In today's world, where competition is becoming increasingly fierce and consumer choices are becoming more diverse, it is essential for companies to develop strategies that understand consumer behavior in order to achieve sustainable growth.

What is important in this situation is precise analysis based on consumer awareness and behavior data. In this article, we will explain in detail about "Consumer Mix Modeling (CMM)" developed by XICA, including its overview, benefits of using it, and how to put it into practice. We will provide useful information for companies and marketers who are looking to optimize their marketing strategies.

What is CMM (Consumer Mix Modeling)?

Definition and characteristics of CMM

CMM is an abbreviation for Consumer Mix Modeling, and is an analytical method developed by XICA that statistically analyzes the mechanism of consumer brand selection based on consumer awareness data (questionnaire surveys). The greatest feature of this method is that it can maximize the probability that your brand will be selected by clarifying the strengths and weaknesses of your brand through comparison with competitors.

Specifically, we statistically clarify how each of the factors that influence brand selection, such as the 4Ps of marketing, CX, and brand assets, impacts selection behavior. This makes it possible to clarify the "levers in which to invest" that are effective in encouraging customers to switch from each competitor to your company.

Main features of CMM

• Utilizing consumer awareness data
• Competitive analysis
• Clarifying the mechanism of brand choice using statistical methods
• Maximizing the probability of choosing our own brand

Differences from MMM

Although CMM has a similar name to the well-known MMM (Marketing Mix Modeling), they have different approaches and objectives. Below we explain the main differences between CMM and MMM.

Key Differences Between CMM and MMM

CMM and MMM each use different perspectives and data for analysis, so by appropriately combining these methods, companies can carry out more precise marketing.

Basic implementation process of CMM (Consumer Mix Modeling)

To implement CMM effectively, you need to follow three basic steps:

STEP 1: Organize your strategy

First, organize your target segments and competitors you want to switch to. In this step, make hypotheses about the strengths of your brand and the weaknesses of your competitors, and solidify the direction of your analysis.

STEP 2: Analysis design and data acquisition

We then conduct analysis and design surveys to obtain data on the attitudes of our target segments, which will provide the basis for understanding how consumers make brand choices.

STEP 3: Model construction and simulation

Finally, we build a consumer behavior model and perform a switching simulation to determine the degree to which each factor influences consumers' brand choices, as well as the probability of switching when each factor's score is increased by +1 point, and draw suggestions.

Benefits of Consumer Mix Modeling (CMM)

Using CMM provides the following benefits:

Overcoming the challenges of traditional surveys

When conducting general questionnaire surveys such as cross-tabulation, there is a problem in not knowing which factors are effective levers for brand selection. In this case, the evaluation tends to be "higher than average/competitors is good," but this does not mean that the probability of brand selection increases. On the other hand, CMM can statistically clarify which factors have a great impact on brand selection, making it possible to formulate a strategy that guarantees validity and reproducibility.

Data-driven decision making

By using CMM, companies can plan strategies that are supported by statistics, not just by intuition and experience. This allows companies to make data-based decisions and execute effective strategies while minimizing risks.

Clear differentiation from competitors

By analyzing consumer awareness data, you can understand your company's strengths and weaknesses in comparison with your competitors and develop an effective differentiation strategy. For example, by clarifying which elements of your brand are superior to your competitors, you can develop communications that emphasize those elements. Furthermore, by regularly monitoring your competitors' marketing activities and brand image, you can quickly respond to the strategies they adopt.

Rapid response to market changes

Consumer attitudes are greatly influenced by changes in the market. By utilizing CMM, companies can regularly grasp changes in consumer attitudes and make appropriate strategic adjustments to respond to market changes. The ability to respond quickly is a key factor in determining the success of a company, especially in today's world of fierce competition.

Increased brand value

Based on consumer awareness data, you can understand your company's strengths and your competitors' weaknesses, and develop a strategy to improve your brand value. This will increase your brand's competitiveness and contribute to expanding your market share.

CMM (Consumer Mix Modeling) Use Cases

Consumer Goods Manufacturer

A consumer goods manufacturer used CMM to develop a strategy to encourage brand switching from competitor A and competitor B to their own company.

The analysis revealed that the switching lever from competitors A and B was common to both, "functionality," and that when the company's brand's functionality score increased by 1 point, the switching rate from each competitor was 17% and 18%. In addition, it was found that other important factors for switching from competitor A were "design (switching rate 16%)" and "brand attractiveness (switching rate 14%)." On the other hand, it was found that "price (switching rate 14%)" and "convenience (switching rate 13%)" were important for switching from competitor B. Furthermore, when analyzing the important factors for increasing "brand attractiveness," it was found that it was important to appeal to the image of "experience and expression."

Based on the results of this analysis, the consumer goods manufacturer decided that it was important to first emphasize the "good functionality." Furthermore, in order to encourage customers to switch from competitor A, which has a particularly large market share, the communication message would focus on "good design" in addition to good functionality, and the branding was also decided to be strengthened. In terms of branding, the policy was to appeal to the viewpoint of "allowing for self-expression" in order to boost the "attractiveness."

By utilizing CMM in this way, future strategies and effective measures to strengthen the company's market position by switching from competitors became clear.

CMM (Consumer Mix Modeling) Challenges

Difficulty in collecting sufficient, high-quality data

To effectively utilize CMM, a sufficient amount of high-quality data is required. However, in reality, collecting such data is often difficult. In particular, to obtain data on consumer awareness and behavior, it is important to design a survey to comprehensively collect the necessary data. Furthermore, if the number of survey respondents is insufficient or the quality of the responses is low, the reliability of the data will be reduced, affecting the analysis results.

Sample bias can distort analytical results

Collecting data through questionnaire surveys also carries the risk of sample bias. Sample bias is inappropriate sampling, where a large or small number of samples are collected for a particular group or attribute. For example, if too much consumer data is collected from a particular age group or region, the analysis results will be biased toward that group and may not accurately reflect overall consumer awareness and behavior. If such bias exists, the results of the CMM analysis will be distorted, increasing the risk of formulating an incorrect strategy.

Requires advanced statistical knowledge

CMM uses advanced statistical analysis techniques, so specialized knowledge is required to execute it. For example, it is necessary to understand advanced techniques such as Bayesian statistics and regression analysis, and a deep knowledge of statistics and data science is required.

Results are difficult to interpret

Accurately interpreting the results of CMM analysis is also a challenge. Understanding the results in the business world and using them for decision-making requires not only expertise in statistical analysis, but also a deep understanding of the actual business environment and marketing strategies. If the results are interpreted incorrectly, there is a risk of formulating an incorrect strategy, which could have a negative impact on the business. Therefore, the skills to correctly interpret the results and translate them into concrete actions are required.

Summary

CMM is an important tool for companies to strengthen their competitiveness and achieve sustainable growth in the modern business environment. This article provides a detailed explanation of the definition, characteristics, benefits, practice methods, and actual use cases of CMM. In particular, specific success stories from consumer goods manufacturers concretely demonstrate how CMM contributes to business.

However, there are several challenges to using CMM. These include ensuring the quality and quantity of data, the risk of sample bias, the need for advanced statistical knowledge, and the difficulty of interpreting the results. To overcome these challenges, it is essential to establish appropriate data collection methods and develop personnel with specialized knowledge and skills.

Overall, CMM is a powerful tool for companies to optimize their marketing strategies and increase their competitiveness. As it is expected to continue to be used and developed further in the future, if you want to effectively incorporate CMM and promote data-driven decision-making, please consult with experts such as XICA.

XICA has been providing services in the field of data science in marketing for over 10 years, and has a track record of supporting over 250 companies, mainly domestic enterprise companies. Our analysts and consultants with extensive and deep expertise in a wide range of industries use data science to help clients make better decisions.

XICA's CMM ServiceDetailed information about "COMPASS"For inquiries about specific applications, please contact us.Contact us here.