NEW DISTRIBUTION RESEARCH TOPIC
How can AI support front-line sales and real business decisions?
Practical analysis of AI-assisted sales visits, sales analytics, business diagnosis, and accountable human-AI workflows.
QUESTIONS THIS TOPIC ANSWERS
AI Sales Execution & Business Analytics
- How can AI find business issues in sales reports?
- Which field-sales tasks are suitable for AI?
- How should companies govern AI-assisted decisions?
Related research and analysis
20 articles · Page 1 of 1
Promotions Boost Sales, but Why Should Brands Be More Cautious?
After a promotion ends, with sales growth, sales targets met, and decent ROI, many teams conclude that "the campaign was effective and should be continued." However, an increase in sales does not necessarily mean the brand has truly achieved growth. Promotional sales may include natural sales that would have occurred anyway; consumers may simply be pulling future demand forward; growth in one product may come at the expense of other products in the brand's portfolio; and increased shipments to distributors and stores may not reflect actual sell-through. Therefore, the first step in a promotion review is to break down the components: promotional sales = natural sales + true incremental sales + purchase acceleration + product substitution + channel stocking. What brands should truly focus on is how much of that represents sustainable, profitable true incremental growth. This requires teams to establish a baseline of normal sales, distinguish between orders, shipments, inventory, and point-of-sale sell-through, and observe the continuous changes before, during, and after the promotion—avoiding misinterpreting demand pull-forward and inventory shifts as growth. A comprehensive promotion review should also examine four layers of metrics: the results layer looks at sales volume, sell-through, gross margin, expenses, and inventory; the process layer looks at store participation, distribution, display, conversion, and out-of-stocks; the diagnostic layer assesses customer segments, product mix, promotional mechanics, discount depth, and channel take-up; and the long-term layer observes new customer acquisition, repeat purchases, sales at regular prices, and whether future demand is being borrowed. At the same time, don't just look at the overall average. Only by breaking it down to "product × region × channel × store type × time × promotional mechanic" can you identify where growth is occurring, what conditions it depends on, and whether it is replicable. The review should ultimately lead to four types of actions: keep effective programs, optimize specific issues, pause campaigns lacking incremental sales and profit, and re-validate and replicate those with clear success conditions. AI can help teams consolidate data on sales, sell-through, inventory, expenses, and in-store execution, establish sales baselines, identify performance variations across products, regions, and channels, and quickly generate a draft review report. The brand team remains responsible for business judgment and final decisions. 📅 September 17-18, 2026, Zhengzhou, China, the second session of "AI Bootcamp for FMCG Growth" will focus on real brand operating scenarios, helping teams build promotional review and AI-driven business workflows that can be continuously optimized. Interested parties can first obtain the "AI Implementation Handbook for FMCG Enterprise Decision-Makers," complete an AI application diagnosis for their enterprise, and then learn more about the course details.
New DistributionThe Real Growth Differentiator Is Store Management
With limited budgets, manpower, and time, which stores should investments actually go to? In practice, most manufacturer sales teams often adopt an "equal distribution" approach: the same display policies, similar promotion plans, and uniform visit frequencies. The result is often that significant resources are invested, but only a few stores actually achieve growth. The first step in managing high-potential stores is not to look at who currently has the highest sales, but to determine who is most worth sustained operation. Identifying high-potential stores requires simultaneous observation of five dimensions: market opportunity, operational foundation, operational capability, willingness to cooperate, and growth space. Sales figures can only reflect how much a store is selling now, but cannot fully indicate how much more it can grow in the future. Whether the trade area matches, whether there is category opportunity, whether the store owner is willing to cooperate, and whether the team has execution capability—all these determine whether resource investment can be converted into results. After identification, tiered management is also necessary. ⭕️ Stores with high potential but low performance should be the focus for breakthrough; stores with high potential and high performance are suitable for building benchmarks and replicating experience; stores with limited potential but stable operations should focus on maintenance and stability; stores with low potential and low performance require controlled investment. ⭕️ Store tiering is not about labeling stores, but answering three specific questions: Which stores deserve key investment? What business actions should be configured? How to verify results after investment? ⭕️ Correspondingly, brands need to check distribution, display, conversion, execution, and repurchase in sequence. First look at data anomalies, then verify on-site, lock onto the real operational issues, and finally choose product, display, pricing, promotion, or personnel actions. This is also the core of a "one store, one strategy" approach. It is not about planning different activities for each store, but configuring different actions targeting different bottlenecks, with clear goals, responsible persons, verification cycles, and review standards. 👉 Only by forming a closed loop of "establishing a baseline—finding gaps—developing actions—on-site execution—tracking indicators—reviewing and replicating" can store operations shift from relying on experience to becoming a continuously operable method. What is truly difficult in this process is that as the number of stores increases, data sorting, scoring and tiering, anomaly identification, and solution output become very time-consuming. 💡 This is exactly where AI can add value. AI can help teams batch process sales, distribution, inventory, and store visit data, identify anomalous stores and growth opportunities, and generate diagnostic hypotheses and draft action lists; while the team is responsible for on-site verification, business judgment, resource decisions, and action implementation. 💡 AI processing efficiency, combined with human business judgment, can form truly executable store strategies. 📅 On September 17–18, 2026, in Zhengzhou, China, in the second session of the "FMCG Growth AI Bootcamp", we will focus on real business scenarios to help brand teams transform scattered data into store scoring models, high-potential store lists, tiered management strategies, one-store-one-strategy action cards, and review templates, establishing a sustainable AI-driven operational workflow. Interested friends are welcome to join the "Sales Report AI Toolkit - Community" for pre-registration.
New DistributionWhy Can't We Find the Key Issues Despite All the Data Analysis?
Many brands conduct business analysis every month: sales revenue, target attainment rate, year-over-year, month-over-month, and channel share. The data is abundant, and so are the analyses. But at business review meetings, the team still cannot answer three key questions: Where exactly is the problem? Why did it happen? And what should be done next? ⭕️ The core reason is that business analysis is often stuck at “reporting numbers”: it only reports results — you know how much was accomplished but not why; it only looks at totals — the overall seems on track, but local risks are hidden; it only describes phenomena — you see sales decline but haven’t found the real cause; it only gives direction — without specifying the target, responsible person, and timeline. ❗️ Effective business analysis does not start by opening Excel. The first step is to determine what decision this analysis will support. Then, establish four types of comparisons: targets, year-over-year, month-over-month, and benchmarks. Next, drill down layer by layer across time, region, channel, customer, product, and store to find anomalies in the overall figures, and identify contributors and drags from those anomalies. 💡 When it comes to selecting metrics, more is not always better. Business outcome metrics answer how the final results look; growth metrics indicate trends and sources of increment; structure metrics identify who contributes and who drags; terminal metrics reflect whether stores are actually selling through; efficiency and risk metrics help assess whether expenses, inventory, and resource investments are healthy. Business data analysis is time-consuming not only because of complex calculations. Data is scattered across different systems and files, with inconsistent fields, codes, and statistical definitions. Multiple metrics must be cross-tabulated with dimensions such as region, channel, customer, product, and store. Once anomalies are found, the causes must be validated against frontline business facts. Finally, conclusions, priorities, and concrete actions need to be formed. 📅 On September 17–18, New Distribution will hold the second session of the “FMCG Growth AI Bootcamp” in Zhengzhou. Brand teams can bring their anonymized real business data and run through the complete pipeline on-site — from data organization, anomaly detection, cause validation, to business actions — and produce a real business data analysis report. 📝 It is recommended that leaders of sales, marketing, brand, channel, KA (key accounts), data analysis, and enterprise digitalization bring their core team members and join as a group to truly bring AI into brand operations and team collaboration.
New DistributionIndustry Milestone | FMCG KBS Officially Launched: Large Models Don't Understand FMCG, So FMCG Professionals Take Matters into Their Own Hands
At the AI Application Forum of the 2026 China FMCG Conference (CFC) held in Hangzhou on May 27, 2026, Ren Wenqing, CEO of New Distribution, delivered a keynote speech highlighting the core challenge facing the FMCG industry: large models are powerful but don't understand FMCG; companies want to keep up with this major technological transformation but generally don't know where to start—so how should the FMCG industry navigate its AI path? After the speech, representatives from five parties jointly launched FMCG KBS. Tsinghua University's Institute for Digital Government and Governance, Turing Artificial Intelligence Research Institute, and Qingtu Data jointly initiated the KBS concept in July 2025...
New DistributionProfit Soars 312%: Jierou's Three-Year AI Transformation Offers the FMCG Industry an Answer
In 2025, C&S Jierou's annual profit surged 312% year-on-year, and in Q1 2026, profit continued to rise 53%. Behind this counter-trend growth lies a complete AI-driven growth system honed over three years, which will be publicly detailed by Vice President Yang Senlin at a closed-door session of the 2026 China FMCG Conference AI Application Forum.
陈思廷Agentic Commerce Takes Off: 2025, the Last Year Humans Shop Online Themselves
Visa's announcement that the 2025 shopping season marked the end of an era signals the rise of Agentic Commerce, where AI agents autonomously complete purchases. By 2026, AI agents will not only recommend products but also execute purchases, reshaping retail and requiring brands to adapt to AI-driven discovery and decision-making.
戚特While Chinese Supermarkets Study Store Overhauls, Walmart Uses AI to Drive New Growth
While 2024-2025 Chinese retail focuses on learning from Pang Donglai for store renovations, Walmart is undergoing a more radical AI-driven transformation. This article explores how Walmart uses generative AI to rebuild its search, supply chain, store operations, and employee tools, creating a new growth flywheel.
戚特The Growth Logic of the FMCG Industry Is Being Completely Rewritten by AI!
In recent years, the FMCG industry has invested in digital transformation, completing the initial stages of 'data online' and 'system integration.' Now, this process is accelerating into a new 'AI-driven' phase—companies are no longer satisfied with 'seeing' data but seek to make data flow in real time and actively empower business. AI is shifting from a tool attribute to a capability attribute, penetrating key links such as supply chain, channel, and marketing, driving enterprises from static analysis to dynamic intelligent decision-making. How to make AI move from 'foreseeing the future' to 'achieving growth' and truly become a business engine has become a key industry issue.
秦亚峰Cyber Dafu: Using AI to Re-engineer Marketing
“I didn't become a CMO; I became an AI prompt engineer. I can't draw, but I became an AI artist, and 'Cyber Dafu' has become a top influencer on WeChat, Xiaohongshu, and Video Account. All this is thanks to the new possibilities that the AIGC creation era has given me.” While serving dozens of industry-leading companies such as P&G, Yili, Coca-Cola, COFCO, Moutai, Hongxing Erguotou, Gujing, Shede, Novo Nordisk, Nanfu, HANSHU, and SAIC-GM in their brand marketing AI evolution, she directly confronts the pain points and aspirations that companies have regarding AI. Facing the overwhelming wave of super artificial intelligence, business operators are being chased by the relentless pace of the times, with one pressing question hanging over their heads: How can companies keep up?
赛博大福AI-Driven Perfect Market Closed Loop
Trax, one of the earliest companies in product image recognition, founded in 2010, now processes over 1.5 million stores in more than 90 countries daily, converting product displays, promotional materials, and sales activities into readable structured digital assets for real-time, transparent sales operations and decision support. Although online business is growing rapidly, offline remains the mainstay, with about 3.3 million stores in China's urban areas.
Mike Wang5 Seconds to Influence Purchase Decisions: Building an AI-Driven Model for Retail Terminals
At the FMCG2019 China FMCG Conference hosted by New Distribution, founder Zhao Bo opened with a discussion on digital supply chain applications and practices in the FMCG industry, highlighting deep distribution as an effective solution for many FMCG brands. This aligns with Pinlan's advocacy for an AI-driven retail terminal model, which uses AI and data to manage terminals and influence both consumers and store owners.
李剑Wahaha's Zong Qinghou's New Beverage "Plan"!
At the 2019 APEC Business Leaders China Forum, Zong Qinghou, Chairman and General Manager of Wahaha Group, delivered a speech detailing the company's strategy for intelligent beverage manufacturing and outlined three strategic upgrade directions, including a "Big Health" strategy, integration with artificial intelligence, and active development of "Intelligence+".
New Distribution14 Business Analysis Reports a Distributor with Sales Over 10 Million Must Review at Month-End
When a distributor's sales reach 10 to 30 million, they are at a critical juncture between the 'entry stage' and 'growth stage.' After surpassing 10 million, distributors often face challenges such as expanded business scale, increased personnel, more complex processes, and deeper market penetration, making old management methods ineffective and leading to confusion and inefficiency. This passive situation mainly arises because management tools and methods haven't kept pace with the widened management scope, causing information failures and decision-making problems. To regain control and avoid declining efficiency, distributors need to systematically track business information using 14 key reports, starting with the monthly performance summary and detailed reports.
马兴强Efficiency is King: The Key Breakthrough for Distributor Transformation!
The development of the times always replaces traditional models with more efficient ones. In recent years, all enterprises have been seeking new models that are "more economical, faster, more abundant, and better." Let's look at the industry changes related to distributors over the past three years: In 2014, the real economy generally struggled with declining sales; in 2015, the "Internet+" strategy led to the rise of numerous B2B platforms, making 2015 a golden year for B2B; in 2016, "supply-side reform" was proposed, and the related change for distributors was the emergence of unified warehousing and shared distribution; in 2017, the "artificial intelligence" strategy made new retail a hot topic.
徐永刚14 Business Analysis Reports Distributors with Sales Over 10 Million Must Review at Month-End
When sales reach 10 to 30 million, distributors are at the watershed between the 'entry stage' and 'maturity stage.' After sales exceed 10 million, distributors often face a situation where, as business scale expands, staff grows, processes multiply, and markets deepen, old management experience no longer works, leading to confusion, inefficiency, and a sense of losing control. This passive situation mainly arises because management tools and methods have not kept pace with the widening management span, causing information failure and frequent problems in the decision-making system.
马兴强Chen Haibo: Artificial Intelligence, Changing Retail
The 2018 4th 'FMCG + Internet Conference' hosted by New Distribution was held on March 18-19 in Chengdu, attracting thousands of industry distributors, manufacturers, and internet companies. The following is the speech by Chen Haibo, CEO of DeepBlue Technology, at the conference, organized by New Distribution for readers.
陈海波Field Service 365 AI: A New Gateway to Sales Efficiency
As we step into the era of artificial intelligence, Field Service 365 is pioneering the application of AI in enterprise sales management, starting with photo recognition to detect fraudulent photos in offline sales, thereby enhancing efficiency and market control.
New DistributionTen Business Trends You Must Watch in 2017!
This article is republished with permission from Shengjing Wanglian (ID: sjwl360). For reprints, please contact Shengjing Wanglian for authorization. Unauthorized reproduction is prohibited. As 2017 approaches, it's time to discuss future business trends. In 2016, we saw significant shifts: consumption upgrades, the rise of B2B, the boom in artificial intelligence, and investment in fintech and blockchain. Technology innovation is re-emerging, the sharing economy is being reshaped, and many bubbles are bursting, while other trends gradually prove their value.
传刚14 Business Analysis Reports Distributors with Sales Over 10 Million Must Review at Month-End (with Table Download)
This article contains 14 tables in total. Friends who need these tables can reply "tables" in the official account to get them. Sales reaching 10 to 30 million is the watershed between the "entry stage" and "formation stage" for distributors. After sales exceed 10 million, distributors easily encounter a situation: as business scale expands, personnel increases, links multiply, and markets deepen, original management experience no longer works, leading to confusion, low efficiency, and difficulty controlling many matters, suddenly feeling powerless. This passive situation is mainly due to the widening of management span without corresponding management tools, causing information failure and frequent problems in the distributor's decision-making system.
马兴强16 Business Analysis Tools to Make You Look Professional
This article introduces 16 business analysis tools, including VRIO, 3C, Five Forces, SWOT, 7S, PEST, Balanced Scorecard, ERRC, GE Matrix, Value Chain Analysis, Advantage Matrix, PPM, Ansoff Matrix, Value Portfolio, Investment Portfolio Technology Analysis, and Scenario Planning, to help professionals analyze business operations and strategy.
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