How to build a data based operation system, and the three levels of an enterprise to build a data ba

The core of building a data-based operation system is to find a digital company with professional global marketing, which can connect online and offline and gain global data insights, and drive the fine operation and lean growth of the brand with data.

The functions of the data-based operation system need to include the following:

1. Marketing and customer acquisition: The brand headquarters uniformly controls the operation of the store display screen, and the materials are put on with one click, which improves the efficiency of store publicity and reduces the cost of product promotion content release management.

2. Customer flow analysis: Accurately analyze multi-dimensional customer flow data such as entry and exit, store passing, flow direction, and hot spots, to help managers quickly locate store customer flow problems and take marketing measures in a timely manner.

3. Private domain retention: The spatial data platform SDP + Curtain Marketing Cloud WMC provides panoramic member portraits and online contact points to realize automated marketing for thousands of people and thousands of faces, and guide customers to the store to consume again.

4. Inspection and supervision: on-site inspection, first-class inspection, AI automatic inspection, can be initiated anytime and anywhere, strict monitoring all day and time, and more flexible inspection methods can help brands effectively supervise store operations and greatly reduce the cost of store inspections.

5. Business review: Based on a powerful analysis platform, it provides multi-dimensional visual business analysis data to help brands accurately grasp the actual operation of each store and make timely business adjustments and improvements.

Data-based operation system. Take it apart and look at it.

1. Dataization. First you need a database, maybe a simple sql will suffice. See for yourself your size, and user base.

2. Operations. If you're a traditional or commercial company, and the business drives the technology, then operations is responsible for manufacturing the business units.

If it's an Internet company, it's an operation-driven business. I can't finish three sentences or two sentences.

The first layer: know what it is1, the data is scattered, and there needs to be a framework to see the data. Data presentation is very particular, and only by putting data into the business framework can reflect business analysis can the overall value be exerted.

An effective framework has at least two functions: (1) People at different levels have different data needs. For example, for marketing data, the business layer needs to guide the completion of its daily indicators and rank rankings, and needs to submit daily, weekly, and monthly data.

Leadership needs to know the performance completion rate, sales by region, marketing costs, and performance rankings within the group for a fixed period. Management, CEO level may need to know some key metrics for each business unit, such as total revenue, market growth rate, important R&D progress, etc. An effective framework allows different people to take what they need.

2) A good framework can locate the problem and guide decision-making. For example, if e-commerce sales drop by 30%, it's likely that there is a major problem with the business. We need to analyze the cause of the problem, but if it is difficult to explain the problem only from the customer unit price, the number of transactions, and the conversion rate, a good business analysis framework can support us to drill down and find the problem from the category, traffic channel, etc., and find the corresponding person.

This is also what we usually say, to see the data to land. 2. Data can only be considered if there is comparison. Daily sales of 1 million, do you say more or less?

It's hard to tell the story of a single piece of data. There is either an indicator for reference, or there is an indicator data that can accurately judge the trend, such as the growth rate and the increase rate. Such a benchmark can be the historical summary of the same period of Lingna data, or the average level of the industry, or it can be a pre-set goal, and all data analysis that deviates from the goal is "playing hooligan".

The second layer: know why it is, find the reason for the problem, this is a very smooth connection. But it's not enough to get to this point, it's the truth that solves the problem.

Data is combined with business to find the real reason behind the data appearance and solve it. The process of solving problems will involve data collation, processing, and the establishment of data analysis models and tools, which has been introduced enough in the previous pages.

It is very important to find that the way of thinking is really strong, and many times before I was receiving needs, responding to one temporary demand after another, and lacking the transformation of thinking and balance. The whole person is very busy every day, and he is very bad for himself. People should sometimes stop and jump out to see if their work is efficient and meaningful.

Data managementData application is a large system, from the underlying collection, data cleaning to application, the value of analysis lies in the energy of the business. The whole process also needs a data monitoring system, to build a monitoring system needs to first clarify the index system, and the second needs to support the application of the management process, it can be said that the monitoring system finds problems, analyzes the system to solve problems, and the final product and service are continuously iteratively optimized and upgraded, and we have also grown in this process.

It is hoped that in the later chapters, the knowledge system of data operation and analysis can be summarized in combination with the work.

1. Why do we need to do data-based operations?

2. How to establish a data-based operation system.

3. Measurement of the effect of data-based operation.

Your food, clothing, housing and transportation will generate data.

Every step of the way within the enterprise will have data. Some data is drowned in an irritable society over time, and some data is shrugged and thrown out of our minds. However, no one has ever realized that what we ignore is wealth, and it is wealth that needs to be accumulated over a long period of time.

Choose a good data metric.

Good data indicators usually have two basic characteristics: one is the relevance of data indicators to goals, which is used to measure the expected value of goals; The second is the accuracy and stability of the data indicators, which reflect the target results with long-term stability and accuracy.

In addition to this, a good data indicator should also include the following characteristics: The first is easy to obtain and understand. The second is that it is adaptable, suitable for different operational activities, suitable for horizontal and vertical comparison, and closely related to the business.

In addition, the sustainability of the indicators is also very important, and the sustainability is reflected in the consistency of the caliber and the long-term availability. Although the indicators that are focused on at different stages are different, these indicators must meet the above characteristics.

The construction of the data operation indicator system.

There are usually two routines for building a data indicator system in the industry: one is the first indicator method represented by lean data analysis, which is to find the key indicators and then use the DuPont analysis method to build an operational data index system around the first key indicator by dismantling the first key indicator; The other is the pirate data indicator framework formed according to the business evolution process (logic): AARR, which is similar to AARR, similar to PRAPA, AMAT and other data indicator frameworks.

The above two routines ultimately lead to the same goal, and ultimately point to the core demand of the business: revenue. In the end, the process of splitting the income and naming different routines for different influencing factors is the process of building a data indicator system.

Taking B2C e-commerce as an example, the target revenue is split into indicators such as customer flow, conversion rate, customer unit price, purchase frequency, gross profit margin, and cost, and then these core indicators are split into unit impact modules such as SEM and EDM according to the influencing factors, and finally the core indicators and influence module indicators constitute a complete data operation system.

Three-dimensional data indicator system.

Core indicators, influencing factors and development stages make the data indicators three-dimensional. The data module, which is composed of core indicators and influence modules, changes with the changes in the business development stage, and finally forms a three-dimensional data indicator system.

The three-dimensional data index system can be understood from the perspective of four-dimensional space, the first three-dimensional is the two-dimensional data index system composed of the core data indicators and the corresponding factors, with the development of the business and the subdivision of the division of labor, and on this basis, the introduction of post-level attention, so far the two-dimensional data index system is transformed from two-dimensional to three-dimensional, and finally forms a data indicator module. Secondly, with the passage of time, the core indicators that different business development stages focus on are different, and finally the dynamic evolution of the data indicator module is formed, and finally the data indicator module is evolved into a three-dimensional data indicator system.

The enterprise is still under construction.

Marketing management. From the planning of marketing activities to the execution and monitoring of marketing activities, to the verification and approval of marketing expenses, to the analysis and evaluation of marketing effects. In the era of big data, the information asymmetry of the Internet makes the online information varied, and all walks of life are producing countless fragments of information all the time.

Improve the efficiency of marketing management.

Sales management. As we all know, the sales staff is an important part of determining the operation of the enterprise. As the business expands and the sales team grows, how to learn and apply the management experience and behavior of the best salespeople becomes a key issue.

The CRM system can achieve the segmentation and precision of good sales behavior. Systematic management, fine management of marketing activities, at the same time, according to the system to screen out the target customers, accurate positioning on the target customers, according to the distinction of different marketing objects to plan marketing activities and promote marketing levels. At the same time, the evaluation mechanism of marketing activities is completed.

Reduce business operating costs, improve work efficiency, expand market share and increase sales.

Service management. Service management is a piece that is easily ignored in the enterprise module, especially after-sales service, but the added value brought by after-sales service to enterprises is great, and many enterprises are not aware of this. The application of CRM can establish a variety of customer communication channels, collect customer feedback and needs in a timely manner, improve the customer service request processing process, improve the response speed and service quality, and effectively monitor and evaluate the sales execution process.

3. Exchange traffic, data or services between ** and third parties**, or strategic cooperation alliances, and increase ** traffic and popularity.

4. Formulate the overall and phased promotion plan, complete the phased promotion tasks, and be responsible for the number of registered users, PV, PR, visits, brand promotion and other comprehensive indicators.

5. Cooperate with various departments, especially the marketing department, to have a penetrating understanding of the company's business, and be responsible for increasing sales.

6. Adjust the optimization strategy in combination with the best data analysis.

Web SEO Specialist Job Responsibilities:

1. Formulate and organize the implementation of the company's SEO optimization plan and columns, and participate in the planning of the column structure;

7. Formulate the best SEO optimization plan and analysis;

9. Formulate keyword strategies according to the implementation situation, and continuously optimize the plan;