9 basic methods for brand marketing data analysis!

"What is your methodology for data analysis ?" Whether it is at work or in an interview, this question will stump many students. Oh my god, you can figure it out by just running the numbers every day, but what the hell is methodology! Today I will introduce to you nine basic methods. Let’s start with the beginning.

01 What is the basic method

In essence, almost all jobs are related to data and require some data analysis methods to a greater or lesser extent. But data itself has a threshold. Many people were afraid of math classes when they were in school, let alone complex theories.

Therefore, the so-called basic data analysis method should be:

1. Does not involve advanced mathematics, statistics, operations research, and algorithm principles

2. Does not involve complex business logic or causal inference logic

3. Not limited to specific business scenarios, it is universal

In short, the basic method is the one that everyone can use.

Based on this concept, I asked my operations sister, Xiao Xiongmei, to organize nine basic analysis methods for everyone, which are simple and easy to use.

02 Start with an indicator

Periodicity Analysis

The most basic analysis method can start with one indicator, which is the "cyclical analysis method". The so-called "cyclical analysis method" is very simple to operate. It is to extend the observation time of an indicator to see whether it has a cyclical change pattern.

This method is simple to analyze, but very practical. Because newbies often become jokes because they don’t know how to read the cyclical changes.

Such as:

"I noticed that the indicator fell sharply yesterday" - Yesterday was the weekend, so it should have fallen (natural cycle changes)

“I found that product A sells very well” – Product A has just been launched, so it should sell well (product life cycle)

Often, the indicators we look at are overall indicators, which are composed of several parts, such as:

Head office - Branch A, Branch B, Branch C

Total sales - Product A, Product B, Product C

Therefore, after seeing an overall indicator, you can break down the whole according to its components and understand the composition of each part. This is the structural analysis method (analyzing the internal structure of the whole).

Structural analysis

The structural analysis method is useful in many cases. For example, if you ask, "Why is the performance declining?" The answer is, "Because XX area is not done well!" By looking at the structure, you can quickly find the person responsible.

Stratified analysis

In addition to simply looking at the structure, people also like to make rankings and distinguish between high, medium and low levels. This is the hierarchical analysis method.

Many students confuse stratification and structure. Just remember the following two sentences:

The structure exists objectively, just ask clearly

Stratification is subjective, and it is necessary to determine the high and low

These three methods are the basics of the basics. First, they are all analyzing an indicator, and second, they are all based on factual statements and do not require any calculations. When we first arrive at a company or first come into contact with new data, we can use these three methods to establish basic knowledge.

03 From one indicator to two indicators

Matrix analysis

When the number of indicators increases from one to two, the best method is matrix analysis. The matrix analysis method constructs an analysis matrix through the intersection of two indicators, and uses the average value to cut out four quadrants to discover problems (as shown in the figure below).

The biggest advantage of matrix analysis is that it is intuitive and easy to understand. It is easy to find problems from the cross-comparison of the two indicators. Especially when these two indicators are input/cost indicators, the two categories of high cost + low income and low cost + high income can directly indicate the direction of improvement for the business, thus greatly avoiding the problem of "not knowing how to evaluate good or bad".

Many consulting companies like to use this method, which is similar to the KANO model or the Boston Matrix. The essence is to find two good evaluation indicators, construct a matrix by cross-constructing the two indicators, and classify the business. The classification effect was very good and it became widely circulated.

04 From 2 indicators to multiple indicators

When there are more analytical indicators, the most important task is to figure out the relationship between these indicators. There are two typical relationships.

The first one: parallel relationship.

Several indicators are independent of each other and are components of the indicators at the upper level.

For example, we often say: Performance = Number of customers * Consumption rate * Average customer price

In this formula

1. Primary indicator: performance

2. Secondary indicators: number of customers, consumption rate, and average order value

3. Number of customers, consumption rate, and average order value are independent of each other

At this point, the number of customers, consumption rate, and average order value are three parallel indicators, and are all sub-indicators of performance.

The second type: serial relationship.

Several indicators are interrelated and have a chronological relationship.

For example, we often say: Number of new registered users = number of ad views * landing page conversion rate * registration page conversion rate.

1. Primary indicator: number of newly registered users

2. Secondary indicators: number of ad views, landing page conversion rate, registration page conversion rate

3. Users must first see the ad, then click on the ad to enter the landing page, and then complete the registration

At this point, the indicators of the advertising page, landing page, and registration page are interrelated, and users have to proceed step by step.

These two relationships correspond to two basic analysis methods:

Parallel relationship: indicator decomposition method, by decomposing a first-level indicator, problems can be found from the second-level indicators.

Funnel analysis method: By observing a series of processes, understand the process conversion rate and find out the shortcoming of the conversion rate.

Indicator decomposition method

The indicator decomposition method is generally used more in business analysis. To give a simple example, a mini program mall had sales of 1.5 million last month and 1.2 million this month. If you only look at the results, you won't know anything except that 300,000 is missing. But after breaking down the indicators, you can find a lot of things (as shown below)

After breaking it down, it is clear that although the number of registered users increased this month, the consumption rate dropped significantly, so the revenue was lower. We can further think about how to increase the consumption rate.

Funnel analysis

The funnel analysis method is more commonly used in Internet product/promotion/operation analysis, because Internet products can record more user data, and thus can present the entire user conversion process for analysis.

To give a simple example, when we see a product advertisement online, we are very interested and click on it to make the purchase. You need to go through several steps: advertising page → details page → shopping cart → payment. With each additional step, some users will be lost, just like a funnel.

At this point, you can use a conversion funnel to graphically represent this relationship (as shown below).

With the conversion funnel, we can further analyze the funnel to guide business improvements:

1. Which link misses the most users and needs improvement?

2. What is the funnel shape for different products, and which one is more suitable for promotion?

3. Has the number of missed users been reduced after the new product revision?

Correlation analysis

Of course, there are some indicators that may not have a direct parallel/serial relationship, but at work, I would also like to know if they are related, such as:

1. Advertising investment and sales performance

2. Rain, wind and store traffic

3. User click and consumption behavior

At this time, you need to master the correlation analysis method. Note: Indicators may be inherently correlated with each other.

There are three common forms of innate correlation:

1. In the structural analysis method, the relationship between the overall index and the partial index

2. In the indicator decomposition method, the relationship between the main indicator and the sub-indicators

3. In the funnel analysis method, the relationship between the previous and next step indicators

These three situations are called: directly related. Direct correlation does not require data calculation, and the relationship can be clearly seen through indicator sorting. The correlation analysis method mainly uses scatter plots/correlation coefficients to find potential correlations (as shown below).

But please note: correlation does not equal causation. How to interpret the correlation coefficient needs to be combined with the specific business meaning, and you cannot draw conclusions randomly.

05 From indicators to business logic

Tag analysis

All of the above methods are based on data indicator calculations, but in actual business, many relationships cannot be directly expressed by data indicators.

for example:

● Do community stores do better business than pedestrian street stores?

● Does private domain traffic convert better than public domain traffic?

● Are sales better when it’s windy and rainy than when the sky is clear?

Community stores/private domain traffic/rain or shine, it is difficult to measure them with a single data indicator. But these factors do have an impact on business operations. How should we analyze them? This requires the use of: label analysis method.

To give a simple example, in a certain southern province, heavy rains often occur in August. Everyone thinks that rain will affect store performance. So how to analyze it? According to the five-step method, we can analyze the stores in this province as shown below:

So we can conclude that rain has little impact on performance, and that’s it.

Note that in the example above, the labels are very crude, with only two simple categories: raining/not raining. In addition to rain, there may also be typhoons, hail, high temperatures, etc. Therefore, the precision of labeling determines the accuracy of label analysis. Whether or not the appropriate label can be selected tests the analyst's understanding of the business.

So far, a total of eight basic methods have been introduced. In actual work, a combination of methods is generally used. Because the questions raised by the business are likely to be complex and may involve multiple indicators and multiple labels. At this point, there are so many things to think about. To sort out your thoughts, you have to use the ninth method: the MECE method.

MECE Method

MECE is the abbreviation of (Mutually Exclusive Collectively Exhaustive), which refers to the classification principle of "mutually independent and completely exhaustive". By classifying problems using the MECE method, you can be clear and accurate, making it easier to find answers.

The MECE method is the watershed between basic analysis and advanced analysis, and is also the channel to upgrade from basic to advanced. All complex problems need to be carefully sorted out and broken down into small solvable problems. The so-called business analysis model is actually the MECE decomposition of business problems.

Seeing this, I am sure many friends want to see the operating details of these nine methods. You can follow Xiao Xiongmei. Xiao Xiongmei has written a detailed operation article for each method. Next, Little Bear Sister will organize the nine analysis methods into a PDF for everyone to download and use.

Of course, some students are definitely curious: After mastering these nine methods, how can we go deeper?

There are generally three routes to in-depth analysis.

06 After mastering the basic methods

Route 1: Business analysis model.

Business models are used to solve problems such as vague definitions, poor data, and the need for business guidance.

For example, the business is struggling:

1. What kind of users are better?

2. How can we motivate sales?

3. How big of an impact did the activity have?

These questions sound simple, but the definitions are actually very vague. What is considered good? How does it work? Are incentives without money really useful? The various issues are complicated and may be mixed with the business department’s own little thoughts. Therefore, it is necessary to carefully sort out the business logic and derive a feasible problem-solving logic.

Route 2: Algorithm analysis model.

Algorithmic models are used to solve problems with clear definitions, rich data, and complex computational processes.

For example, the problem of identifying high-value users has been clearly defined in the business:

1. What indicators are used to measure user value?

2. What criteria are used to evaluate the value of "high"

3. Collected rich data (gender, age, interests, related products, interactions, comments...)

4. There are a group of manually labeled "high-value" users as positive samples

At this point, various algorithms can be used for modeling. The purpose of modeling is not to increase the depth of analysis, but to improve the efficiency from analysis to business application. With relatively accurate model judgment, the business can automatically trigger marketing rules through CDP+MA, without having to write a long PPT every time. The algorithm model requires some

Route three: statistical inference.

Statistical inference methods are used to solve problems that are clearly defined, have no data, and require testing and data collection.

For example, to launch a new version of a product, the business has already defined that the new version is to increase the average online time of users (average problem). Now a test is needed and one of the two preliminary versions must be selected. At this time, we need to use the method of double population mean comparison hypothesis test.

Of course, actual problems are more complicated, requiring consideration of various control variables, assumptions, system development, and data collection plans, and cannot be solved solely by analysts.

The above are three routes for in-depth learning after mastering the basic methods.

Author: Down-to-earth Academy

Source: Down-to-earth Academy