Policy Support Device, Policy Support Method, and Program

Abstract

Provided is a measure assistance device or the like capable of assisting in creating a measure effective for moving the segment of a customer. This measure assistance device comprises: a measure plan identification unit that identifies a measure plan relevant to a factor that has caused a customer in hierarchical segments to move to a higher-level segment; a measure selection unit that selects a measure from among a plurality of the measure plans on the basis of the number of use intentions acquired on the assumption that the measure plan is executed; and a measure inspection unit that inspects the measure on the basis of a change in customer distribution of the hierarchical segments through execution of the measure.

Description

Technical Field

[0001] The present disclosure relates to a policy support device and the like.

Background Art

[0002] Various measures for sales promotion are implemented for new customers and existing customers. For effective marketing activities, it is important to approach appropriately according to customer segments. A customer segment is, for example, a group of customers divided by age, gender, area of residence, tendency of behavior, etc. Patent Document 1 discloses a tool that can easily perform the formulation of an approach for sales promotion according to customer segments and the segment analysis necessary as a premise for the formulation.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a hierarchical customer segment as described in Patent Document 1, sales of excellent customers belonging to the upper segment may account for 80% of total sales. On the other hand, customers do not always belong to the same segment but move dynamically in the long term. A certain percentage of customers in the top segment also defect. For this reason, there is a need for measures to develop new customers included in the lower segment and to make existing customers into excellent customers as much as possible.

[0005] If appropriate measures, such as an approach for sales promotion, cannot be selected as a result of implementing measures for customers, so that customers in a segment become customers in a higher segment, it will be a wasteful measure and the implementation cost will increase. On the other hand, if an objective evaluation of the measures cannot be made as to how effective the measures are, it cannot be reflected in the next measures.

[0006] One of the objects of the present disclosure is to provide a measure support device and the like that can support the formulation of measures effective for the movement of customer segments.

Means for Solving the Problem

[0007] The measure support device according to the first aspect of the present disclosure includes a measure plan specifying unit that specifies a measure plan related to the factor for which a customer in a hierarchical segment has moved to a higher segment, a measure selection unit that selects a measure from among a plurality of the measure plans based on the number of usage intentions when the measure plan is hypothetically implemented, and a measure verification unit that verifies the measure based on a change in the customer distribution of the hierarchical segment before and after the implementation of the measure.

[0008] The measure support method according to the second aspect of the present disclosure specifies a measure plan related to the factor for which a customer in a hierarchical segment has moved to a higher segment, selects a measure from among a plurality of the measure plans based on the number of usage intentions when the measure plan is hypothetically implemented, and verifies the measure based on a change in the customer distribution of the hierarchical segment before and after the implementation of the measure.

[0009] The measure support program according to the third aspect of the present disclosure causes a computer to specify a measure plan related to the factor for which a customer in a hierarchical segment has moved to a higher segment, select a measure from among a plurality of the measure plans based on the number of usage intentions when the measure plan is hypothetically implemented, and verify the measure based on a change in the customer distribution of the hierarchical segment before and after the implementation of the measure.

[0010] The program may be stored in a non-transitory computer-readable recording medium.

Advantages of the Invention

[0011] According to the policy support device and the like of the present disclosure, it is possible to support the formulation of policies effective for the movement of customer segments.

Brief Description of the Drawings

[0012]

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Mode for Carrying Out the Invention

[0013] <First Embodiment> The policy support system of the first embodiment will be described with reference to the drawings. FIG. 1 is a block diagram showing a configuration example of the policy support system of the first embodiment. As shown in FIG. 1, the policy support system includes a database 10, a policy support device 20, and a terminal 40. The policy support device 20 and the terminal 40 are communicably connected via a network 30. Note that the policy support device 20 and the database 10 may be configured to be communicably connected via the network 30. An example of the hardware configuration of the database 10 is composed of a memory, a storage, or a network storage. Also, the policy support device 20 and the terminal 40 are configured by, for example, a computer.

[0014] (Database) The data stored in the database 10 will be described. FIG. 2 is a block diagram showing a configuration example of the database of the first embodiment. The database 10 stores market research data 11, hierarchical segment data 12, face-to-face survey data 13, and policy plan data 14.

[0015] The market research data 11 includes questionnaire data. The questionnaire data includes, for example, questions and answers to the questions, and attribute information of the respondents who answered (for example, region, age, etc.). The market research data is collected and accumulated at predetermined monthly intervals (for example, 1 month, 6 months, 1 year, etc.). The questions are, for example, brand awareness, purchase experience, and purchase amount or purchase frequency. A brand is generally a label by which a product or service is identified by customers. A brand in this specification may be a product or a service, or may be a provider who provides a product or a service.

[0016] The hierarchical segment data 12 stores data related to hierarchical segments. For example, it stores the data of hierarchical segments before and after implementing a certain measure. The hierarchical segment data 12 may be the data of the hierarchical segment to which the customer allocation unit 21 of the measure support device 20 allocates customers based on the questionnaire data. The data of the hierarchical segment includes the number of customers in each segment, or the ratio of the number of customers in each segment to all segments or all customers. The data of the hierarchical segment may be, for example, the number of customers in each segment among 10,000 people, or the ratio of the number of customers in each segment in all segments. The hierarchical segment is, for example, a customer pyramid. The hierarchical segment may be a 6-segment map or an 11-segment map.

[0017] The face-to-face survey data 13 is the data of interviews with customers assigned to a higher segment compared to the segment assigned based on past market survey data among the customers assigned to the hierarchical segment. In the interview, for example, the reason for purchasing the brand's product or service, the reason for purchase, and the satisfaction after purchase are questioned. The face-to-face survey data 13 may include, in addition to the answers to the questions, the attribute information (region, age, etc.) of the customers who answered.

[0018] The measure plan data 14 includes the data of measure plans that have been implemented or the data of measure plans that have not been implemented. Alternatively, the measure plan created based on the face-to-face survey data 13 may be included in the measure plan data 14. Also, the measure plans specified by the measure plan specifying unit 22 of the measure support device 20 in the past may be included in the measure plan data 14.

[0019] (Measure support device) The measure support device 20 of the first embodiment will be described with reference to the drawings. FIG. 3 is a block diagram showing a configuration example of the measure support device of the first embodiment. The measure support device 20 shown in FIG. 3 includes a customer allocation unit 21, a measure plan specifying unit 22, a measure selection unit 23, and a measure verification unit 24.

[0020] The customer allocation unit 21 allocates customers to hierarchical segments based on brand awareness, purchase experience, and purchase amount or purchase frequency of the customers. As a specific example, the customer allocation unit 21 acquires questionnaire data for brands included in the market research data 11 from the database 10. The customer allocation unit 21 allocates customers to hierarchical segments based on brand awareness, purchase experience, and purchase amount or purchase frequency included in the questionnaire data.

[0021] Figure 4 is a diagram showing an example of a hierarchical segment. The hierarchical segment shown in Figure 4 is an example of a 6-segment map. The 6-segment map is a map that classifies customers into six segments: excellent customers, general customers, defecting customers, prospective customers, aware customers, and unaware customers. The 6-segment map is determined based on questionnaire data regarding the presence or absence of brand awareness, the presence or absence of purchase experience (including purchase consideration), purchase amount, etc. Note that the item of purchase amount may be purchase frequency.

[0022] Figure 5 is a diagram showing another example of a hierarchical segment. The hierarchical segment shown in Figure 5 is an example of an 11-segment map. The 11-segment map is composed of 11 segments, where among the six segments shown in Figure 4, the top five segments are each divided into two segments according to brand preference, and the segment of unaware customers. Specifically, as shown in Figure 5, excellent customers, general customers, defecting customers, prospective customers, and aware customers excluding unaware customers are divided into those indicating positive (+) or negative (-) with respect to the axis of "high" and "low" preference.

[0023] The preference is determined as positive or negative for the brand based on affirmative and negative answers to questions for customers. Questions about preference for customers are, for example, whether they like or dislike the target product, whether they have the intention to repurchase the target product, or whether they recommend the target product to others, etc. It is possible to determine whether branding is successful in each segment based on the ratio of preference.

[0024] In addition, when using an 11-segment map as the hierarchical segment, the customer allocation unit 21 allocates customers to the hierarchical segment based on brand awareness, purchase experience, purchase amount or purchase frequency, and preference indicating the intention to purchase next time.

[0025] The customer allocation unit 21 generates the number or ratio of customers in each segment as data of the hierarchical segment. The data of the hierarchical segment may be, for example, the number of customers in each segment per 10,000 people, or the ratio of the number of customers in each segment in all segments.

[0026] In the above, as examples of the hierarchical segment for allocating customers, the examples of a 6-segment map and an 11-segment map are used for explanation, but it is not limited to this, and other segment maps can be applied.

[0027] In addition, in the description of the first embodiment, the customer allocation unit 21 classifies customers into a plurality of ranked segments based on the questionnaire data, but it is not limited to this. For example, if the change in customer distribution can be visualized for each segment, the data of the hierarchical segment to which the customers are allocated may be used without using the customer allocation unit 21.

[0028] The measure plan specifying unit 22 specifies a measure plan related to the factor for which the customers in the hierarchical segment have moved to a higher segment. Specifically, the measure plan specifying unit 22 specifies a measure plan based on the face-to-face survey data of the customers who have moved to the higher segment.

[0029] FIG. 6 is a diagram showing an image of a face-to-face survey of customers allocated to a higher segment. In FIG. 6, it represents conducting a face-to-face survey on customers who have moved from the general customer segment to the excellent customer segment. Here, moving to a higher segment means moving to at least one higher segment. For example, customers who have moved from the churn customer segment to the excellent customer segment may be the target of the face-to-face survey.

[0030] FIG. 7 is a diagram showing an example of face-to-face survey data. The face-to-face survey data shown in FIG. 7 are questions from an interviewer and the responses of customers who have moved to a higher segment in response to the questions. In the interview, the reasons for purchase, the reasons for purchase, the satisfaction after purchase, etc. are questioned, and specific content is answered to the questions.

[0031] The measure plan identification unit 22 searches for measure plans related to the responses from the measure plan data 14 in the database 10 using, for example, the products or services included in the responses of customers who have moved to a higher segment as keywords. Based on the search results, the measure plan identification unit 22 identifies measure plans related to the factors for which customers have moved to a higher segment. Note that the identification of measure plans by the measure plan identification unit 22 is not limited to searches using products or services as keywords. For example, similar measure plans may be identified based on the attributes of each measure plan. Specifically, variables such as "discount-based / contact opportunity-based", "direct face-to-face / mass media-based", "period continuation-based / single occurrence-based", etc. may be assigned to each measure, and those that match many of the attributes included in the responses of customers who have moved to a higher segment may be identified as measure plans.

[0032] FIG. 8 is data showing an example of the measure plans identified by the measure plan identification unit. The measure plan data shown in FIG. 8 are "Measure A: Free inspection guide", "Measure B: Pipe cleaning guide", "Measure C: 5-fold point reduction", "Measure D plan: Air conditioner cleaning guide", "Measure E: Introduction campaign", etc.

[0033] The measure plan identification unit 22 sends the identified measure plans to the measure selection unit 23. The measure plan identification unit 22 may temporarily store the identified measure plans in the measure plan data 14 of the database 10.

[0034] The measure plan identification unit 22 can grasp the factors of customers who have moved to a higher segment, and extract measure plans related to the factors. Also, by implementing the identified measure plans, the possibility that other customers who purchase products or services will move to a higher segment also increases.

[0035] The policy selection unit 23 selects a policy from among a plurality of policy proposals based on the number of usage intentions when the policy proposal is hypothetically implemented. Specifically, the policy selection unit 23 selects a policy from the policy proposals based on the market research data 11 for the policy proposals.

[0036] The market research for the policy proposal is an investigation regarding the availability of usage when the policy proposal is hypothetically implemented. The number of usage intentions means the number of respondents who answered "will use" in the market research if the policy proposal is implemented. The number of respondents indicating the usage intention of the policy proposal can be estimated as the number of demanders for the policy proposal.

[0037] The policy selection unit 23 acquires the market research data for the policy proposal specified from the market research data 11 in the database 10. The policy selection unit 23 calculates the number of usage intentions when the policy proposal is hypothetically implemented based on the acquired market research data. The policy selection unit 23 selects a policy from among a plurality of policy proposals based on the number of usage intentions. The policy selection unit 23 may calculate the number of usage intentions for each attribute of the respondents indicating the usage intention. The policy selection unit 23 selects a policy from the policy proposals based on the number of usage intentions for each policy proposal.

[0038] FIG. 9 is a diagram of data showing an example of the results of the market research for the policy proposal. The results of the market research shown in FIG. 9 show the distribution of the number of respondents indicating the intention of "will use" for each policy proposal, that is, the distribution of the number of usage intentions. The usage intentions "small", "medium", and "large" shown in FIG. 9 are obtained by adding up the number of respondents indicating the usage intention for each policy proposal, and classifying the added number of respondents into three levels by a predetermined threshold.

[0039] The results of the market research in FIG. 9 include cells showing the overall (regardless of the attributes of the respondents) and cells showing the attributes of the respondents (by the number of years since construction, by region, and by gender and age). In the cell showing the overall, the distribution of the usage intention of Policy C: 5-fold point campaign or Policy E: referral campaign is larger than that of other policies.

[0040] By selecting a policy from among a plurality of policy proposals based on the number of usage intentions when the policy proposal is hypothetically implemented, the policy selection unit 23 can implement a more effective policy. In addition, by stopping the implementation of ineffective policies, it is possible to reduce the implementation cost.

[0041] Also, by reflecting the attributes of the respondents in the usage intention, it is possible to further narrow down the target layer for implementing the policy. For example, in Policy C: 5-fold point reduction, in terms of region, the usage intention is high in the capital region and Kansai, and in terms of gender and age, the usage intention is high among women. From this, it is possible to further reduce the implementation cost by limiting the region and target layer for implementing the policy.

[0042] The policy selection unit 23 may temporarily store the selected policy in the database 10 or transmit it to the terminal 40. Also, the output destination of the selected policy may be a display device (not shown) or a printer (not shown).

[0043] The policy verification unit 24 verifies the policy based on the change in the customer distribution of the hierarchical segments before and after the implementation of the selected policy. Specifically, the policy verification unit 24 acquires the data of the hierarchical segments before and after the implementation of the selected policy from the hierarchical segment data 12 in the database 10. The policy verification unit 24 compares the customer distribution of the hierarchical segments before the implementation of the policy with the customer distribution of the hierarchical segments after the implementation, and extracts the change in the customer distribution of each segment in the hierarchical segments.

[0044] FIG. 10 is a diagram showing an example of the change in customer distribution before and after the implementation of the policy. In FIG. 10, it is understood that before and after the implementation of the policy, excellent customers, general customers, and aware customers increased, while defecting customers and considering customers remained flat or slightly decreased, and unaware customers decreased. Also, it is understood that by implementing the policy, it was possible to convert considering customers into purchasing customers and defecting customers into general customers or excellent customers. Furthermore, it is understood that the increase in aware customers and the decrease in unaware customers were effective in increasing the awareness.

[0045] The policy verification unit 24 can grasp the difference in customer distribution of each segment before and after the implementation of the policy, and can also quantitatively evaluate the fluctuations between segments.

[0046] In addition, the policy verification unit 24 verifies the influence of the policy on the preference degree for each of the segments based on the change in the customer distribution of the hierarchical segments before and after the implementation of the policy.

[0047] FIG. 11 is a diagram showing an example of changes in other customer distributions before and after the implementation of the policy. In FIG. 11, in the comparison of preference degrees among excellent customers, general customers, and aware customers, the increase amount of the segment showing negativity (-) is larger than the segment showing positivity (+). In the consideration customers, the decrease amount showing negativity (-) is large. It is grasped that the policy has a certain positive effect on the preference degrees of the defecting customers and the consideration customers, but there may be a reverse effect on the preference degrees of the excellent customers, general customers, and aware customers.

[0048] The policy verification unit 24 can grasp the difference before and after the implementation of the policy for the customer distribution with the axis of preference added, and can quantitatively evaluate the influence of the implementation of the policy on the preference degree.

[0049] The policy verification unit 24 may temporarily store the verified results in the database 10, or may transmit them to the terminal 40. Also, the output destination of the verified results may be a display device (not shown) or a printer (not shown).

[0050] Next, the operation of the policy support device 20 of the first embodiment will be described with reference to the drawings. FIG. 12 is a flowchart showing an operation example of the policy support device 20.

[0051] The customer assignment unit 21 executes a customer assignment process (step S11) and assigns customers to hierarchical segments based on brand awareness, purchase experience, and purchase amount or purchase frequency of the customers. As a specific example, the customer assignment unit 21 acquires questionnaire data for brands included in the market research data 11 from the database 10. The customer assignment unit 21 assigns customers to hierarchical segments based on brand awareness, purchase experience, and purchase amount or purchase frequency included in the questionnaire data. The hierarchical segment is, for example, a 6-segment map.

[0052] When an 11-segment map is used as the hierarchical segment, the customer assignment unit 21 assigns customers to the hierarchical segments based on brand awareness, purchase experience, purchase amount or purchase frequency, and preference indicating the intention to purchase next time.

[0053] The customer assignment unit 21 generates the number of customers or the ratio of the number of customers in each segment as data of the hierarchical segment.

[0054] The measure plan specifying unit 22 executes a measure plan specifying process (step S12) and specifies a measure plan related to the factor for which the customers in the hierarchical segment have moved to a higher segment. Specifically, the measure plan specifying unit 22 specifies a measure plan based on the face-to-face survey data for the customers who have moved to the higher segment. The measure plan specifying unit 22 searches for a measure plan related to the answer from the measure plan data 14 in the database 10 using, for example, the products or services included in the answers of the customers who have moved to the higher segment as keywords. Based on the search result, the measure plan specifying unit 22 specifies a measure plan related to the factor for which the customers have moved to a higher segment. The measure plan specifying unit 22 sends the specified measure plan to the measure selection unit 23. The measure plan specifying unit 22 may temporarily store the specified measure plan in the measure plan data 14 in the database 10.

[0055] The policy selection unit 23 executes a policy selection process (step S13) and selects a policy from among a plurality of policy proposals based on the number of usage intentions when the specified policy proposal is hypothetically implemented. Specifically, the policy selection unit 23 selects a policy from the policy proposals based on the market research data 11 for the policy proposals.

[0056] The policy selection unit 23 acquires the market research data for the policy proposals specified from the market research data 11 in the database 10. The policy selection unit 23 calculates the number of usage intentions when the policy proposal is hypothetically implemented based on the acquired market research data. The policy selection unit 23 selects a policy from among the plurality of policy proposals based on the number of usage intentions. The policy selection unit 23 may calculate the number of usage intentions for each attribute of the respondents who indicated their usage intentions.

[0057] Also, regarding the selection of policies, by reflecting the attributes of the respondents in the usage intentions, it becomes possible to further narrow down the target layer for implementing the policies.

[0058] The policy selection unit 23 may temporarily store the selected policy in the database 10, or may transmit it to the terminal 40. Also, the output destination of the selected policy may be a display device (not shown) or a printer (not shown).

[0059] The policy verification unit 24 executes a policy verification process (step S14) and verifies the policy based on the change in the customer distribution of the hierarchical segments before and after the implementation of the policy selected by the policy selection unit 23. Specifically, the policy verification unit 24 acquires the data of the hierarchical segments before and after the implementation of the selected policy from the hierarchical segment data 12 in the database 10. The policy verification unit 24 compares the customer distribution of the hierarchical segments before the implementation of the policy with the customer distribution of the hierarchical segments after the implementation of the policy, and extracts the change in the customer distribution of each segment in the hierarchical segments.

[0060] The policy verification unit 24 can grasp the difference in the customer distribution of each segment before and after the implementation of the policy, and can also quantitatively evaluate the variation between each segment.

[0061] In addition, the policy verification unit 24 verifies the influence of the policy on the preference for each segment based on the change in the customer distribution of the hierarchical segment before and after the implementation of the policy.

[0062] The policy verification unit 24 can grasp the difference in the customer distribution before and after the implementation of the policy with respect to the axis of preference, and can quantitatively evaluate the influence of the implementation of the policy on the preference.

[0063] (Effect of the First Embodiment) According to the policy support device 20 of the first embodiment, the policy plan specifying unit 22 specifies a policy plan related to the factor for which the customer of the hierarchical segment has moved to a higher segment. The policy selection unit 23 selects a policy from among a plurality of policy plans based on the number of usage intentions when the policy plan is hypothetically implemented. The policy verification unit 24 verifies the policy based on the change in the customer distribution of the hierarchical segment before and after the implementation of the policy. With this configuration, the policy support device 20 can support the formulation of a policy effective for the movement of the customer segment. Specifically, it is possible to select a policy for the customer and objectively evaluate the effect of the policy. It is possible to grasp an effective policy in advance and reduce the cost of the policy by avoiding the implementation of a wasteful policy.

[0064] <Second Embodiment> The policy support device of the second embodiment will be described with reference to the drawings. FIG. 13 is a block diagram showing a configuration example of the policy support device of the second embodiment. The policy support device 50 shown in FIG. 13 includes a policy plan specifying unit 22, a policy selection unit 23, and a policy verification unit 24. The policy support device 50 of the second embodiment has a configuration obtained by removing the customer allocation unit 21 from the policy support device 20 of the first embodiment.

[0065] The policy plan specifying unit 22 specifies a policy plan related to the factor for which the customer of the hierarchical segment has moved to a higher segment. Specifically, the policy plan specifying unit 22 specifies a policy plan for sales promotion based on the face-to-face survey data of the customers who have moved to the higher segment.

[0066] The policy plan identification unit 22 searches for policy plans related to the responses from the policy plan data 14 in the database 10 using, for example, the products or services included in the responses of customers who have moved to a higher segment as keywords. Based on the search results, the policy plan identification unit 22 identifies policy plans related to the factors for which customers have moved to a higher segment.

[0067] The policy selection unit 23 selects a policy from among a plurality of policy plans based on the number of intentions to use in the case where the policy plan is hypothetically implemented. Specifically, the intention to use is the intention of customers to use the policy plan. Specifically, the policy selection unit 23 calculates the number of intentions to use based on the market research data 11 for the policy plan and selects a policy from the policy plans.

[0068] For example, the policy selection unit 23 acquires the market research data for the policy plan specified from the market research data 11 in the database 10. The policy selection unit 23 calculates the number of intentions to use in the case where the policy plan is hypothetically implemented based on the acquired market research data. The policy selection unit 23 selects a policy from among a plurality of policy plans based on the number of intentions to use. The policy selection unit 23 may calculate the number of intentions to use for each attribute of the respondents who indicated the intention to use.

[0069] The policy selection unit 23 selects a policy from among a plurality of policy plans based on the number of intentions to use in the case where the policy plan is hypothetically implemented. As a result, it becomes possible to implement a more effective policy. Also, by stopping the implementation of policies for which no effect can be expected, it is possible to lead to a reduction in implementation costs.

[0070] The policy verification unit 24 verifies the policy based on the change in the customer distribution of the hierarchical segments before and after the implementation of the selected policy. Specifically, the policy verification unit 24 acquires the data of the hierarchical segments before and after the implementation of the selected policy from the hierarchical segment data 12 in the database 10. The policy verification unit 24 compares the customer distribution of the hierarchical segment before implementing the policy with the customer distribution of the hierarchical segment after implementation, and extracts the change in the customer distribution of each segment in the hierarchical segment.

[0071] The policy verification unit 24 can grasp the difference in customer distribution of each segment before and after the implementation of the policy, and can also quantitatively evaluate the fluctuations between segments.

[0072] In addition, the policy verification unit 24 verifies the influence of the policy on the preference degree for each segment based on the change in the customer distribution of the hierarchical segment before and after the implementation of the policy.

[0073] The policy verification unit 24 can grasp the difference before and after the implementation of the policy regarding the customer distribution with the axis of preference added, and can quantitatively evaluate the influence of the implementation of the policy on the preference degree.

[0074] Next, the operation of the policy support device 50 of the second embodiment will be described with reference to the drawings. FIG. 14 is a flowchart showing an operation example of the policy support device 50.

[0075] The policy plan identification unit 22 executes a policy plan identification process (step S31) to identify a policy plan related to the factor that customers in the hierarchical segment have moved to a higher segment. Specifically, the policy plan identification unit 22 identifies a policy plan based on the face-to-face survey data of the customers who have moved to the higher segment. The policy plan identification unit 22 sends the identified policy plan to the policy selection unit 23. The policy plan identification unit 22 may temporarily store the identified policy plan in the policy plan data 14 of the database 10.

[0076] The policy selection unit 23 executes a policy selection process (step S32) to select a policy from among a plurality of policy plans based on the number of usage intentions when the identified policy plan is hypothetically implemented. Specifically, the policy selection unit 23 selects a policy from the policy plans based on the market survey data 11 for the policy plans.

[0077] The policy selection unit 23 acquires the market research data for the policy proposals identified from the market research data 11 in the database 10. Based on the acquired market research data, the policy selection unit 23 calculates the number of usage intentions when the policy proposal is hypothetically implemented. Based on the number of usage intentions, the policy selection unit 23 selects a policy from among a plurality of policy proposals. The policy selection unit 23 may calculate the number of usage intentions for each attribute of the respondents who indicated a usage intention. The policy selection unit 23 selects a policy from the policy proposals based on the number of usage intentions for each policy proposal.

[0078] The policy verification unit 24 executes a policy verification process (step S33) and verifies the policy based on the change in the customer distribution of the hierarchical segments before and after the implementation of the policy selected by the policy selection unit 23. Specifically, the policy verification unit 24 acquires the data of the hierarchical segments before and after the implementation of the selected policy from the hierarchical segment data 12 in the database 10. The policy verification unit 24 compares the customer distribution of the hierarchical segments before the implementation of the policy with the customer distribution of the hierarchical segments after the implementation of the policy, and extracts the change in the customer distribution of each segment in the hierarchical segments.

[0079] The policy verification unit 24 can grasp the difference in the customer distribution of each segment before and after the implementation of the policy, and can also quantitatively evaluate the variation between each segment.

[0080] In addition, the policy verification unit 24 verifies the influence of the policy on the preference degree for each segment based on the change in the customer distribution of the hierarchical segments before and after the implementation of the policy.

[0081] The policy verification unit 24 can grasp the difference before and after the implementation of the policy for the customer distribution with the preference axis added, and can quantitatively evaluate the influence of the implementation of the policy on the preference degree.

[0082] (Effect of the Second Embodiment) According to the policy support device 50 of the second embodiment, the policy plan specifying unit 22 specifies a policy plan related to the factor for which the customers in the hierarchical segment have moved to a higher segment. The policy selection unit 23 selects a policy from among a plurality of policy plans based on the number of usage intentions when the policy plan is hypothetically implemented. The policy verification unit 24 verifies the policy based on the change in the customer distribution of the hierarchical segment before and after the implementation of the policy. With this configuration, the policy support device 50 can support the formulation of policies effective for the movement of customer segments. Specifically, it is possible to select a policy for customers and objectively evaluate the effect of the policy. It is possible to grasp effective policies in advance and reduce the cost of policies by avoiding the implementation of wasteful policies.

[0083] (Hardware Configuration) In the embodiment, some or all of the components in the policy support device 20 shown in FIG. 3 or the policy support device 50 shown in FIG. 13 can also be realized using an arbitrary combination of, for example, the computer 60 shown in FIG. 15 and a program. The computer 60 includes, as an example, the following configuration.

[0084] ·CPU 61 ·ROM 62 ·RAM 63 ·Storage device 65 for storing program 64 and other data ·Drive device 67 for reading and writing recording medium 66 ·Communication interface 68 ·Input / output interface 69 for inputting and outputting data For example, each component of the policy support device 20 in the first embodiment is realized by the CPU 61 acquiring and executing the program 64 that realizes these functions. The program 64 that realizes the functions of each component of the policy support device 20 is stored, for example, in the storage device 65 or the RAM 63 in advance, and is read by the CPU 61 as needed. Note that the program 64 may be supplied to the CPU 61 via a communication network, or may be stored in the recording medium 66 in advance, and the drive device 67 may read the program and supply it to the CPU 61.

[0085] There are various modifications to the implementation methods of each device. For example, the policy support device 20 may be implemented by any combination of separate information processing devices and programs for each component. Also, a plurality of components included in the policy support device 20 may be implemented by any combination of one computer 60 and a program.

[0086] In addition, some or all of the components of the policy support device 20 are realized by other general-purpose or dedicated circuits, processors, etc. and combinations thereof. These may be configured by a single chip or by a plurality of chips connected via a bus.

[0087] Some or all of the components of the policy support device 20 may be realized by a combination of the above-described circuits, etc. and a program.

[0088] When some or all of the components of the policy support device 20 are realized by a plurality of information processing devices, circuits, etc., the plurality of information processing devices, circuits, etc. may be centrally arranged or may be distributed. For example, the information processing devices, circuits, etc. may be realized in a form in which each is connected via a communication network, such as a client and server system, a cloud computing system, etc.

[0089] As described above, the present invention has been described with reference to the present embodiment, but the present invention is not limited to the above embodiment. Various changes that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

Explanation of Reference Numerals

[0090] 10 Database 11 Market Research Data 12 Hierarchical Segment Data 13 Face-to-Face Survey Data 20, 50 Policy Support Device 21 Customer Allocation Unit 22 Policy Plan Identification Unit 23 Policy Selection Unit 24 Policy Verification Unit

Claims

Claims 1. A measure identification means for identifying a measure related to the factor for a customer to move to a higher segment by searching for measure plan data using, as a keyword, a product or service included in face-to-face survey data which is data of an interview with a customer assigned to a segment higher than a hierarchically assigned segment in the past; A measure selection means for selecting a measure from among a plurality of the measure plans based on the number of utilization intentions calculated based on market survey data which is data of a questionnaire regarding the availability of utilization when the measure plan is hypothetically implemented; A measure support apparatus comprising a measure verification means for extracting a change in the customer distribution by comparing the customer distribution which is the number or the ratio of the number of customers assigned to the segment before and after the implementation of the measure, and verifying the measure based on the change in the customer distribution. 【Claims 2】 The measure support apparatus according to claim 1, further comprising a customer assignment means for assigning the customer to the segment based on brand awareness, purchase experience, and purchase amount or purchase frequency of the customer. 【Claims 3】 The customer assignment means according to claim 2, assigns the customer to the segment based on brand awareness, purchase experience, purchase amount or purchase frequency, and preference indicating the intention to purchase next time. 【Claims 4】 The measure selection means according to any one of claims 1 to 3 calculates the number of utilization intentions for each attribute of respondents who indicated the utilization intention in the market survey data for the measure plan. 【Claims 5】 The measure verification means according to claim 3 verifies the influence on the preference for each segment by the measure based on the change in the customer distribution before and after the implementation of the measure. 【Claims 6】 A computer identifies a measure plan related to the factor for a customer to move to a higher segment by searching for measure plan data using, as a keyword, a product or service included in face-to-face survey data which is data of an interview with a customer assigned to a segment higher than a hierarchically assigned segment in the past, selects a measure from among a plurality of the measure plans based on the number of utilization intentions calculated based on market survey data which is data of a questionnaire regarding the availability of utilization when the measure plan is hypothetically implemented, A policy support method for extracting changes in the customer distribution, which is the number or percentage of customers assigned to the hierarchical segment before and after the implementation of the policy, and verifying the policy based on the changes in the customer distribution by comparing the customer distributions.

7. By searching for policy plan data using products or services included in face-to-face survey data, which is interview data of customers assigned to segments higher than the hierarchical segments assigned in the past, as keywords, identify policy plans related to the factors for the customers to move to higher segments. Select a policy from among a plurality of the policy plans based on the number of usage intentions calculated based on market research data, which is questionnaire data on the availability of usage if the policy plan were to be implemented. A program that causes a computer to extract changes in the customer distribution by comparing the customer distribution, which is the number or percentage of customers assigned to the hierarchical segment before and after the implementation of the policy, and verify the policy based on the changes in the customer distribution.

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