Marketing is "making sales unnecessary." These are the words of Peter Drucker, the father of modern management.[English-Japanese Translation] Definitive Edition of Drucker's Quotations / Diamond Inc.Than).

Data analysis is essential for this type of marketing. Most marketing activities that rely on intuition and experience end in failure and waste costs.

However, even though it is said that data analysis is essential for marketing, many people probably don't even know what kind of data analysis methods are available.

In this article, we will introduce 13 data analysis methods that are essential for improving marketing results, and provide an overview of each, for those new to marketing. We will introduce them by marketing scenario, so please try incorporating them into your work.

13 Data Analysis Techniques Essential for Marketing

So, let's take a look at 13 data analysis techniques that are essential for marketing.

The main scenarios in which data analysis is used are as follows:

• Create a marketing strategy
• Analyze customer data
• Uncover trends in your data
• Uncover data relationships
• Identify data classifications
• Discover hidden details in your data

We will introduce important data analyses for each scenario, so please pick out the data analyses that you think are necessary for your company's marketing.

Create a marketing strategy

1. 4P Analysis

4P analysis is a data analysis method for developing marketing strategies by organizing information along the four axes of "Product," "Price," "Place," and "Promotion."

For example, when releasing a new product or service, we design the functions and performance, appropriate price, sales channels, promotional activities, etc. to suit the target users.

The important thing is to have a balance between the four Ps. A marketing strategy that is biased towards any one of them is likely to fail.

2. STP Analysis

STP analysis is a data analysis method that organizes information along three axes: "Segmentation (segmenting the market)," "Targeting (determining the target market)," and "Positioning (confirming your position in the market)," in order to clarify your position in the market.

Since business is a competition to gain market share, when formulating a marketing strategy it is necessary to understand the position of your company and its products and services.

3. SWOT Analysis

 SWOT analysis is a data analysis method that organizes information along four axes - Strengths, Weaknesses, Opportunities, and Threats - to develop a marketing strategy that is based on reality.

 By organizing information based on SWOT, you will be able to develop an appropriate marketing strategy while taking into account the positive and negative factors of your company and the market.

Analyze customer data

4. Decile Analysis

Decile analysis is a data analysis method that divides customers into 10 groups based on product and service purchasing data, and visualizes purchase ratios, sales composition ratios, etc.

Analyzing customers not only as a whole but also by dividing them into various segments enables you to analyze customer data from a different angle.

This identifies the customer segments in which you should prioritize your marketing investments, allowing you to "select and focus" your investments.

5. RFM Analysis

RFM analysis is a data analysis method that classifies customer groups by scoring and ranking customers based on three pieces of data: "Recency (number of days since the last purchase)," "Frequency (number of purchases and purchase frequency)," and "Monetary (total purchase amount)."

After organizing the data based on RFM, customers are classified as follows:

*Created by our company based on the following:
Source: https://tech.pepabo.com/2017/12/06/tableau-rfm/

This will enable you to identify customers to whom you should actively upsell or cross-sell, and customers to whom you should work to prevent from dropping out.

Uncover trends in your data

6. Cross-tabulation analysis

Cross-tabulation analysis is one of the basic data analysis methods. It uses data obtained through questionnaire surveys to aggregate data by combining multiple axes such as respondent attributes and question items.

Example: An ice cream manufacturer is running a campaign targeting women, and they conduct a cross-tabulation analysis to decide which products to target for the campaign.

① Comparing the sales data for vanilla ice cream and strawberry ice cream, which are candidates for the target product, vanilla ice cream accounts for twice as much sales as strawberry ice cream. If you look at this data alone, you might be tempted to make vanilla ice cream the target product for the campaign.

However, this time the campaign is targeted at women. Looking at the sales data by gender, we can see that men make more purchases than women.

So, let's look at the sales data broken down by gender of the purchaser. We found that 9% of vanilla ice cream purchasers were men, while 8% of strawberry ice cream purchasers were women. Based on these results, we decided to target strawberry ice cream for this campaign aimed at women.

Cross-tabulation analysis involves combining various axes in order to extract the information needed for data analysis.

Source: [Data analysis from scratch #2] Three analysis methods that data analysis beginners should remember | XICA Co., Ltd. https://xica.net/xicaron/data-analysis-for-beginners-2/

Uncover data relationships

7. Multiple Regression Analysis

In statistics, "multiple" means plural and "regression" means causation.

Multiple regression analysis is a data analysis method that understands the relationship between one outcome (target variable) and multiple factors (explanatory variables).

How much should we spend on advertising to reach our sales goals?

"How much effect can we expect from each of Measure A and Measure B?"

"How can we allocate our budget this fiscal year to maximize our marketing results?"

This is an analytical method that is essential in the marketing field, as it can be used for sales forecasting, formulating marketing strategies, and more.

For example, by analyzing the degree of influence of various factors (explanatory variables) that affect sales (objective variable), you can consider the optimal budget allocation for marketing initiatives.

Source: [Data analysis from scratch #2] Three analysis methods that data analysis beginners should remember | XICA Co., Ltd. https://xica.net/xicaron/data-analysis-for-beginners-2/

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8. Logistic regression analysis

Logistic regression analysis is an analytical method for predicting and explaining the probability of a binary response variable (outcome) occurring from multiple explanatory variables (factors). Binary means that there are only two response variables, such as "YES" or "NO."

For example, you can use data about a potential customer (sales, number of employees, profit margins, etc.) to predict the likelihood that that customer will purchase your products.

<Difference between multiple regression analysis and logistic regression analysis>

• Multiple regression analysis
  Predict the value of the objective variable from multiple explanatory variables
• Logistic regression analysis
  Predict the probability that the objective variable will be "1" from multiple explanatory variables

Although multiple regression analysis and logistic regression analysis have some similarities, their use cases are clearly different.

9. Association Analysis (Basket Analysis)

Association analysis (basket analysis) is a data analysis technique that uses purchasing data to analyze how often a certain product is purchased together with other products.

By identifying combinations of products that are frequently purchased together, you can use this information in your marketing strategies, such as placing or recommending products that are likely to be purchased together nearby.

*Created by our company based on the following:
Source: What are the benefits and implementation methods of basket analysis that anyone can easily use? | Cross Marketing for research and market research https://www.cross-m.co.jp/column/data_marketing/dtm20220722/

Identify data classifications

10. Cluster analysis

Cluster analysis is a data analysis method that discovers features (variables that provide clues for prediction) for each piece of data from a population of data and identifies groups (clusters) of data according to these features.

Cluster analysis can be applied to not only customer information, but also any other data such as companies, products/services, regions, or survey items.

It also makes it possible to simplify huge amounts of data, and in marketing it is possible to segment consumers and implement appropriate measures for each cluster.

11. Decision Tree Analysis

Decision tree analysis is a data analysis method that classifies data using a tree structure. It is often used in data analysis because it can produce relatively accurate results even with simple analysis and the results are easy to interpret.

This is a data analysis method primarily used to discover potential customers and understand the impact that products and services have on customer satisfaction.

Discover hidden details in your data

12. Factor Analysis

Factor analysis is an analytical method for finding hidden common factors (components) from observed element data.

It is used when you want to understand your customers' essential needs, such as quantifying the user intentions hidden behind survey response results.

13. Correspondence Analysis

Correspondence analysis is a data analysis method in which the results obtained from questionnaire surveys, cross-tabulation analysis, etc. are plotted as a scatter plot.

*Created by our company based on the following:
Source: What is correspondence analysis? | Intage Market Research https://www.intage.co.jp/glossary/400/

You often see this in manufacturers' product descriptions and marketing proposals.

The relative positions of attributes allow you to understand the relationships between them, so you can use this to understand the relationships between competing products, such as "if you have this attribute, you're more likely to have this attribute as well."

The necessity and benefits of data analysis in marketing

In mass marketing, which has traditionally been mainstream, such as television commercials and newspaper advertisements, marketing activities have been carried out through rough targets and broad market analysis.

However, with the spread of the Internet, the situation has changed dramatically as anyone can freely send and receive information. Consumer needs have rapidly diversified, and marketing activities have become impossible without detailed targeting and detailed market analysis.

As marketing has shifted digitally, the need for data analysis has increased and data analysis technology has developed, making data analysis the key to successful marketing today.

Using data analytics in marketing also provides the following benefits:

Data-driven decision making

Traditional marketing that relies on intuition and experience is an activity with a strong element of gambling. Marketing activities incur large costs, so it can be said that "if it hits, it's heaven, if it doesn't, it's hell."

Data analysis is essential to eliminate this gambling element.Data-driven decision-making can dramatically increase the success of your marketing efforts.

Enabling personalized marketing

As consumer needs have become more diverse, the need for "personalized marketing" has increased.

"Personalized marketing" refers to the analysis of customer data, prospect data, market data, etc.Implementing different marketing for each segment (target classification).

In order to achieve "personalized marketing," it is essential to deepen understanding of the market and customers by making full use of the analytical methods mentioned above.

Improving marketing through repeated hypothesis and verification

The benefits of data-driven marketing include:You can quickly "implement measures" and "check results"You may.By repeating the hypothesis and verification cycle in detail, you can quickly improve your marketing activities.It looks like

If you can use various data analysis methods depending on your marketing objectives and speed up the hypothesis-verification cycle, you can expect better marketing results.

Steps for conducting marketing data analysis

Marketing data analysis involves the following main steps:

Start with the above steps and follow the flow carefully to proceed with your data analysis. Go back to the step before you feel something is wrong and proceed with the flow again. If you do not follow this flow, your marketing data analysis will most likely fail, so be careful.

For details of each step,[Data analysis starting from zero #1] “8 steps of analysis” that data analysis beginners should know first' is explained in detail in.

This article contains important basic knowledge not only for marketing but also for data analysis in business in general, so please read it together with the main article.

Recommended data analysis tools for marketing

When analyzing marketing data, if the amount of data is small, it is possible to use tools such as Excel. However, if you need to analyze a large amount of data, we recommend using a dedicated data analysis tool.

Here we will introduce four data analysis tools that are recommended for marketing, so please take a look.

MMM (Marketing Mix Modeling) Tool

MMM is a statistical method that comprehensively analyzes marketing-related big data and visualizes the direct and indirect impact that each marketing initiative had on results.

As the number of media and channels for marketing continues to increase, it is impossible to understand accurate marketing results without integrated data analysis. MMM solves this problem and allows you to understand the overall picture of your marketing activities from all data, and the MMM tool is a tool that incorporates this mechanism so that anyone can use it.

"MAGELLAN" has been adopted by companies in a wide range of industries and sectors to visualize advertising effectiveness and optimize budget allocation.

MA (Marketing Automation)

MA is a system that enables lead generation and lead nurturing through predefined scenarios and imported lead data.

While analyzing online and offline prospect data, you can automate parts of your marketing initiatives by triggering pre-defined scenarios.

By utilizing big data, we can improve the efficiency of marketing operations, allowing marketers to focus on creative work.

DMP (Digital Management Platform)

A DMP is a data analysis tool for managing and analyzing the vast amounts of data accumulated on the Internet.

DMP providers primarily use audience data (third-party data) collected independently to optimize advertising operations, but they can also combine this data with user data they own to identify likely potential customers.

In addition, by utilizing a DMP, it is possible to deliver advertisements and emails to segmented users, making one-to-one marketing possible.

CRM (Customer Relationship Management)

CRM is a data analysis tool that comprehensively manages user data and allows for customer analysis from various angles. In addition to data analysis, it also allows for cross-departmental use of user data.

Originally, it was a tool for managing user data and building good customer relationships. However, in recent years, data analysis functions have been strengthened in many CRMs, making it an indispensable data analysis tool for customer analysis.

Marketing and Data Science

In recent years, data science is a field that has seen increasing demand in the marketing industry.

What is Data Science?An approach to analyzing the vast amounts of data held by companies and deriving insights that will benefit the business.Say.

As data analysis becomes more common in business, the realization that "everything can be explained by analyzing data" has become widespread.

This is half right and half wrong. Because data itself is a collection of impersonal information, and you cannot explain anything just by looking at the data. In other words, "data does not give you answers."

To get answers from data, business people themselves need to breathe life into the data. Using a variety of approaches, we derive insights from the data and use them to guide the next action. This "breathing life into" part is what data science does.

As the need for data utilization in marketing increases, data science has also become indispensable.

For data science,"What is Data Science? A Data Professional's Easy-to-Understand Explainer [3 Recommended Books for Beginners] (Internal Link)"We've provided a detailed explanation on this, so please take a look.

In conclusion

Making sales unnecessary and creating a system that allows sales.

Although they use different words, many prominent management scholars and marketers have similar interpretations of the role of marketing.

Data analysis is essential to fulfill this essential role of marketing. In today's world, where user needs are so diverse, data-based decision-making and personalized marketing are necessary to make sales unnecessary or create systems that enable sales.

We hope that this article will help you to reaffirm the importance of data analysis in marketing.