Business model VS algorithm model, how to use them?

When talking about data, we must mention various models. How should business models and algorithm models be used? If you have any questions about this issue, I recommend reading the following article, hoping it will be helpful to you.

When talking about data, we have to mention various models. Friends often wonder: from 4P, SWOT, RFM to linear regression, decision number, Kmean clustering, some people call them models, so what are the differences between these models? Today's article explains it clearly, and you will never get lost after reading it.

An example to understand the difference between the two.

The word model itself refers to "an abstraction of the real world" that describes complex problems through a few key pieces of information.

There are two ways to extract key information: if you extract it from a business perspective, it is a business model; if you extract it using mathematics, statistics, operations research, and machine learning methodology, it is an algorithm model.

To give a simple example, we often talk about the "marketing 4P model", which actually starts from business logic. From a business perspective, as long as I do a good job of product, channel, price, and promotion, I can sell the goods.

However, when it comes to data, there are big differences. It is difficult to quantify the attributes of products and channels. We can only roughly compare the sales indicators under different labels by labeling them (as shown below):

The relationship between product price/promotion and sales volume can be easily quantified using data, which gave rise to a classic algorithm model: the price elasticity model. First, collect the sales volume of products at different prices; second, fit the function and summarize the quantity-price model; third, use the model to infer the effect of price increases or find the price that maximizes profits (as shown below):

Note! Both methods are designed to solve problems, and both methods have their own advantages.

1. Advantages of the business model

1. The biggest advantage of the business model is that it can explain the problem from a business perspective

for example:

• Is my strategy wrong?
• Is it because my selection of products is not good?
• Is it because my execution ability is not good enough?

Business thinking such as "strategy", "product selection" and "execution" is difficult to measure directly using the addition, subtraction, multiplication and division relationship between x and y. At this time, you have to construct a business analysis model, first quantify the terms such as "strategy" and "product selection", then use the logic tree method to split the problem, construct a layer-by-layer analysis logic, and use the elimination method to find the correct answer (as shown below).

2. The second advantage of the business model is that it is easy to observe the effects of business actions

For example, in the figure above, based on the logic tree, the business improved the execution action and increased the manpower input. We can directly observe whether the problem at the top of the logic tree has improved, so as to judge whether the analysis is really in place (as shown in the figure below).

3. The third advantage of the business model is that it clarifies the responsibilities of the business entities

For example, if we use regression or smoothing algorithms to come up with a conclusion, the business department will not be able to see the effect of their actions. They may even ask in confusion: "Will the result be the same if I work overtime or not next week?" "If I can't handle customer A and switch to customer B, will the prediction be different?" (as shown below)

At this time, if you use the business model to predict, you can directly split the overall indicators by department and let each department fill in its own expectations. Although the specific parameters may need to be figured out in your head, each department can directly see what level they need to achieve, thereby reversely motivating the business to take action. Even if the task is not completed, you can clearly see "who did not complete it". This allows you to think of countermeasures more quickly.

2. Advantages of Algorithm Model

1. The biggest advantage of the algorithm model is fast computing speed and ease of use

The biggest advantage of algorithmic models is not that they are smarter than humans, but that they are fast and easy to use. For example, in the classic Internet promotion problem, there are a lot of constraints: "total promotion budget, conversion rate of each channel, number of appointments available for each channel", etc.

At this point, if we use manpower to arrange it, it may take half a day to calculate, but students who are familiar with operations research know that this is a typical linear programming model. As long as the modeling assumptions are clearly written, it is easy to get the results (as shown in the figure below).

What's more convenient is that if there are adjustments later, such as:

• Modify total delivery cost
• Changes in the number of channel schedules
• Channel conversion rate changes

Then, as long as you modify the model parameters, you can quickly get the results, which is very convenient (as shown below):

2. The second advantage of the algorithm model is that it can discover situations that the business has not noticed

For example, when doing product analysis, the business can manually output a "Product Association Rule Table", but the rules of this table are fixed. If the association rule algorithm is used, it can break through the limitations of business thinking and discover more potential associated sales logic. Although it may not be something as exaggerated as "beer and diapers", it is also very helpful to inspire business ideas (as shown in the figure below).

3. The third advantage of the algorithm model is processing large-scale data

In the typical business model RFM, when doing user segmentation, if each indicator is divided into 3 categories, then there are 3*3*3=27 categories, which is so complex in business that it is difficult to match the corresponding strategy. However, if the collaborative filtering algorithm is used, it is completely possible to achieve a thousand faces for a thousand people, which is also a huge advantage of the algorithm model.

The reason why Internet companies tend to use algorithms to make recommendations is mainly because there are billions of products on Internet platforms, and it is extremely difficult to match the rules manually.

How to perfectly match business and algorithms.

In order to achieve smooth cooperation, it is recommended that you spend time to sort out what problem you want to solve before starting the project, rather than saying "I want a model" right away. Make a model first, and then use a hammer to find nails.

If the problem to be solved is not clear, such as:

• Diagnostic questions: Is the indicator change due to internal or external reasons?
• Standard questions: How do we define “high-value users” and “effective strategies”?
• Testing questions: I have a new idea, but I haven’t implemented it yet. I wonder if it will be effective?

At this time, it is recommended to make a business model, sort out the problem clearly, make the definition clear, get the test data, and then see how to proceed?

If the problem to be solved is clearly defined and has accumulated data, it is very suitable for algorithm modeling. Especially when it is difficult to improve the results even if the business works hard, such as retaining users and selling new users by phone, the response rate is naturally low. Using the model to select the target group can greatly improve business efficiency, and the effect is good at this time.

Author: Down-to-earth Teacher Chen

Source: WeChat public account "Down-to-earth Teacher Chen"