Unravel the reasons for being chosen and design a winning strategy.
-- CMM (Consumer Mix Modeling)
compass
The survey scores are high. Even so, it's not selling.
Quantify the impact of the 4Ps, CX, and brand assets on purchasing decisions, and identify the structure of switch and loyalty.
*Part of the COMPASS technology is currently under patent application.
We are listening to the "voice of the consumer."
Still, why aren't there any results?
Customers' true feelings and purchasing motivations are not visible.
We incorporated the elements that were most frequently requested in our survey. But when we launched it, it didn't sell. There was a contradiction between what customers said in interviews and their actual actions, and we failed to grasp the purchasing motivations that customers themselves couldn't articulate.
Hypotheses can be formed, but they cannot be substantiated.
When a plan is decided upon, there's no way to explain why it's better than the others. The decision is based on experience and intuition alone. While awareness is reported to have increased, there's no explanation for how that translates to increased sales.
Judgments of good and bad vary from person to person.
Works are submitted without a clear standard for what constitutes "good." No one's definition of "good" is necessarily wrong. That's precisely why progress is made without a decisive factor.
It's not that there isn't enough data. The data isn't in a format that allows for answers.
I have the survey data and reports at hand. Yet, I still can't grasp "why it sold" or "what factors drove the purchase." When a project approved based on research fails to deliver results, the blame falls not on the research, but on the person who made the decision. It becomes even harder to get the next project approved.
We will use statistics to explain "why it was chosen."
Quantifying customers' true feelings and identifying the next step to increase sales
COMPASS is an analytical consulting service that uses its unique CMM (Consumer Mix Modeling) analysis to unravel the mechanisms behind consumer brand selection. It analyzes the impact of the 4Ps of marketing, CX, and brand assets on consumer purchasing behavior. Rather than simply aggregating data, it uses statistical analysis to calculate the probability of switching from competitors or increasing customer loyalty.
Visualizing customers' "unconscious" purchasing motivations.
High scores do not necessarily lead to purchases. COMPASS quantifies which factors have the greatest impact on brand selection (purchase).
Identify the "who, what, and where" of your strategy
We derive winning patterns by combining target attributes (Who), the message to be conveyed (What), and the optimal touchpoint (Where).
From "intuitive discussions" to "data-driven agreements"
Objective evidence based on statistical models becomes the "common language" in internal meetings and agent briefings, enabling quick decision-making and reliable execution.
*Part of the COMPASS technology is currently under patent application.
Two analytical approaches to choose from depending on the issue
"Reasons for being chosen" and "Reasons for being continuously chosen"—we support strategy formulation with two approaches tailored to your objectives.
Analysis of the mechanism of "brand choice" among competitors:
Brand Switch Analysis
Identify the key factors that will persuade customers to switch from your competitors' products to your brand.
Analysis PerspectiveAnalyze which elements of each competitor need to be strengthened to maximize the likelihood of switching to your company
Suggestions gainedIt is possible to perform simulations such as "If the score is increased by one point, the market share will increase by a certain percentage," and to derive specific measures to seize market share.
Analysis of your company's "loyalty" mechanism:
Loyalty Driver Analysis
We will develop "light users" into "medium/heavy users" and clarify the loyalty factors to prevent them from leaving.
Analysis PerspectiveAnalyze not only behavior but also what forms the psychological loyalty behind it
Suggestions gainedIt is possible to conduct simulations such as "What percentage of customers will become loyal if the score is increased by one point?", and to design measures to improve LTV.
Identify your selling points to beat your competitors
- Data-driven strategy that connects analysis to the next step
Addressing the challenge of "competitive analysis not leading to action," this presentation explains a methodology for identifying winning "selling points" based on data. It will cover how to calculate the probability of consumers switching from competitors (switch rate) and how to develop strategies to maximize return on investment. A practical framework will be presented, including examples from consumer goods manufacturers.
DownloadCommon pitfalls with traditional surveys
In general surveys, a higher score than the average or competitors is considered a good score, but this does not necessarily mean that the brand will be chosen more often.
Although it provides an understanding of the current situation for each item's score, it is often difficult to translate it into actionable information, such as the impact of improvement or predictions based on that information.
Even if the degree of impact is considered using a statistical approach, it is difficult to take into account the heterogeneity of respondents, and the results tend to be weak as suggestions for specific actions.
CMM Consulting Process and Analysis Flow
From hypothesis design to simulation, data scientists and consultants will work alongside you, verifying each step along the way to guide you towards results.
Strategic planning and hypothesis discussion
We clearly define our target audience and segments, and determine which groups to approach. After organizing our current marketing situation and challenges, including competitor market share and our own customer loyalty levels, we design our analysis strategy.
Data acquisition and analysis
We design surveys to structurally and numerically grasp the driver elements for increasing "switches" and "loyalty," collect asking data, and then perform analysis.
Model building and simulation
We will construct a consumer behavior model and simulate the switch rate. Through this simulation, we will quantitatively evaluate how much each factor influences consumers' brand choices.
An example of CMM analysis output Patent pending
We convert complex consumer psychology into "numbers" and "structures" that can be used for decision-making and present them to you.
Identify numerically what you need to focus on to win
We visualize in a ranking format which elements (4P, CX, brand image) are most effective in switching customers from competitors/converting them into loyal customers.
This is the point
- Calculating the "switch rate*" makes priorities clear
- Identify the differences in effective "attack methods" against competitors A and B (Brand Switch Analysis)
*Switch rate: The percentage of people who switch/loyal when their survey response score increases by 1 point
Organizing "strengths to maintain" and "opportunities to exploit"
Plot elements on two axes: "your company's evaluation score" and "difference with competitors." This clarifies the "maintenance elements (critical success factors)" where you are already winning, and the "growth elements" where you are competing closely.
This is the point
- At a glance, you can see where you should focus your investment resources
- Avoid inefficient investments (which have already won or have little impact)
Clarifying the structure of brand choice
We identify not only the direct impact on consumer behavior, but also the "indirect effect" via a specific brand image, and structurally clarify which measures contribute to building brand assets.
This is the point
- Identify the specific experiences and messages needed to increase brand loyalty and trust
- The entire mechanism of measures → change in attitude → purchasing behavior can be quantified.
Collaboration with Megane Top Co., Ltd.
── Eyeglasses market
Situation
"Megane Ichiba" operates 1,063 stores nationwide.*The company, which operates in the market and holds the top market share in the industry, faced a challenge as its main customer base of 30-40 year olds shifted to older generations, making the acquisition of younger customers a key focus. Judging solely based on past successes would not allow them to determine what would work with this new customer base. Faced with market changes, they needed to objectively re-examine the factors that drive purchasing.
Approach
We separated the conditions for customers to choose our company over competitors (new customer acquisition) and the conditions for customers to continue choosing us (retention of churn), and analyzed them along two axes. We quantified the impact of each element of the 4Ps, CX, and brand assets on purchasing decisions by generation. By calculating the probability that consumers will act when a specific brand image improves, we have derived decision-making data that does not rely on intuition.
Outcome
What motivated younger generations was not price advantage, but rather unique value propositions tailored to the customer and a brand image shaped throughout the purchasing process.*By combining market importance with our company's competitive advantages, we were able to define the key points of what to communicate to which target audience and prioritize what message to convey.
The identified key drivers are now being disseminated throughout the company as a "common language" shared by everyone from management to stores. Objective support has been added to judgments based on experience and intuition, creating a foundation for data-driven discussions.
*Store count is as of March 2025. Specific analysis results and strategic policies are not publicly available, therefore, modified and masked expressions are used. Titles are as of the time of the interview.
