Seasons like Christmas and Black Friday put your customer service team to the test. The demand for solutions grows like never before, forcing you to scale your response capacity to rise to the occasion and not neglect your consumers’ experience.
Hiring talent just to handle demand spikes is not a solution, nor is maintaining a reserve that you will deploy only at specific moments. There is only one way to overcome the challenges of this season and optimize your budget in the process. We’ll explain it to you.
The problems of stationary support
There is no area that suffers more from a lack of scalability in customer support than ecommerce. When launches, product discounts, or peak season arrive, the increase in demand is simply overwhelming.
Regardless of forecasts, the sudden rise in traffic slows down operations and disrupts the usual workflow. This happens for several reasons:
- Increase in user inquiries.
- Possible errors in the supply chain.
- Channel and system saturation.
- Physical and mental burnout of the support team.
- Longer response times.
This combination leads to widespread disorganization that directly affects your company’s reputation and, above all, makes your users consider your competition as an alternative due to the poor experience you offer them.
In a survey designed by Cyntexa, 76% of people stated that they stopped buying from a brand because of very bad experiences. These not only harm customer satisfaction, they also impact the repeat purchase rate (RPR).
Hiring more human talent is no longer a solution
Customer support is an area centered on empathy. There, you directly manage the doubts and inquiries of those who trust you, seeking to offer an optimal solution that resolves every possible issue.
The human factor will always be necessary, but that does not necessarily mean that the ideal strategy to overcome demand spikes is to hire more staff to manage tickets. Here’s why.
Low scalability in the face of demand spikes
When your operations increase and key seasons or events concentrate heavy user traffic in just a few days, traditional hiring stops being a real response.
On the one hand, traditional selection and onboarding processes are not suitable for reacting instantly to a key launch or peak-season offers. You need to scale support immediately, not two months from now.
When ticket volume explodes, even a reinforced team becomes saturated, which leads to longer wait times and frustrated customers—two serious situations during a time when you most need to capitalize on every sales opportunity.
Inconsistency in service quality
Imagine that, for several reasons, you don’t lose as much time integrating talent in the traditional way. That could be the ideal scenario if it didn’t give rise to a critical risk for your business: inconsistency in customer support.
Think about it—you are mixing experienced agents with new profiles who do not yet fully master all your products, policies, and, what may be even more serious, your brand’s tone of voice.
As a result, the customer experience becomes a roulette: some receive clear and decisive answers; others get incomplete, contradictory, or outright incorrect messages that create more doubts than they had before.
Higher operating costs
From a financial perspective, traditional scalability in customer support critically increases operating costs, and it’s not just about the regular salary. Your investment goes beyond that.
Behind every new hire there are recruitment expenses; training costs for the person to reach the level of previous talent; overtime, night shifts, and other elements that only a Total Cost of Ownership (TCO) calculation can truly reveal.
In practice, you allocate budget to maintain a rigid structure that does not match the real elasticity of your demand, which limits the potential investment you can make in growth, product, or innovation.
The best way to scale customer support in 2026
It’s a fact: you won’t solve customer support demand spikes by hiring more people. On the contrary, you will hinder the little agility you have left while your budget takes on expenses with no immediate real return.
In this context, strengthening the scalability of your support team forces you to think outside the box and find a novel solution that boosts you instantly. We know what it is: incorporating artificial intelligence into the operational layer.
A Zendesk report from 2023 anticipated it. At that time, 43% of companies were investing in chatbots, AI, and automation to improve scalability in their customer support. The current figure is much higher.
By combining human agents with virtual agents that you can “clone” as many times as you need, you create a support system that will match and exceed the increase in user traffic at the moments when you need it most.
24/7 availability to handle user tickets
The arrival of chatbots in customer support makes full availability a completely real scenario without requiring overnight costs.
Think about it: a chatbot can take tickets, answer frequently asked questions, and manage basic operations at any time, across different channels and time zones. Even when your human team is offline, no interaction goes unanswered.
This capability is especially critical in global operations, where clicks, shopping carts, and claims happen without respecting your local schedule. In a scenario like this, intelligent automation is the ideal path.
First-contact resolution of inquiries
Today, AI-powered assistants autonomously resolve a large portion of level 1 requests, significantly reducing handling times and easing the load on human agents. We’re talking about issues such as:
- Password resets.
- Login and access problems.
- Basic software and hardware configurations.
- Questions about system usage.
In addition, when they are integrated with your order, payment, and logistics systems, they can inform about the status of a delivery, initiate a return, or update customer data in seconds, without the need to escalate the case.
The result is a more agile user experience and a human team that dedicates its time to complex problems, not repetitive tasks.
Scalability without friction or budget stress
Does the week start and you need to increase support by Wednesday? No problem. You can clone an AI assistant as much as you need to manage the flow of users smoothly and then scale it down without major drama or friction.
Letting go of a human agent from one day to the next involves operational tensions. There will be a gap in your organizational chart that other talents will have to fill, leading them to neglect their own functions and causing their work to lose quality.
This doesn’t happen when artificial intelligence assistants enter the equation. You can increase or reduce support without friction or compensations that pull you away from your business goals and drain budget allocated to strategic matters.
Intelligent automation based on LLMs
Automation based on Large Language Models modernizes this process. The AI assistant better understands natural language and maintains context throughout the conversation, moving away from a scheme dependent on rigid decision trees.
These models generate more natural responses, better interpret nuances, rephrase explanations, and rely on business rules to prioritize and route tickets based on their urgency and impact.
Under these guidelines, assistants improve as copilots for human agents, suggesting response drafts and queries to internal systems that the agent only needs to review and adjust before sending.
Greater focus on customer personalization and segmentation
Artificial intelligence deepens personalization and segmentation, something especially valuable for managing demand spikes, closing high-value purchases, and, above all, for customer satisfaction.
A McKinsey & Company report indicates that 67% of customers feel frustrated when their interactions with a brand are generic. It couldn’t be otherwise—being treated like just a number or a random person is unpleasant.
The models analyze various factors to identify critical segments and adapt the support message along with the offers that accompany the user experience on the platform, improving conversion opportunities. Some of these factors include:
- Browsing behavior.
- Purchase history.
- Past customer responses.
Thanks to this, human agents prioritize strategic customers in the queue. In this way, you build a system that sends relevant suggestions and designs support experiences that solve problems and drive new sales within the same service flow.
