This Year's Singles' Day, the Upgraded Version of the Popular Merchant Checklist Is Here

DingTalk Launches Industry's First AI Spreadsheet with "Million-Row Real-Time Performance"

By Shen Songnan

The arrival of the 17th Singles' Day has once again plunged the e-commerce industry into its annual high-intensity operational cycle. From marketing campaigns and live-streaming operations to warehousing, logistics, customer service, and after-sales support, every link in the chain operates tightly together, with data generation and real-time processing demands increasing exponentially.

Under these conditions, multi-threaded business operations have become the norm. A brand representative recently told Tianxia Wangshang that their birthday coincides with Singles' Day: "I haven’t properly celebrated it for years. On my birthday, everyone shows up—except the birthday person."

Beneath the continuous growth in sales during shopping festivals and the expansion of brands lies not only consumer demand but also the evolution of digital tools. On November 5, DingTalk launched the industry’s first AI-powered spreadsheet capable of handling millions of rows in a single sheet with real-time performance. This enables enterprises to aggregate, compute, and act on massive datasets within one unified table, eliminating the previous need to manually split and stitch multiple spreadsheets due to large data volumes—a capability perfectly aligned with e-commerce’s urgent need for real-time data processing during the Singles' Day period.

In fact, prior to this launch, Tianxia Wangshang had already observed companies such as Semir, Intime Department Store, and designer brand Almond Rocks using “a single spreadsheet” as the core engine of daily operations.

Semir: From Manual Entry to Real-Time Data-Driven Operations

In the apparel sector, capturing and responding to consumer feedback is directly linked to identifying bestsellers and avoiding inventory risks. As a category highly driven by emotion and fashion trends, customer reviews, user-generated photos, and even subtle emotional shifts reflect genuine signals about changing fashion preferences—especially during major promotions, when massive inquiries and an influx of feedback test a company’s operational resilience.

Lü Wanlong, customer service supervisor at Semir Co., Ltd., and his team have experienced firsthand the pain of slow data response during Singles’ Day sales. In the past, they relied on Excel spreadsheets, requiring manual input for multidimensional information—an inefficiency greatly amplified during peak seasons. Each round of feedback required regenerating charts and analysis before cross-departmental sharing, resulting in slow and cumbersome processes. Moreover, customer feedback came from diverse sources—chat logs, voice recordings, images, social media content—and consolidating these into long-term analyzable data was nearly impossible under traditional methods.

Semir using DingTalk AI Spreadsheet

Today, through deep integration of RPA (Robotic Process Automation) and the DingTalk AI spreadsheet, Semir has built a “real-time data-driven” business middleware platform.

First, it enables real-time aggregation of public sentiment across all channels. After automatically collecting and summarizing brand-related content from platforms like Xiaohongshu, RPA writes the data directly into the DingTalk AI spreadsheet and sends instant notifications. The AI spreadsheet then automatically determines sentiment orientation and accurately categorizes issues—whether related to products, logistics, or promotions.

Wanlong gave an example: “We sold 10,000 units, and 500 customers shared their experiences, over 90% of which included positive tags like ‘good-looking’ or ‘comfortable.’ As soon as this data emerges, we push it instantly to the operations team for decision-making, enabling restocking or adjusting promotional strategies.”

Real-time collection and analysis of user feedback allow Semir to quickly adapt resources during the multi-wave rhythm of Singles’ Day, minimizing the gap between “customer voice” and “operational decisions.” With extended promotion cycles, predicting sales peaks has become significantly harder. For instance, an unexpected drop in temperature at the end of October, combined with promotional discounts, rapidly boosted winter clothing sales for brands like Semir and Bosideng. Such sudden events create not only inventory pressure but also strain pre-sales inquiries and after-sales handling capacity on Tmall flagship stores. Now, by integrating sales trends with external data like weather forecasts, the AI spreadsheet can predict operational needs in real time, allowing teams to flexibly adjust staffing across different functions.

“The longer the cycle, the higher the accuracy required in data—do we increase or reduce resources? Should they go to pre-sales or after-sales? These decisions require precise, dynamic data,” explained Wanlong.

For Semir, the value of the AI spreadsheet lies in building a “real-time data-driven” business middleware that transforms vast amounts of dynamic information into immediate action amid high-concurrency operations. And this smooth operation relies precisely on DingTalk AI spreadsheet’s ability to process millions of interactions per second.

Tianxia Wangshang believes that behind the narrative of “millions of hot rows,” the deeper significance of DingTalk’s AI spreadsheet for the e-commerce industry is that “this is undoubtedly the spreadsheet that best understands e-commerce.” China’s e-commerce sector is thriving, vast, and complex, spanning countless industries with distinct characteristics. From front-end marketing to back-end inventory and after-sales, the data chain is long and fragmented. To centrally manage such a complex network requires deep immersion in and long-term understanding of the e-commerce industry—the very advantage embedded in DingTalk’s DNA, given its origins within Alibaba.

Intime Department Store: Breaking Down Collaboration Silos for Cross-Organizational Efficiency

While Semir solved the issue of data timeliness, Intime Department Store uses the same spreadsheet to manage the complexity of large-scale, cross-organizational collaboration.

A major retail live stream often involves multiple independent parties: brands, platforms, malls across various locations, and operational teams. Different systems and inconsistent data formats lead to frequent errors in transmitting and aligning critical elements such as product details, pricing, coupons, and schedules. Any minor change requires repeated confirmation across organizations.

According to Li Kai, who oversees content operations at Intime, this was once a “collaboration nightmare.”

Recently, Li managed a large-scale live stream involving over 60 malls, more than ten beauty brands, and over 80 types of coupons. Following traditional practices, he had to set up multiple temporary communication groups to collect and verify information—like rowing a boat from one data island to another...

“I send out standardized templates, but the collected data comes back in wildly different formats,” Li lamented, adding that traditional collaboration methods are extremely labor-intensive. For example, if a product detail changes temporarily, all documents must be updated manually.

Intime using DingTalk AI Spreadsheet

Now, Li uses the AI spreadsheet as a unified information hub for all collaborators:

Brand and mall staff update information directly in the sheet, with data automatically aggregated in real time—powered by the AI spreadsheet’s “bidirectional linking” feature. For example, when product details in Table A are modified, all related entries in Table B—such as inventory dashboards or scheduling plans—are automatically synchronized. This fundamentally solves a core pain point in modern collaborative work.

Today, with the help of the AI spreadsheet, Li has fully automated key workflows. For instance, the AI spreadsheet automatically filters out prohibited terms in product descriptions and adds location-specific labels to products for different malls, ensuring consistency and compliance from the outset. After each live stream ends, analytical results are instantly pushed to workgroups, compressing what used to take days of post-event review into mere minutes.

“Now they call me the ‘hardest worker,’ because I often post review reports in the middle of the night right after a live stream. Partners think I stayed up late analyzing results, but they don’t know it’s actually the AI spreadsheet automatically generating insights based on key performance indicators.”

Li explains that even the act of sending the review report to relevant business groups is done automatically by the AI spreadsheet. Additionally, influencer payment settlements are now automated—by linking live streaming duration and schedule data, the system generates monthly salary details automatically, making manual reconciliation obsolete.

Efficiency gains are directly reflected in business scale. When Li first joined Intime last year, his team could barely support around 20 malls at full capacity. Today, with standardized processes enabled by the AI spreadsheet, the same team efficiently manages live streaming operations across more than 60 malls nationwide—with room to spare. “I think managing over 100 malls wouldn’t be a problem. Anyone working in retail today should absolutely adopt this tool.”

Almond Rocks: End-to-End Influencer Collaboration Efficiency, Building a Business “Command Center”

The day before weekly meetings, Almond Rocks’ business development and operations teams are likely burning the midnight oil: facing over 6,000 influencers’ contact lists, sample shipment tracking numbers, and screenshots of sales performance scattered across different folders—vast, chaotic information that tests frontline employees’ patience and attention to detail.

Different pieces of information are spread across Excel sheets, social media chat histories, and emails. Just compiling them into a weekly report “drains your energy.”

“We used Excel before, but the file kept growing larger and slower, lacking real-time capabilities and data-fetching functions. Employees spent hours every day just making tables,” explained Zhang Qi, founder of the brand. Beyond tedious organization, the bigger issue was that this brand, which heavily relies on influencer partnerships, lacked a comprehensive tool to oversee the entire influencer lifecycle—from initial contact and management to collaboration and performance tracking.

“We want to gradually build a relatively efficient workflow so people can focus more on what they do best and what truly matters,” added Wu Xian, project director at Almond Rocks, speaking from the perspective of frontline employees. “For example, someone responsible for influencer outreach shouldn’t waste time on backend data sorting. They just need to know which products are selling well. Their strengths lie in negotiation skills and brand understanding.”

Starting with socks, Almond Rocks aims to become a Chinese original design brand combining quality, design, and affordability. However, inefficient collaborations with external influencers and internal management bottlenecks began hindering further growth. Now, the DingTalk AI spreadsheet is helping this brand-focused enterprise achieve operational agility.

Almond Rocks’ Command Center

In the “influencer seeding” scenario, the company’s database of over 6,000 influencers was previously managed by just 4–6 business coordinators, most of whose time was spent exporting data, making tables, and searching for information. Now, business staff can view all influencers needing follow-up in one AI spreadsheet, prioritizing them via sorting. Fragmented data such as sample shipment counts and output performance are now centralized.

“With the RPA plugin, entering a note link automatically pulls点赞 and comment data; using the AI field ‘Courier Logistics Tracking,’ inputting a sample tracking number instantly generates the delivery status,” said Wu Xian. Furthermore, the core KPI for business staff—the number of viral notes produced—is now monitored in real time by the AI spreadsheet, with alerts proactively sent via DingTalk messages.

In live streaming scenarios, operations staff previously had to jump between four or five platforms to manually integrate and clean conversion data, leading to disjointed workflows. Now, the AI spreadsheet uses RPA plugins to automatically consolidate multi-platform conversion data into a unified dashboard, allowing operations teams to easily compare this week’s and last week’s sales figures and details. Internally, this is referred to as the “command center”—one dashboard providing a holistic view of the brand’s entire business.

More than that, Zhang Qi noted that the AI spreadsheet helps generate influencer performance heatmaps to identify long-term partners, dynamically monitors individual product sales and inventory turnover rates to spot potential hits early, and even tracks competitor activities to keep tabs on market competition...

Zhang told Tianxia Wangshang that half of Almond Rocks’ team now works on the AI spreadsheet. Especially during promotional periods, he said, “For example, when scaling up production of a fast-selling item, deciding how many samples to send and posts to publish this week versus next—previously, data lagged behind, making it unsuitable for fast-paced environments like Singles’ Day (posting after the event is meaningless). Now, data flows in real time, perfectly matching e-commerce’s need for rapid adjustments.”

When data from over 6,000 influencers, cross-platform live streams, and rapidly changing inventory information converge in one place and flow in real time, the brand’s responsiveness finally matches the speed of e-commerce.

AI Spreadsheets: Elevating from Tool to New Productive Force

Semir’s “data real-time,” Intime’s “process collaboration,” Almond Rocks’ “business agility”—these three brands’ AI implementations across different dimensions collectively demonstrate a trend: AI spreadsheets are becoming the new paradigm supporting e-commerce operations.

Hence, it’s no surprise that this year is being called “the first Singles’ Day powered by AI spreadsheets.”

Tianxia Wangshang argues that beneath these practical applications of DingTalk’s AI spreadsheet in complex scenarios like e-commerce, retail, and shopping festivals lies a deeper truth: Alibaba has completed an AI capability application loop within its own ecosystem. At the top is Alibaba’s foundational large AI model technology; this is concretized into products like the DingTalk AI spreadsheet; and e-commerce operations—especially extreme scenarios like Singles’ Day, characterized by multi-threaded, data-intensive demands—provide the highest-intensity, fastest-feedback environment for validating AI applications.

The successful implementation and maturation of these AI capabilities matter not just for empowering the industry but also serve as strong blueprints for extending AI into new scenarios and businesses. Plausible extensions include deploying AI spreadsheet capabilities across other complex operations within Alibaba’s ecosystem, such as Cainiao’s logistics network (warehousing, trunk, last-mile delivery) or Hema’s retail operations (procurement, sales, inventory).

Thus, “the use of this single spreadsheet during Singles’ Day” extends far beyond e-commerce or promotional events—it clearly outlines how Alibaba transforms its top-tier AI technologies into new modes of production through concrete products within its ecosystem.

In e-commerce—an industry where China leads globally—the AI spreadsheet is evolving from a “tool” to a “new productive force.” This is both a showcase of Alibaba’s AI muscle and a prototype for how it will empower both internal and external ecosystems at scale in the future.

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