Kentrix SKU Assortment Intelligence

Generated: 27 Jan 2026, 16:10
317 Stores Analyzed
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How to Read This Dashboard
This dashboard compares each store's SKU range against top performers in the same archetype (similar store type). Under-ranged stores have fewer SKUs than successful peers and may benefit from expansion. Over-ranged stores carry more SKUs than needed, which can dilute productivity. Focus on stores with the largest gaps for maximum impact.
Total Stores
317
4 distinct archetypes
Top Performers
81
Benchmarks for recommendations
Stores Needing Action
296
29 expand, 8 optimize
Avg Sales/SKU
Rs 534,986
Benchmark: Rs 806,546
Total SKU Gap
13,623
Across all stores vs benchmark

Key Findings & Recommendations

Range Alignment

129 stores are under-ranged and 95 are over-ranged compared to top performers in their archetype.

Productivity Gap

Top performers achieve Rs 806,546 per SKU vs Rs 441,782 for others - a 83% gap.

Brand Portfolio Health

277 category-archetype combinations show high brand concentration risk, while 7 are fragmented.

Category Opportunities

5962 category-store combinations identified for range expansion based on top performer benchmarks.

Store Distribution by Archetype

Shows how stores are grouped. Larger segments = more stores of that type. Each archetype gets its own benchmark.

Recommended Actions Distribution

EXPAND = add more SKUs | MAINTAIN = on track | OPTIMIZE = reduce excess SKUs.

Store Performance by Archetype

Faceted View

Stores grouped by archetype to reveal patterns within each store type. Top Performers | Other Stores | Larger bubbles = higher sales.

Productivity Quadrant Analysis

Summary

Stores classified by SKU range and productivity. High Range + High Productivity = ideal. High Range + Low Productivity = over-ranged, needs optimization.

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Store-Level Analysis
Select a store from the dropdown to see its complete profile: current vs benchmark range, category-level gaps, and specific recommendations. Use this view to understand exactly what actions are needed for each store and which categories to focus on.
Select Store:

Select a store from the dropdown above

View detailed insights including category gaps, benchmarks, and specific recommendations

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Strategic Store Insights
Select a store to view strategic performance insights including sales attribution breakdown, performance classification, quick win opportunities, and actionable recommendations based on location potential vs actual performance analysis.
Select Store for Strategic Analysis:

Select a store for strategic analysis

View performance classification, sales attribution, quick wins, and strategic recommendations

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What are Store Archetypes?
Archetypes group similar stores together based on catchment affluence (customer income levels) and store size. For example, "Premium-Large" stores serve wealthy areas with big floor space, while "Mass-Small" stores serve budget-conscious areas with limited space. Why it matters: A store should only be compared to peers in the same archetype - a small store in a mass market shouldn't be benchmarked against a premium flagship.

Understanding Store Archetypes

Multi-Dimensional Segmentation

Stores are segmented across 4 dimensions: Affluence Level, Store Size, Competition Intensity, and Category Focus - creating actionable archetypes.

Data-Driven Thresholds

Unlike fixed rules, thresholds are derived from actual data distribution using quantile-based segmentation.

Benchmark-Based Recommendations

Each archetype has its own benchmarks from top 25% performers within that segment.

Archetype Performance Comparison

Sales/SKU measures productivity - how much revenue each product generates. Higher is better. Compare bars to identify which archetypes are most efficient at converting range into sales.

Archetype Profiles

Summary metrics for each archetype. Top Performers = stores in top 25% of sales within their archetype - these set the benchmark. Use Avg Sales/SKU to compare productivity across archetypes.

Archetype Stores Avg Sales Avg Sales/SKU Avg Range Top Performers
Flagship Premium (Competitive) 40 Rs 416,097,314 Rs 574,797 672 10
Flagship Premium (Competitive) (Atypical) 10 Rs 677,713,198 Rs 762,312 814 3
Large Format (Competitive) 262 Rs 374,907,226 Rs 520,719 649 66
Large Format (Competitive) (Atypical) 5 Rs 437,759,106 Rs 509,474 660 2

Sales by Archetype

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Understanding Gap Analysis
Gap % = (Benchmark - Current) / Benchmark x 100. A positive gap (e.g., +25%) means the store has fewer SKUs than top performers - opportunity to expand. A negative gap (e.g., -20%) means the store carries more SKUs than needed - consider optimizing. Stores within +/-15% are considered well-aligned and marked as "MAINTAIN".
Under-Ranged Stores
129
Expansion opportunity
Over-Ranged Stores
95
Optimization needed
Well-Aligned Stores
93
Within 15% of benchmark
Avg Gap %
-0.0%
Range: -102% to 100%

Gap Distribution

Histogram showing how gaps are distributed. Green bars (right of 15%) = expansion opportunities. Red bars (left of -15%) = over-ranged stores. Center = well-aligned stores.

Current vs Benchmark Range by Archetype

Compares average current range vs benchmark for each archetype. Large gaps between bars indicate systemic under/over-ranging.

Top Expansion Opportunities

10 Stores

These stores have the largest positive gaps - they carry significantly fewer SKUs than top performers in their archetype. Action: Prioritize adding SKUs in high-performing categories to capture missed sales potential.

Store ID City Archetype Current Range Benchmark Gap Action
A506 Surat Large Format (Competitive) 0 605 +605 (+100%) DATA QUALITY REVIEW
A609 Ludhiana Flagship Premium (Competitive) 251 630 +379 (+63%) SELECTIVE EXPANSION
A059 New Delhi Large Format (Competitive) (Atypical) 334 662 +328 (+49%) DATA QUALITY REVIEW
A064 New Delhi Flagship Premium (Competitive) (Atypical) 462 769 +307 (+43%) SELECTIVE EXPANSION
A698 Ambernath Large Format (Competitive) (Atypical) 356 662 +306 (+46%) MONITOR
A406 Ahmedabad Flagship Premium (Competitive) (Atypical) 464 769 +305 (+43%) SELECTIVE EXPANSION
A612 Agra Large Format (Competitive) 313 605 +292 (+52%) SELECTIVE EXPANSION
A703 Bangalore Large Format (Competitive) 321 605 +284 (+51%) SELECTIVE EXPANSION
A513 Indore Large Format (Competitive) 325 605 +280 (+50%) SELECTIVE EXPANSION
A307 Ahmedabad Flagship Premium (Competitive) (Atypical) 497 769 +272 (+39%) SELECTIVE EXPANSION

Stores Needing Range Optimization

8 Stores

These stores carry more SKUs than top performers in their archetype, which may dilute productivity. Action: Review slow-moving SKUs and consider delisting underperformers to improve sales per SKU.

Store ID City Archetype Current Range Benchmark Excess Action
A207 Bangalore Large Format (Competitive) 865 605 -259 (-33%) OPTIMIZE & REDUCE
A134 Hyderabad Large Format (Competitive) 814 605 -208 (-25%) OPTIMIZE & REDUCE
A169 Hyderabad Flagship Premium (Competitive) 803 630 -173 (-20%) OPTIMIZE & REDUCE
A029 Hyderabad Large Format (Competitive) 767 605 -161 (-18%) OPTIMIZE & REDUCE
A028 Hyderabad Flagship Premium (Competitive) 786 630 -156 (-17%) OPTIMIZE & REDUCE
A385 Kolkata Large Format (Competitive) 702 605 -96 (-8%) OPTIMIZE & REDUCE
A660 Mumbai Large Format (Competitive) 691 605 -85 (-6%) OPTIMIZE & REDUCE
A225 Hosur Large Format (Competitive) 685 605 -79 (-6%) OPTIMIZE & REDUCE
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Category-Level Analysis
This section breaks down SKU gaps by product category (e.g., Mobiles, TVs, Laptops). Instead of just knowing a store needs 50 more SKUs, you'll see which categories need expansion. Use the "Categories to Expand" chart to identify where to add range, and "Categories to Optimize" to find where to trim.

Category Performance Overview

Top 10 categories by total sales across all stores. Longer bars = higher revenue contribution. Focus expansion efforts on high-revenue categories.

Categories to Expand

Growth Opportunity

Categories with the highest % gap vs benchmark across stores. Higher % = more under-represented relative to potential.

Categories to Optimize

Reduce Range

Categories with the highest % excess SKUs vs benchmarks. Higher % = more over-ranged relative to current assortment.

Category Share by Archetype

Heatmap

Shows what % of sales each category contributes within each archetype. Darker cells = higher contribution. Use this to understand category importance by store type - Premium stores may skew toward different categories than Mass stores.

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Brand Portfolio Health
This section analyzes brand concentration within each category-archetype combination. High Concentration = Too dependent on 1-2 brands (risk if brand exits or underperforms). Fragmented = Too many brands diluting focus (operational complexity, weak brand partnerships). Balanced = Healthy mix with good brand diversity and focus.
Balanced Portfolios
84
Healthy brand mix
High Concentration
277
Dependency risk
Fragmented
7
Too many brands

Brand Portfolio Health Distribution

Distribution of portfolio health across all category-archetype combinations. Aim for more "Balanced" and fewer "High Concentration" segments.

Brand Portfolio Analysis by Category

Category Archetype Brands Top Brand Top Brand Share Top 3 Share Status
Air Conditioners Flagship Premium (Competitive) 23 Croma 15.9% 38.6% FRAGMENTED
Air Purifier Flagship Premium (Competitive) 16 Dyson 73.7% 94.9% HIGH_CONCENTRATION
Apple Audio Flagship Premium (Competitive) 1 Apple 100.0% 100.0% HIGH_CONCENTRATION
BT Speakers Flagship Premium (Competitive) 27 Marshall 49.9% 72.8% BALANCED
Batteries Flagship Premium (Competitive) 1 Croma 100.0% 100.0% HIGH_CONCENTRATION
Cleaning & Hygiene Flagship Premium (Competitive) 17 Dyson 57.5% 92.0% HIGH_CONCENTRATION
Computer Bags OEM Flagship Premium (Competitive) 5 Lenovo 41.1% 88.6% HIGH_CONCENTRATION
Computer Consignments Flagship Premium (Competitive) 1 Canon 100.0% 100.0% HIGH_CONCENTRATION
Connected Homes & Devices Flagship Premium (Competitive) 5 Yale 88.8% 99.5% HIGH_CONCENTRATION
Cookware Flagship Premium (Competitive) 20 Faber 47.4% 91.6% HIGH_CONCENTRATION
Cooling & Heating Appliances Flagship Premium (Competitive) 26 Croma 23.8% 54.2% BALANCED
Croma Bags Plastic & Paper Flagship Premium (Competitive) 1 Croma 100.0% 100.0% HIGH_CONCENTRATION
DSLR Cameras Flagship Premium (Competitive) 4 Nikon 41.5% 99.0% HIGH_CONCENTRATION
Desktops Flagship Premium (Competitive) 5 HP 71.5% 98.2% HIGH_CONCENTRATION
Digital Cameras Flagship Premium (Competitive) 10 Go Pro 87.8% 98.5% HIGH_CONCENTRATION
Dishwashers Flagship Premium (Competitive) 10 Bosch 40.6% 89.0% HIGH_CONCENTRATION
Dryers Flagship Premium (Competitive) 7 Bosch 27.1% 62.8% BALANCED
Dummies KA Flagship Premium (Competitive) 1 Godrej 100.0% 100.0% HIGH_CONCENTRATION
Dummy Laptop / Netbook / Tabs Flagship Premium (Competitive) 1 Microsoft 100.0% 100.0% HIGH_CONCENTRATION
Electric Sewing Machine Flagship Premium (Competitive) 1 Usha 100.0% 100.0% HIGH_CONCENTRATION
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How to Use This Action Plan
Stores are prioritized based on gap size and sales potential. High Priority = Large gaps with high sales potential - act first for maximum impact. Medium Priority = Moderate gaps - address in next planning cycle. Low Priority = Small gaps or already aligned - monitor only. Start with High Priority stores and work down. The "Rationale" column explains why each action is recommended.

Prioritized Action Summary

High Priority

37
Stores requiring immediate attention

Medium Priority

133
Stores for phased implementation

Low Priority / Maintain

144
Stores aligned with benchmarks

Detailed Store Action Plan

Top 30 stores sorted by priority and gap size. Current = store's SKU count. Benchmark = top performer avg in same archetype. Gap = how many SKUs to add (+) or remove (-). Sales/SKU = current productivity.

Store ID City Archetype Current Benchmark Gap Sales/SKU Action Priority Rationale
A506 Surat Large Format (Competitive) 0 605 +605 Rs 0 DATA QUALITY REVIEW Critical Store has zero SKUs - verify data accuracy before planning
A059 New Delhi Large Format (Competitive) (Atypical) 334 662 +328 Rs 0 DATA QUALITY REVIEW Critical Store has SKUs but zero sales per SKU - investigate data quality
A009 Navi Mumbai Large Format (Competitive) 1,163 605 -557 Rs 0 DATA QUALITY REVIEW Critical Store has SKUs but zero sales per SKU - investigate data quality
A670 New Delhi Large Format (Competitive) 413 605 +192 Rs 523,418 EXPAND RANGE High Significantly under-ranged, productivity is strong
A311 New Delhi Large Format (Competitive) 431 605 +174 Rs 598,048 EXPAND RANGE High Significantly under-ranged, productivity is strong
A417 New Delhi Large Format (Competitive) 443 605 +162 Rs 521,280 EXPAND RANGE High Significantly under-ranged, productivity is strong
A313 New Delhi Large Format (Competitive) 459 605 +146 Rs 510,810 EXPAND RANGE High Significantly under-ranged, productivity is strong
A552 Kanpur Large Format (Competitive) 464 605 +141 Rs 507,661 EXPAND RANGE High Significantly under-ranged, productivity is strong
A503 New Delhi Large Format (Competitive) 469 605 +136 Rs 556,559 EXPAND RANGE High Significantly under-ranged, productivity is strong
A545 New Delhi Flagship Premium (Competitive) 516 630 +114 Rs 599,545 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A532 New Delhi Large Format (Competitive) 496 605 +109 Rs 529,271 EXPAND RANGE High Significantly under-ranged, productivity is strong
A555 Noida Flagship Premium (Competitive) 523 630 +107 Rs 560,540 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A393 Faridabad Large Format (Competitive) 500 605 +105 Rs 544,104 EXPAND RANGE High Significantly under-ranged, productivity is strong
A453 Bhopal Large Format (Competitive) 517 605 +88 Rs 501,117 EXPAND RANGE High Significantly under-ranged, productivity is strong
A559 Nagpur Large Format (Competitive) 521 605 +84 Rs 534,486 EXPAND RANGE High Significantly under-ranged, productivity is strong
A626 Vadodara Large Format (Competitive) 530 605 +75 Rs 541,587 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A529 Jabalpur Large Format (Competitive) 548 605 +57 Rs 529,455 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A162 Mohali Large Format (Competitive) 550 605 +55 Rs 522,532 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A478 Mumbai Large Format (Competitive) 552 605 +53 Rs 527,012 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A445 Kalyan Large Format (Competitive) 556 605 +49 Rs 515,703 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A230 Ahmedabad Flagship Premium (Competitive) 591 630 +39 Rs 590,890 EXPAND RANGE High Significantly under-ranged (p=0.022), productivity is strong
A194 Pune Large Format (Competitive) 568 605 +37 Rs 506,825 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A567 Indore Large Format (Competitive) 570 605 +35 Rs 628,975 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A022 Ghaziabad Large Format (Competitive) 576 605 +29 Rs 559,141 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A382 Chandigarh Large Format (Competitive) 586 605 +19 Rs 637,048 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A138 Chennai Large Format (Competitive) 593 605 +12 Rs 513,403 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A240 Gurugram Large Format (Competitive) 596 605 +9 Rs 679,292 EXPAND RANGE High Significantly under-ranged (p=0.000), productivity is strong
A318 Kolkata Large Format (Competitive) 612 605 -6 Rs 523,148 EXPAND RANGE High Significantly under-ranged (p=0.006), productivity is strong
A328 Ahmedabad Large Format (Competitive) 612 605 -6 Rs 537,494 EXPAND RANGE High Significantly under-ranged (p=0.006), productivity is strong
A359 Kanpur Large Format (Competitive) 613 605 -7 Rs 626,394 EXPAND RANGE High Significantly under-ranged (p=0.008), productivity is strong

Category-Level Priority Actions

Top 20

Specific category-store combinations requiring action. This tells you exactly which categories to expand or optimize in each store. Use this for tactical execution - share with merchandising teams for category-specific planning.

Store ID Category Archetype Current SKUs Recommended Gap Action
A506 Hearables Large Format (Competitive) 0 145 +145 INCREASE
A260 Hearables Flagship Premium (Competitive) 49 155 +106 INCREASE
A609 Hearables Flagship Premium (Competitive) 55 155 +100 INCREASE
A490 Hearables Flagship Premium (Competitive) 71 155 +84 INCREASE
A698 Smart Phones (OS Based) Large Format (Competitive) (Atypical) 15 121 +106 INCREASE
A059 Smart Phones (OS Based) Large Format (Competitive) (Atypical) 16 121 +105 INCREASE
A697 Hearables Large Format (Competitive) 63 145 +82 INCREASE
A319 Hearables Large Format (Competitive) 63 145 +82 INCREASE
A572 Hearables Large Format (Competitive) 64 145 +81 INCREASE
A365 Hearables Large Format (Competitive) 65 145 +80 INCREASE
A431 Hearables Large Format (Competitive) 66 145 +79 INCREASE
A506 Smart Phones (OS Based) Large Format (Competitive) 0 107 +107 INCREASE
A595 Hearables Large Format (Competitive) 67 145 +78 INCREASE
A698 Hearables Large Format (Competitive) (Atypical) 79 155 +76 INCREASE
A703 Hearables Large Format (Competitive) 68 145 +77 INCREASE
A350 Hearables Large Format (Competitive) 68 145 +77 INCREASE
A685 Hearables Large Format (Competitive) 68 145 +77 INCREASE
A547 Hearables Large Format (Competitive) 69 145 +76 INCREASE
A585 Hearables Large Format (Competitive) 70 145 +75 INCREASE
A651 Hearables Large Format (Competitive) 70 145 +75 INCREASE
ML
Machine Learning & Advanced Analytics
This dashboard leverages ML clustering instead of rule-based segmentation, statistical significance testing for gap analysis, Monte Carlo simulations for recommendation confidence, and TOPSIS multi-criteria ranking for prioritization. These advanced techniques provide more robust and data-driven insights.
Clustering Method
KMEANS
Optimal clusters: 3
Best Silhouette Score
0.252
Higher = better cluster separation
Outlier Stores Detected
15
Using DBSCAN detection
Confidence Level
95%
For statistical tests

Statistical Significance Testing

STATISTICAL

Gaps are tested for statistical significance using t-tests and bootstrap resampling. Only statistically significant gaps drive recommendations.

Significant Gaps

282 stores have statistically significant gaps (p < 0.05)

Total Analyzed

317 stores evaluated with confidence intervals

Monte Carlo Simulation

200 iterations

Recommendations are stress-tested through 200 Monte Carlo simulations to measure robustness and confidence.

Average Confidence

71.2% average recommendation confidence across all stores

High Confidence Count

69 stores have ≥70% recommendation confidence

TOPSIS Multi-Criteria Ranking

Top 10 Priority

Stores ranked using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) considering financial impact, implementation ease, strategic importance, and risk.

Rank Store ID City TOPSIS Score Action
#1 A041 Mumbai 0.834 REVIEW RANGE
#2 A001 Mumbai 0.780 REVIEW RANGE
#3 A151 Noida 0.780 REVIEW RANGE
#4 A039 Mumbai 0.775 REVIEW RANGE
#5 A035 Mumbai 0.772 REVIEW RANGE
#6 A147 Mumbai 0.737 REVIEW RANGE
#7 A027 Mumbai 0.669 REVIEW RANGE
#8 A153 Mumbai 0.659 REVIEW RANGE
#9 A119 Mumbai 0.639 REVIEW RANGE
#10 A175 Thane 0.624 REVIEW RANGE

Brand Concentration Analysis (HHI)

Herfindahl-Hirschman Index

HHI measures market concentration. High concentration (>2500) suggests dominant brands, low (<1500) indicates healthy competition.

High Concentration

84 category-store combinations dominated by few brands

Moderate Concentration

15 combinations with moderate brand diversity

Low Concentration

5 combinations with healthy brand competition

Recommendation Robustness Analysis

Stress testing validates how stable recommendations are when benchmarks vary by ±10%. Stable recommendations remain consistent across parameter variations.

Stable Recommendations

181
Consistent across stress tests

Unstable Recommendations

136
Sensitive to parameter changes

Avg Robustness Score

79%
Higher = more stable

Resource-Constrained Implementation Schedule

Top 10 Stores

Based on multi-criteria analysis, these stores should be prioritized for implementation given limited resources. Ranked by urgency score combining gap size, sales potential, and strategic fit.

Priority Store ID City Urgency Score Action
#1 A041 Mumbai 1.67 REVIEW RANGE
#2 A001 Mumbai 1.56 REVIEW RANGE
#3 A151 Noida 1.56 REVIEW RANGE
#4 A039 Mumbai 1.55 REVIEW RANGE
#5 A035 Mumbai 1.54 REVIEW RANGE
#6 A147 Mumbai 1.47 REVIEW RANGE
#7 A027 Mumbai 1.34 REVIEW RANGE
#8 A153 Mumbai 1.32 REVIEW RANGE
#9 A119 Mumbai 1.28 REVIEW RANGE
#10 A175 Thane 1.25 REVIEW RANGE