) No

11 ) If Yes, whe r e?

a)

Ho

meb) Wa r eh o usec) Businessd ) Othe r s, Please Specify : ________________________________________

12 ) If yes, was it a Godr ej Ma k e?

a) Yesb) No

If No , Please specify B r an d: ___________________________________________13) Wo uld yo u p r efe r to use an elect ro nic safety s o luti o n o ve r a t r ad itio nal l o ck ?

a) Yesb) No

14) Rate f ro m m o st imp or tant ( 5) to least imp or tant ( 1) f or an elect ro nic secu r ity s o lutio n :

5 4 3 2 1

Pr iceCo nvenience o f UseSer vice QualityTechn o lo gyCeleb r ity Ad ve r tisementFr ee T r ial

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Appe ndix -2 COMMUNALITIES MATRIX WHEN EIGEN VALUE = 0.5

Comm unaliti e s


Br an d name 1 .000 .868

num o f ro b 1 .000 .933



b r an d names o l 1 .000 .87 1

p r ice 1 .000 .825


se r vice 1 .000 .814




Extr acti o n Meth od: Pr incipalCo mp o nent Analysis.

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Appe ndix -3 SCREE PLOT

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Appe ndix -4 DENDOGRAM

* * * * * * * * * * * * * * * * * * * H I E R A R C H I C A L C L U S T E R A N A L Y S I S * * * * * * * * * * * * * * * * ** *

Dendrogram using Average Linkage (Between Groups)

Rescaled Distance Cluster Combine

C A S E 0 5 10 15 20 25

Label Num +---------+---------+---------+---------+---------+66 71 52 61

4 27 28 15 19 12 41 59

1 22

2 69 42 63 67

3 53 58 39 44 35 38 33 34 29

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Appe ndix -5 COMMUNALITIES MATRIX WHEN EIGEN VALUE = 0.5

COMMUNALITIES MATRIX WHEN EIGEN VALUE = 0.5


Br an d name 1 .000 .900

num o f ro b 1 .000 .89 1



b r an d names o l 1 .000 .883

p r ice 1 .000 .796


se r vice 1 .000 .83 0




Extr acti o n Meth od: Pr incipalCo mp o nent Analysis.

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Appe ndix -6

Total Varian ce Exp lain ed

Co mp o

nent



1 4.874 44.3 11 44.3 11 4.874 44.3 11 44.3 11 3.3 00 30 .002 30.002

2 1 .609 14.63 1 58.94 2 1 .609 14.63 1 58.94 2 1 .589 14.44 5 44.448

3 1 .364 12 .405 71.347 1 .364 12 .405 71 .347 1 .588 14.437 58.88 5

4 .9 08 8. 252 79. 599 .9 08 8. 252 79. 599 1 .50 4 13.67 5 72.560

5 .656 5 .964 8 5.562 .656 5 .964 8 5 .562 1 .43 0 13.002 85.562

6 .49 0 4.4 50 90.01 3

7 .4 21 3.8 27 93.839

8 .261 2 .37 2 96. 212

9 .21 6 1 .959 98. 171

10 .12 6 1 .142 99.3 12

11 .076 .688 100 .000

Extr acti o n Meth od: Pr incipal C o mp o nent Analysis.

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Appe ndix -7 SCREE PLOT

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Documents

SRM Report