Data management is a compulsory course for cross-border e-commerce

Management is essentially an iterative and escalating process around organizational goals and value creation: clarify goals, set goals, integrate and allocate resources, use various processes to transform resources, make operational decisions, action execution decisions, and respond to market changes Adjust in time, and then complete the results and re-analysis based on the actual situation. Cross-border e-commerce is no exception. As large as traffic, ranking algorithms, as small as the browsing and clicking of each user, and the time-efficiency indicators of each order, they are all the mapping and reflection of actual business. Data runs through the operation and management. It can be said that data management is an introductory course and a required course for cross-border e-commerce operation management.
1. Use data as goals
To do a thing or a project, you must first clarify the purpose, that is, the goal. For cross-border e-commerce sellers, the goal is to increase sales, increase market share, and increase profits. The purpose is clear, and then the goal needs to be set. The goal setting needs to be prepared in advance of various supporting data, which is mainly divided into two major data
1) Market data: total market data of the target market, annual and monthly trend data of the total market, data on sellers in the target market, data on the pain points of products on the market, data on product distribution channels, product price range and sales volume Percentage data, product audience data;
2) Ownership data: supplier status data, own existing operational quantity and quality data, own supply chain strengths and weaknesses data (mainly warehousing logistics), own funding strengths and weaknesses, own product quality data, etc.;
When the relevant supporting data is ready, it is necessary for the operation manager to compare and analyze the relevant data of the market and its own, analyze the advantages and disadvantages of its own and the market, and combine what kind of results they want to achieve in several stages. To set goals, managers need to first obtain the analysis results of the difficulty of Party B entering the market from the data, obtain the analysis results of their own advantages and disadvantages of entering the market, and then obtain the strategic goals of entering the market at different stages.
To give a simple example: the total trend of a category market is rising year by year, but the market is monopolized by several large sellers. The top rankings are all sellers with Review’s links, but our product resources and supply chain resources are relatively low. Excellent, there is room for concessions in the price, then the general target can be dismantled in stages: the first year of entering the market is mainly to sell sales and promote the brand, and the sales target is set for gradient growth, and the gross profit target is set. A lower limit is enough; in the second year, there is a certain market positioning, but it is not enough to compete with other established brands. The market share target continues to increase gradually, and the gross profit target is appropriately increased; the accumulation of products and brands in the third year has reached To a certain extent, while the market share continues to grow, the gross profit index is also increasing at the same time.
It needs to be particularly emphasized here: whether in the formulation of large goals or stage goals, it must be fully analyzed based on the relevant data of oneself and the market before reasonable settings, so as to avoid too high goals and difficult to achieve, damaging operation enthusiasm, and avoiding goals that are too low. Reduce operational morale. Knowing yourself and the enemy can survive all battles.
2. Use data to integrate resources and transform
Once the goal is set, the next step is the process of integrating resource transformation. Resources include: funds, personnel, supplier resources, supply chain resources, operation support resources (off-site, pictures, videos, design, etc.), integration of resources must also learn to use data integration
1) Use data to plan cash flow to ensure capital efficiency and safety
Cross-border e-commerce has a relatively long payback cycle, so it is necessary to plan and allocate funds in advance to obtain the current disposable total funds data, combined with the monthly sales target of each category and the delivery date of each product Calculate the monthly required stocking cost and the amount of money back, and calculate the monthly expenditure and the amount of money back for all projects.
Under the condition of ensuring the normal turnover of monthly funds, a certain percentage of safe funds is reserved to prevent accidents. Through this calculation, it is analyzed whether there is funds that can meet the normal turnover of funds. If the total funds are insufficient, it needs to be combined The market data analyzed in the first step is combined with our own data to adjust the resource allocation of internal projects, and readjust the target according to the allocation of resources.
2) Integrate human resources with data, equip talented soldiers to improve personnel efficiency
The first is quantitative data. The current number of operators, the number of people required to operate each project according to the required standards, and the average performance that each operation can undertake. Combining these three data to get the current total project goal. The difference between the number of operators required for each position and the number of existing operators. If the number of personnel required is more than the existing number, you need to apply for an increase in the number of personnel resources. If it cannot be increased, you need to combine the data of the first step for each project Prioritize and adjust, and some project goals will be adjusted downwards or cancelled;
The second is quality data. Existing operations need to refine the operational capabilities to the various functional capabilities of each operation, and configure them according to the preferences of the operational capabilities required by each project and the importance of the project. Appropriate personnel, such as those with strong operational attributes for individual categories, should be equipped with operational personnel with strong attributes for promotion and content operations.
3) Integrate supply chain resources with data to reduce costs and increase efficiency to ensure supply
  Delivery time, quality and cost reduction. Supplier resources are mainly based on data statistics of suppliers’ purchase prices, production capacity, product quality, delivery dates, and ability to respond to abnormal events, combined with the historical performance of the supplier’s various attributes and the actual situation of the supplier for analysis and comparison. Promptly upgrade or replace suppliers that cannot meet the requirements of category operations, such as analyzing and solving each after-sale product problem in combination with the after-sales proportion of the product;
  Storage capacity planning and optimization. Regardless of whether they use platform warehouses or self-built warehouses, sellers need to control the storage capacity. Sellers who use platform warehouses need to pre-estimate the difference data between purchases and sales for each time period in accordance with the platform's warehousing rules to ensure that they do not exceed the platform warehouse. Sellers who build their own warehouses need to calculate the storage area and the ability of warehouse personnel in advance, and compare and analyze the volume and weight of each product according to the target and the volume and weight of each product. The quantity and volume that need to be sent to each warehouse at each time point. Combine the target forecast to calculate the outbound volume for each time period, plan the warehouse area and personnel work in advance, if there is a mismatch, carry out the pre-action to expand the warehouse or find a temporary transition in the three warehouses in advance to avoid unplanned The resulting liquidation occurred;
Optimization of warehouse division and beginning and end journeys. Shipment includes first and last shipments. Sellers who use platform warehouses don’t need to worry about this, but sellers who use self-built warehouses need to integrate the piece weight data of the product, and the shipment quantity data for each time period. Data on the proportion of orders in the area covered by overseas warehouses, and the first-haul freight data of each warehouse, comprehensively analyze the optimal delivery warehouse ratio, and the first-to-last freight can be optimized.
4) Use data to integrate operational support resources and equip enough ammunition to grab the market
  Operational support resource integration data level mainly integrates the comparison of supply and demand data, such as off-site promotion resources, various visual-related resources, and calculates the total resources of various types that can be allocated each month, according to the characteristics of the product and the input of each resource Combined with the reasonable allocation of limited support resources to suitable projects, the profitability of the resources can be maximized.
3. Use data to drive decisions and take action
  In the second step of the process of resource integration and conversion, a lot of data results will be analyzed, such as insufficient financial resources, such as supplier resources that do not meet the requirements, such as insufficient operating support resources, etc., as an operation manager, you need to pass these data As a result, make decisions on conflicting points, such as insufficient funds or insufficient operational support resources, you need to comprehensively evaluate the importance of each item based on the data of each item, and sort and choose according to the importance of each item, so as to make the target Achieve a balanced match with all resources; after the decision is completed, tasks can be divided into projects for execution according to the established resource allocation. The execution process also requires continuous follow-up of the data of each project goal achievement, and timely discovery from the data For which projects have abnormal progress, analyze and optimize resource allocation and adjustment strategies from the data continuously generated by each resource input; for example, the video resources of Project A are originally set to account for the largest proportion, but from the comparison of video input and output, it is found that it is better to only give For project B with very few video resources, the proportion of video resource investment can be adjusted in time according to the results of the conversion data, and more video resources can be transferred from project A to project B; this stage is a continuous cycle of data analysis-execution-decision-making The process of correction.
4. Use data as results
Using data as a result can effectively reduce the common ‘bumping problem’, which is mainly reflected in the following two aspects for cross-border e-commerce companies:
The first aspect is the performance results of each person in each responsible department of each project. For the company, the result is whether the set goals have been achieved after the project ends, and what is the achievement rate of the set goals, that is, the results of the project; Regarding the performance results of the responsible departments and individuals, whether the performance indicators within the scope of responsibility of the department are completed and what is the completion rate. The absolute performance results are supplemented by special circumstances as the performance results. This depends on the first step. For the establishment of goals, everyone in each responsible department needs to develop performance indicators that can be quantified with data. Using data results as performance results can highly unify the overall goals of the company's various departments.
The second aspect is to use data for benefit distribution, combined with the goals set in the first step, combined with the final actual completed data results, and calculate the contribution rate of each person in the project based on the completion rate and the proportion of functional contribution. The benefits generated by the contribution rate distribution result on the one hand can ensure the fairness and reasonableness of the benefit distribution, and on the other hand, it can stimulate everyone's motivation to achieve the goal to the greatest extent, thereby promoting the achievement of the collation goal.
5. Use data for re-discovery analysis
  When the result is formed, no matter whether the result is good or bad, the operation manager needs to use data analysis to conduct a review analysis of the entire project from goal setting to result achievement.
Analyze starting from the formulation of the target, which part of the target formulation process is too high or too low, and which part of the target formulation depends on the data analysis deviation, and how to avoid it, such as finding a project in a review analysis At that time, the combined market data and market trend data were very high, so the goal setting was extremely high. However, after the implementation began, the market quickly languished, resulting in the failure to achieve the goal and the availability of more goods. The data analysis of the goal setting was carried out first, and the analysis found that the goal setting was At that time, the total market volume and Google trend were both rising. The reference data was consistent with the target setting. However, in the past few years, the market data found that only the total volume in the past year has risen sharply. Combining the category and specific conditions, it is found that it is because of the pull. The year of the data belongs to the epidemic. This category is rapidly heating up due to the impact of the epidemic. After this project review, it can be concluded that the market data that needs to be combined in the subsequent target setting needs to be pulled for at least 3 years. At the same time, abnormal market growth needs to be analyzed and judged in advance. Analyze some abnormal situations by increasing the amount of data collection, so as to avoid the occurrence of such situations in the future;
Analyze the problem points in each execution process through data and analyze the achievement of the indicators of each responsible department. If it is found that the sales target is reached, but the gross profit index gap is large, first analyze the goal setting, and analyze the execution process after the goal setting analysis has no problems. During the implementation process, it was found that the proportion of advertising expenses in the operating cost structure data was significantly higher than that of the normal category, and then a detailed analysis was carried out to determine the input and output of multiple advertising expense types in the advertising expenses, and which advertising type was input and output Low, find the problem of advertising type and then analyze in detail, whether the bidding of each advertisement is reasonable, how much invalid traffic is, after analyzing the specific responsibilities, then analyze the attributes of the personnel, and the assigned score of the promotion ability of the operating personnel is How much, whether it is above the normal level, and whether the promotion ability scoring standard exists is the parameter that has been taken into account that caused the operator to be assessed as qualified when the promotion ability was evaluated.
In the review analysis, because there is enough support for each data, there are a lot of analyses that can be done, and there are many vulnerabilities in the historical data model that can be found from the results of the analysis, which can assist us in continuous corrections. The parameters of various data indicators reflect various results from the data dimension as comprehensively and accurately as possible.
Cross-border e-commerce is a fast-growing and rapidly changing industry, as well as an industry highly dependent on data. If an operation manager wants to be stable and successful in the field of cross-border e-commerce, he must learn to speak with data, use data to drive decision-making, and use data to empower business.