In an environment where customer values ​​are diversifying and changes in purchasing behavior are accelerating, traditional customer loyalty strategies may not still be effective. What factors should we pay attention to in order to build a "true connection with customers" that cannot be measured by point cards alone? The CMM (Consumer Mix Modeling) introduced here is a method that uses data to clarify the factors hidden behind invisible customer loyalty, and enables us to choose what we should focus on to increase customer loyalty, and conversely, what we do not need to focus on.

In this article, we will introduce the main measurement indicators and analysis methods currently used for customer loyalty, then explain the challenges in traditional analysis, and provide an overview of CMM, a new approach to overcome these challenges, and how to use it. We will also introduce new insights to elucidate the factors that influence customer loyalty.

The Importance of Customer Loyalty

Customer loyalty is a key factor in strengthening a company's economic foundation. Loyal customers generate stable revenue for a company through repeat purchases and high purchase frequency. These customers also tend to spread positive word-of-mouth to other potential customers, which can help attract new customers organically.

In addition, loyal customers generally tend to be less price sensitive, so there is a high possibility that relatively stable profits can be secured without relying on excessive price reductions or frequent discounts. In this way, increasing loyal customers is an important initiative for companies that is directly linked not only to short-term profits but also to long-term growth.

Metrics for measuring customer loyalty

Now that we understand the importance of customer loyalty, the next question we face is, "How do we measure and evaluate it?" Choosing the right metrics and continuous monitoring are essential for businesses to effectively manage and improve loyalty.

Here are some key metrics and analysis methods, but please note that they won't apply to all companies, so it's important for marketers to choose or define new metrics that are appropriate for their own business.

1. Customer Retention Rate (CRR)

It indicates the percentage of customers that were retained over a given period of time.
a formula: CRR = ((Number of customers at the end – Number of new customers) / Number of customers at the start) x 100
A higher CRR indicates greater customer loyalty.

2. Purchase Frequency

It tells you how often your customers make repeat purchases.
a formula: Purchase Frequency = Total Orders / Unique Customers (within a given time period)
Higher frequency suggests greater customer loyalty.

3. Repeat Purchase Rate (RPR)

It represents the percentage of customers who make multiple purchases.
a formula: RPR = (Number of returning customers / Total number of customers) x 100
A higher RPR means higher customer loyalty.

4. Customer Lifetime Value (LTV)

It is a metric that predicts the total revenue a customer will generate for a company over their lifetime.
a formula: LTV = Average Purchase Amount × Purchase Frequency × Average Customer Lifetime
High LTV generally means higher customer loyalty.

5. Net Promoter Score (NPS)

Measure customer satisfaction and loyalty based on willingness to recommend.
a formula: NPS = Percentage of Promoters - Percentage of Detractors
A higher NPS indicates greater customer loyalty and recommendation.

6. Customer Effort Score ("CES")

Measure how easy customers found it to interact with your company. Ask customers to rate the ease of their experience in a survey.
a formula:Voting percentage for top 7 categories out of 2 options – Voting percentage for bottom 3 categories
The higher the CES, i.e., the lower the burden, the greater the tendency for customer loyalty to increase.

7. Customer Satisfaction Score (CSAT)

This is an index that measures the overall level of customer satisfaction with a product or service. Customers are asked to rate their satisfaction through a survey.
Higher CSAT scores are often associated with increased customer loyalty.

8. Sentiment Analysis

Analyze customer feedback and reviews to gauge overall sentiment towards your brand.
Positive sentiment is often an indication of high customer loyalty.

9. Engagement Metrics

Track customer interactions across touch points, including website visits, app usage, and social media engagement.
Higher engagement tends to lead to greater customer loyalty.

How to Identify What Drives Customer Loyalty

Understanding what makes some customers loyal and others not is key to developing an effective strategy.

To identify factors that improve customer loyalty, a comprehensive approach can be used that combines various methods, such as data analysis, customer feedback, and industry best practices. It is important for marketers to select the most appropriate method based on their hypothesis and conduct analysis. Below are some typical methods.

1. Analyzing customer behavior data

Finding commonalities between high LTV customers and repeat customers can lead to understanding customer characteristics and the factors that drive customer loyalty.

[Usable data]

• Purchase frequency and most recent purchase date
• Average order value
• Frequently purchased product categories and services
• Customer Lifetime Value (LTV)
• Your participation in loyalty programs and rewards

2. Collect customer feedback

It's important to actively gather feedback to understand what motivates your customers, and a good way to do this is to look at the factors that customers frequently cite as reasons for returning.

[Usable feedback]

• Conduct a survey on satisfaction, willingness to recommend, and reasons for purchase
• Focus groups and interviews with repeat customers
• Analysis of customer success interactions and complaints
• Monitoring social media mentions and reviews

3. Customer Experience Assessment

It allows you to comprehensively evaluate the experience at every touch point that customers come into contact with. Excellent customer support is considered one of the most important factors in increasing customer loyalty.

[Usable evaluation]

• Usability and functionality of the website or app
• In-store experience (if there is a store)
• Quality and speed of customer service
• Personalization Initiative
• Post-purchase support system

4. Verify the quality of products and services

While quality isn't the only factor, it's still important: it helps ensure that your products and services meet customer needs and provide value.

[Usable data]

• Analysis of return rates and reasons for them
• Customer feedback on product features and performance
• Comparison with competing products

5. Evaluating loyalty programs and rewards

If you have a loyalty program in place, evaluate its effectiveness. An effective program can significantly increase customer loyalty.

[Usable data]

• Participation rate
• Usage rate of benefits, points, etc.
• Influence on purchasing behavior

6. Consider emotional and social factors

It's okay to focus on emotional connections as well as transactional ones, as emotional connections and shared values ​​can be powerful sources of loyalty.

[Useful information]

• Brand values ​​and social responsibility initiatives
• Emotional marketing
• Emotions and thoughts associated with the brand

7. Benchmarking with Industry Standards

You can also compare your performance against industry benchmarks and best practices, which may help you identify areas where you excel and areas where you lag behind your competitors.

[Usable data]

• Customer Satisfaction Score
• Net Promoter Score (NPS)
• Customer Retention Rate

The challenge of identifying what drives customer loyalty

Many companies focus on loyalty-enhancing measures that have clear effects, such as point reward programs and other benefits. While these certainly have a certain effect, there is a risk that customers will easily move to a competitor if they offer a higher point reward rate or discount. In order to build long-term, strong customer loyalty, it is essential to not only take visible measures, but also to foster deep relationships of trust with customers.

For example, it is important to consider factors that may not be visible but have a subtle impact on customer psychology, such as the store's atmosphere and friendly communication with employees. Although the effects of these factors are difficult to measure at first glance, they can contribute to long-term customer loyalty.

Previous analytical methods have made it difficult to fully understand the complex factors that affect customer loyalty. For example, even if you can identify what customers value through surveys, it is difficult to quantitatively evaluate the extent to which they affect business outcomes (purchases of your company's brand). To address these challenges, more comprehensive and advanced analysis is required.

Overview of CMM and its use to improve customer loyalty

Consumer Mix Modeling (CMM) has been attracting attention in recent years as a way to overcome the limitations of traditional analytical methods and achieve a deeper understanding of customers. This innovative approach makes it possible to analyze complex customer behavior from multiple angles and comprehensively evaluate the various factors that affect loyalty.

What is CMM?

CMM is a method to clarify the purchasing mechanisms of your own brand and your competitors' brands based on customer awareness data (surveys). It is an approach that quantitatively clarifies "how to make customers choose your brand over other brands" and "how to improve customer loyalty."
By using CMM, you can comprehensively evaluate various factors that affect customer loyalty and quantitatively show their impact on business results. For example, you can obtain specific insights such as "By improving a specific factor (such as brand image) by one point, you can make XX% of the target segment (e.g., middle class) loyal (e.g., move from middle class to heavy class)." This allows you to prioritize marketing measures and efficiently allocate and execute resources.

・Related articles:What is CMM (Consumer Mix Modeling)? Explains the characteristics, implementation process, and use cases of this scientific approach to clarifying consumer behavior.

Comparison with conventional analysis methods

Conventional survey and analysis methods can provide hints about what factors customers value, but it is difficult to quantitatively show the extent to which those factors affect business results (in other words, how much purchasing (sales) will increase by promoting those factors). On the other hand, CMM uses consumer awareness data from questionnaire surveys to comprehensively analyze the relationship between various factors and business results, and can structurally and quantitatively clarify the impact that each factor has on improving loyalty. This makes it possible to efficiently select which factors to focus on and which factors do not require excessive focus.

Statistical analysis of factors influencing loyalty using CMM

CMM analysis identifies factors that influence loyalty in a particular customer segment, for example, promoting a customer from a "light user" to a "medium user" and then to a "heavy user." The variables analyzed include the 4Ps of marketing, such as product features and design, pricing, number of stores and sales channels, and promotional measures (advertising and campaigns), as well as CX (elements in the customer experience) and brand assets (emotional and functional brand image). This allows for a deep understanding of hidden customer psychology and behavior that could not be achieved through conventional superficial analysis. It also makes it possible to quantitatively evaluate the extent to which each factor affects changes in customer status.

Examples of CMM use

Now that we have a better understanding of the concept and methods of CMM, let's take a look at how it is used in practice and what results it can bring. Below is an example of how CMM is used by a credit card service company.

Faced challenges

The challenge was not knowing what key factors unique to our company we should focus on to improve customer loyalty.

• The most important issue is the transition from the light segment, which is the volume zone, to the middle segment (middleization), followed by the transition from the middle segment to the heavy segment (heavyization).

Analysis content

We clarified the key factors (drivers) for each switch by dividing them into the following two axes:

• Analysis axis 1: Switch to middle management
• Analysis axis 2: Switch to heavier

result of analysis

By using CMM to analyze the drivers of customer loyalty, we were able to gain the following insights:

The impact of rewards and point reward programs

• We had long recognized the impact that rewards and point reward programs have on customer loyalty, but CMM enabled us to quantitatively evaluate their importance.

Impact of travel-related services

• It turns out that the services available during travel are a key factor in the transition from light to medium tier travelers, and from medium to heavy tier travelers. This information can be used as a selling point for marketing strategies targeting travelers, and as a hint for strategically acquiring and promoting this travel demographic.

The impact of prompt and courteous service

• We found that speed and courtesy of customer service is an important factor in moving from the light to middle tier.

The influence of others' opinions

• It turns out that praise and recognition from others is an important factor in moving from the middle to the heavy tier.

Utilizing the analysis results

Insights based on these analytical results clarified which actions should be prioritized, leading to the consideration of the following measures:

Optimizing marketing strategies

• Strengthen advertising and promotions that highlight the convenience of travel-related services and implement campaigns aimed at tourists
• In order to promote middle-class work, we will emphasize our fast and courteous service.
• Plan measures and events to promote awareness and understanding of member benefits and point programs in order to promote heavy use.

Product and service improvement

• Strengthen employee training to improve customer service quality
• Increase customer satisfaction by offering additional travel-related services

In this way, by utilizing CMM, companies can develop specific and effective strategies that will increase customer loyalty and create highly precise measures for each target demographic.

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Benefits of using CMM to improve customer loyalty

CMM enables companies to make efficient and effective decisions to improve loyalty. Here are some key benefits:

Comprehensive factor analysis and impact quantification

CMM comprehensively analyzes all factors, from visible measures such as point reward programs and special offers to factors such as store atmosphere and communication with employees. What is particularly important is that it can quantitatively evaluate the impact of each factor on business results. For example, it is possible to make specific predictions such as "what percentage of a target segment will become more loyal by strengthening efforts on a specific factor." This allows you to prioritize measures with the highest return on investment.

Building a competitive advantage

Analysis using CMM provides insights to build your own unique competitive advantage beyond simple price wars and increased rebate rates, which are easily imitated. By quantitatively understanding which factors have the greatest impact on improving customer loyalty (e.g., the shift from light to heavy customers), you can develop a strategy focused on providing unique value that competitors cannot easily imitate.

Improve data-driven strategy planning and explanation capabilities

CMM can show the impact of a measure in concrete numbers, making it a powerful persuasive tool when proposing to management or formulating a strategy. For example, it is possible to make proposals based on statistical relationships, such as "increasing investment in a specific measure by XX% is likely to increase customer loyalty by XX%." This can enhance the strategic nature and explanatory power of marketing measures.

Realizing continuous optimization of marketing activities

CMM allows you to regularly monitor the effectiveness of your marketing activities and adjust your strategy based on the results. In particular, by combining it with traditional MMM (Marketing Mix Modeling), you can achieve more precise optimization of your marketing activities from both the qualitative factors (factors that increase the probability of brand selection) and quantitative factors (factors of investment volume and allocation). This continuous improvement cycle (PDCA) allows you to flexibly adjust your strategy in response to changes in the market environment.

・Related articles:What is MMM (Marketing Mix Modeling)? Explaining its features, procedures, examples, etc.

In conclusion

When it comes to improving customer loyalty, many companies are faced with the challenge of "what should we focus on?" Even if traditional analysis can identify factors that customers value, it is difficult to quantitatively evaluate the extent to which they affect business outcomes.

CMM is an innovative approach that fills this gap. It comprehensively analyzes various elements such as the 4Ps of marketing, CX, and brand assets, and structurally and quantitatively shows the impact that each factor has on improving loyalty.

What's particularly important is that it makes it possible to make concrete predictions, such as "how much increased loyalty can be expected by increasing investment in a particular measure." This allows companies to make strategic decisions based on data, not just intuition or rule of thumb.

If you want to understand or review the current effectiveness of your company's strategies, or have a hypothesis to verify regarding issues related to improving customer loyalty (such as not knowing which measures to prioritize investment in order to improve customer loyalty), it may be a good time to consider implementing CMM.

As a pioneer in the use of data science in the marketing field, XICA has supported data-driven marketing for over 10 companies for over 270 years. Our analysts and consultants with extensive expertise in a wide range of industries, mainly domestic enterprise companies, will help our clients make better decisions.

XICA's CMM SolutionsService documentation including details about "COMPASS"For inquiries regarding specific use and implementation, please contact us.Contact us here.