Survey Findings Report
November 2024
| Agency | Kapa Research |
|---|---|
| Principal | CEGID / DATANOESIS |
| Survey Type | Quantitative B2B Survey in Greece |
| Sample | Fashion retail firms (apparel, footwear, accessories, luxury or beauty/cosmetics) with at least 3 physical stores / Respondent position: Retail manager / Other top management position with include responsibility for management, operations, and digitalization of the retail solution |
| Sample Size | 189 firms nationwide |
| Data collection period | 4 – 25 November 2025 |
| Data collection method | Data collection implemented via Computer-Assisted Web Interviewing (CAWI)and Video-Conference Interviewing (VCI) |
| About Kapa Research | A member of the Greek Association of Opinion and Market Research Companies (SEDEA). Complies with the ESOMAR codes of conduct and publication of opinion surveys |
| 2–3 stores | 34% |
| 4–10 stores | 25% |
| 11–30 stores | 21% |
| 31–100 stores | 14% |
| 101+ stores | 6% |
| Physical stores | 97% |
| E-shop | 86% |
| Franchise | 29% |
| Marketplaces | 28% |
| Wholesale | 11% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Physical stores | 92 | 100 | 100 | 100 |
| E-shop | 81 | 88 | 92 | 87 |
| Franchise | 20 | 21 | 49 | 32 |
| Marketplaces | 19 | 29 | 33 | 37 |
| Wholesale | 9 | 6 | 15 | 13 |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Physical stores | 61 | 58 | 33 | 18 |
| E-shop | 39 | 42 | 67 | 82 |
| Staff scarcity / lack of skills | 63% |
| Operating costs | 53% |
| E-commerce competition | 39% |
| Price competition | 29% |
| Customer experience | 28% |
| Supply chain disruptions | 14% |
| Digital transformation | 9% |
| Demand unpredictability | 6% |
| Security & cybersecurity | 6% |
| Financing (Capex & IT) | 5% |
| Regulatory compliance | 4% |
| DK/NA | 1% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Staff scarcity / lack of skills | 55 | 65 | 77 | 63 |
| Operating costs | 55 | 67 | 38 | 47 |
| E-commerce competition | 53 | 44 | 23 | 26 |
| Price competition | 27 | 25 | 28 | 37 |
| Customer experience | 23 | 25 | 31 | 34 |
| Supply chain disruptions | 11 | 10 | 18 | 18 |
| Digital transformation | 11 | 10 | 8 | 5 |
| Demand unpredictability | 6 | 8 | 10 | 0 |
| Security & cybersecurity | 3 | 8 | 5 | 11 |
| Financing (Capex & IT) | 8 | 2 | 8 | 3 |
| Regulatory compliance | 2 | 0 | 10 | 8 |
| DK/NA | 0 | 0 | 0 | 3 |
| 1 – Very bad | 6% |
| 2 | 19% |
| 3 | 51% |
| 4 | 22% |
| 5 – Very good | 3% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| 1 – Very bad | 5 | 10 | 5 | 3 |
| 2 | 23 | 15 | 26 | 8 |
| 3 | 50 | 56 | 38 | 58 |
| 4 | 17 | 17 | 31 | 29 |
| 5 – Very good | 5 | 2 | 0 | 3 |
| Integration of ERP, POS, e-shop and app via APIs | 47% |
| Channel performance measurement and attribution | 30% |
| Buy Online, Pick Up In Store (Click & Collect), returns and exchanges | 29% |
| Real-time stock accuracy and OMS | 25% |
| Single customer view through CRM or CDP | 17% |
| Unified pricing and promotion rules | 11% |
| Cloud scalability and 24×7 availability | 10% |
| IT resourcing and timelines | 6% |
| DK/NA | 8% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Integration of ERP, POS, e-shop and app via APIs | 48 | 46 | 38 | 55 |
| Channel performance measurement and attribution | 28 | 29 | 28 | 34 |
| Buy Online, Pick Up In Store (Click & Collect), returns and exchanges | 34 | 38 | 15 | 24 |
| Real-time stock accuracy and OMS | 25 | 17 | 41 | 21 |
| Single customer view through CRM or CDP | 14 | 19 | 23 | 16 |
| Unified pricing and promotion rules | 6 | 4 | 23 | 16 |
| Cloud scalability and 24×7 availability | 6 | 8 | 18 | 8 |
| IT resourcing and timelines | 8 | 0 | 8 | 11 |
| DK/NA | 3 | 8 | 13 | 11 |
| AI-driven personalization and recommendations | 39% |
| In-store “Clienteling” application | 33% |
| Workforce and task management | 25% |
| RFID and real-time stock visibility | 25% |
| Cloud POS delivered as Software as a Service (SaaS) | 21% |
| Order Management System (OMS) | 15% |
| Loyalty and marketing automation | 15% |
| Customer Data Platform or single customer profile | 14% |
| Integration with marketplaces | 9% |
| Unified pricing and promotion management | 8% |
| Role-based dashboards and business intelligence | 8% |
| DK/NA | 2% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| AI-driven personalization and recommendations | 33 | 40 | 51 | 37 |
| In-store “Clienteling” application | 34 | 40 | 23 | 32 |
| Workforce and task management | 20 | 29 | 28 | 26 |
| RFID and real-time stock visibility | 11 | 15 | 46 | 39 |
| Cloud POS delivered as Software as a Service (SaaS) | 23 | 25 | 18 | 13 |
| Order Management System (OMS) | 16 | 15 | 18 | 13 |
| Loyalty and marketing automation | 13 | 13 | 18 | 21 |
| Customer Data Platform or single customer profile | 9 | 13 | 18 | 21 |
| Integration with marketplaces | 11 | 15 | 5 | 3 |
| Unified pricing and promotion management | 9 | 2 | 5 | 18 |
| Role-based dashboards and business intelligence | 14 | 2 | 10 | 3 |
| DK/NA | 0 | 6 | 0 | 3 |
| Onboarding and training | 47% |
| Staff engagement and motivation | 41% |
| Staff rostering and staffing | 37% |
| Task management, checklists and audits | 28% |
| KPI and productivity tracking | 25% |
| Consistent visual merchandising | 22% |
| Loss prevention and shrink | 22% |
| Process standardization and discipline | 7% |
| Headquarters to store communications | 2% |
| DK/NA | 2% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Onboarding and training | 44 | 48 | 51 | 47 |
| Staff engagement and motivation | 36 | 40 | 49 | 45 |
| Staff rostering and staffing | 33 | 31 | 41 | 45 |
| Task management, checklists and audits | 14 | 40 | 31 | 32 |
| KPI and productivity tracking | 31 | 29 | 13 | 21 |
| Consistent visual merchandising | 27 | 21 | 21 | 18 |
| Loss prevention and shrink | 23 | 23 | 28 | 13 |
| Process standardization and discipline | 9 | 2 | 10 | 8 |
| Headquarters to store communications | 0 | 2 | 5 | 0 |
| DK/NA | 3 | 0 | 3 | 0 |
| 1 = Not at all | 15% |
| 2 | 17% |
| 3 | 13% |
| 4 | 23% |
| 5 = Very | 32% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| 1 = Not at all | 30 | 10 | 10 | 0 |
| 2 | 20 | 21 | 15 | 8 |
| 3 | 9 | 10 | 26 | 11 |
| 4 | 14 | 29 | 28 | 24 |
| 5 = Very | 27 | 29 | 21 | 57 |
| Loyalty program and benefits | 37% |
| Fast and contactless checkout | 35% |
| Easy returns and exchanges | 35% |
| “Clienteling” and appointments | 34% |
| Personalization using artificial intelligence | 34% |
| After-sales support and repairs | 16% |
| Sustainability, transparency, and traceability | 16% |
| Mobile app and digital wallet | 12% |
| In-store navigation and signage | 11% |
| Omni-channel services such as Reserve, and BOPIS | 4% |
| DK/NA | 1% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Loyalty program and benefits | 25 | 27 | 56 | 47 |
| Fast and contactless checkout | 44 | 38 | 15 | 39 |
| Easy returns and exchanges | 34 | 42 | 23 | 42 |
| “Clienteling” and appointments | 39 | 27 | 36 | 34 |
| Personalization using artificial intelligence | 20 | 38 | 49 | 37 |
| After-sales support and repairs | 23 | 15 | 13 | 8 |
| Sustainability, transparency, and traceability | 11 | 21 | 13 | 21 |
| Mobile app and digital wallet | 11 | 13 | 13 | 13 |
| In-store navigation and signage | 5 | 8 | 18 | 16 |
| Omni-channel services such as Reserve, and BOPIS | 3 | 2 | 5 | 5 |
| DK/NA | 0 | 2 | 0 | 0 |
| AI personalization and recommendations | 40% |
| Workforce and task management | 30% |
| RFID and real-time stock | 23% |
| Clienteling | 21% |
| CDP or CRM and loyalty | 21% |
| Cloud POS | 20% |
| Order Management System, OMS | 15% |
| Marketplaces integration | 14% |
| Unified pricing and promotion management | 13% |
| Dashboards and business intelligence | 11% |
| DK/NA | 3% |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| AI personalization and recommendations | 27 | 44 | 49 | 47 |
| Workforce and task management | 27 | 25 | 38 | 34 |
| RFID and real-time stock | 20 | 17 | 23 | 37 |
| Clienteling | 23 | 27 | 21 | 11 |
| CDP or CRM and loyalty | 14 | 27 | 33 | 13 |
| Cloud POS | 30 | 19 | 10 | 13 |
| Order Management System, OMS | 16 | 13 | 10 | 21 |
| Marketplaces integration | 19 | 17 | 10 | 8 |
| Unified pricing and promotion management | 14 | 17 | 15 | 5 |
| Dashboards and business intelligence | 14 | 13 | 15 | 0 |
| DK/NA | 3 | 0 | 0 | 8 |
| 2–3 stores | 4–10 stores | 11–30 stores | 31+ stores | |
|---|---|---|---|---|
| Yes | 34 | 27 | 33 | 32 |
| No | 66 | 73 | 67 | 68 |
Small retailers are trapped in a defensive cycle: constrained resources, pessimistic expectations, and limited readiness for expansion reinforce each other and suppress technological investment. Their heightened vulnerability to e-commerce competition suggests that without targeted interventions, either through consolidation, investment partnerships, or strong digital adoption, they may face progressive marginalization. The market appears to be moving faster than their capacity to adapt.
Mid-sized retailers sit at the uncomfortable frontier where operational complexity surpasses organizational structure. Their challenges (OMS performance, real-time stock visibility, price consistency, cloud scalability), are not symptoms of failure but indicators of being “stuck in transition.” They have outgrown legacy systems but lack the integrated digital architecture of large players. Without decisive strategic choices, they risk entering a period where complexity accumulates faster than capability, weakening competitiveness despite growth in store count.
Large retailers, by contrast, are already behaving like regional operators rather than local ones. Their high readiness for international expansion, strong appetite for RFID and AI-driven personalization, and interest in unified management systems suggest that a segment of the Greek market is preparing to compete on a European or global stage. These companies are moving toward a model where data consistency, real-time visibility, and customer intelligence form the core of business strategy. The gap between them and smaller peers is widening, and the sector may experience a future where competitive advantage is determined primarily by digital infrastructure and wholistic customer experience innovations.
The broader implication is that the sector’s evolution will likely be uneven and discontinuous. Rather than converging around a common standard, retailers may polarize – some advancing into high-efficiency, omnichannel maturity, and others remaining structurally constrained. This divergence is reinforced by the neutral sector outlook (51%) and the bifurcation in expansion readiness, suggesting a market where confidence and capability correlate strongly, and where low expectations can become self-fulfilling.
Ultimately, the critical insight is that fashion retail in Greece is not defined by a lack of awareness of what must be done, since the priorities are clear, and the solutions are widely understood. The challenge is capability: the ability to fund, absorb, and operationalize digital transformation in serving their customers in an individualized way. Retailers that can align strategic intention with technological execution are likely to shape the next phase of the industry. Those unable to break the cycle of defensive behavior may find themselves increasingly exposed in a market where consumer expectations and international competition continue to accelerate.
In this environment, AI-driven digital transformation is no longer a choice but a sorting mechanism, one that will determine which retailers scale, which survive, and which gradually exit the competitive landscape.
