With the increasing popularity of mobile internet, consumers' fragmented time and attention have shifted rapidly from traditional media to mobile phones, iPads, etc., accelerating the exodus of FMCG advertising from traditional media. The traditional FMCG marketing model faces unprecedented challenges and opportunities. According to the report "2008-2019 China Advertising Market and Communication Trends" released by CTR, in recent years, the FMCG industry has been accelerating its departure from traditional media, shifting to digital marketing platforms such as mobile internet, WeChat, Weibo, and big data. Advertising spending on the five traditional media has dropped by more than 30%. At the same time, the report points out that FMCG brands have launched numerous advertising wars, price wars, and promotional wars over the years to boost performance and seize market share. However, in recent years, the effectiveness of promotions in traditional channels such as supermarkets, hotels, and nightclubs, where companies have concentrated their efforts, has been declining. FMCG marketing seems to have entered a bottleneck period. -01- Data Rules: Digital Marketing Becomes a Major Trend in FMCG Big data will be another disruptive technological revolution in the IT industry after cloud computing and the Internet of Things. In the movie "A World Without Thieves," there is a classic line: "What is the most expensive thing in the 21st century? Talent!" Now, the answer may also include: data. Because "those who have data rule." Against the backdrop of increasingly segmented consumer demand and the growing prominence of the "people, goods, and places" consumption experience, FMCG manufacturers have gradually realized that traditional media, due to their one-way communication, closed nature, mandatory delivery, and difficulty in evaluating communication effects, can no longer meet the precise marketing needs of brands. Meanwhile, the continuous emergence of various social media platforms led by WeChat, coupled with the rapid growth of internet users, has generated and accumulated massive user data and business data. For the FMCG industry, since the vast majority of heavy consumers are concentrated among the post-80s, post-90s, and even post-00s generations who are active online, the online population highly matches FMCG consumers. More and more leading FMCG companies are placing increasing importance on advertising and marketing through digital media, primarily the internet. Corporate marketing models are accelerating their upgrade to consumer-centric online marketing models. Figure 1: China's online shopping scale and penetration rate are rapidly increasing Online, data-driven, and intelligent will be important "faces" of future enterprises. With the improvement of big data infrastructure and the rapid advancement of big data technology in recent years, humanity is moving from the IT era to the DT era, causing the data generated by human society to explode exponentially. Marketing applications driven by big data can be better realized and are becoming increasingly important. More and more FMCG companies are integrating user analysis and even enterprise applications with big data, using data to optimize and enhance their processes, products, and decisions, making operations and management more effective. In the past, FMCG companies did not notice how a small online information change could produce different results. But with the advent of the digital economy era, FMCG companies generate massive amounts of data every day and urgently need to process it. The industry increasingly believes that those who have data rule. Clearly, competition in the FMCG industry has shifted from brand, channel, and product competition to a battle over data mining and usage capabilities. Therefore, FMCG companies need to deploy and build data systems that can accelerate business expansion and build core competitiveness as early as possible, so as to effectively mine the value of collected data and maximize the effect of data-driven business growth. -02- Big Data Helps Create Phenomenal Products like Bestore and Three Squirrels So how can FMCG companies leverage big data to accelerate business expansion, cultivate core competitiveness, and create phenomenal products? Let's look at a few successful cases. Few snack companies can knock on the door of the domestic securities market, but Bestore, a leading snack company, successfully IPO'd on February 24 this year, becoming the second listed leisure snack company in China. Data shows that in recent years, domestic snack market demand has been sluggish and industry dividends have gradually declined. However, Bestore has grown against the trend in the past three years, with expanding market scale and continuous growth in revenue and net profit, showing strong growth momentum and ranking among the top three in the market. According to the "2019 China Leisure Snacks Research Report," in terms of market share, Baicaowei, Three Squirrels, and Bestore rank as the top three in the domestic snack market. Why has Bestore been able to stand out and overtake on curves? What is the main reason behind its successful IPO? Thirteen years ago, it was an unknown small shop in Wuhan; now it is a domestic snack giant and a listed company. One important breakthrough stems from Bestore's continuous innovation in digital expansion and continuous optimization of intelligent construction, which empowers and brings traffic to its chain stores. This is the biggest difference between Bestore and its peers. One key factor is the skilled application of big data, which makes its products frequently go viral as "best-sellers." The value of big data exploded when the company spared no expense to cooperate with international software service providers like IBM and SAP, investing in a comprehensive integrated order management system (OMS) that integrates more than 10 systems and 30+ online and offline platforms, including the omnichannel product system, store order system, inventory system, user system, cashier system, POS system, logistics system, financial system, and big data center. Figure 2: Bestore OMS System Over the years, to capture the ever-changing consumer trends, Bestore has gone to great lengths. It uses the big data module in OMS to monitor, statistically analyze consumer behavior on a weekly basis, randomly capturing over 1 million customer opinions and comments on average each month, and analyzing these massive data to gain insights into consumer behavior preferences and consumption tendencies. At the same time, it uses big data for user profiling, and through user profiles, behavior, and preference data, combined with personalized recommendation algorithms, it recommends different products or items based on users' different interests and needs, truly achieving "tailoring to preferences" and maximizing the efficiency and effectiveness of promotional resources. As of the first half of 2020, Bestore has fully integrated an omnichannel system consisting of 37 online channels and 2,100 offline physical retail stores, with a middle-platform system OMS connecting six key elements: membership, products, promotions, orders, inventory, and logistics: Online, Bestore has completed integration with over 200 platform channels, from rapid order acquisition, intelligent review, to timely synchronization after shipment, forming a closed-loop sales process. Every step, from when a product enters the store, when it is sold, and to which consumer, is digitally recorded. The APP has over 100,000 daily active users and over 5 million installations per year, providing a foundation for omnichannel digitalization. Through big data tracking on its own channels such as APP and mini-programs, it collects a series of behavioral data including user login, registration, clicks, browsing, ordering, coupon collection, coupon redemption, and shopping reviews, precisely understanding consumer behavior and providing personalized products and services. In the past two to three years, it has created multiple phenomenal products such as crispy winter dates and lotus root products, with annual sales exceeding 100 million bags. It is reported that the OMS middle platform not only unifies membership data and rights management across the original 30+ channels but also supports full interaction with consumers, offering new internet gameplay such as flash sales, shopping while watching shows, and group buying with free orders. In addition, in supporting precise marketing activities, member profiling and marketing campaign initiation have evolved from manual to automatic. The technical team's time for customer selection has been reduced from half a day to 15 minutes, system response time shortened to one-fifteenth, and result analysis accelerated to hourly level, with continuous optimization of delivery models, making Bestore's product marketing increasingly precise. Three Squirrels, which has grown rapidly in recent years and ranks second in leisure snacks, has also seen big data become increasingly prominent and frequent in its marketing operations. Its biggest feature is the establishment of a central product control cloud platform, which has over 500 suppliers, each equipped with two to three R&D personnel, and professors from 15 universities across the country are also on this platform. The actual number exceeds 1,000, jointly promoting the R&D and industrialization of Three Squirrels' leisure foods. Relying on the central product control cloud platform, Three Squirrels has transformed from a pure e-commerce company into a digital supply chain platform enterprise, enabling real-time and efficient accurate data transmission. On one hand, it connects many domestic food production enterprises through the product cloud platform; on the other hand, it connects consumers through broader channels, shortening the link between the two. With the help of platform big data, it jointly develops and customizes best-selling products based on consumer preferences. Specifically, every consumer review online and complaint feedback on channels like WeChat and Weibo are automatically captured by the central product control cloud system. If a particular flavor or packaging receives a lot of consumer feedback, the system automatically captures it and traces it back to which supplier produced it and which step went wrong. Even through this system, consumers can scan a code to learn about more than 30 information points, including the origin of raw materials, warehousing time, raw material quality inspection reports, the specific vehicle transporting to Three Squirrels' packaging factory, the testing center staff, and customer service chat records. Recently, Three Squirrels released a big data report on daily nuts consumption, stating that its daily nuts product has become the best-selling single item on Tmall snacks. Since its launch in September last year, this upgraded daily nuts product has sold over 100 million bags cumulatively, becoming the sales champion for daily nuts across the internet, with sales exceeding the sum of the second and third place combined. Behind becoming the single-product sales champion is the Three Squirrels R&D team, which used the data center under the central product control cloud platform to spend over a year capturing opinions and feedback from tens of millions of consumers to comprehensively improve and enhance the original product. The R&D team first improved the product mix of daily nuts. They integrated consumer opinions from online and offline, conducted numerous blending experiments, and improved the taste, appearance, and nutrition, finally determining a mix of 6 premium nuts and 3 dried fruits. This is a strong combination that distinguishes this daily nuts product from other mixed nuts on the market, aiming to make nutrition more sufficient and balanced. On this basis, the R&D team also sorted out the product standards for daily nuts, setting requirements for particle size, plumpness, and moisture content far above industry levels. Taking the walnut kernels used in daily nuts as an example, based on continuous consumer feedback, the thin skin on the surface brings a bitter taste. To solve this pain point, the R&D team conducted repeated experiments and finally achieved peeling of the walnuts, improving the taste of the product. Listening to the opinions of many overseas merchants on the Amazon platform, in terms of packaging, Three Squirrels and its partners jointly invented a dry-wet separation packaging design, using partitioned freshness-locking technology to package nuts and dried fruits separately, preventing mutual influence. Before consumption, tearing open the middle seam and shaking gently to mix yields an optimal taste. Additionally, based on feedback from the data center, they developed a "one-day" small bag packaging for nuts, which is convenient to carry and ready to eat, deeply loved by consumers. Sales have been rising year by year, and it now occupies nearly a quarter of the domestic nut market. In addition, Three Squirrels also thoughtfully developed a pregnancy version to meet the blood-nourishing needs of pregnant women. Figures 3 and 4: Three Squirrels' information system platform enables the transmission, mining, and sharing of data across business processes Recently, Three Squirrels also announced that the latest cooperation between Three Squirrels and Alibaba Cloud, the Three Squirrels snack store, has achieved the first global application of perceptual retail, where products can be automatically priced based on customers' moods. The application scenario of Three Squirrels' new-generation snack store: When consumers enter the offline snack store, the backend big data system activates cameras set up in the store, and the membership card system starts. When consumers approach the shelves, the backend system uses shelf cameras to detect members' moods, gestures, and membership levels, and electronic tags automatically recommend products and set prices for consumers. Let's look at other cases. Some leading FMCG companies have a "war room" at their headquarters to monitor sales and other operational data in real time, similar to P&G's famous "business crystal ball" TV wall, where key performance indicators are graphically displayed on large screens to provide a basis for new market decisions at any time. The maternity clothing brand "October Mom" also uses big data analysis of comments from its Weibo followers to identify fans with comments containing keywords like "love," then tags them and pushes precise marketing information to them. Xiaoye Cosmetics uses its own website as a radar for collecting consumer information, recommending corresponding skin solutions to different consumers, hoping that in the future, big data marketing can replace the website's role and truly become the front end facing customers. The recent internet-famous product Satuday Coffee also used big data to capture the pain points of massive consumer demand, successfully developing the only instant coffee on the market that can be brewed in cold water without affecting taste, and currently holds a market position second only to Nestlé. The key to Wangbaobao's explosive popularity across the internet is using various internet data to precisely capture the pain points of young consumers, successfully positioning the selling point of "soaking goji berries in a thermos, burning my calories," breaking the taste defects of ordinary oatmeal and the high sugar and heat limitations of puffed oatmeal, finding a balance between taste and health, filling the gap in the traditional oatmeal category, and gaining high consumer favor. In the 2019 Double 11, it defeated old brands like Quaker and Calbee to take the top spot on Tmall oatmeal. In summary, for the FMCG industry with massive consumer data, leveraging big data analysis is a powerful tool to gain business advantages and build new competitiveness. -03- Mining the Value of Big Data through "Establish, Aggregate, Connect, and Integrate" to Achieve Precision Marketing For the time being, for most small and medium-sized FMCG enterprises, how can they cleverly find a shortcut to build a big data mining and analysis system, empower data, cultivate core competitiveness, and remain invincible? In the future, small and medium-sized FMCG enterprises should focus on "Aggregate, Connect, Use, and Integrate" to build a low-cost, high-efficiency BI business intelligence data analysis system, mine the value of production big data, help enterprises reduce costs and increase efficiency, enhance overall competitiveness, accurately predict market trends, and quickly seize market opportunities.

  1. Establish: Determine the company's short- and medium-term goals and standards Big data resources are extremely complex and abundant. If an enterprise does not have clear goals, it will at least feel confused if not lost. Therefore, first, determine the short- and medium-term goals for using big data, define the enterprise's value data standards, and then use tools that can solve specific domain problems. Promote gradually, step by step, and do not set ideals too high, otherwise disappointment will be greater.
  2. Aggregate: Start with a BI system and do a good job of data collection and accumulation "Aggregate" mainly reflects data acquisition and collection, which is the foundation of data analysis. "Aggregate" does not mean blindly acquiring data, but clarifying the types of data that need to be acquired. This requires clarifying the main types of data acquisition based on the business development needs and strategy of the FMCG enterprise. Generally, FMCG enterprises mainly focus on data needs in personnel, terminals, channel distributors, expenses, assets, and business behaviors. They can start by introducing a small BI analysis system, model and collect specific data types according to the actual situation of the enterprise, lay a good foundation, and then further expand the enterprise's big data intelligent analysis system.
  3. Connect: Breaking the "island phenomenon" and achieving data sharing is key "Connect" mainly reflects connectivity and sharing. Currently, most small and medium-sized FMCG enterprises do not lack business analysis systems, but rather have many and complex business systems that form an "island phenomenon," each operating independently, unable to form effective integrated analysis. This requires the data analysis system of FMCG enterprises to be unified and coordinated, with strong data integration capabilities, able to integrate data from various different sources and structures, such as customer relationship management, search, mobile, social media, web analytics tools, census data, and offline data. The integrated data is the basis for targeting a larger audience. A large platform system is needed to connect and integrate business data from various systems, such as Bestore's OMS, to achieve multi-dimensional integrated data insights, helping enterprise managers truly discover the internal connections between problems and their causes, and then helping managers find positive factors for discovering and solving problems.
  4. Integrate: Leverage external forces to easily achieve goals Many small and medium-sized FMCG enterprises lack the capability and financial resources to build systems for analyzing massive data. At this time, they can cooperate with big data R&D companies. Currently, there are companies such as Yonyou, IBM, Teradata, and Teamsun that provide big data analysis and mining services, which are forces that traditional enterprises can leverage for big data analysis. Many e-commerce merchants on Taobao purchase the part of the massive data collected by Taobao that is related to their own operations to expand their online business. Another example is Kraft, which cooperated with IBM to capture 479,000 discussions about its products from blogs, forums, and discussion boards, and used big data analysis to determine consumers' affection for Kraft foods and their consumption patterns. In addition, FMCG enterprises can use modern advanced technologies for efficient and comprehensive data collection, such as the rapidly developing AI technology in recent years, whose advanced algorithm capabilities can efficiently and comprehensively assist FMCG enterprises in mastering terminal data. -04- Conclusion Although big data shows extraordinary prospects and great value, big data marketing still faces many problems and challenges. The first challenge is technical difficulties. After all, big data technology is still in its early active stage, and various aspects of technology are not yet solid, requiring further improvement and enhancement of various tools. But the reality is that truly launching big data marketing, FMCG enterprises face not only technical and tool issues, but more importantly, they need to transform their business thinking, management processes, and organizational structure to truly mine that data gold mine. "Planning strategies and winning battles a thousand miles away" may lie in big data!