I built a powerful data early warning model

The first thing to do in data analysis is to clarify the role, then collect actions, assist in judgment, and make accurate alerts. According to the warning, a plan is formulated to make decisions quickly. The author of the following article explains to us how to make data analysis. Let's take a look.

Everyone's ultimate hope is that data analysis can proactively issue early warnings. But the reality is very bleak. Often, the indicators have already fallen, the business department is busy, and the data analysis department is only slowly analyzing "why did DAU drop by 30% yesterday?"

After all the hard work, you end up complaining, "I knew it earlier, why didn't I do it earlier?" So how can you make early warnings? Today I will explain the system.

1. Clarify roles

First of all, it should be clear that early warning is an alarm given to people. Therefore, who needs to hear this alarm is the first priority to consider. In actual work, there are four major roles:

The first step in issuing an early warning is to first clarify the business scenario to be warned, taking into account all relevant responsible persons in a scenario to avoid missing any roles (as shown in the figure below).

2. Collect Actions

Secondly, we should pay attention to the fact that problems start with people and end with people. Therefore, the second thing to consider is the impact of these four roles on the indicator trend. We need to collect the actions planned by these four types of people in different categories to prepare for the third step of evaluation (as shown in the figure below).

Note: There is a sequence for collecting influencing actions, which should be from high-level → front-end → middle-end → back-end.

Because the order in which these four types of people influence the indicators is as follows:

• The goals set by the top management determine the difficulty of front-end execution
• The quality of front-end execution determines the difficulty of middle-end assistance
• The actual performance results determine the difficulty of backend support
• The quality of backend support determines whether the problem will expand

At work, these four links are closely linked. If they work well together, even if problems arise, they can be resolved. If they work poorly together, they will drag each other down and make more mistakes (as shown in the figure below).

In actual work, it is not a simple and clear closed loop of work, starting from the beginning and then moving on to the next one. Each department has a lot of work going on at the same time. Therefore, the collection department's actions are not achieved overnight, but require a smooth information channel, especially when the indicators begin to show signs of problems, the stress response made by the business department must be understood to a certain extent. In this way, better judgment can be made and accurate alarms can be issued.

3. Data Evaluation

With the second step of action collection, the third step of data evaluation can begin. Data evaluation is the most critical step in early warning and is the key to avoiding being confused when seeing data fluctuations during execution.

The reason why we can perceive the risk of indicator fluctuations in advance is based on the simple truth that what happened in the past will happen again in the future. Therefore, it is necessary to conduct an in-depth assessment of what happened in the process. (As shown below)

There are many specific analysis methods involved here, which have been shared in previous articles and will not be repeated here.

4. Make early warning

With the above preparations, you can make early warnings. After the early warning model is established, it can be put into operation from the planning stage. It runs through the various stages of planning, execution, and review, and covers the main and auxiliary roles (as shown in the figure below).

For example, if you set a sales target, then:

Plan-making stage: Can the sales plan (including sales plan, human resources allocation, supporting materials/sales support products) support the current goal? If not, then a warning can be issued directly to remind the problem.

Execution stage - as the main sales: Have all sales teams implemented the plan? If not, who is responsible? How much of the market is affected? As the main force, as long as there is a problem in some of its branches, it is necessary to issue a warning directly to prevent the problem from getting worse.

Execution stage - as an auxiliary supply chain: If the target achievement rate is too good, is there enough purchase? If the target achievement rate is too poor, is there a risk of backlog? If sales performance is good, but the related auxiliary departments are about to face the risk of out-of-stock, timely warnings should also be issued!

Note: When issuing warnings during the process, the business department’s response actions should be considered. For example, the performance may be very good, but the inventory is about to run out. At this time, you should find out whether there is a replenishment plan. If there is, you should mention this when issuing warnings, and give warnings and estimated results for each of the optimistic (100% on-time completion of the plan), conservative (50% or less on-time completion of the plan), and pessimistic (unable to complete the plan) scenarios (as shown in the figure below).

This kind of early warning gives management a very good experience

1. Management can gain overall control of the business through the work of analysts.
2. Risks can be foreseen during the planning stage so that adequate preparations can be made.
3. During the process, not only can you see the warning values, but you can also roughly identify the direction of the problem, reducing the feeling of being "hit by a club" during the process.
4. When you see the warning results, you can also see the expected treatment plan at the same time, so you can make decisions quickly.

This is much better than asking why after seeing results such as "DAU dropped a lot" or "Sales failed to meet the target for 4 consecutive days". In an ideal situation, as long as one or two related indicators deteriorate, the risk of the main indicator diving can be immediately perceived.

5. Wrong Approach to Data Warning

The wrong approach to data early warning is, of course, to expect a powerful and invincible general model to predict all indicators 100% accurately. There are too many factors that actually affect indicators, and external environmental pressures and internal proactive behaviors are difficult to quantify. Therefore, it is completely unreliable to bet entirely on a model that relies on a very limited number of predictions.

The truly reliable approach is for data analysts to stay highly informed and follow the four steps above to prepare in advance to cope with changes. You can't predict the future, but if you predict, you have the opportunity to discover problems.

Many people who don’t know much about the industry think that data can really give people insight into everything. In fact, it is just the opposite. A data analyst must first have a thorough understanding of the company’s situation before he can make judgments on data trends.

This point must be remembered.

Of course, there are three basic conditions for the operation of this model:

1. The data department has sufficient communication with the business and management and has sufficient information
2. The data department has a thorough review and experience accumulation of past goals, methods, and execution situations.
3. The business process is highly digitized, and the actions of each department can be recorded in the form of data

These three points just correspond to management problems, analytical method problems, and infrastructure problems. Therefore, not all companies can apply them. If the company's atmosphere is really bad, only some short-term and fast early warning methods can be used.

Author: Down-to-earth Teacher Chen

Source: WeChat public account "Down-to-earth Teacher Chen (ID: gh_abf29df6ada8)"