How Growth-Driven E-commerce Businesses Stay Steady During Traffic Peaks with an AI-Powered Spreadsheet

In the retail industry, whether online or offline, every major promotion is an all-hands effort across the entire company. Customer service must hold the front line, logistics must stabilize the back end, and operations need to ensure live streaming proceeds smoothly. Amid the chaos, the most vulnerable aspect is often the efficiency and accuracy of information flow.

How can teams handle workloads several times larger within limited time?

At internet brand ZHR Zeze and lifestyle concept store 55°N FIFTYFIVE, both share a common goal: freeing up time from repetitive tasks so they can focus energy on what truly matters.

They turned to DingTalk's AI-powered spreadsheets to reorganize their respective business processes. What stands out isn't just the tool itself, but their determination and practical application of technology to "catch" business demands before traffic surges hit.

ZHR Zeze: Transforming Customer Service from "Messengers" into Problem Solvers

ZHR Zeze is an internet brand focused on casual women's footwear, operating across multiple e-commerce platforms with fast-paced sales cycles and complex after-sales procedures.

Jiu'er, head of the customer service team, not only coordinates frontline support across multiple stores but also acts as a "messenger and dispatcher," manually passing customer requests accurately to finance, operations, warehouse, quality inspection, courier partners, and other departments.

During peak pre-sale and after-sales periods, one of the biggest fears for customer service is missing a complaint—requests had to be sent manually into different departmental groups, package interception required constant manual monitoring, progress tracking relied on follow-ups, and feedback needed to be relayed back to customers to close the loop. The entire process involved extensive coordination and follow-through, and during high-traffic periods, the workload became overwhelming.

So Jiu'er, with over ten years of e-commerce experience, rebuilt the workflow using AI spreadsheets.

Return order management has always been a core pain point for e-commerce retailers. Previously, each return involved multiple departments and steps: customer service staff had to check the order system, verify addresses and shipping status, then notify warehouse personnel; upon receiving returned items, warehouse staff had to cross-check tracking numbers, determine if products could be restocked, and update customer service accordingly. When exceptions occurred—such as wrong items returned or damaged goods—extensive communication between warehouse, customer service, and customers was required to resolve the issue.

With AI spreadsheets, everything changed.

All customer service agents need to do is enter the courier tracking number into the AI spreadsheet. An AI field automatically retrieves the latest logistics status—delivered, in transit, or abnormal—at a glance. There’s no longer a need for manual monitoring to intercept packages—the system tracks and evaluates them directly within the sheet.

When warehouse staff identify an abnormal return, they simply @ an automation assistant in the group chat and provide the tracking number and rejection type. This instantly populates the spreadsheet, where the system automatically looks up the associated order and store, assigns it to the correct after-sales agent, who then updates the resolution in the table. The system automatically synchronizes the result back to the group. Automation replaces much of the manual labor, eliminating tedious communication and preventing oversight.

Swipe left and right to see how AI spreadsheets manage return orders

Negative Feedback Loop: From “Finding the Right Group” to “Automatic Task Assignment”

Simplified workflows have significantly improved cross-team collaboration, especially in managing negative feedback.

The ZHR Zeze customer service team regularly reports issues such as inaccurate shoe sizing, delivery damage, or incorrect product images to operations, logistics, and other departments. With more than 30 different groups across platforms, stores, and departments, simply identifying the correct group and following up consumed valuable time.

Now, when customer service sends feedback to the AI assistant, the information is automatically captured in the AI spreadsheet. AI fields identify the issue type, match it with the responsible person, and deliver the alert precisely to the relevant group. No more digging through message lists—feedback flows seamlessly from detection to assignment, forming a complete closed loop. All feedback data is now centralized in one table instead of scattered across chats, enabling easier analysis and optimization.

Swipe left and right to see how AI spreadsheets manage negative feedback

Previously, group chats were cluttered with raw data dumped by each department: how many returns processed by after-sales, how many received by the warehouse. Determining which agent was responsible for which store’s orders required extracting key details manually from massive amounts of messages. Everyone spent at least three to five hours daily trapped in this cycle.

Now, after-sales agents simply open their personalized task view to quickly address pending issues. Filtered views replace manual scanning, reducing processing time from hours to minutes. Even better, AI fields in the spreadsheet automatically flag overdue tasks based on submission time and deadlines—no manual checks required—ensuring rejected items are handled promptly every day.

A dashboard displays real-time metrics including task volume, processing progress, and overdue alerts—all visualized for clarity. Anyone can instantly see how many issues remain unresolved and how much time is left. This also solves the previous problem of being unable to track feedback once submitted.

During promotional peaks, the customer service team saw far fewer communication loops and follow-up questions. Missed task rates dropped nearly to zero, return processing efficiency increased by 40%, task delays decreased by 70%, task assignment speed improved by 80%, and overall feedback efficiency rose over 70%. These aren’t just numbers—they mean that during traffic spikes, the same-sized team can handle significantly more customer inquiries and after-sales cases.

"By reducing unnecessary communication and follow-ups, AI helps our team operate more comprehensively and to higher standards," said Jiu'er, customer service manager at ZHR Zeze. "Finally, we can spend our time where it matters most."

Live Streaming Efficiency: From 2 Hours to 10 Minutes

This improvement caught the attention of other teams, such as the operations team responsible for daily live stream setup.

Previously, the process was long and tedious: before each live stream, operators manually prepared scripts, compiled benefit points and images, and pasted product information one by one—often over a dozen items per session.

When live streaming frequency doubled during peak seasons, one person might prepare several streams a day. Missing even one item could cause problems during the broadcast.

After shifting this workflow to AI spreadsheets, operators now only need to input product codes, influencers, timing, and upload promotional images. The AI automatically identifies product IDs, extracts selling points from the images, and generates live-streaming scripts. These scripts are synced directly to the customer service team without manual editing. What used to take two hours now takes just ten minutes.

For ZHR Zeze, this change goes beyond efficiency—it makes every detail during peak periods more manageable. They can run more live streams with more influencers, without delays caused by preparation bottlenecks. By the time users tune into a live stream, backend inefficiencies have already been resolved. Even customer service can prepare proactively.

55°N FIFTYFIVE: One Spreadsheet Powers Logistics Across 30+ Stores

55°N FIFTYFIVE, a lifestyle brand offering over 25,000 SKUs, expanded from launch to over 30 physical stores in less than three years.

Such rapid growth placed significant pressure and challenges on warehouse and logistics management.

For logistics manager Liu Fengshou, managing diverse product types and long supply chains became increasingly difficult as store count and shipment frequency grew. Inventory counting emerged as the most time-consuming task:

Employees had to photograph product labels, manually enter production dates, calculate shelf life, and especially for bundled products, repeatedly recalculate expiration dates.

Processing hundreds of entries per day led to high error rates.

Shipment discrepancies and damages between stores and warehouses were common. Broken, missing, or incorrect deliveries occurred several times a month. Pinpointing responsibility alone took days—exchanging screenshots, comparing Excel sheets, and manual audits. Worse, logistics and warehousing work was inherently hard to quantify. Employee evaluations often relied on subjective judgment rather than objective metrics. As a startup, choosing management tools was tricky: they needed systematic control, yet lightweight and affordable solutions.

That’s when Liu Fengshou turned to AI spreadsheets.

He built a comprehensive "system" covering the entire process—from production planning, shipping, in-transit tracking, receipt confirmation, claims handling, to reconciliation—using a single AI spreadsheet.

Next month’s shipping plans, estimated loading times, number of packages, and volume—all live in this one table. To suit various scenarios, he designed multiple views: a detailed view for stores to verify goods, a Gantt chart showing shipping frequency and progress, and a calendar view clearly marking daily pickup schedules. Different permissions were set for logistics providers and departments, ensuring each role sees only relevant data.

Swipe to explore the full logistics management dashboard

In inventory checks, AI became a true productivity booster. Warehouse staff upload photos of product labels, and AI automatically recognizes production dates and shelf life, calculates expiry dates, and triggers alerts. Counting time dropped dramatically, and error rates fell sharply.

Automated Handling of Shipment Discrepancies and Reconciliation

Resolving shipment losses and damages became automated. When store employees find damaged or missing items, they simply upload the signed delivery note. AI recognizes the timestamp, automatically determines if the delivery was late, and if so, triggers the compensation process immediately. What used to take days to assign responsibility now happens in minutes. All loss and damage records are automatically aggregated in the table—nothing gets missed.

Even the most cumbersome task—monthly reconciliation—was solved. Liu Fengshou previously reconciled with over ten logistics companies each month. Now, by setting up a "print view," he filters data to generate ready-to-use reconciliation statements—complete with mall name, outbound order number, tracking number, volume, and settlement amount. Exporting to PDF, stamping, and invoicing—this entire process is now several times faster.

Data-Driven Improvement: Making Service Measurable and Actionable

Liu Fengshou believes the real value lies in data. A dashboard displays quantifiable KPIs like on-time delivery rate and damage rate, and presents service quality via scoring. More importantly, he integrated direct feedback from end-store employees, conducting monthly employee reviews. Logistics providers can now see not only their own scores but also those of competitors. Naturally, they strive to improve—boosting punctuality, reducing breakage, enhancing service attitudes.

When rolling out this system, Liu Fengshou avoided rushing. In the first month, he personally entered all data to help staff get familiar. In the second month, he started delegating basic inputs to employees. Only in the third month did he teach advanced features like permissions. The most welcomed functions were the most practical ones—like handling damage reports. Previously, employees had to search through over ten group chats to find tracking numbers. Now, all related information is linked in the table—accessible instantly. Productivity improved visibly.

In just three months, Liu Fengshou transformed a logistics department once managed by experience into one driven by data, where everyone’s contributions are visible and measurable. A startup achieved what normally requires a dedicated enterprise system—with minimal investment.

In his words, the AI spreadsheet “makes repetitive work simple, simple work automatic, and automatic work accurate.”

Conclusion: The AI Spreadsheet, the Frontline Team’s Most Reliable Ally

The practices of ZHR Zeze and 55°N FIFTYFIVE show us that AI spreadsheets don’t bring radical transformation—they centralize scattered information, automate processes that should be automated, and make work visible and traceable.

Seemingly simple, yet highly effective. Reduced redundant queries, eliminated manual handoffs, prevented information loss. During traffic peaks, these improvements directly translate into capacity—when orders double and stores multiply, the same team can handle more.

A single misstep can lose a customer, but these two companies refined every link in the chain, resulting in unleashed individual productivity and elevated team-wide output.

An AI spreadsheet that understands business, enables collaboration, and closes loops can become the frontline team’s most reliable ally—

Stabilizing rhythm during peaks,

Maintaining standards amid chaos,

Building efficiency through growth.

An AI spreadsheet that understands business, enables collaboration, and closes loops can become the frontline team’s most reliable ally:

Stabilizing rhythm during peaks,

Maintaining standards amid chaos,

Building efficiency through growth.

This is the kind of AI businesses truly need.

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