How to build an efficient operation analysis system?

In the era of data-driven operations, building an efficient operational analysis system is crucial for enterprises. However, many teams often face the dilemma of rich data but unable to transform it into effective analysis and decision-making in actual operations. This article will explore in depth how to extract valuable insights from the ocean of data and transform these insights into practical operational strategies.

There are indicators but no system

Numbers, no analysis

There are charts, but no conclusions

This is the biggest pain point for students who work with data in their actual work. Today, we will take operations as an example to systematically explain how to break through. There are eight branches of operations. Today, we will take content operations as an example. Because content operations are the most representative of "a lot of indicators, but no analysis" (as shown below).

What is the problem?

When it comes to content operation, many students instinctively think of WeChat public accounts, Weibo, and Douyin. So data indicators come out of their mouths: number of fans, number of new fans, number of regular fans, number of readings, opening rate, forwarding rate... The more they talk, the more excited they become, and the words in their throats are almost bursting out: "Today's reading is low, we need to increase it."

However, operations colleagues can silence the data with just one sentence: “Try to record a high-quality video or write an high-quality article!” If they add another sentence: “I already know these routine data, what’s the use!”, the data will most likely be at a loss.

What's the problem?

The problem is (emphasis added):

1. The indicator itself is just a data measurement, not an explanation of the problem

2. The problem itself does not come with a solution, so you need to design a solution

3. The plan itself cannot prove its effectiveness, and needs to be supported by evidence

4. Operations require problem prompts, solution assistance, and effect verification, not just one or a few isolated numbers.

So from a bunch of numbers to useful conclusions, we need to work step by step, using data to look at the current situation, derive solutions, and test the effects, rather than simply hoping to calculate a super magical number that will do the trick.

Step 1: State the problem

Data alone does not explain the problem, only data + standards can explain the problem. Where do standards come from? Of course, they come from business goals. If you can achieve the goals, you are doing well.

So the first step is to ask three questions to understand what the goal is:

1. What is the difference between Internet content operations and traditional enterprises?

2. What are the tasks of Internet content operations?

3. What is the current task for our company?

Among them, questions 1 and 2 are business common sense, and you need to do your homework. Question 3 is a conclusion derived from "the current situation of the enterprise + the requirements of the leadership". In simple terms, it can be summarized in one sentence: VS traditional enterprises, Internet content operations have more fish farming processes, so they are divided into three major goals: dissemination, fan acquisition, and conversion (as shown in the figure below).

Once you understand your mission, you can set specific goals.

Note that the working mode of Internet content operation determines that it will not pursue a single goal, nor will it only look at one indicator. When setting goals, there is often an overall reading goal, which is then allocated to each content release, using a main goal + an assessment condition method (as shown below).

This step is very important because in actual work, operations always like to go to extremes, for example:

1. Over-emphasis on a single indicator, and like to make up stories like "1 million followers for 0 yuan" or "100 million yuan in sales from one article". They don't care if other indicators collapse.

2. Various indicators are mixed together. They will talk about the number of readings, forwarding numbers, conversion rate, etc., and then report whichever indicator is good this time. They say: Although I did not achieve XX, the performance of YY indicator is very good.

This sneaky approach is a huge destruction to data-based operations, scientific management, and data analysis. Because it messes up standards and confuses right and wrong. Even the judgment of "right/wrong" is erratic, how can we summarize experience and improve results. So if you want to get things done, you must resolutely promote the 1 main + 1 deputy evaluation model. Each task focuses on whether the main goal is achieved. Not good is not good, and only by admitting mistakes can we make progress.

Step 2: Develop a plan

With the first step, we can judge the quality of operations. However, knowing whether it is good or bad is not enough, and it cannot guide the details of the work. If you want to guide the details of the work, you must first understand what the operation is doing, which involves sorting out the work process. Many students think that the work of operations is very simple, but upon closer inspection, there is a lot to it (as shown in the figure below).

Understanding the workflow is the key to avoiding the problem of "going for the best". When you find that there are so many factors to consider when writing an article, you will never dare to say "going for the best" lightly again. There are really too many details to consider, and one wrong move will lead to a complete failure.

But another problem arises: article writing itself is too creative, and many hot topics are only effective at the time of hype and expire. In such a complex environment, how to use data as an aid? First of all, let's make it clear: data itself represents rational, objective, and logical thinking, but content creation is likely to be a product of sensibility, subjectivity, and emotion. Therefore, data does not replace creation, but provides opportunities for creation and helps creation avoid risks.

To achieve this, you need to do three things:

1. Label the content and extract quantifiable labels

2. Based on labels, test results and accumulate experience

3. Collect external data based on tags to indicate opportunities

Let's take a simple example. One day, an operation editor was writing an article and found that Nothing But Thirty was very popular at the time. He wanted to take advantage of the hot topic and spread it. If data is used to help, it can be done from the following three angles (as shown below):

This can greatly improve the efficiency of the operation editor. And to be honest, the creative ability of most operation editors is not strong enough to be imaginative, and they are more like copycats.

So if content tags are really established, many editors will probably just add or subtract tags:

1. For communication-related topics, write about your personal reading experience and make up a story!

2. For increasing followers, distributing materials is effective, and the PDF package is ready!

3. Conversion type, good at triggering gender conflict, start to criticize straight male chauvinism!

Strictly speaking, we do not recommend blindly copying, as this devalues ​​the value of operations work. It is better to let data analysts write articles directly. But it is so good! At this time, we need to establish a continuous monitoring system for operational effects, and remind operations to change tactics in time when a certain routine fails (as shown in the figure below).

Step 3: Verify the effect

After designing the content, you can observe the effect of the delivery. This is something that many students will do, so I will not go into details. What's interesting is that looking back at the beginning, the students casually said: the number of fans, the number of new fans, the number of regular fans, the number of readings, the opening rate, the forwarding rate... are actually generated in this step. These are all result indicators. Only result indicators cannot be analyzed in depth. As far as content operation is concerned, we must at least have clear classification goals and a content label system to determine the effect and assist the plan.

Some students will say: No need to go through so much trouble, why don’t I just ask the business directly? Asking is a good communication habit, but the premise is that we have clear business common sense and judgment. Otherwise, if we just ask directly, what if the operation itself is confused? What if the operation is very speculative? What if the operation will fool everyone with the means at the beginning? What if the operation puts the blame on the data: "We don’t have artificial intelligence big data methods" so the operation ability is not good? Only if you have basic knowledge will you not be fooled, it is the same everywhere.

summary

The process of establishing a content operation analysis system:

1. Understand work goals and processes

2. Establish results observation indicators

3. Establish evaluation criteria

4. Set up content tags

5. Evaluate content dissemination/increase in followers/conversion effects

6. Accumulate questions and effective labels

7. Continuous iteration to improve analysis accuracy