factors affecting the consumer purchase for private label apparel brands in india

STUDYING THE FACTORS AFFECTING CONSUMER PURCHASE DECISIONS

FOR PRIVATE LABEL APPAREL BRANDS

Prepared & Presented By,Group No-01

Strong growth in the Indian Retail Industry

• The retail sector in India is emerging as one of the largest sectors in the economy• By 2015, the total market size is estimated to be around US$ 600 billion, thereby registering a CAGR of 7.45 per cent since 2000.• Retail industry is expected to grow to US$ 1.3 trillion by 2020, registering a CAGR of 9.7 per cent between 2000-2020

Organised retail in nascent stage

• The Indian retail market is in its nascent stage; unorganised players accounted for 92 per cent of the market during 2015• There are over 15 million mom-and-pop stores• Compound Annual Growth Rate (CAGR) of 16.7 per cent over 2015-20. • Organised retail is expected to account for 24 per cent of the overall retail market by 2020

Private Label Products are commonly referred to as name brand, store brand, own label, retailer brand or generics. These are brands

owned by the retailer rather than the producer or manufacturer.

Apparel Sector, it is the second largest segment in terms of its contribution to the retail market.

The overall size of the textile and apparel industry in India is currently estimated at $70bn and is expected to grow to $220bn by 2020 with a CAGR of 11%.

Currently, menswear is the biggest segment of the domestic apparel market with 43% share of the total pie while women’s wear constitutes 38%.

The private label market in India is currently estimated at

INR13 billion, which accounts for 10-12% of organised

retail in India. Retailers such as Pantaloon, Trent, Shoppers

Stop and Spencer’s have increased focus on private label

retailing. Private labels constitute 90% of Trent’s, 80% of

Reliance’s and 75% of Pantaloon’s overall sales.

Challenges Faced by Private Label Brand Apparels

The biggest challenge in building a private label brand is to establish credibility and aspiration for the brand. When consumers buy a brand,

they are aware about their expectations, but when they buy a new label they are bound to be a quite a few ‘doubters’.

Most consumers see private label apparel as ‘undifferentiated’. Private label are more popular among lower-income household as well

as larger families owning to the need to economize.

Some Challenges faced by Private Labels Apparel are as follows:• Higher risk of inventory.• Higher expense in Research and Development.• Markdown or return allowances will not be available.• Failure of the product will create a negative image about the

retailer.• More marketing expenses

Objectives of the Study

The Indian apparel industry in retail is growing rapidly, with an increasing focus on private labels (PLBs).

Recently, in a sharp contrast to earlier periods, consumers have started considering purchase of PLBs as smart shopping.

Considering these aspects, this research aims to analyse the association of PLBs with customers and retailers so as to gauge customer loyalty, consumer preferences, shopping behaviour and also to understand the marketing strategies and objectives of PLB retailers.

It provides an insight into the positioning of brands and PLBs in the minds of consumers and recommends suitable strategies to enhance the market share of private labels.

Problem Statement

To present a better understanding of the factors influencing consumer behaviour towards private labels in

apparel retailing.

To study the emergence, growth and future of private labels in India

To examine the respondents awareness about private label brand apparels

To bring out the major factors that affect the perception of consumers related to private label apparels

To identify major price related dimensions influencing the purchase of private label apparels

To determine the importance of quality related perceptions (extrinsic and intrinsic cues) influencing the purchase of private label apparels

To analyse whether there is any relation between price and quality dimensions related to private label apparels

To segment the consumers on the basis of factors affecting their perceptions towards private labels brought out from the study

To determine the relationship between customer loyalty and private label branding

25 Diverse Literature Review

Study

Visit to nearby Malls, Retail Stores to observe Buying

Pattern

Employees / Friends working in

Retail firms, Qualitative FGD

21 Factors-Initial Questionnaire

1. Brand Name2. Style3. Availability4. Quality5. Discount Offers6. Price7. Value for Money8. Location9. Return on Damage Policy10. Comfort/Fit/Size11. Lifestyle12. Reliability13. Referrals14. Shelf Position of Merchandize15. Store Popularity16. Loyalty Points17. Word of Mouth18. Online Shopping19. Supportive in-store personnel20. Advertisements on TV21. In-store Promotions

Sampling Method Used: Non Probability – Convenience Sampling

Simple Random Convenience sampling is used in Exploratory Research, where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample (304 Respondents) is selected because they are convenient.

This non probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

Research Plan

To study the emergence, growth and future of private label Brands in India

To examine the respondents awareness about private label brand apparels

To bring out the major factors that affect the perception of consumers related to private label apparels

To identify major price related dimensions influencing the purchase of private label apparels

To determine the importance of quality related perceptions (extrinsic and intrinsic cues) influencing the purchase of private label apparels

To analyse whether there is any relation between price and quality dimensions related to private label apparels

To segment the consumers on the basis of factors affecting their perceptions towards private labels brought out from the study

To determine the relationship between customer loyalty and private label branding

The main data collection methods that was used for the study is online & offline questionnaires, forms which are completed and returned by respondents. This method is useful & inexpensive, this method was implied as literacy

rates were high and respondents were cooperative.

DemographicsFactor

AnalysisLabelling the

07 FactorsData Analysis

DemographicsFactor



Since the KMO Measure of Sampling Adequacy meets the minimum criteria, we do not have a problem in conducting a factor analysis.

The Sig. value for this analysis leads us to reject the null hypothesis and conclude that there are correlations in the data set that are appropriate for factor analysis.

Communalities

Initial Extraction

Brand name 1.000 .555

Private Label brand vs

National brands 1.000 .657

Style 1.000 .649

Quality 1.000 .649

Availability 1.000 .669

Discounts 1.000 .684

Price 1.000 .542

Value for money 1.000 .606

Location 1.000 .462

Return/damage policy 1.000 .672

Product fitting 1.000 .715

Lifestyle 1.000 .766

Reliability 1.000 .641

Referral from friends

and family 1.000 .635

Size flexibility 1.000 .536

Shelf position 1.000 .627

Store popularity 1.000 .761

Loyalty points 1.000 .732

Prefer online over

offline 1.000 .587

Word of mouth 1.000 .501

In store personnel

support 1.000 .249

Television

advertisements 1.000 .698

In store promotion 1.000 .626

Print media 1.000 .610

Outdoor media 1.000 .612

Satisfaction level 1.000 .621

Extraction Method: Principal Component

Analysis.

As highlighted, Lifestyle, Store Popularity, Loyalty points and Product fitting contributes to over 70% of variance. Whereas, In-store personnel support gives the least variance in the model of about 20%.

It must also be observed and noted that the total sum of communalities is 16.062 with an average of 0.617769. Which indicates that this model has about 61.78% of variance, and this can be confirmed from the ‘Total variance Explained’ results in the next table.

The Total Variance Table shows that total 26 factors were observed through the Principal Component Analysis. However only the 7 factors with Eigen value greater than 1 will be taken into consideration.

Factor - Not all 26 factors will be retained. Only the first seven factors will be retained.

Total Variance Explained

Componen

t

Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of

Variance

Cumulative

%

Total % of

Variance

Cumulative

%

Total % of

Variance

Cumulative

%

1 6.833 26.282 26.282 6.833 26.282 26.282 3.311 12.735 12.735

2 2.398 9.221 35.504 2.398 9.221 35.504 3.310 12.732 25.467

3 1.678 6.453 41.956 1.678 6.453 41.956 2.735 10.519 35.986

4 1.617 6.217 48.174 1.617 6.217 48.174 2.303 8.858 44.844

5 1.314 5.053 53.227 1.314 5.053 53.227 1.472 5.663 50.507

6 1.182 4.547 57.774 1.182 4.547 57.774 1.469 5.649 56.156

7 1.039 3.998 61.772 1.039 3.998 61.772 1.460 5.616 61.772

8 .986 3.791 65.563

9 .958 3.685 69.248

10 .859 3.305 72.554

11 .801 3.082 75.636

12 .784 3.014 78.650

13 .673 2.588 81.237

14 .644 2.478 83.716

15 .580 2.231 85.947

16 .530 2.037 87.984

17 .476 1.832 89.815

18 .430 1.652 91.468

19 .369 1.418 92.886

20 .360 1.385 94.270

21 .332 1.278 95.548

22 .323 1.243 96.791

23 .248 .952 97.743

24 .214 .824 98.568

25 .211 .811 99.379

26 .161 .621 100.000

Extraction Method: Principal Component Analysis.

DemographicsFactor



The Component Matrix table contains component loadings, which are the correlations between the variable and the component. Because these are correlations, possible values range from -1 to +1.

The Components are the columns under this heading are the principal components that have been extracted. As you can see by the footnote provided by SPSS (a.), 7 components were extracted (the 7 components that had an eigenvalue greater than 1).

Rotated Factor Matrix table contains the rotated factor loadings (factor pattern matrix), which represent both how the variables are weighted for each factor but also the correlation between the variables and the factor.

Because these are correlations, possible values range from -1 to +1.For orthogonal rotations, such as varimax, the factor pattern and factor structure matrices are the same.

Here also, 7 factors are extracted. From this table we will label the factors. The variable considered for this factors will have a minimum value of 0.6 (cut-off).

Component Matrixa

Component

1 2 3 4 5 6 7

Brand name .121 .181 .261 .375 .285 -.427 .185

Private Label brand vs National

brands .534 .119 .435 .124 .311 .211 .111

Style .440 .287 .397 .123 -.215 .372 -.125

Quality .401 .609 -.141 -.066 .192 .224 -.077

Availability .602 .354 .014 -.057 .366 -.171 .122

Discounts .634 .316 .135 -.202 .322 .032 -.033

Price .647 .270 .228 .057 .073 -.010 .082

Value for money .287 .441 .327 -.332 -.293 -.142 -.080

Location .570 -.250 .088 -.224 -.100 -.026 -.082

Return/damage policy .582 .426 -.250 -.044 -.144 .177 -.188

Product fitting .323 .207 -.210 .259 -.352 .202 .540

Lifestyle .496 .106 -.303 .470 -.124 -.076 -.418

Reliability .545 .352 -.258 -.071 -.325 -.196 -.067

Referral from friends and family .517 -.064 -.426 -.382 .184 .036 -.027

Size flexibility .440 .027 -.160 .413 .030 -.314 .215

Shelf position .567 -.260 -.187 .000 .410 -.054 -.179

Store popularity .680 -.409 -.163 -.222 -.075 -.140 .176

Loyalty points .556 -.111 -.228 -.503 -.105 -.125 .282

Prefer online over offline .148 -.275 -.123 .212 .227 .537 .299

Word of mouth .608 -.120 .091 .201 -.156 -.167 .128

In store personnel support .257 -.071 .307 .044 -.156 -.227 .074

Television advertisements .513 -.520 .325 -.160 .121 -.075 -.111

In store promotion .535 -.334 -.155 .316 .053 .045 -.315

Print media .548 -.314 .306 .157 -.279 -.008 -.119

Outdoor media .553 -.320 .280 -.161 -.152 .277 .010

Satisfaction level .660 -.232 -.228 .238 -.099 .100 .055


a. 7 components extracted.

Rotated Component Matrixa

Component

1 2 3 4 5 6 7

Brand name .136 .011 -.158 .004 -.039 -.043 .712


brands .597 .410 -.056 -.034 .253 -.021 .253

Style .546 .426 -.289 .118 -.062 .214 -.149

Quality .739 -.211 .108 .166 -.048 .118 -.052

Availability .619 .035 .338 .084 -.040 -.004 .402

Discounts .566 .186 .301 .053 -.036 -.098 .153

Price .722 .321 .107 .112 -.067 .144 .262

Value for money .412 .223 .018 -.154 -.592 .099 -.051

Location .139 .518 .381 .144 -.067 -.036 -.055

Return/damage policy .567 .010 .215 .415 -.185 .257 -.177

Product fitting .142 .041 .082 .082 .121 .810 .091

Lifestyle .177 .064 -.007 .840 -.089 .094 .094

Reliability .309 .049 .322 .366 -.439 .335 .026

Referral from friends and family .262 .005 .708 .198 .073 -.044 -.133

Size flexibility .059 .083 .166 .365 .036 .274 .537

Shelf position .217 .199 .455 .388 .264 -.294 .163

Store popularity -.008 .481 .687 .173 .057 .125 .091

Loyalty points .133 .239 .769 -.072 -.124 .210 -.028

Prefer online over offline .073 .095 .056 .026 .723 .199 -.082

Word of mouth .116 .470 .191 .274 -.036 .244 .307

In store personnel support .012 .390 -.002 -.022 -.188 .062 .238

Television advertisements .029 .697 .299 .054 .113 -.311 .092

In store promotion .043 .348 .153 .646 .223 -.097 .052

Print media .033 .720 .015 .282 -.041 .078 .058

Outdoor media .190 .692 .198 .024 .136 .066 -.184

Satisfaction level .111 .360 .317 .490 .229 .281 .080

Component Score Coefficient Matrix

Component

1 2 3 4 5 6 7

Brand name .006 -.030 -.070 -.051 -.026 -.049 .531


brands .247 .113 -.136 -.146 .229 -.053 .108

Style .209 .191 -.289 .020 .007 .093 -.218

Quality .305 -.174 -.006 .041 .057 -.013 -.107

Availability .188 -.115 .122 -.086 .021 -.082 .256

Discounts .157 -.033 .065 -.085 .028 -.162 .044

Price .262 .057 -.047 -.063 -.006 .036 .121

Value for money .101 .115 -.031 -.120 -.377 .019 -.067

Location -.026 .155 .093 -.004 -.083 -.066 -.089

Return/damage policy .162 -.085 -.003 .184 -.087 .069 -.221

Product fitting -.034 -.021 .012 -.111 .148 .619 .049

Lifestyle -.027 -.064 -.142 .506 -.110 -.072 -.032

Reliability -.018 -.058 .092 .144 -.301 .148 -.029

Referral from friends and family .057 -.136 .317 .020 .053 -.088 -.121

Size flexibility -.102 -.064 .040 .102 .006 .160 .374

Shelf position .051 -.061 .140 .155 .145 -.292 .065

Store popularity -.135 .086 .274 -.061 -.007 .079 .048

Loyalty points -.060 -.001 .373 -.206 -.083 .153 -.010

Prefer online over offline .089 -.009 -.013 -.084 .542 .194 -.089

Word of mouth -.086 .126 -.004 .032 -.054 .141 .176

In store personnel support -.074 .167 -.043 -.070 -.156 .041 .162

Television advertisements -.037 .240 .049 -.047 .013 -.244 .024

In store promotion -.046 .050 -.066 .351 .086 -.158 -.052

Print media -.085 .283 -.137 .096 -.086 .019 -.035

Outdoor media .036 .262 -.027 -.095 .084 .041 -.204

Satisfaction level -.061 .040 .035 .161 .132 .156 -.013


Rotation Method: Varimax with Kaiser Normalization.

Component Scores.

DemographicsFactor



Column 1 2 3 4 5 6 7

Attributes Quality,

Price

Television

Advertisement,

Print Media,

Outdoor Media

Referral from

friends &

Family, Store

Popularity,

Loyalty

Points

Lifestyle

(Add some

income stats

here to

support)

Prefer

Online

over

Offline

Product

Fitting

Brand

Name

Factor

Formed

Perceived

Quality

with Price

Influence of

Advertisement

Trust Factor

in Shopping

Consumer

Lifestyle

Online

Shopping-

an

Emerging

Trend

Comfort

level

Brand

Consciou

sness

By collating the variables above 0.6 in each factors. These factors were given appropriate labels described in the 3rd row of the table above.

Scree Plot Analysis

The Scree Plot graphs the eigenvalue against the component number.From the seventh component on, you can see that the line is declining, meaning the each successive component is accounting for smaller and smaller amounts of the total variance. In general, we are

interested in keeping only those principal components whose eigenvalues are greater than 1.

The variables that played a vital role in contributing to the factors are :

a. Quality, Priceb. Television Advertisement, Print Media, Outdoor Mediac. Referral from friends & Family, Store Popularity, Loyalty Pointsd. Lifestylee. Prefer Online over Offlinef. Product Fittingg. Brand Name

Seven Factors were found to influence the buying decision of consumer in the purchase of Private Label Apparels.

a. Perceived Quality with Priceb. Influence of Advertisementc. Trust Factor in Shoppingd. Consumer Lifestylee. Online Shopping- an Emerging Trendf. Comfort levelg. Brand Consciousness

Conclusion

In Sync with the earlier findings

Limitations

Larger Sample Size would have given us more conclusive results.

Expanding the study to tier-1,2 cities & not only Bengaluru, Lack of Time & Resources.

Lack of Awareness for Private Label Brands, Awareness Programs were needed.

Celebrity Endorsement & similar factors couldn’t be included because of less awareness about PLB

Discriminant Analysis of consumers was needed

Managerial Implications

1. Private label brands should focus primarily on the quality of the fabric & add-ons (like button, laces) to ensure the primary concern of the consumers is satisfied.2. Pricing strategy should be reviewed with these concerns in mind. If higher quality is demanded, pricing points can be raised to eliminate the cause of concern.3. If a PLB is not allocating its budget to marketing/ATL activities, now is the time to re-consider. Advertising plays a huge amount of role in convincing the consumer about the credibility of the brand.4. PLB should focus on building a referral pipeline. They should collect and build a testimonial bank through campaigns (Online and Offline) to advertise about customer satisfaction with a particular product.5. PLB should focus on its marketing and product mix by clearly defining their target market. They should be able to display the accurate lifestyle their products reflect.6. Online vs Offline seems to play an important role. PLB should Venture in Omni Channel Sales.7. Product design and comfort is vital. PLBs should make sure they are competitive to national brands in terms of product fitting. Their R&D should come with innovative designs to satisfy consumer comfort