Shulou(Shulou.com)06/01 Report--

Introduction: data source thinking is a way of thinking that provides non-professional data technicians with effective use of data to solve problems. The simple description of this approach is "come from the business and go back to the business". Let non-technical personnel understand the data, use the data, and restore the real market behind the data. In this paper, the data source thinking can well integrate the data and its methods into the solution of business problems through the four steps of asking, dismantling, solving and scheming, so as to enhance the business problem-solving ability from the experience era to the data era.

This article is selected from "data Source thinking: business-oriented data thinking Secrets".

The formulation of work goals is one of the basic work of all positions at all levels, but it is not easy to do well. Because although the formulation of principles is simple-both feasible and challenging, feasibility and challenge are requirements in opposite directions, so the intersection space between the two is very small, and it is very difficult to predict accurately. So what can be done to make it look more reliable than slapping the head to set a goal?

First, make the question more operable.

Original question-what is the target value?

The work goal is actually the result expected to be achieved by the target assessment object or index on the timeline to the characteristic time point (the cycle of goal setting, for example, the annual plan is the time point in a year's time). Therefore, the goal setting problem can be described as the increment of the target assessment object in the unit time, that is, the growth rate of the assessment index.

In this way, we transform the original problem of predicting the target area of the future time point into the problem of estimating the growth rate from now on. In this way, it is much less difficult to estimate the feasibility and challenges.

Question after conversion: what is the target speed?

There is another uncertain element in this problem-goals. What does the goal assess specifically? That is, what are the assessment indicators?

Make the question more specific.

We might as well assume the most complex situation, that is, how a newly founded enterprise, or an enterprise operating a new product, new business, and facing a new market, sets its own work goals. Because this situation has to think about the goal from the beginning, other cases are some kind of simplification of this extreme situation.

Think from the beginning, the key is from which end?

Start thinking from your existing resources? How much resources do you have and how much do you do? This static, conservative way of thinking is obviously not in line with the assumption of new business. For creative projects and careers, they tend to strive for as many resources as they want to do, so the source of thinking should be the expectation of what they do. How big the enterprise project can be, the most fundamental depends on how big the market is. The size of the market is mainly affected by two factors, one is the number of users, the other is purchasing power. Purchasing power is actually determined by the enterprise profit model, which can be regarded as a constant when setting annual targets, that is, it has no effect on the numerical changes of the assessment objects. As a result, there are more specific indicators for the objects and constraints of goal-setting decisions.

Decision object: set the target for the growth rate of users this year

Constraints: number of potential users

To set goals, it is necessary to make credible predictions of user growth at different levels of effort. However, the number of potential users is only an upper limit, and it is only possible to become their own users in the extreme cases of theory. In practice, it is a number from 0 to the upper limit, and the growth path of actual users in the new year is generally not a straight line between 0 and potential users. Because the growth of users / market will not be linear, it is generally a slow process in the early stage, fast in the middle stage, and then slow in the later stage, so it is necessary to establish a user growth model to predict the growth under different conditions. Then we need to consider the main variables that affect the growth of users, how these variables will change, and how they will affect the growth of users. To sum up, it is:

Core factors: factors affecting the growth of users

Data decision-making: build a growth model and predict the growth rate of users under different conditions

The problem of setting targets for this year is visualized as "how to build a user growth model based on core factors to predict the growth rate of users under different conditions, and then set feasible and challenging goals".

This is the first step of digital source thinking-- the practical thinking of "ask" in goal setting.

"how to build a user growth model based on the core factors to predict the user growth rate under different conditions, and then set a feasible and challenging goal" is a problem that contains too much content. It's not easy to read it in one breath, and if you want to solve it, you have to break it down into small problems.

Split the problem so that it can be solved one by one

How to build a user growth model based on core factors

What is the growth rate of users under different conditions?

Which of the above growth rates is feasible and challenging?

However, these three smaller problems contain more than one element and cannot be solved directly, which means that they still need to be further subdivided. Take question 1 as an example.

Core factors-what exactly does it refer to (clear object)

How the core factors affect the growth of users (qualitative structure)

What is the influence of core factors on user growth in a particular way (quantitative relationship)

These step-by-step splitting of the problem is the second step of digital source thinking-"split" in the practice of goal setting.

Looking for targeted methods to crack one by one.

Analyze the content of core factors

The first factor affecting the growth of users is the state of the users themselves. The status can be divided into two sub-factors: the product use state of the potential user and the purchase intention, that is, the objective state and subjective state of the product. Because the meaning of user growth is that users who have not yet purchased our products have become users who have already purchased our products, and what determines this change is the subjective and objective state of users.

The second factor affecting the growth of users is the state of market competition. Specifically, it can refer to what is the comparative advantage over the competitors, that is, the comparison of the ability of the competitors and our competitors in the market.

Analysis of influence mode

■ user factor is defined by two dimensions: whether potential users have (competitors') products and whether they want to buy (new products), which can constitute the four quadrants of potential users' status.

The most favorable potential user status quadrant in the image above is the "no product, willing" on the upper right, in which potential users are transformed into actual users. The transition from existing products to no products means that users have abandoned the original products, so the left quadrant can be changed to the right.

The core driving force of the unwillingness to change from the lower side to the upper side is the marketing work. That is, marketing promotes the willingness to purchase, which in turn promotes the growth of users, which is estimated to be a positive correlation.

The core driving force for the transformation from existing products on the left to no products on the right in the fourth quadrant of ■ is competitive advantage. That is, competitive advantage promotes product replacement, which in turn promotes the growth of users, and the estimate is also a positive correlation.

So the process of potential users becoming actual users can be transformed into the process of pushing potential users upward or to the right through marketing and competitive advantage in the four-quadrant diagram of potential user status. In this way, the qualitative structure hypothesis of the model comes out. Finally, the growth rate of the number of users is related to the intensity of promotion and competitive advantage.

Analyze the degree of influence.

This requires specific historical data to be calculated. Finally, through the verification and adjustment of the data, the growth prediction model of the number of users can be established.

After the establishment of the model, and then gradually split, crack the problem [2] [3], we can get a growth rate target. This is the third step of digital source thinking-the practical thinking of "solution" in goal setting.

To achieve this step, the goal has been set, does that mean that the goal-setting work has been completed?

Go one more step forward to promote the implementation of the problem plan

If you set this goal only for managing your own work, it will be accomplished when it is set. But in most cases, goal setting is planned for the team and requires approval from superiors or resource providers. If the recognition of the goals by the relevant parties cannot be achieved, the previous target results may be abandoned. Therefore, the theoretical solution to the problem does not guarantee that the problem can be really solved in the enterprise practice. We must further give the practical operation plan of the problem in order to win the resources or opportunities to solve the problem in practice.

In order to convince the boss, we need to focus on the content, form, timing and other aspects.

For example, in the problem of goal setting, the key to the content is whether the theoretical derivation of goal setting is correct in reality. The best and most direct way to achieve this is to conduct small-scale testing. Through the test, take out the actual market data to verify what intensity of resource input and under what conditions can get the target results. In this way, the boss can believe that the goals set are feasible and challenging.

It is the last step of digital source thinking-the practical thinking of "Mou" in goal setting.

Through the four steps of "asking", "dismantling", "solving" and "scheming", data and its methods can be well integrated into the solution of business problems, so as to enhance the ability of business problem solving from the era of experience to the era of data.

This article is selected from "data Source thinking: business-oriented data thinking Secrets".

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