Retail data in 2026: Turning insight into action at scale

By 2026, retail success will depend less on how much data retailers collect and more on how effectively insight is turned into action across merchandising, forecasting, customer experience, and operations. As channels and SKU complexity grows, data maturity is becoming a practical advantage, enabling faster decisions and stronger execution at scale.

That shift is already underway. A Deloitte survey shows that 96% of global retail leaders expect revenue growth in 2026, with 81% also anticipating margin improvement, as retailers focus on agility, smarter customer engagement, and resilient supply chains. 

For many organizations, making this real will require extending internal capabilities with offshore teams that support consistent, scalable execution – helping insights translate into impact across the business.

Retail data maturity will define competitive advantage in 2026

1. Why having data is no longer enough

Most retailers already collect huge volumes of information from every point of interaction: online behavior, point-of-sale systems, inventory movements, and customer engagement metrics. That raw volume, however, doesn’t automatically improve decisions.

Investment in data continues to accelerate. The retail analytics market is projected to grow from about $7.56 billion today to more than $31 billion by 2032, yet the results vary widely as many organizations struggle to translate analytics into consistent operational decisions.

Even with more advanced tools, analytics often stop at dashboards that explain past performance rather than guiding next actions, leaving teams reacting instead of planning with confidence – a clear signal that data alone is no longer enough to compete.

2. The shift from data visibility to data execution

Gaining a competitive advantage increasingly depends on how quickly and consistently retailers act on insights. Real-time analytics and AI-powered forecasting are accelerating this shift, with more than 70% of retail businesses already piloting or partially deploying agentic AI to improve operational efficiency. What once took days in spreadsheets can now inform pricing, inventory allocation, and personalization decisions almost immediately.

Insight only creates value when it drives operational change. As data is embedded into daily workflows, teams move faster from observing patterns to taking action. This shift defines retail data maturity in 2026 and elevates analytics from reporting to performance.

Merchandising decisions will be increasingly data-led – or costly

1. How data will shape product and range decisions

Merchandising decisions are increasingly driven by demand signals rather than instinct. Retailers use sales performance and customer behavior to optimize SKUs and ranges by channel, enabling faster responses to shifting demand. McKinsey shows that data-led merchandising can deliver 2-5% sales growth and 5-10% margin improvement in the U.S. market.

Pricing and promotions are following the same shift. Real-time data allows teams to adjust faster, reduce excess stock, and protect margin as customer demand changes.

2. The execution challenge behind data-led merchandising

Execution remains the bottleneck. Many merchandising teams still depend on manual processes, spreadsheets, and disconnected systems, while limited analyst capacity slows continuous optimization as demand shifts.

Data-led merchandising only delivers value when insights flow into daily workflows. Retailers that extend internal teams with offshore analytical and merchandising support can sustain ongoing optimization, ensuring data drives execution at scale, not just reporting.

Forecasting accuracy becomes critical as retail complexity increases

1. Why forecasting errors will become more expensive

As retailers expand across more channels, marketplaces, and fulfillment models, forecasting errors carry greater financial risk. Stockouts quickly lead to lost sales, while excess inventory ties up capital and erodes margin as customer tolerance continues to shrink. Improving forecast accuracy can reduce lost sales by up to 65% and lower inventory levels by around 20%.

With rising SKU counts and faster demand shifts, even small inaccuracies can ripple across the network. Forecasting becomes a critical control point for managing complexity and protecting performance, not just a planning exercise.

2. Turning forecasting insight into operational action

Forecasts create value only when they guide decisions across buying, inventory, and fulfillment teams. Aligned demand signals allow retailers to adjust volumes and rebalance stock before issues surface.

That requires forecasts to be embedded into daily workflows. With scalable execution through remote analytical teams, retailers can ensure forecasting drives consistent action rather than remaining a static metric.

Customer experience will be shaped by data in real time

1. Using customer data to improve experience, not just reporting

Customer experience is increasingly shaped in real time. Retailers use live behavioral and operational data to identify friction across the journey, from discovery to fulfillment and support. 80% of customers say experience is as important as products, underscoring why speed and consistency now matter as much as price.

When customer data reaches frontline teams, issues can be addressed faster, fulfillment accuracy improves, and delays are resolved before they impact satisfaction. Real-time insight allows teams to act while customers are still engaged, strengthening loyalty.

2. Why CX data often fails to drive change

Many retailers still struggle to act on CX insights. Data often sits with analytics teams, disconnected from service and fulfillment, while limited capacity slows monitoring and follow-through.

CX data creates value only when it informs daily decisions. By adding remote retail expertise, retailers can apply customer insight more consistently across teams, rather than relying on ad hoc interpretation.

Operational efficiency will depend on actionable insights

1. Where data creates the biggest operational gains

Operational data delivers the greatest value when it enables teams to act earlier. Retailers rely on actionable insights to reduce fulfillment errors, improve workforce planning, and meet SLAs more consistently, especially as the industry continues to lose an estimated $1.73 trillion annually to out-of-stocks and overstocks.

By flagging demand shifts and process bottlenecks sooner, data helps prevent issues before they slow operations or impact customers.

2. The hidden cost of not acting on data

When insights fail to drive action, leadership teams spend more time firefighting recurring problems. Issues like fulfillment errors or repeated missed SLAs, even though the data has already identified the root causes.

The cost of inaction shows up in wasted labor, margin leakage, and slower response times. Adding remote operational expertise allows retailers to apply insights more consistently and avoid repeating the same issues over time.

The growing gap between data ambition and data execution

1. Why retailers struggle to operationalize insights

Many retailers have ambitious data goals but struggle to translate insight into action. McKinsey research shows that fewer than 20% of organizations have achieved advanced analytics at scale, pointing to a persistent gap between strategy and operational impact, especially in the U.S. market.

As a result, data teams produce valuable analysis, but limited capacity and slow scaling prevent insights from being applied consistently across merchandising, planning, and operations, widening the gap between what retailers know and what they do.

2. Why data maturity is an operating model challenge

Data maturity goes beyond tools. While modern platforms already deliver rich insights, value is created only when teams can act on them. Research shows high-performing companies are 3× more likely to embed analytics into daily operations, reinforcing that people and processes matter most. 

When analytics are aligned with everyday workflows, data becomes a practical driver of performance rather than a reporting layer.

How outsourcing supports data-driven retail execution

1. Extending internal teams with data-enabled capability

Retailers are increasingly extending internal teams with experienced analysts and operational specialists to apply insights more effectively. This trend is reflected in the rapid growth of the data analytics outsourcing market, driven by demand for scalable analytics capacity alongside in-house teams.

The global data analytics outsourcing market size is projected to grow from about $29.85 billion in 2026 to nearly $249.79 billion by 2034. Building offshore teams give retailers faster access to skills that take months to hire in the U.S. market, supporting consistent decisions across merchandising, forecasting, customer experience, and operations.

2. Where outsourced teams add the most value

Outsourced teams create the most impact in roles that support data-driven action across retail and eCommerce. Data analysts, data engineers and customer insights analysts help with data preparation, reporting, and quality assurance, ensuring insights are accurate, reliable, and ready to use. They also support catalogue and product data optimization, improving listing accuracy and consistency across channels.

In outsourcing retail and eCommerce models, remote analysts play a key role in translating insight into day-to-day work. By keeping functional teams aligned with operational data signals, they help ensure insights inform daily decisions rather than remaining confined to reports.

Why Vietnam fits modern, data-enabled retail delivery

1. Outsourcing 2.0 for analytics-driven retail

Vietnam is a strong fit for retailers building data-enabled teams embedded directly into core tools and workflows. With a modern model – Outsourcing 2.0, moves beyond cost-focused thinking toward access to global talent that strengthens teams and improves product and business outcomes. It is built on partnership, with teams in Vietnam aligned to business priorities and accountable for measurable results.

In practice, remote analysts and operational specialists contribute continuously to daily retail operations, from maintaining data pipelines to monitoring forecasts and surfacing actionable signals. With clear ownership and governance, data moves beyond reporting to become a consistent driver of performance.

2. Why Vietnam is well-suited to data-led retail operations

• Strong analytical talent pipeline

Vietnam produces around 55,000-60,000 computer science and IT-related graduates each year, creating a deep talent pool well-suited for data engineering, analytics, and detail-oriented retail operations.

• Clear communication and ownership

Vietnam-based professionals demonstrate solid English proficiency and a strong sense of ownership, proactively flagging risks, clarifying requirements, and contributing ideas beyond assigned tasks.

• Built for cross-time-zone collaboration

Vietnamese teams are experienced in working as offshore extensions of U.S. retail organizations, collaborating effectively across time zones using agile practices and retail platforms such as Shopify, Salesforce, NetSuite, alongside tools like Teams, Slack, Jira, and shared reporting environments.

• Adaptable, team-first mindset for retail data work

With growing experience on U.S. retail projects, teams bring a collaborative, feedback-driven mindset suited to data-heavy work across merchandising, inventory, forecasting, and customer analytics. This makes it easier to integrate into daily workflows and build long-term partnerships.

3. Scaling product and data delivery: A retail technology company’s offshore model

A retail technology provider serving fashion and lifestyle brands partnered with Away Digital Teams to address growing product and data capacity constraints as demand outpaced local hiring.

By extending its team with offshore full-stack and data engineers in Vietnam, embedded into existing tools and sprint workflows, the company accelerated parallel product and data work while retaining ownership of technical direction and code quality.

• The results:

– Increased throughput across product and data initiatives.

– Faster access to engineering and data capability without added fixed cost.

– Greater flexibility as priorities evolved.

– Strong onshore–offshore collaboration with clear visibility and quality control.

This case shows how an embedded offshore model can sustain delivery momentum in a fast-moving retail technology environment without sacrificing control.

Preparing for retail data maturity 

1. What leading retailers are doing now

Leading retailers are shifting focus from adding more tools to strengthening how insights drive decisions. As analytics investment continues to rise, advantage comes from speed, clarity, and ownership, not access to data alone. Leaders are tightening decision cycles and setting clearer accountability across merchandising, planning, and operations.

2. Designing operating models that support insight-to-action

Retail data maturity depends on operating models that connect insight to everyday work. High performers embed data into routine workflows, supported by shared metrics and clear decision rights, so teams can respond faster to demand changes, inventory risk, and customer signals.

3. Aligning teams with scalable capability

As complexity increases, many retailers are complementing local teams with embedded offshore capability in markets like Vietnam to sustain momentum. This allows organizations to scale analytical and operational capacity, maintain consistent follow-through, and ensure insights continue to inform merchandising and operations as the business grows.

Conclusion

In 2026, retail leaders won’t be defined by how much data they have, but by how consistently insight turns into action. As complexity grows, success depends on operating models that strengthen internal teams by tapping offshore talent, enabling insights to move from analysis into action at scale.

For many retailers, this shift helps explain why Vietnam is becoming retail’s global back office, providing the scalable talent, embedded ways of working, and day-to-day support needed to keep insight flowing into action at scale.