The rapid development of China's Internet industry in the past 20 years has led to great changes in the retail industry, especially the emergence of e-commerce platforms, which have greatly subverted people's shopping experience and shopping habits. With the rise of short video platforms such as Douyin and Kuaishou, new shopping forms have further stimulated people's consumption desire. However, for traditional retail enterprises such as shoes and clothing, under the impact of the Internet wave, brands have basically formed an operation mode of online and offline coordination. E-commerce accounts for almost half of national consumption. However, according to industry research data, online sales of shoe and clothing brands only account for 10%-40%, which means that the shoe and clothing industry, which focuses on trying on the experience, The main battleground is still below the line.
After all, offline space is an important channel to connect customers and experience products, so the quality of offline operation also has a direct impact on the overall growth of a brand. However, as the traffic growth of mature brand stores is gradually weak, digital operation has become the key to reduce costs and increase efficiency, among which, data-driven intelligent marketing system has become an important means to improve the breakthrough. Whale Strategy, as a service provider started with retail digital marketing technology, features "offline data feeding online operation" and is good at building intelligent marketing system for brands with offline data collection, analysis and insight. It is popular with retail and new consumer brands and many retail industry giants have become its customers.
01 "Space intelligent" operation, based on data capture
Under the trend that brand offline stores are gradually upgrading to digital and intelligent operation, Whale's characteristic proposal of "spatial intelligence" operation solution can be understood as an upgraded version of the "people, goods and field" operation in the retail industry, that is, centering on the link between brand, customer and store operation. Focusing on the operation links that brands focus on, such as attracting new customers, acquiring customers, visiting shelves, commodity interaction, transaction transformation, etc., Whale's strategy helps brands to have a deeper understanding of customers, quantify product attraction, and help managers to have a clearer understanding of the operation from individual stores to the overall brand through the collection, analysis and insight of customer flow and interactive data of offline stores. Therefore, under the core goal of increasing the overall sales, we can assist the brand to complete the planning and implementation of the single-store to overall marketing strategy.
Therefore, the core of "spatial intelligence" solution lies in data and intelligent model, among which data collection and precipitation is the premise of building intelligent model. However, for a long time, the data precipitation of brand offline stores has been seriously missing. On the one hand, most brands still have a traditional operation mode for offline stores and lack of awareness of digital operation. On the other hand, limited by imperfect privacy protection and data collection equipment, offline data collection is more difficult. Whale Strategy started from the digital operation of offline stores of retail brands in 2017, and has accumulated many years of industry reputation with a thorough understanding of the industry and rich practical experience.
Whale's strategy for offline store data collection in the retail industry mainly focuses on two dimensions of comprehensiveness and security. From the perspective of comprehensiveness, Whale strategy data collection can be divided into two aspects: customer visit track and interaction information with commodities. From the inbound and outbound customer flow indicators, the collection of store attendance, attention, store attendance statistics and store attendance rate analysis can be used to set up marketing programs according to the store attendance under the influence of time period, holidays, weather and other factors. From the index of commodity interaction, the trajectory of customers' in-store activities is associated, including the multi-dimensional detailed data collection, such as staying area, picking up commodity category, picking up duration, picking up times, trying times, days of commodity display, turnover, and connection rate, so as to comprehensively capture the interaction information between customers and commodities. It is convenient for the subsequent brand to provide data support for product selection, display and overall operation planning of promotional activities.
From the perspective of security, it is mainly about the compliance of data acquisition. Firstly, security level cameras are used in data acquisition equipment, and high-performance edge computing AI cameras are used to ensure the security of private data. AI camera edge computing analyzes passenger flow data without collecting and saving any video data. The "fuzzy algorithm" is applied to the edge of the front-end processing to ensure the effective protection of user data privacy.
02 Conversion funnel model, series the whole operation link
In order to further refine the effective data of the operation of the offline stores, Zuoji also designed the funnel analysis model based on the transformation of customer visits for shoe and clothing brands, which has become a sharp tool for Zuoji to break out from a number of digital operation service providers. "Transformation funnel model" means that each link of customers' journey through the store is integrated and progressive through the detailed customer activity track and commodity interaction data. Finally, through effective operational data insight, intelligent operation model is built to help brands complete the formulation and implementation of precision marketing strategies, so as to improve the overall operation quality.
The first step is to collect the number of customers visiting the store and establish a relationship model between the store customer flow data and routine variables such as working days, holidays, weather and temperature, which can be used to evaluate the daily store operation and the conversion effect of marketing activity traffic.
The second step is to collect the number of customers visiting the shelves, establish a baseline model of the store-to-shelf customer turnover rate according to the store's thermal and dynamic data, and further analyze the possible problems of display and product selection for the category shelves whose actual turnover rate is lower than the predicted value of the model, and make targeted improvements.
The third step is to collect the number of interactions between customers and products, and establish a baseline model of interaction rate of large categories according to store data, which can be used to find sub-categories with low interaction rate for targeted improvement, or to evaluate the direct improvement rate of interaction between categories by operating means such as intelligent shelves. Brand level interaction rate model can be used to find the matching problem between product selection and store customer group.
The fourth step is to collect the commodity trying data, establish the multi-level interactive trying rate baseline model according to the store/category/sub-category trying data at all levels, find out the store/category/sub-category with significant problems at all levels of data, and carry out targeted optimization such as product selection and promotion;
The fifth step is to collect the transaction number of interactive customers, which is the core commodity ordering link, establish the baseline model of the try on transaction rate at all levels of the brand/store/category/sub-category, find out the unit with low try on transaction rate, further analyze the quality, pricing and other issues, and optimize the targeted operation.
Of course, the ultimate goal of the whole solution is to improve the overall GMV and profit margin of the brand. The "transformation funnel model" is only a means to collect and refine operational data layer by layer. The final data analysis will be fed back to the basic Kanban board. Through horizontal comparison across stores, shelves and commodities in each link, as well as longitudinal comparison across time, targeted operation improvement will be carried out according to business problems such as single store operation, shelf display and commodity category. Establish the contribution weight model of conversion rate at all levels, evaluate the allocation of operational resources and input priority at all levels; Establish the relationship model of marketing operation input and output, so as to select the right time, city, store mix, category mix, etc., so as to improve the overall brand GMV and profit margin.
Accurate grasp of passenger flow data, improve business transformation
In terms of specific cases, Whale masterly served an international famous luxury brand covering 70+ stores in first-tier and second-tier cities across the country, providing accurate and reliable customer flow data for the management to master the store operation situation, optimizing the store display and improving the floor efficiency, and helping the brand operation efficiency to increase by 60%. For another internationally famous sportswear brand entering the Chinese market, a more comprehensive digital marketing demand is proposed: collect data of various marketing channels, activity process and effect, analyze and compare the transformation effect, precipitate brand data assets, and improve marketing quality. Whale's strategy helped build the online and offline digital marketing system, among which there were more than 30 monitoring indicators of the marketing effect. Besides, collect and analyze customer flow and commodity interaction data of offline stores through intelligent AI identification and intelligent display system, optimize store operation strategy, and increase efficiency by 20%; Establish offline activities drainage online precipitation fan member process, guide drainage precipitation fan operation effect increased by more than 50%.
About Whale strategizing
As a professional omni-digital marketing operation platform in China, Whale, through key technological innovations in artificial intelligence (AI), large-scale Internet of Things (IoT) and Data model (Data), provides future-oriented retail brands with data-driven, collaboration-first, simple and easy to deploy omni-brand marketing solutions. It aims to enable sustainable fine operation and lean growth of retail brands and open up the "last mile" of MarTech. At present, Whale strategy service system has widely covered food and beverage, beauty makeup and skin care, fashion shoes and clothing, light luxury jewelry, digital electrical appliances, catering and tea drinking, business super convenience, automobile service, medicine and health and other industries. It has accumulated benchmark customers such as Unilever, Watsons, Sibei, Midea, Paopamart, Nextev, Carrefour and more than 300 Top brands in the industry. The company was founded in 2017 in Hangzhou, and has offices in Shanghai, Shenzhen and Beijing.
