Offline retail has a love-hate relationship with Traffic data. Stores know the importance of traffic data, but how does it help stores improve their performance? Managers often struggle to answer. As a result, there are some voices in the industry that passenger flow data is useless:
Is insight just empirical conclusion?
Operators can intuitively see when there are more people and when there are fewer people through passenger flow data, and then judge the attribute labels of customer groups, such as tourists, office workers, etc. But this is a cliche, from the preparation of the store, to the actual operation, the operators observe and verify every day, such insight is just a test of their correct experience.
In fact, such insight thinking is too narrow, ignoring the breadth of insight and multidimensional analysis of passenger flow data. The changes of attention rate and entry rate are often impossible to be accurately measured by experience, and the duration of customers' stay in the store is often far from the reality due to subjective evaluation. At the same time, with the combination of holiday, weather, temperature and other influencing factors, operators can not only have an insight into the changing trend of customers' behavior, but also quantify the degree of influence and guide store operation and brand marketing.
At the same time, although some extensive customer group attribute labels are empirical verification for a single store, they are a model for store location and operation for multi-store chain management. A brand often has different types of stores, and the models applied are naturally different. At this time, the evaluation and comparison of different model stores and the evaluation and optimization of a single store can be carried out.
Is it difficult to help "reduce cost" or "increase efficiency"?
Some operators have discovered anomalies in customer flow data and made corresponding adjustments to store operations and marketing, which sometimes seem to be effective and sometimes seem to be in vain. From passenger flow to transaction, from passenger flow to improve efficiency, it takes a long period of time, and it is difficult for operators to clearly grasp the impact of passenger flow on the results, what kind of impact, how to have an impact? Passenger flow statistics are like punching cotton with a fist. After its essence, or because passenger flow data and GMV or other specific indicators can not achieve open loop, become data island.
To build a refined offline operation index system, it is imperative to get through customer flow data, customer behavior data and order data; Then through the operation and marketing adjustment, monitoring data, to achieve "efficiency". At the same time, the real-time warning of abnormal passenger flow data, rapid response of stores, combined with random inspection and inspection of stores to ensure the response quality of stores, can gradually achieve "cost reduction" in the actual operation process.
03 Is User Data Collection Risky?
The restriction of the Personal Information Protection Law makes it difficult to establish the offline customer portrait, and the offline precision marketing is difficult. Some enterprises establish a deep trust relationship with customers through service improvement strategies, allowing users to voluntarily authorize their personal data to the enterprise, so that the enterprise can provide users with better products and services and achieve a win-win situation. However, due to privacy concerns, some customers still have a strong natural resistance, and the data collected must be greatly compromised.
Apart from biometric identification, how accurate can we be about customer insight? Based on human feature recognition, we can make vague prediction on common age, gender and height. At the same time, combined with the number of accompanying consumers, we can make portrait prediction on the characteristics of the crowd, such as defining the labels of parent-child, colleague, classmate, partner, etc., which can effectively guide retail brands to make planning and adjustment at the commodity and operation level.
To realize the value of passenger flow, it is necessary not only to continuously achieve more detailed customer insight through CV and other technologies, but also to excavate and explore the existing passenger flow indicators in combination with the actual business and linkage with the overall index system, so as to give full play to its value. Below I will list common traffic metrics and values.
• The in-store (Enter) phase
1) Customer flow through the store: insight into customer behavior outside the store, can evaluate the quality of the business area, marketing promotion effect and quantitative analysis of some common objective factors on the impact of customer flow. Such as the impact of different times of the day, holidays, weather changes, temperature changes and so on passenger flow.
2) Pay attention to customer flow: observe the behavior of customers outside the store and evaluate the attractiveness of the store.
3) inbound customer flow/inbound rate: Observe customers' inbound behavior and evaluate store attraction, brand influence and inbound(inbound marketing) effect. For inbound, customers usually enter the store directly after online contact, which can be intuitively demonstrated by changes in the inbound rate.
• The Shopping phase
1) Stay time: to observe customers' stay behavior in different dimensions such as region/category/commodity, and evaluate regional characteristics, category attraction and commodity attraction through average stay time. The in-depth visit rate is a fine classification of the length of stay. By setting different time nodes, the brand can find the proportion of the in-depth stay customers, so as to achieve a more accurate measurement of the attractiveness of the product/category and avoid the interference of some invalid customers (too short stay).
2) Dynamic diagram and thermal map: insight into customers' action trajectories and staying trends in the store, and evaluation of the attractiveness of regions or categories. Combined with store plans, it provides a more intuitive representation of customer interest than length of stay. And take into account the factors other than the product of the moving line design to assess the impact on customer stay. At the same time, the heat map can effectively reflect the utilization rate of stores and timely remind stores to adjust the region or display.
3) Regional relationship chart: insight into the flow of customers between specific regions and assessment of inter-regional relevance and attraction. Guide the store to optimize the layout of the area.
4) Customer base portrait: Customer base portrait is a comprehensive evaluation of a number of indicators, including age, gender, number of people, time and other factors. Combined with different industries and categories, the combination of factors may also form different labels to form the customer base portrait in line with the brand itself, which can be used to evaluate the quality of customers entering the store and has great guiding significance for the store operation.
If we want to verify the value of customer flow data, we must be inseparable from the long-term operation practice of retail brands, which is short step, no thousand miles. After that, we will continue to deepen our research in the field of customer flow value, and explore more optimization possibilities and inspirations for operators with the support of practical experience in retail customer flow value.
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.
