A comprehensive statistical analysis exploring store attributes, visual merchandising impact, and consumer segmentation in modern retail settings
Understanding consumer behavior and preferences in retail environments through rigorous statistical analysis
To investigate the relationship between store attributes, visual merchandising elements, and consumer experience in retail environments, identifying key factors that influence shopping behavior and purchase decisions.
Rigorous statistical procedures ensuring validity and reliability of findings
Survey-based data collection with N = 154 respondents. Structured questionnaire with 5-point Likert scales measuring store attribute importance and visual merchandising effectiveness.
Two primary scales developed: Store Attribute Importance (16 items) and Visual Merchandising Impact (7 items). Both scales underwent rigorous reliability and validity testing.
Analysis conducted using Python with scipy, statsmodels, pingouin, and factor_analyzer libraries. Significance level set at α = 0.05.
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Understanding the composition of our survey respondents (N = 154)
The sample shows a female-dominant distribution with 62.3% female and 37.7% male respondents.
Young adults (18-25) represent the largest segment at 43.5%, indicating a predominantly younger sample.
Employed individuals (42.9%) and students (36.4%) dominate the sample, providing diverse perspectives.
The sample is highly educated with 95.5% holding a degree or postgraduate qualification.
Female respondents dominate across all age groups, with the 18-25 age bracket having the highest representation in both genders.
The demographic profile represents educated, urban consumers with diverse occupational backgrounds.
With 62.3% female respondents, findings may better capture female consumer preferences.
The 18-25 age group dominance provides insights into millennial/Gen-Z shopping behaviors.
Deep dive into shopping behaviors and attribute importance ratings
78% of respondents shop at least once per quarter, indicating relatively frequent retail engagement.
"Variety in life" is the dominant motivation (45.5%), followed by "keeping up with trends" (24.7%).
Mean importance ratings on a 5-point Likert scale (1 = Not at all important, 5 = Extremely important)
Highest rated attribute with lowest variance (σ=0.61)
Essential infrastructure requirement for shoppers
Reflects modern payment preferences
Time efficiency is highly valued
Risk reduction drives confidence
Agreement ratings on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree)
Most effective VM element for enhancing shopping experience
Growing acceptance of tech-enhanced shopping experiences
Promotes impulse buying behavior among consumers
Hygiene and operational excellence factors dominate: Store Cleanliness (#1), Parking (#2), and Digital Payment (#3) are the most valued attributes.
Top 5 attributes relate to cleanliness, parking, payments, checkout speed, and return policies—all operational excellence factors.
Price-related attributes (offers, vouchers, branded merchandise) rank lowest (mean ~3.0), suggesting value-seeking is less critical than experience.
Lighting & music rate highest among VM elements, highlighting the importance of creating an immersive sensory shopping environment.
Ensuring measurement quality through rigorous reliability and validity testing
Testing prerequisites for factor analysis
All scales exceed the 0.60 threshold for factor analysis suitability.
All tests significant (p < 0.001), confirming correlations exist in the data.
| Scale | r (Odd-Even) | Spearman-Brown | Status |
|---|---|---|---|
| Store Importance (16 items) | 0.723 | 0.839 | ✓ Reliable |
| Visual Merchandising (7 items) | 0.639 | 0.780 | ✓ Reliable |
| Full Scale (23 items) | 0.784 | 0.879 | ✓ Reliable |
All scales meet minimum reliability thresholds (α ≥ 0.68)
KMO values indicate adequate sampling for factor analysis
Bartlett's tests confirm significant inter-item correlations
All prerequisites met for Exploratory Factor Analysis
Uncovering the underlying structure of consumer preferences through Exploratory Factor Analysis
47.4% variance explained
54.8% variance explained
Oblique rotation allowing correlations
Store Importance: 5 factors (λ > 1)
Visual Merchandising: 2 factors retained
Store Importance: 3 factors
More rigorous threshold than Kaiser
Store: 62.46% (5 factors)
VM: 54.8% (2 factors)
Promax rotation allows factors to correlate. Low-to-moderate correlations support discriminant validity.
Strongest correlation: F4 (External) ↔ F5 (In-Store) at φ = 0.157, confirming VM dimensions are related but distinct.
* p < .05, ** p < .01, *** p < .001
Service infrastructure and operational convenience elements
Visual appeal and sensory store environment elements
Price-related and value-seeking preferences
Elements that draw customers into the store
Elements enhancing the shopping journey inside
Key Insight: The factor structure reveals that consumers evaluate retail stores on three distinct dimensions: operational convenience, aesthetic appeal, and value for money. This multidimensional view should inform targeted retail strategies.
Testing differences across demographic groups using t-tests, ANOVA, and Chi-square analyses
Comparing factor scores between male (n=58) and female (n=96) respondents
| Factor | Male (M) | Female (M) | t-statistic | p-value | Cohen's d | Result |
|---|---|---|---|---|---|---|
| Facilities & Service | -0.17 | +0.10 | -1.65 | 0.102 | -0.27 | Not Sig. |
| Store Atmosphere | -0.15 | +0.09 | -1.46 | 0.148 | -0.24 | Not Sig. |
| Value Proposition | +0.10 | -0.06 | 0.94 | 0.347 | 0.16 | Not Sig. |
| VM - External Appeal | -0.00 | +0.00 | -0.01 | 0.992 | -0.00 | Not Sig. |
| VM - In-Store Experience | +0.00 | -0.00 | 0.04 | 0.968 | 0.01 | Not Sig. |
Conclusion: No significant gender differences found across all five factors. Males and females have similar retail preferences.
Comparing factor scores across occupation groups (Employed, Student, Business, Housewife)
Business owners show different aesthetic preferences than other groups
Post-hoc: Business vs Student (p = 0.022)
Facilities & Service (p = 0.258), Value Proposition (p = 0.689), and VM In-Store Experience (p = 0.053) showed no significant occupation differences.
| Variables | χ² | df | p-value | Cramér's V | Result |
|---|---|---|---|---|---|
| Age × Shopping Reason | 30.84 | 12 | 0.002** | 0.26 | Significant |
| Age × Shopping Frequency | 22.96 | 9 | 0.006** | 0.22 | Significant |
| Occupation × Shopping Reason | 22.94 | 12 | 0.028* | 0.23 | Significant |
| Gender × Shopping Reason | 1.24 | 4 | 0.872 | 0.09 | Not Sig. |
| Gender × Shopping Frequency | 2.64 | 3 | 0.451 | 0.13 | Not Sig. |
| Education × Shopping Frequency | 9.95 | 6 | 0.127 | 0.18 | Not Sig. |
Comparing effect sizes across different statistical tests
Values near 0 indicate no practical difference
η² ≥ 0.01 (small), ≥ 0.06 (medium), ≥ 0.14 (large)
Different age groups have distinct shopping motivations and frequencies. Young adults (18-25) shop more for variety, while older groups focus on trends.
Business owners value store atmosphere and external visual merchandising significantly differently than students.
No significant gender differences across any factor or shopping behavior—suggesting universal appeal strategies work equally well.
Examining how store importance factors predict visual merchandising perceptions
| Predictor | β | SE | z | p-value | Sig |
|---|---|---|---|---|---|
| F1: Facilities & Service | 0.085 | 0.081 | 1.05 | 0.293 | ns |
| F2: Store Atmosphere | 0.134 | 0.081 | 1.66 | 0.097 | ns |
| F3: Value Proposition | 0.218 | 0.078 | 2.80 | 0.005 | ** |
| Predictor | β | SE | z | p-value | Sig |
|---|---|---|---|---|---|
| F1: Facilities & Service | 0.033 | 0.082 | 0.40 | 0.689 | ns |
| F2: Store Atmosphere | 0.146 | 0.082 | 1.78 | 0.076 | ns |
| F3: Value Proposition | 0.193 | 0.079 | 2.46 | 0.014 | * |
All VIF values < 1.2
No issues detectedCFI = 1.00, RMSEA = 0.00
Excellent fitN = 154 observations
Adequate for analysisF3 (Value Proposition) is the only significant predictor of both VM dimensions. Consumers who value pricing, promotions, and return policies also respond more positively to visual merchandising elements.
Store importance factors explain 9.1% of External Appeal variance and 7.1% of In-Store Experience variance. Other unmeasured factors likely contribute to VM perceptions.
Significant correlations exist between Store Atmosphere and both VM factors (φ = 0.12), suggesting overlapping perceptual dimensions.
K-Means clustering identifies two distinct consumer segments with significantly different profiles
Optimal cluster solution
p < 0.001 (Highly significant)
Balanced cluster sizes
The radar chart visualizes how each segment differs across all five factor dimensions. Value-Seeking Visual Shoppers (green) score consistently above average while Low-Involvement Shoppers (gray) fall below average on all dimensions.
Consumers with below-average scores across all store importance and visual merchandising dimensions. They tend to be more utilitarian and less engaged with the shopping environment.
Consumers with above-average scores on all dimensions. They are highly engaged with the shopping experience and respond strongly to both store attributes and visual merchandising elements.
The two segments are distinguished primarily by engagement intensity, not by which factors they value. Both segments rank factors in the same order—they simply differ in how strongly they respond to store attributes and visual merchandising.
VM In-Store Experience (d=1.46) shows the largest effect size, indicating Value-Seeking Visual Shoppers are particularly responsive to creative displays, lighting, music, and technology (AI/VR/AR) elements in-store.
Chi-square tests reveal no significant demographic differences between segments (Gender χ²=0.04, p=0.84; Age χ²=1.17, p=0.76; Occupation χ²=5.33, p=0.15). Both segments share similar profiles: predominantly female, 18-25 years, employed.
The near-equal split (46.8% vs 53.2%) suggests the market is roughly divided between convenience-focused shoppers who prioritize efficiency and experience-seeking shoppers who engage deeply with the retail environment.
| Factor | F-statistic | p-value | η² (Eta-squared) | Cohen's d | Effect Size |
|---|---|---|---|---|---|
| VM - In-Store Experience | 81.12 | <0.001 | 0.348 | 1.46 | Large |
| VM - External Appeal | 68.60 | <0.001 | 0.311 | 1.35 | Large |
| Value Proposition | 36.17 | <0.001 | 0.192 | 0.98 | Large |
| Store Atmosphere | 34.91 | <0.001 | 0.187 | 0.96 | Large |
| Facilities & Service | 20.95 | <0.001 | 0.121 | 0.74 | Medium |
Single factor explains only 18.3% of variance (threshold: <50%)
5-factor solution explains 51.6% — confirming distinct constructs exist beyond common method variance.
Key findings and strategic recommendations for retail practitioners
All scales demonstrate acceptable reliability (α = 0.68–0.80) and the 5-factor structure is confirmed through both EFA and CFA.
Value Proposition (pricing, promotions, returns) is the only significant predictor of visual merchandising perceptions.
Clear segmentation into "Low-Involvement" and "Value-Seeking Visual" shoppers with large effect sizes (d = 0.74–1.46).
Significant differences by occupation in Store Atmosphere (p=0.036) and VM External Appeal (p=0.036) perceptions.
Factor analysis revealed five distinct dimensions that capture how consumers perceive retail store importance and visual merchandising effectiveness:
Physical amenities and service quality including parking, washrooms, water facility, changing rooms, fast checkout, and store cleanliness.
Aesthetic and sensory elements including store design/layout, merchandise display arrangement, and overall store ambience.
Economic value factors including location, price offers, vouchers/coupons, return policy, digital payment, loyalty programs, and branded merchandise.
Exterior visual elements that attract customers including window displays, signage/graphics, and entrance promotional displays.
Interior sensory and technological elements including creative displays, lighting/music, communication elements, and AI/VR/AR technology.
Key Insight: Together, these 5 factors explain 51.6% of total variance. The Path Analysis reveals that only Value Proposition (F3) significantly predicts both VM factors, suggesting that consumers who prioritize economic value are more responsive to visual merchandising efforts.
Store cleanliness, parking, and fast checkout are the top priorities. Retailers should invest in maintaining clean environments and streamlining the checkout process.
Since value perception drives VM response, visual merchandising should emphasize pricing, promotions, and value-for-money messaging to maximize impact.
Target "Value-Seeking Visual Shoppers" with immersive store experiences. For "Low-Involvement" shoppers, focus on efficiency and convenience.
Different occupational groups respond differently to store atmosphere and external VM. Consider tailoring marketing communications by occupation.
Sample limited to specific retail environment; results may vary across regions and cultures.
Female-skewed sample (62.3%); future research should aim for balanced representation.
Regression models explain modest variance (7-9%); other factors influence VM perceptions.
Longitudinal studies could reveal how preferences evolve over time.