Why Peak Season Is Getting Harder for Retail Contact Centers

Holiday demand is growing. So is the work behind each order. Here’s how retailers can get their customer support ready for both.

An order is late. The shopper opens a chat and gets a tracking link, only to see that the order hasn’t moved in five days. They send an email. Then they call. By the time a human agent picks up, one delivery problem has become three contacts, and the product is still stuck at a warehouse.

Most retailers plan their peak season around the number of orders. Far fewer count how many of those orders turn into unresolved problems that keep coming back to customer support. That second number is a big reason peak season gets harder for contact centers every year. Every repeat contact raises operating costs, adds to agent workload and erodes the customer experience. Left unresolved, it leads to customer churn and agent attrition at the busiest time of the year.

How is peak season changing for retail contact centers?

Three pressures are building at once: shopping starts earlier, demand arrives in sharper spikes, and shoppers expect problems to be solved fast. Together, they put customer service under strain before peak season even begins, and repeat contacts compounds to it.

The holiday forecast for 2026 shows the scale:

• $95.8 billion in U.S. online spending in October 2026, ahead of the November–December holiday period.
• Online holiday sales are forecast to reach $275.1 billion, up 6.7% from 2025, with $47.5 billion concentrated in Cyber Week alone.
• Shoppers are getting used to instant help. Gartner found that customers were about three times more likely to use third-party GenAI tools than a company’s own chatbot in their most recent service interaction. If your support is slower than the AI in their pocket, they notice.

Every one of those orders is a potential “where is my order?” contact, address change or return. Staffing isn’t keeping pace. The National Retail Federation (NRF) expects retailers to hire 325,000 to 375,000 seasonal workers in 2026, roughly the same as 2025 and well below the 442,000 hired in 2024 as retailers take a cautious approach amid economic pressure. Seasonal hires are also costly to recruit and slow to train, so adding more people is no longer the default answer.

For service teams, the pressure shows up in specific questions: Can you change the address before dispatch? Will the replacement arrive before Christmas? Can you refund one item from a split shipment? Resolving each request requires the right information and permission to act: updating an order, issuing a refund or arranging a replacement.

Why do traditional service models struggle during peak season?

Temporary staff, longer hours and self-service channels have been the standard way to deal with peak season demand. But their value depends on how much of the customer’s problem they can resolve.

Self-service is where the gap shows most. Gartner found that only 14% of customer service issues are fully resolved in self-service. When the parcel hasn’t moved for five days, the next step means checking stock, applying a replacement policy and arranging a new shipment. A chatbot that can only look things up, leaves that work unfinished.

During peak, an unfinished request can come back through another channel. The agent then has to piece together what happened, repeat checks and help a customer who has already spent time trying to get an answer. This is the hidden cost of peak: repeat contacts. They land on agents who are already stretched thin and absorbing the stress of shoppers racing a Thanksgiving or Christmas deadline. That’s a recipe for burnout at exactly the wrong time.

What should retailers change before the next demand spike?

Our view: at peak, the most important number isn’t how many contacts you deflected. It’s how many problems you finished the first time.

• Measure resolution alongside deflection. Track repeat contacts and first contact resolution and check whether customers are returning with the same unresolved issue.
• Carry the context through. Keep the order details, previous checks and actions attached to the request. If a human specialist joins, the customer should be able to continue from where they left off.
• Make proactive updates useful. Tell shoppers when a shipment is delayed and explain their available options. A notification that leaves them wondering what to do next can still lead to a call.
• Build human judgment into the process. Give specialists a clear role in policy exceptions, sensitive situations and cases where automation needs approval. In Gartner’s 2026 survey, 87% of customers said companies using GenAI for service must provide access to a human agent.

How can retailers streamline peak season operations?

Before increasing automation, check whether your systems and policies allow the request to be completed.

• Connect the systems needed for resolution. Order management, inventory, carrier tracking, returns and payments need to support authorized actions as well as lookups.
• Define what automation can approve. Set clear rules for refunds, replacements and exceptions, including when a human must review the decision.
• Route requests to the right expertise. Use the customer’s intent and the complexity of the case to decide who should handle it.
• Review unresolved demand daily. During Cyber Week, watch repeat contacts, resolution rates and customer effort alongside volume and handling time.

What should retail contact center leaders do next?

Together, these changes point to a different operating model: routine requests resolved through automation, and complex issues reach a human specialist with the full story, in one continuous interaction. Agentic AI is what makes that model practical at peak scale. When routine requests are resolved the first time, repeat contacts drop, and so do the costs, agent burnout and customer frustration that come with them. That helps retailers protect both customer loyalty and their support teams through the busiest weeks of the year. In our next post, we’ll follow the same order delivery issue through an Agentic AI system and look at the tasks it can take over and the KPIs that show it’s working.