Customer analytics device

Abstract

To rationally evaluate a person in charge of a service.SOLUTION: A customer analyzer comprises: an answer acquisition unit that obtains main answers from a user to an inquiry for surveying willingness to purchase a new service from a person currently in charge of providing or mediating a predetermined service, an inquiry for surveying willingness to continue to use the service provided by the person, and an inquiry for surveying willingness to recommend the person in charge to other users, and also obtains sub-answers on preconditions for the main answers; and an achievement calculation unit that calculates an achievement value for the person on the basis of a plurality of main answers related to the person. The achievement calculation unit selects main answers to be used for the calculation of the achievement value on the basis of the sub-answers.SELECTED DRAWING: Figure 14

Description

[Technical Field]

[0001] The present invention relates to customer analysis, and more particularly to a customer analysis technique for realizing customer-oriented business operations. [Background technology]

[0002] In recent years, the importance of customer experience has been increasing. Customer experience value is not just the objective and rational material and monetary value of a product or service, but also the subjective and emotional value that appeals to customer emotions and covers the entire customer experience, from pre-purchase promotions to post-purchase support.

[0003] Bernd H. Schmitt, a professor at Columbia University, classifies emotional value into five categories: Sense, Feel, Think, Act, and Relate (lifestyle). Customers recognize emotional value, or customer experience value, when their five senses are stimulated by something being delicious or pleasant to the touch (Sense), when their internal emotions are stimulated by something being cool or cute (Feel), when their intellectual desires or self-development are stimulated (Think), when they experience a different lifestyle than before (Act), and when they have a sense of belonging or sharing, such as participating in an activity (Relate).

[0004] Customer experience value is said to have a strong correlation with a company's profitability, and leading companies are said to be focusing on improving customer experience value (see Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-268405 Summary of the Invention [Problem to be solved by the invention]

[0006] However, there is no established method for measuring customer experience value. For example, one proposed method is to ask customers, "Were you satisfied with our company's service?" and have them rate it on a five-point scale. However, companies that receive high ratings in this way do not necessarily boast high profitability. A more rational method for measuring customer experience value is needed.

[0007] The present invention was completed based on the above-mentioned problem recognition, and its main purpose is to provide a technology for rationally evaluating customer representatives, particularly when measuring customer experience value. [Means for solving the problem]

[0008] A customer analysis device in one embodiment of the present invention includes a behavioral index calculation unit that obtains responses from users who have received a service to one or more type 1 questions defined for investigating behavioral motivation for a specified service, and calculates a behavioral index based on the one or more responses; an emotional index calculation unit that obtains responses from users who have received the service to a plurality of type 2 questions defined for investigating customer feelings toward the service, and calculates multiple types of emotional indexes based on the multiple responses; a correlation calculation unit that calculates a correlation coefficient between the behavioral index and each of the multiple types of emotional index; and an influence display unit that graphically displays the influence of each of the multiple emotional indexes on the behavioral index based on the calculated correlation coefficients.

[0009] In another aspect of the present invention, a customer analysis device includes an answer acquisition unit that acquires main answers from the user to questions to investigate the willingness to purchase a new service from a current representative who provides or mediates a specified service, questions to investigate the representative's willingness to continue using the service, and questions to investigate the representative's willingness to recommend the service to other users, as well as secondary answers regarding the prerequisites for the main answers, and a performance calculation unit that calculates the performance value of the representative based on multiple main answers regarding the representative. The result calculation unit selects a main answer to be used for calculating the result value based on the secondary answers. [Effects of the Invention]

[0010] According to the present invention, it becomes easier to rationally evaluate service personnel. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a schematic diagram illustrating the relationship between business indicators, behavioral indicators, and emotion indicators. [Figure 2] FIG. 1 is a schematic diagram for explaining a behavior index. [Figure 3] FIG. 10 is a diagram showing the survey results regarding the strength of correlation between recommendation intention and CX and business indicators. [Figure 4] FIG. 10 is a data structure diagram of an emotion index. [Figure 5] FIG. 2 is a functional block diagram of a customer analysis device. [Figure 6] 10 is a flowchart showing a process for calculating a psychological correlation coefficient. [Figure 7] FIG. 10 is a diagram showing an expected value screen. [Figure 8] FIG. 10 is a diagram showing an evaluation value screen. [Figure 9] FIG. 10 is a screen view of an emotion index screen. [Figure 10] FIG. 10 is a screen diagram of a psychological correlation screen. [Figure 11] FIG. 10 is a diagram showing a customer psychology analysis screen for an Internet bank. [Figure 12] FIG. 10 is a diagram showing a customer psychology analysis screen for a brick-and-mortar bank. [Figure 13] FIG. 10 is a functional block diagram of a customer analysis device according to a modified example. [Figure 14] An excerpt from the questionnaire regarding willingness to recommend is shown below. [Figure 15] An excerpt from the questionnaire regarding intention to continue is shown below. [Figure 16] An excerpt from the purchase intention questionnaire is shown below. [Figure 17] 10 is a flowchart showing a process for calculating a result value. DETAILED DESCRIPTION OF THE INVENTION

[0012] FIG. 1 is a schematic diagram illustrating the relationship between business indicators, behavioral indicators, and emotional indicators. A business indicator is an indicator that indicates the profitability of a service provided by a company. Examples of business indicators include sales, operating profit margins, and repeat customer rates. In this embodiment, customer experience value is expressed using two types of indicators: "behavioral indicators" and "emotional indicators." Note that in this embodiment, "service" is not limited to intangible services such as meals and accommodation, but also includes the purchase of tangible items such as merchandise.

[0013] Behavioral indicators indicate the strength of behavioral intentions toward a service (e.g., willingness to purchase). Emotional indicators indicate the type of emotions felt by users who actually received the service, and to what extent. When users' positive behavioral intentions toward a service are strengthened, in other words, when the behavioral indicator is large, it is believed that business indicators showing profitability will also improve. Furthermore, when users have positive emotions toward a service, in other words, when the emotional indicator is large, it is believed that the behavioral indicator will also be high. It is believed that the emotional indicators of users who sense customer experience value will be enhanced. The psychological model shown in Figure 1 models the correlation between the three elements: emotions prompt behavior, and behavior leads to revenue.

[0014] The customer analysis device in this embodiment analyzes customer sentiment in order to improve profitability (business indicators), and based on the analysis results, suggests which areas of the service should be improved.

[0015] The following description focuses on financial services provided by financial institutions such as banks and securities companies.

[0016] One indicator for exploring customer experience value is the Net Promoter Score (NPS). It was proposed in 2003 by Frederick F. Reichheld of the American consulting firm Bain & Company. NPS first asks users to answer the question, "How likely are you to recommend this company (product, service, or brand) to a close friend or colleague?" on a scale of 0 to 10. NPS considers scores of 9 or above to be "promoters," scores of 8 or 7 to be "passives," and scores of 6 or below to be "detractors." The index value is calculated by subtracting the percentage of detractors from the percentage of promoters in the user group surveyed. NPS has proven to be somewhat effective and is now the de facto standard.

[0017] However, NPS also has its weaknesses. Financial services are particularly weak points for NPS. It has been found that when NPS is used to measure customer experience in financial services, the NPS tends to be very low. In a survey conducted by the applicant, when asked, "Do you think that financial institutions are not something you would recommend to others?", approximately 80% of people answered "I think so" or "I think so to some extent" (sample size: 12,612).

[0018] When we asked users who have the kind of thinking that hinders the analysis of customer psychology described above (hereafter referred to as "noise thinking") why they do so, many of them responded with answers such as "(The choice of financial institution) is my own responsibility," "I can't take responsibility if something happens," "People have different expectations and priorities," and "I don't like talking about money with other people."

[0019] NPS is difficult to use in services such as financial services, where it is easy for the user to be influenced by the noise of "this is not something I would recommend to others." Therefore, in this embodiment, we propose the following behavioral indicator to compensate for the shortcomings of NPS.

[0020] FIG. 2 is a schematic diagram for explaining the behavior index. In this embodiment, the behavioral index is broken down into three elements: "intention to recommend," "intention to continue," and "intention to purchase." Like NPS, recommendation intention is measured by asking users to rate the question "How likely are you to recommend the service to a close friend or colleague?" on a scale of 1 to 10. Hereinafter, the evaluation value of recommendation intention will be referred to as the "recommendation value." A recommendation value is obtained for each user.

[0021] The noise thought in recommendation intention is "(This service) is not something I would recommend to others." As mentioned above, because such noise thoughts exist in recommendation intention, there is a tendency for the recommendation value to be low (decreasing bias).

[0022] Continuance intention indicates the user's willingness to continue using the service. For continuance intention, users are asked to rate the question "How likely is it that you will continue to use our service in the future?" on a scale of 1 to 10. For example, 100% is 10 points, 90% to less than 100% is 9 points, and less than 10% can be 0 points. Hereinafter, the evaluation value of continuance intention is called the "continuance value." A continuance value is obtained for each user.

[0023] Even if a user has a noisy opinion about recommendation intention, if they like the service, they will likely continue using it. Therefore, the continuation value acts as a complement to the decreasing bias of the recommendation value.

[0024] On the other hand, a noise thought in continuation intention is, "I don't like the service, but it's a hassle to change services." Because of this noise thought (inertial continuation) in continuation intention, there is a tendency for the continuation value to be high (upward bias).

[0025] Purchase intention indicates a user's willingness to use a service for the first time. Regarding purchase intention, users are asked to rate the question "How likely is it that you will continue to buy, sell, trade, and manage risky products with our company?" on a scale of 1 to 10. For example, 100% is 10 points, 90% to less than 100% is 9 points, and less than 10% is 0 points. Hereinafter, the evaluation value of purchase intention is referred to as the "purchase value." A purchase value is obtained for each user.

[0026] Even if a user has noise thoughts about their continuation intention, they are unlikely to purchase a new financial product if they do not like the service. Therefore, the purchase value functions as a complement to the upward bias of the continuation value.

[0027] The noise thought in purchase intention is, "There is nothing else I want to buy." Because of this noise thought, the purchase value tends to be low (decrease bias).

[0028] Even if a user has noise thoughts about their purchase intention, if they like a service, they are likely to recommend it to others. Therefore, recommendation value acts as a complement to the decrease bias of purchase value.

[0029] In this embodiment, a recommendation value, a continuation value, and a purchase value are obtained based on the above-mentioned three questions regarding recommendation intention, continuation intention, and purchase intention (hereinafter referred to as "type 1 questions"), and the average of the recommendation value, continuation value, and purchase value is calculated as a behavioral index. The behavioral index is calculated for each user. Hereinafter, the behavioral index calculated in this manner will be referred to as a "CX index" or simply "CX." The inventor hypothesized that the CX index may be able to more appropriately index customer psychology by canceling out the noise thoughts contained in recommendation intention, continuation intention, and purchase intention.

[0030] Figure 3 shows the survey results regarding the strength of correlation between recommendation intention and CX and business metrics. The inventor conducted a survey by dividing people into those who deposit the most of their financial assets in banks (A: bank-preference persons) and those who deposit the most of their financial assets in securities companies (B: securities-preference persons). Note that people with noise thoughts regarding recommendation intentions were excluded from the survey subjects.

[0031] First, we defined five types of business indicators: (R1) amount of deposits at the target financial institution, (R2) amount of investment in risky products at the target financial institution, (R3) wallet share (the ratio of the amount of deposits at the target financial institution to the customer's total financial assets), (R4) percentage change in the amount of deposits at the target financial institution compared to five years ago, and (R5) percentage change in the amount of investment in risky products at the target financial institution compared to five years ago.

[0032] When the correlation coefficient between recommendation values ​​only (NPS: Intention to Recommend) and business indicators was calculated for 862 bank preference respondents, the correlation coefficient of NPS with "(R1) Deposit Amount" and "(R2) Investment Amount in Risky Products" was over 0.7, confirming a relatively strong correlation. On the other hand, the correlation coefficient of recommendation values ​​with "(R3) Wallet Share," "(R4) Rate of Change in Deposit Amount," and "(R5) Rate of Change in Investment Amount in Risky Products" was below 0.7, indicating that no particularly strong correlation could be found.

[0033] Meanwhile, when the correlation coefficient between CX (average of recommendation value, purchase value, and continuation value) and business indicators was calculated for 2,980 bank preference respondents, the correlation coefficient was 0.7 or higher for all four business indicators except for "(R1) deposit amount." The correlation coefficient for "(R1) deposit amount" was also 0.68, which is slightly lower than the recommended value. As far as the survey of bank preference respondents is concerned, it was confirmed that CX (average of the three values) has a stronger positive correlation with various business indicators than the recommendation value alone.

[0034] When the correlation coefficient between recommendation values ​​and business indicators was calculated for 1,758 securities preferences, the correlation coefficient of NPS with all five business indicators was below 0.5. For this reason, NPS, which is based only on recommendation values, cannot be said to be an appropriate indicator for exploring the customer experience value of securities preferences.

[0035] Meanwhile, when the correlation coefficient between CX (average of three values) and business indicators was calculated for 6,150 securities-preference individuals, the correlation coefficient was 0.7 or higher for all four business indicators except for "(R3) Wallet Share." The correlation coefficient for "(R4) Wallet Share" was also 0.67, which is at least a better result than the recommendation value alone (NPS). For securities-preference individuals, the clear superiority of CX over NPS was confirmed.

[0036] As mentioned above, even though the survey subjects were people without noise thinking, overall CX has a stronger positive correlation with business indicators than NPS. CX was confirmed to be particularly effective for securities-preference individuals. Since the hypothesis that improving CX as a behavioral indicator can improve profitability has been confirmed, the next step is to consider what measures should be taken to improve CX. In this embodiment, as described in relation to Figure 1, emotional indicators that drive behavioral indicators (CX) are defined, and measures to improve behavioral indicators are considered by analyzing the emotional indicators.

[0037] FIG. 4 is a data structure diagram of the emotion index. The applicant has collected and accumulated tens of thousands of questionnaires from customers who have received financial services. These questionnaires include free comments. The inventor analyzed the vast amount of free comments and summarized the diverse evaluations and requests regarding financial services into three categories: "reliability," "convenience," and "economic rationality."

[0038] Credibility includes the following five types of evaluation points (hereinafter referred to as "emotional points"). (P1) Empathy (Does the servicer show empathy for the customer's problems?) (P2) Capability (Does the servicer have the expertise to meet the customer's needs?) (P3) Personality (Do you think the service provider is kind and enthusiastic about their work?) (P4) Effectiveness of risk management (Are risks in financial services being managed appropriately?) (P5) Transparency (Are they being treated honestly, without lies or secrets?)

[0039] Convenience includes the following five emotional points: (P6) Anytime (Can I receive the service at any time?) (P7) Ease (Can you easily receive the desired service?) (P8) Speed ​​(Is the service executed quickly?) (P9) Understandability (Are the procedures and steps for receiving the service easy to understand?) (P10) Useful (Was the service useful?)

[0040] Economic rationality includes the following three types of emotional points: (P11) Costs (Are fees and other costs appropriate?) (P12) Profit (Were you able to make a profit through the service?) (P13) Added value (Did you feel the added value?)

[0041] For each of the 13 emotional points above, users are asked to input the level of their expectations before receiving the service (hereafter referred to as "expectation value") and their level of satisfaction after actually receiving the service (hereafter referred to as "evaluation value") on a scale of 1 to 10.

[0042] In this embodiment, the emotion index is calculated by subtracting the expected value from the evaluation value. 13 types of emotion indexes are calculated for each user, corresponding to the 13 types of emotion points. Next, the correlation coefficients of each of the 13 types of emotion indexes with the behavior index (CX) are calculated (details will be described later).

[0043] To analyze the emotional index, various questions (hereafter referred to as "type 2 questions") are prepared for the user in advance. For example, for "(P1) Empathy," a prepared question is "Do you think the servicer makes an effort to understand your needs?" The emotional index for "(P1) Empathy" is calculated based on the user's answer to this question (expectation value and evaluation value).

[0044] Other examples include questions such as "Were you able to trade without missing the instantaneous timing of buying and selling" for "(P8) Speed" and "Do you think the fees are low" for "(P11) Cost." One emotional point may be associated with one Type 2 question, or multiple Type 2 questions may be associated with one emotional point. For example, for a certain emotional point PX, the weighted average expected value and weighted average evaluated value may be calculated by weighting the answers (expected value and evaluated value) to multiple Type 2 questions, and the weighted average evaluated value minus the weighted average expected value may be calculated as the emotional index for the emotional point PX.

[0045] By calculating the difference between the evaluation value and the expected value as the emotional index, the emotional index indicates the size of the gap between expectations and reality. If the result is above expectations, the emotional index will be a positive value. On the other hand, if the result is below expectations, the emotional index will be a negative value. When the gap between expectations and reality is large, as a psychological tendency of humans, people tend to be moved or dissatisfied. The emotional index in this embodiment numerically indicates how much the customer's emotions have been stirred for multiple emotional points.

[0046] FIG. 5 is a functional block diagram of the customer analysis device 100. The components of the customer analysis device 100 are implemented by hardware including computing units such as a CPU (Central Processing Unit) and various coprocessors, storage devices such as memory and storage, and wired or wireless communication lines connecting them, as well as software stored in the storage devices that supplies processing instructions to the computing units. The computer program may be configured by device drivers, an operating system, various application programs located at higher levels, and libraries that provide common functions to these programs. Each block described below represents a functional block, not a hardware configuration.

[0047] The customer analysis device 100 includes a user interface processing unit 110, a communication unit 114, a data processing unit 112, and a data storage unit . The user interface processing unit 110 accepts operations from the user and is responsible for user interface-related processing such as image display and audio output. The communication unit 114 is responsible for communication processing with external devices via the Internet. The data storage unit 116 stores various data. The data processing unit 112 executes various processes based on data acquired by the user interface processing unit 110 and the communication unit 114 and data stored in the data storage unit 116. The data processing unit 112 also functions as an interface between the user interface processing unit 110, the communication unit 114, and the data storage unit 116.

[0048] The user interface processing unit 110 includes an input unit 118 that receives input from the user, and an output unit 120 that outputs various types of information such as images and sounds to the user. The output unit 120 includes an influence display unit 122 and an improvement notification unit 124. The influence display unit 122 displays the degree of influence that each of a plurality of emotion indicators has on a behavior indicator. Specifically, the influence display unit 122 displays the screens (described below) shown in FIGS. 7, 8, 9, 10, 11, and 12. The improvement notification unit 124 notifies the analyst of emotion points that are effective in improving the behavior indicators.

[0049] The communication unit 114 includes a transmission unit 132 that transmits data and a reception unit 134 that receives data.

[0050] The data processing unit 112 includes a behavior index calculation unit 126, an emotion index calculation unit 128, and a correlation calculation unit 130. The behavior index calculation unit 126 calculates a behavior index based on the above-mentioned first-type questions. Specifically, the behavior index calculation unit 126 collects response data to the first-type questions for the user group to be surveyed, calculates the recommendation value, continuation value, and purchase value using the above-mentioned method, and calculates the average value of these three values ​​as the behavior index. The behavior index calculation unit 126 calculates the behavior index for each user. The behavior index calculation unit 126 may calculate the average value of the behavior indexes of the user group to be surveyed, thereby calculating a behavior index for the entire group (average behavior index).

[0051] The emotion index calculation unit 128 calculates an emotion index based on the above-mentioned type 2 question. Specifically, the emotion index calculation unit 128 calculates an emotion index for each user on an individual basis for each of the 13 emotion points. The emotion index calculation unit 128 calculates an emotion index for each user. The emotion index calculation unit 128 may calculate an average emotion index for the user group being surveyed, thereby calculating an emotion index for the entire group for each emotion point.

[0052] The correlation calculation unit 130 calculates the correlation coefficient between the emotional index and behavioral index obtained from multiple users. Specifically, the correlation calculation unit 130 first collects the emotional index (hereinafter referred to as "emotional index (P1)") of multiple users for "(P1) empathy." Next, the correlation calculation unit 130 calculates a correlation coefficient (hereinafter referred to as "psychological correlation coefficient") based on the Pearson product-moment correlation coefficient formula for the set of emotional indexes (P1) and the set of behavioral indexes of these multiple users. The correlation calculation unit 130 calculates 13 types of psychological correlation coefficients in the same manner for each of the 13 types of emotional indexes.

[0053] FIG. 6 is a flowchart showing the process of calculating the psychological correlation coefficient. First, the receiving unit 134 collects answer data based on the first-type questions and the second-type questions from users who have received financial services. When there is unprocessed answer data (Y in S10), the behavioral index calculation unit 126 first calculates behavioral indexes based on the answers to the first-type questions included in the new answer data (S12). Next, the emotion index calculation unit 128 calculates 13 types of emotion indexes based on the answers to the second-type questions included in the answer data of the same user (S14). One type of behavioral index and 13 types of emotion indexes are calculated for each user. The same process is repeated for all answer data.

[0054] After calculating the behavioral index and emotional index from all the response data (N in S10), the correlation calculation unit 130 calculates a psychological correlation coefficient for each emotional index (S16). The influence display unit 122 graphically displays the influence of the emotional index in a predetermined format based on the calculation results (S18). At this time, the improvement notification unit 124 also displays improvement points. The improvement notification unit 124 notifies the analyst of emotional points where the psychological correlation coefficient is equal to or greater than a first threshold, for example, 0.5, and the emotional index is equal to or less than a second threshold, for example, 0.

[0055] FIG. 7 is a diagram showing the expected value screen 140. The influence display unit 122 displays the expectation screen 140 in accordance with instructions from the analyst. The vertical axis shows the average value of the expectation (hereinafter referred to as "average expectation"). Graph L1 shows a branch-based bank, and graph L2 shows an internet bank. For example, in the internet bank user group, the average expectation value for "(P3) Personality" is "6.44." In contrast, in the branch-based bank user group, the average expectation value for "(P3) Personality" is "8.42." Therefore, it can be seen that users of branch-based banks have higher expectations for the personality of the servicer than when using an internet bank.

[0056] Additionally, in the Internet bank user group, the average expectation for "(P11) Cost" is "8.08." This shows that users have expectations for low costs when using Internet banks. In contrast, in the branch bank user group, the average expectation for "(P11) Cost" is "7.40." This shows that branch bank users have lower expectations for costs than Internet bank users.

[0057] In the user group of brick-and-mortar banks, the average expectation for "(P2) Ability" is 7.20. In contrast, in the user group of internet banks, the average expectation for "(P2) Ability" is 6.43, indicating relatively low expectations.

[0058] FIG. 8 is a diagram showing the evaluation value screen 142. The influence display unit 122 displays the evaluation value screen 142 in accordance with instructions from the analyst. The vertical axis indicates the average value of the evaluation values ​​(hereinafter referred to as the "average evaluation value"). For the Internet bank user group (graph L2), the average evaluation value for "(P2) ability" is "5.64," and the average evaluation value for "(P3) personality" is "5.78." In contrast, for the branch bank user group (graph L1), the average evaluation value for "(P2) ability" is "7.00," and the average evaluation value for "(P3) personality" is "8.12."

[0059] FIG. 9 is a screen diagram of the emotion index screen 144. The influence display unit 122 displays the emotion index screen 144 in accordance with instructions from the analyst. The vertical axis indicates the average value of the emotion index (evaluation value - expectation value) of multiple users (hereinafter referred to as the "average emotion index"). As mentioned above, a high emotion index indicates "satisfaction beyond expectations," and a low emotion index indicates "dissatisfaction below expectations."

[0060] In the user group of branch-based banks (Graph L1), the average emotional index (P4: Effectiveness of risk management) was "-1.30." This means that users were highly dissatisfied with the "effectiveness of risk management (P4)" at branch-based banks. On the other hand, in the user group of branch-based banks, the average emotional index (P3: Personality) was "-0.30." This means that users' expectations for "personality (P3)" at branch-based banks were generally met.

[0061] Additionally, for the Internet bank user group (Graph L2), the average sentiment index (P8: Speed) was 0.04, which means that Internet banks are largely able to meet users' expectations regarding "(P8) Speed."

[0062] FIG. 10 is a screen diagram of the psychology correlation screen 146. The impact display unit 122 displays the psychological correlation screen 146 in accordance with instructions from the analyst. The vertical axis indicates the psychological correlation coefficient between the emotional index and the behavioral index (CX). In the case of a branch-based bank (graph L1), the psychological correlation coefficient for "(P3) Personality," one of the emotional points, is "0.95." Therefore, it is believed that leaving customers with a favorable impression of the servicer's personality is likely to lead to an improvement in the behavioral index. According to the emotional index screen 144 in Figure 9, the average emotional index (P3: Personality) is "-0.30," which is by no means low, but it is clear that there is merit in further improvement in "(P3) Personality."

[0063] In branch-based banks, the psychological correlation coefficient for "(P4) Effectiveness of risk management" is "-0.45." Therefore, it is thought that improvements in risk management are unlikely to lead to improvements in behavioral indicators. According to the emotion indicator screen 144 in Figure 9, the average emotion indicator (P4: Effectiveness of risk management) is "-1.30," which is very low. Although there is significant room for improvement in "(P4) Effectiveness of risk management," such improvements are unlikely to lead to improvements in customer experience value.

[0064] The improvement notification unit 124 refers to the emotion index and the psychological correlation coefficient and notifies the analyst of improvement points. As described above, in this embodiment, the improvement notification unit 124 notifies the analyst of emotion points where the psychological correlation coefficient is 0.5 or greater and the emotion index is 0 or less as improvement points. Therefore, according to the emotion index screen 144 of FIG. 9 and the psychological correlation screen 146 of FIG. 10, the improvement notification unit 124 suggests "(P3) Personality," "(P5) Transparency," "(P6) Anytime," and "(P8) Speed" as improvement points for the brick-and-mortar bank (graph L1). Furthermore, the improvement notification unit 124 suggests "(P3) Personality," "(P4) Effectiveness of Risk Management," and "(P7) Simplicity" as improvement points for the Internet bank (graph L2).

[0065] FIG. 11 is a screen shot of a customer psychology analysis screen 148 for internet banking. The influence display unit 122 displays a customer psychology analysis screen 148 as the analysis results for the Internet bank user group in accordance with instructions from the analyst. The horizontal axis indicates the average emotional index, and the vertical axis indicates the psychological correlation coefficient. The (average) emotional index is higher toward the right, which indicates higher satisfaction with expectations. The psychological correlation coefficient is higher toward the top, which indicates a strong positive correlation between the emotional index and the behavioral index.

[0066] According to the customer psychology analysis screen 148, the sentiment index regarding convenience is generally large, and the psychology correlation coefficient is also large. Therefore, it is thought that Internet banks can achieve profitability by concentrating management resources on further improving convenience.

[0067] FIG. 12 is a screen view of a customer psychology analysis screen 150 for brick-and-mortar banks. The influence display unit 122 displays the customer psychology analysis screen 150 as the analysis result for the user group of the branch bank in accordance with instructions from the analyst. The horizontal axis indicates the average emotion index, and the vertical axis indicates the psychology correlation coefficient.

[0068] According to the customer psychology analysis screen 150, some of the emotional indicators related to convenience are thought to contribute significantly to improving behavioral indicators and, ultimately, profitability, but the effect is not as great as when targeting Internet banks. On the other hand, although "(P3) Personality" already provides a high level of satisfaction, it is thought to be worth working on further improvement. Also, although the emotional indicator for "(P01) Empathy" is high, it is difficult to see how it will lead to an improvement in behavioral indicators.

[0069] The customer analysis device 100 has been described above based on the embodiment. According to this embodiment, by defining behavioral indicators using multiple elements that offset each other's noise thinking, it is possible to increase the positive correlation between profitability and behavioral indicators. In particular, it was found that for "services that are difficult to recommend to others," such as financial services, CX, a new behavioral indicator, has a higher correlation than NPS.

[0070] In this embodiment, multiple emotional indices are set as emotional factors that affect the behavioral index. By calculating a psychological correlation coefficient as the degree of influence of each emotional index on the behavioral index, it is possible to identify emotional points that are effective in improving the behavioral index. This control method allows limited management resources to be focused on improving appropriate emotional points, making it easier to reform organizational operations efficiently and rationally.

[0071] The present invention is not limited to the above-described embodiments and modifications, and the components can be modified without departing from the spirit of the invention. Various inventions can be formed by appropriately combining multiple components disclosed in the above-described embodiments and modifications. Furthermore, some components can be omitted from all the components shown in the above-described embodiments and modifications.

[0072] [Variations] In the present embodiment, the behavior index is calculated as the average of the recommendation value, continuation value, and purchase value. As a modification, the behavior index may be calculated by taking a weighted average of these three values. For example, the weighting coefficients of the three values ​​may be adjusted so that the positive correlation coefficient with the business index indicating profitability is high.

[0073] [Evaluation of the person in charge] As a result of further consideration, the inventor came to the conclusion that a behavioral indicator (CX) that indicates behavioral intentions toward a service would not only be beneficial to companies that provide services, but could also be used to evaluate the work of frontline staff who actually interact with customers and introduce, provide, and broker services, such as customer service representatives and branch managers, and other employees who are in a position to interact with customers.

[0074] The strength of recommendation intention, continuation intention, and purchase intention is greatly influenced not only by the attractiveness of the service, but also by the ability of the agent. As mentioned above, these behavioral indicators include noise thoughts, but recommendation intention, continuation intention, and purchase intention can complement each other's noise thoughts. For example, by aggregating the recommendation value, continuation value, and purchase value from customers for agent A and calculating the total or average value, it is thought that it would be possible to evaluate agent A's work.

[0075] However, some customers may give the recommendation value a zero (the lowest score) because they think, "No matter who the agent is, I have no intention of recommending them to others. I can't take responsibility." Although noise thoughts about recommendation intentions can be compensated for by the continuity value, it is unfortunate for the agent to receive the lowest rating for such reasons, and there may be situations in which it is not appropriate as a performance evaluation. In particular, when a single agent deals with a small number of customers, the performance evaluation is more susceptible to the influence of noise thoughts. Therefore, the inventors felt that improvements were needed when using behavioral indicators to evaluate agent performance.

[0076] FIG. 13 is a functional block diagram of a customer analysis device 100 according to a modified example. In the customer analysis device 100 of this modified example, the data processing unit 112 further includes a performance calculation unit 200. The performance calculation unit 200 performs a work evaluation of the person in charge. The customer analysis device 100 uses the input unit 118 or the receiving unit 134 as an "answer acquisition unit" to acquire response data regarding behavioral indicators from the customer.

[0077] Figure 14 shows an excerpt from the questionnaire regarding willingness to recommend. The customer inputs a 10-point scale for a first-type question regarding the agent: "How likely is it that you would recommend your current agent to a close friend or relative?" The input value becomes the recommendation value. The questionnaire may be provided on paper, or the transmission unit 132 of the customer analysis device 100 may provide the questionnaire as a web page to a tablet computer or the like. The questionnaire includes various questions regarding the emotion indexes described above as well as three types of first-type questions regarding the agent (recommend, continue, purchase).

[0078] Hereinafter, the answer to the first-type question regarding the recommended values ​​etc. will be referred to as the "main answer." The recommended values ​​etc. will be calculated based on the main answer.

[0079] In addition to the first-type question, a question that should be a prerequisite for the first-type question is defined: "If the reason you selected the above number is that you would not recommend the bank / securities company representative to others, not just your current representative, please check this box." Hereafter, the answer to this prerequisite question will be called the "secondary answer."

[0080] Figure 15 shows an excerpt from the questionnaire regarding continuance intentions. Regarding the representative, customers input a score out of 10 in response to the first type question, "How likely is it that you will continue to use... through your current representative?" As with the recommendation intention, the prerequisite question for continuation is also defined: "If the reason you selected the above number is that you will not use a representative from the bank / securities company, not just your current representative, please tick this box."

[0081] Figure 16 shows an excerpt from the purchase intention questionnaire. Regarding the representative, the customer enters a score out of 10 for the first type question, "How likely is it that you will purchase financial products through your current representative again?". For purchase intention, as with recommendation intention, a prerequisite question is defined: "If the reason you selected the above number is that you will never purchase financial products from a bank / securities company, not just from your current representative, please tick this box."

[0082] The performance calculation unit 200 calculates the work evaluation of the person in charge by tallying up the main answers and secondary answers to the first-type questions shown above. At this time, the main answer with a check mark in the secondary answer is excluded from the work evaluation calculation.

[0083] FIG. 17 is a flowchart showing the process of calculating the result value. First, the receiving unit 134 collects answer data based on the first type question, the prerequisite question, and the second type question from a user who has received financial services. When there is unprocessed answer data (Y in S20), the performance calculation unit 200 determines whether a check mark has been placed in the secondary answer for any of the recommended value, the continuation value, and the purchase value (S22). When a check mark has been placed (Y in S22), the main answer at that time is excluded from the calculation target for business evaluation (S24). When no check mark has been placed (N in S22), the main answer is included in the calculation target.

[0084] The average value of the recommendation value, continuation value, and purchase value included in the main answer that is the calculation target is calculated as the performance value for each person in charge (S26). The output unit 120 displays the performance value for each person in charge (S28).

[0085] The above control method makes it possible to more appropriately evaluate the performance of individual employees by excluding recommended values ​​that are low due to reasons beyond the individual employee's efforts. If performance values ​​are calculated on a branch-by-branch basis rather than on a per-employee basis, it can also be applied to the performance evaluation of branch managers.

[0086] As shown in FIG. 14, the prerequisite questions may include a question such as "Please tell us why you selected the above numbers," and the customer may be asked to enter or write a free comment. In this free comment section, reasons that cannot be attributed to the staff member, such as "I'm old, so I don't intend to purchase any new products," or "The current staff member is new, so I don't really understand yet," may be written. Questionnaires containing such comments may be excluded from the scope of business evaluation. The analysis of comments may be performed by a human, or the outcome calculation unit 200 may use natural language processing to detect and exclude questionnaires containing comments that include any exclusion condition (e.g., "because I'm old"). [Explanation of symbols]

[0087] 100 Customer analysis device, 110 User interface processing unit, 112 Data processing unit, 114 Communication unit, 116 Data storage unit, 118 Input unit, 120 Output unit, 122 Impact display unit, 124 Improvement notification unit, 126 Behavioral index calculation unit, 128 Emotion index calculation unit, 130 Correlation calculation unit, 132 Transmission unit, 134 Reception unit, 140 Expectation value screen, 142 Evaluation value screen, 144 Emotion index screen, 146 Psychological correlation screen, 148 Customer psychology analysis screen, 150 Customer psychology analysis screen, 200 Outcome calculation unit, L1 graph, L2 graph

Claims

[Claim 1] An answer acquisition unit that acquires a main answer from a user to each of questions to investigate willingness to purchase a new service from a current person in charge of providing or mediating a predetermined service, questions to investigate willingness of said person in charge to continue using said service, and questions to investigate willingness of said person in charge to recommend said service to other users, along with secondary answers regarding prerequisites for the main answer; a performance calculation unit that calculates a performance value of the person in charge based on a plurality of main answers related to the person in charge, The customer analysis device is characterized in that the result calculation unit selects a main answer to be used for calculating a result value based on the secondary answers.

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