The captain told the Americans, "If you dive in, you'll be a hero," the Germans, "The rule is to dive," the French, "Never dive," the Italians, "A beautiful woman just jumped in," and the Japanese, "Everyone's already jumping in." So everyone jumped into the sea to safety, and they escaped disaster, or so the story goes.
Some of you may already know this "sinking ship joke" because it was quoted in this book . Setting aside the ethical right or wrong, wouldn't it be fair to say thatgreece telegram phone number list the captain who took the most "resonant" approach to the passengers, who were divided into categories based on their nationality, got a perfect score from a marketing perspective?
The attribute of "country" that the captain used to divide the passengers in this story is one of the "demographic data" that we will introduce this time. Please read to the end and use it for your company's marketing activities.
Demographic data ( Demographics ) refers to fact-based statistical information that describes an individual, such as a customer's age, gender, race, age, and occupation, and is called "demographic attribute information" in Japanese.
To understand demographic data, it may be easier to first learn about the "STP theory."
STP theory (STP analysis) is a marketing theory proposed by American economist Philip Kotler , which divides a company's marketing activities into three processes: "S: Segmentation," "T: Targeting," and "P: Positioning."
Demographic data can be considered one of the data used in the first step of STP, "segmentation."
As it is called "attribute information," demographic data is used as one of the allocation indicators when segmenting customers based on their characteristics in marketing.
By dividing up markets and customers into smaller segments and evaluating and analyzing them through segmentation, you can clearly classify the customer groups that match your company's products from those that do not. This allows you to determine which groups your company should truly target (targeting), and also allows you to consider the optimal approach for your company in the targeted segment (positioning).
Therefore, demographic data, one of the indicators that determines segmentation, the starting point of STP, is important data that greatly influences the success or failure of a company's marketing activities.
For more information on STP theory, please see this article on our blog.
Representative segments of demographic data
So what kind of demographic data is there? Here are some of the most common segments of demographic data.
sex
Information about your customer's gender is one of the biggest components of demographic data.
According to the Statistics Bureau of the Ministry of Internal Affairs and Communications , Japan's total population as of August 2022 is approximately 125.08 million. Of these, approximately 60.81 million (49%) are men and approximately 64.27 million (51%) are women. In addition, the United Nations ' world statistics also show that the ratio of men to women in the total population is approximately 50:50, which is almost similar to the statistics for Japan.
Customer purchasing behavior changes greatly depending on gender. For example, here are some examples of female purchasing behavior according to Forbes :
70-80% of consumer purchasing decisions are made by women
Women are 50% more likely to watch "how-to" videos online than men
70% of travel spending is done by women
The next most common home buyers after married couples are single women (18%), twice as many as single men (9%).
In this way, gender can be very useful data for predicting customer preferences, habits, and decision-making characteristics. Segmenting customers by gender can also be meaningful when analyzing the "customer journey," which is the thoughts and experiences that customers go through before arriving at your product.
Recently, the importance of analyzing the purchasing behavior of "LGBT" in addition to the classification of men and women has been advocated . Although it is a little more difficult to obtain the data, this should also be considered if possible.
age
Like gender, information about a customer's age is one of the demographic data that greatly influences customer purchasing behavior.
Even the same person's tastes change as they get older. For example, there are probably many people who loved bugs when they were young, but now that they're adults, they hate even looking at them. There is also data like the one below that shows that favorite colors vary by age.
color-preferences-by-age (2)
(Source: Scott Design Inc. )
Classifying customers into generations based on age is also useful for analyzing customer purchasing power, thought patterns, and even market trends.
For example, the " Millennials (Generation Y) " refers to those born between 1981 and 1996. This generation experienced the IT revolution at a relatively young age and is characterized by being far more familiar with digital technology than the previous generation, "Generation X." Furthermore, the Millennials already account for approximately 60% of tech-related B2B buyers in the United States , and their purchasing power is expected to grow even more as the generational change progresses.
Gen Z is the generation born between 1996 and 2015, following the Millennial generation. This generation is characterized by being born digital natives, with IT technology already widespread by the time they were old enough to understand. In the United States, they already make up 25% of the workforce , and over the next 10 years, they will continue to grow in both purchasing power and influence.
In this way, age can be extremely useful data for companies to use as an indicator to determine not only short-term but also medium- to long-term policies when formulating their marketing strategies.
Family Composition
Customer purchasing behavior also changes depending on family composition.
For example, it is important to consider whether a customer lives alone or with their family. If they live alone, they may have some freedom in making decisions about products and services. However, customers with elderly parents or small children will have different challenges and product needs than customers who live alone, and may be more cautious in their decisions due to a sense of responsibility.
Similarly, whether a customer is single or married, whether they have siblings, or whether they have pets can also be important factors depending on the products and services your company offers.
Nationality and culture
Differences in nationality and culture are also elements that can be broken down into demographic data. Different nationalities have different cultures. And different cultures have different things that people tend to like and dislike.
For example, a 2005 study found that people in Switzerland and Germany, on average, tend to seek new experiences, while Hong Kong, Ireland, and Kuwait were less likely to do so. Another 2007 study found that people in Japan and Argentina were more likely to be neurotic, while people in the Congo and Slovenia were less likely to be neurotic.
The "sinking ship joke" introduced at the beginning of this article is a humorous way of expressing the differences in characteristics based on nationality and culture.
The above are just a few examples, but they are enough to understand that preferred products, services, and marketing approaches vary depending on nationality and culture.
However, as you can see from the fact that it is a joke, analysis of nationality and culture is highly susceptible to bias due to preconceived notions, so care must be taken when actually classifying and analyzing targets.
Occupation/Annual income
Customers' occupations and annual incomes are also demographic data. Classifying customers by occupation or job type is important for clearly targeting your products.
For example, if your company sells customer relationship management tools (CRM) and sales force automation (SFA) in B2B, you will naturally give higher priority to customers such as sales managers of companies with many potential and existing customers. On the other hand, engineers who mainly work in development and research may be better classified as lower priority.
In addition, a customer's annual income is directly linked to their purchasing power. In the case of B2B, you may want to base your sales on account (company) sales rather than individual sales. No matter how much they like you, if they don't have the budget to purchase your products or services, you can't expect to get an order in the end. Classify them based on individual annual income and company sales, and try to approach them efficiently by priority.
