Analysis program, analysis system, computer device, and analysis method

Abstract

The purpose of the present disclosure is to provide an analysis program capable of more accurately estimating the marketing outcome.　The present disclosure involves: estimating a first effect, which is the effect of prescribed causes, from the results of a consumer survey; and correcting the first effect and thereby estimating a second effect using the total number of people who came into contact with each of the prescribed causes and the number of unique people who came into contact with any of the prescribed causes, which can be tallied from the survey results.

Description

Analysis program, analysis system, computer device, and analysis method

[0001] The present disclosure relates to an analysis program, an analysis system, a computer device, and an analysis method.

[0002] Marketing mix modeling (hereinafter also referred to as MMM) is a conventional method for statistically analyzing and modeling data on marketing measures, using sales data and time-series data on the amount of advertising such as television commercials, to estimate the impact of such measures on sales. The inventor works as a consultant using MMM.

[0003] There is also a method for reading causal relationships from observational data, particularly from real-world data that cannot be reproduced (for example, Non-Patent Document 1).

[0004] "Iwanami Data Science Vol. 3," edited by the Iwanami Data Science Publication Committee, published in June 2016

[0005] However, previous analytical methods based on time-series data could not be said to grasp the mechanisms of consumer behavior, and were insufficient for estimating the effect of each marketing measure, such as advertising, on increasing sales. Therefore, the inventor devised a method that uses consumer survey data to grasp the mechanisms of consumer behavior and estimate the effects, such as increasing sales by visiting a brand's store as a result of a measure such as a television commercial.

[0006] An object of at least one embodiment of the present disclosure is to provide an analysis program that can utilize consumer surveys to more accurately estimate marketing results as incremental sales.

[0007] From a non-limiting perspective, the analysis program of the present disclosure is an analysis program executable on a server device in an analysis system including a terminal device and a server device connectable to the terminal device via communication, and causes the server device to function as a first effect estimation means that estimates a first effect, which is the effect of a specified cause, from the results of a consumer survey, and a second effect estimation means that corrects the first effect using the total number of contacts with each of the specified causes and the number of unique contacts with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

[0008] From a non-limiting perspective, the analysis system according to the present disclosure is an analysis system comprising a terminal device and a server device connectable to the terminal device via communication, and comprising: a first effect estimation means for estimating a first effect, which is the effect of a specified cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of contacts with each of the specified causes and the number of unique contacts with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

[0009] From a non-limiting perspective, the analysis program of the present disclosure is an analysis program that causes a computer device to function as a first effect estimation means that estimates a first effect, which is the effect of a specified cause, from the results of a consumer survey, and a second effect estimation means that corrects the first effect using the total number of contacts with each of the specified causes and the number of unique contacts with any of the specified causes, which can be compiled from the survey results, and estimates a second effect.

[0010] From a non-limiting perspective, the analytical method according to the present disclosure is an analytical method having a step of estimating a first effect, which is the effect of a specified cause, from the results of a consumer survey, and a step of correcting the first effect using the total number of people who came into contact with each of the specified causes and the number of unique people who came into contact with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

[0011] Each embodiment of the present disclosure addresses one or more of the deficiencies.

[0012] FIG. 1 is a block diagram illustrating a configuration of a server device corresponding to at least one embodiment of the present disclosure. FIG. 2 is a flowchart of a program execution process corresponding to at least one embodiment of the present disclosure. FIG. 3 is a block diagram illustrating a configuration of an analysis system corresponding to at least one embodiment of the present disclosure. FIG. 4 is a block diagram illustrating a configuration of an analysis system corresponding to at least one embodiment of the present disclosure. FIG. 5 is a diagram illustrating an example of survey data corresponding to at least one embodiment of the present disclosure. FIG. 6 is a diagram illustrating an example of survey data corresponding to at least one embodiment of the present disclosure. FIG. 7 is a diagram illustrating an example of survey data corresponding to at least one embodiment of the present disclosure. FIG. 8 is a diagram illustrating an example of survey data corresponding to at least one embodiment of the present disclosure. FIG. 9 is a diagram illustrating an example of survey data corresponding to at least one embodiment of the present disclosure. FIG. 10 is a flowchart of an execution process corresponding to at least one embodiment of the present disclosure. FIG. 11 is a diagram illustrating an example of cross-tabulation of contact rates by attribute derived from consumer survey data corresponding to at least one embodiment of the present disclosure. FIG. 12 is a diagram illustrating an example of tabulation of single reach rates by attribute derived from consumer survey data corresponding to at least one embodiment of the present disclosure. FIG. 1 is a diagram collating contact rates by age group from multiple perspectives, corresponding to at least one embodiment of the present disclosure. FIG. 2 is a diagram for explaining correction of data regarding contact rates for women in their twenties, corresponding to at least one embodiment of the present disclosure. FIG. 3 is a diagram for explaining a method for calculating a coefficient for overlap adjustment, corresponding to at least one embodiment of the present disclosure. FIG. 4 is a diagram showing an example of a calculation result of a sales contribution amount for each measure, corresponding to at least one embodiment of the present disclosure. FIG. 5 is a diagram showing an example of a calculation result of a sales contribution amount for each factor, corresponding to at least one embodiment of the present disclosure. FIG. 6 is a diagram showing an example of a calculation result of a sales contribution amount for each measure and factor, corresponding to at least one embodiment of the present disclosure.

[0013] Hereinafter, examples of embodiments of the present disclosure will be described with reference to the accompanying drawings. The following description of the effects is one aspect of the effects of the embodiments of the present disclosure and is not limited to those described here. In addition, the content described as an example of one embodiment may be omitted in other embodiments. Furthermore, the description of operations and processes unrelated to the characteristic parts of each embodiment may be omitted. The order of each process constituting the flowcharts described below is random as long as no contradictions or inconsistencies occur in the process content.

[0014] [First embodiment] In the following, as a first embodiment, an analysis system including a terminal device and a server device connectable to the terminal device via communication will be described. The terminal device and / or the server device may be connectable to each other via communication. The server device and / or the terminal device may be connectable to a distributed ledger network.

[0015] 1 is a block diagram illustrating a configuration of a server device 10 according to at least one embodiment of the present disclosure. The server device 10 may include at least a first effect estimator 101 and a second effect estimator 102.

[0016] The first effect estimation unit 101 has a function of estimating a first effect, which is the effect of a predetermined cause, from the survey results of a consumer survey. The second effect estimation unit 102 has a function of correcting the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be calculated from the survey results, to estimate a second effect.

[0017] 2 is a flowchart of a program execution process corresponding to at least one embodiment of the present disclosure. The server device 10 estimates a first effect, which is the effect of a predetermined cause, from the results of a consumer survey (step S1). Next, the server device 10 corrects the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be calculated from the survey results, to estimate a second effect (step S2), and the process ends.

[0018] As one aspect of the first embodiment, an analysis program can be provided that utilizes consumer surveys to more accurately estimate the results of marketing as an increase in sales.

[0019] In the first embodiment, the term "terminal device" refers to, for example, a stationary game console, a portable game console, a wearable terminal, a desktop or notebook personal computer, a tablet computer, a PDA, or a portable terminal such as a smartphone equipped with a touch panel sensor on the display screen. The term "server device" refers to, for example, a device that executes processing in response to a request from a terminal device.

[0020] In the first embodiment, the "cause" refers to, for example, a factor that has the effect of stimulating consumer consumption activity, and more specifically, includes items that could be questions in a consumer survey, such as watching a television commercial, watching an online commercial, visiting a brand store, or seeing a push notification from an app.

[0021] In the first embodiment, the "total number of contacts" refers to, for example, the number of people who answered that they had been exposed to each of the causes. The "number of unique contacts" refers to, for example, the number of people who answered that they had been exposed to any of the causes. In other words, it refers to the number of all responses to the consumer survey questionnaire minus the number of people who answered that they had not been exposed to any of the causes.

[0022] [Second embodiment] In the following, as a second embodiment, an analysis system including a terminal device and a server device connectable to the terminal device via communication will be described. The terminal device and / or the server device may be connectable to each other via communication. The server device and / or the terminal device may be connectable to a distributed ledger network.

[0023] 3 is a block diagram showing the configuration of an analysis system corresponding to at least one embodiment of the present disclosure. As shown in the figure, the analysis system (system) 1 includes a server device 10, a communication network 20, and terminal devices 30 (30A, 30B, ..., 30N: N is any character) used by each of multiple users.

[0024] The configuration of the analysis system 1 is not limited to this, and for example, the server device 10 may be configured by multiple server devices or may be configured by a virtual server device using cloud computing technology. Furthermore, the communication network 20 may be a distributed ledger network.

[0025] The server device 10 and the terminal device 30 are connected to each other so that they can communicate with each other. The terminal device 30 and the server device 10 do not need to be connected to each other all the time, but may be connected to each other as needed.

[0026] [Server Device] The server device 10 includes, as an example, at least a control unit, RAM, storage unit, and communication interface, all of which are connected via an internal bus. The control unit may include an internal timer. The communication interface may also be used to synchronize with an external server, thereby obtaining the actual time.

[0027] [Communication Network] The communication network 20 may be, for example, the Internet or a LAN, as long as communication between connected devices is possible.

[0028] [Terminal Device] The terminal device 30 includes, for example, a control unit, RAM, storage unit, sound processing unit, graphics processing unit, communication interface, and interface unit, all of which are connected via an internal bus. The graphics processing unit is connected to a display unit. The display unit may have a display screen and a touch input unit that accepts input when the user touches the display unit.

[0029] The touch input unit may be capable of detecting the position of contact using any method, such as a resistive film method used in touch panels, a capacitive method, an ultrasonic surface acoustic wave method, an optical method, or an electromagnetic induction method, as long as it can recognize a user's touch operation.The touch input unit is a device that can detect the position of a finger or a stylus when the top surface of the touch input unit is pressed or moved with a finger or a stylus.

[0030] An external memory (e.g., an SD card) can be connected to the interface unit. Data read from the external memory is loaded into RAM, and arithmetic processing is performed by the control unit. The communication interface can be connected to a communication network wirelessly or via a wire, and can receive data via the communication network. Data received via the communication interface is loaded into RAM, just like data read from the external memory, and arithmetic processing is performed by the control unit.

[0031] The terminal device 30 may include a sensor such as a proximity sensor, an infrared sensor, a gyro sensor, or an acceleration sensor. The terminal device 30 may also include an imaging unit that has a lens and captures images through the lens. Furthermore, the terminal device 30 may be a terminal device that can be attached to the body (wearable).

[0032] [Functional Description] The following describes functions provided in the analysis system 1 according to the second embodiment. Fig. 4 is a block diagram showing the configuration of an analysis system corresponding to at least one of the embodiments of the present disclosure.

[0033] The analysis system 1 may include a contact estimation unit 201 , a first effect estimation unit 202 , a second effect estimation unit 203 , and a sales contribution estimation unit 204 .

[0034] The contact estimation unit 201 has a function of estimating the number of contacts that have come into contact with a predetermined cause from the survey results of the consumer survey. The first effect estimation unit 202 has a function of estimating a first effect, which is the effect of a predetermined cause, from the survey results of the consumer survey.

[0035] The second effect estimation unit 203 has a function of correcting the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be compiled from the survey results, and estimating the second effect.The sales contribution estimation unit 204 has a function of estimating the degree of sales contribution for each attribute based on each of the causes, using the number of people who came into contact with the predetermined cause, the second effect, the average purchase price, and the average number of purchases estimated from a predetermined probability model of consumer purchases.

[0036] 5 to 10 are diagrams illustrating examples of survey data corresponding to at least one embodiment of the present disclosure. Data obtained by consumers in the form of a questionnaire is collected in an analyzable number.

[0037] The consumer survey data preferably includes the degree of favorability toward the brand shown in FIG. 5, the time of exposure to the brand shown in FIG. 6, the degree of passive exposure to the brand shown in FIG. 7, the degree of active exposure to the brand shown in FIG. 8, the respondent's exposure time to the media shown in FIG. 9, and the intention to use the brand in the future shown in FIG. 10.

[0038] Furthermore, as shown in FIG. 5, the consumer survey data may be controlled so that brands for which the customer answers "I don't know" are treated as invalid answers for the brands in the subsequent questions shown in FIGS.

[0039] It is preferable that the consumer survey questions include questions that correspond to passive attitudes toward the brand from the consumer's perspective, as shown in Figure 7, and questions that correspond to active attitudes toward the brand, as shown in Figure 8.

[0040] 11 is a flowchart of an execution process corresponding to at least one embodiment of the present disclosure. The server device 10 preferably loads data related to the results of a consumer survey conducted in advance. The data related to the results of the consumer survey preferably includes information on the attributes of the respondents and, if they have purchased the product, their average purchase price.

[0041] The consumer survey data may be the returned data as is, or may be pre-edited data that has been read in. There is no particular limitation on the form as long as the effects of the present disclosure are achieved.

[0042] Below, we will provide a detailed explanation using responses from approximately 6.16 million women in their 20s regarding television commercials for restaurant chains.

[0043] The analysis system 1 estimates the number of people who came into contact with the predetermined cause from the survey results (step S21). In the verified example, the contact rate for women in their twenties was approximately 59%, so it is estimated that approximately 3.63 million people came into contact.

[0044] Next, the server device 10 estimates a first effect, which is the effect of a predetermined cause, from the results of the consumer survey (step S22).

[0045] [First Effect Estimation Process] The estimation of the first effect will now be described. Fig. 12 is a diagram illustrating an example of cross-tabulation of contact rates by attribute derived from consumer survey data, corresponding to at least one embodiment of the present disclosure. The data shown as "Measures" in Fig. 12 is a cross-tabulation of responses to the question shown in Fig. 7, and the data shown as "Factors" in Fig. 12 is a cross-tabulation of responses to the question shown in Fig. 8.

[0046] Next, the "single reach rate" is calculated. The single reach rate is the percentage of respondents who answered that they had only one type of contact for each measure and factor. Fig. 13 is a diagram illustrating an example of calculating the single reach rate by attribute derived from consumer survey data, which corresponds to at least one embodiment of the present disclosure.

[0047] Next, causal inference is used to derive the increase in penetration rate (lift rate) for each cause. For example, a simple comparison between people who watched a TV commercial and those who did not will result in selection bias, so causal inference is used to adjust for the bias. Propensity score analysis is one method that uses causal inference. As propensity score analysis is not the essence of the present disclosure, a detailed explanation will be omitted.

[0048] In the model disclosed herein, emphasis is placed on whether the covariates that cause bias in the estimation of effects are balanced between the two groups, rather than on predictive ability. More specifically, if the sample size when analyzing by age and gender is small (a rough guideline is 40), the balance between the two groups becomes unstable. Therefore, if the sample size is 40 or less, processing is performed to substitute the average effect of the policy or factor.

[0049] In this disclosure, the value estimated by ATT (Average Treatment Effect on the Treated) was used as the increase in penetration rate (lift rate). ATT is the weight value of the IPTW estimator, which calculates the propensity score for each sample using covariates in propensity score analysis and then aggregates them by weighting. Covariates include data such as whether or not the respondent has children, marital status, and living arrangements, which can be obtained as basic attributes from internet surveys, as well as brand favorability (Figure 5), media exposure time (Figure 9), and usage intention (Figure 10), which are obtained from survey responses. Favorability and usage intention are excluded as intermediate variables that play an intermediate role between cause and effect. In the specific example disclosed in this disclosure, it was estimated that approximately 4.22% of the approximately 3.63 million contacts increased their usage rate due to television commercials.

[0050] Returning to the explanation of Fig. 11, the analysis system 1 corrects the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be calculated from the survey results, and estimates the second effect (step S23).

[0051] [Second Effect Estimation Process] First, the analysis system 1 uses the response data of the consumer survey to tally up the "total reach," "unique user reach," "total single reach," and "overlapping unique user reach" by age and gender.

[0052] 14 is a diagram illustrating contact rates by age group from multiple perspectives, corresponding to at least one embodiment of the present disclosure. The reach represents the sum of the contact rates of each campaign. The unique user reach represents the percentage of respondents who were contacted by any of the campaigns. The overlapping unique user reach represents the value obtained by subtracting the total reach from the unique user reach.

[0053] If the effect of each measure (increase in penetration rate, etc.) is, for example, 10%, and there is no overlapping reach of the penetration rate of each measure, the calculation can be done as follows: Population by age and gender x Reach (contact rate) (%) x 10% = Number of people who have lifted the penetration rate. However, in reality, there will be overlapping responses, which will result in an overestimated result, making it impossible to properly evaluate the results of marketing.

[0054] As a specific example, a method for correcting the value of the first effect when the number of unique users reached by females in their twenties is 4.647 million will be described. The number of unique users reached is a value derived from response data of consumer survey data. Figure 15 is a diagram for explaining correction of data related to the contact rate of females in their twenties, corresponding to at least one embodiment of the present disclosure.

[0055] The increase in the number of people due to the penetration rate for a single reach can be estimated by multiplying the population of each age group and gender, the reach (contact rate), and the ATT to estimate the penetration lift number. The penetration lift number taking into account overlaps is calculated by multiplying the total overlap reach number by the overlap adjustment coefficient so that it matches the total number of unique user reach, which is approximately 4,647,000.

[0056] The overlap adjustment coefficient is a value obtained by dividing the overlap unique user reach by the overlap reach for each age group and gender. FIG. 16 is a diagram for explaining a method for calculating the overlap adjustment coefficient, corresponding to at least one embodiment of the present disclosure. The corrected number of contacts is calculated by multiplying the population by the single reach (%) and the population by the overlap reach (%), which is then multiplied by the overlap coefficient. The effect adjustment coefficient is calculated by dividing the corrected number of contacts by the actual number of contacts.

[0057] In the model disclosed herein, the single reach of each media is left as it is. By estimating the lift number of users for each measure using the "number of people reached" x "effect adjustment coefficient (corrected number of contacts / actual number of contacts)" x "penetration lift rate (estimated by ATT)" to calculate the number of unique users reached for the overlapping parts using the method described above, it is possible to estimate the corrected penetration rate, which is the second effect.

[0058] Specific examples of the present disclosure will be described using FIGS. 15 and 17. FIG. 17 is a diagram showing an example of the calculation results of the sales contribution amount for each campaign, corresponding to at least one embodiment of the present disclosure. As shown in FIG. 15, 13 types of campaigns were surveyed, and approximately 75.3% of respondents across all age groups were exposed to any of them. In this case, to correct the number of unique contacts for the campaign name "TV commercial," the lift rate is multiplied by the "effect adjustment coefficient (corrected number of contacts / actual number of contacts)" of approximately 84.4%, thereby correcting the estimated first effect of 4.22% to approximately 3.55%. The corrected value is the second effect.

[0059] Returning to the explanation of Fig. 11, the analysis system 1 estimates the degree of sales contribution for each attribute based on each of the causes using the number of contacts of the predetermined cause, the second effect, the average purchase price, and the average number of purchases estimated from a predetermined probability model of consumer purchases (step S24), and then ends the process.

[0060] [Sales Contribution Estimation Function] The second effect estimation function allows us to estimate the number of lift users for each measure. Next, we calculate the increased number of times and the average cost per use (also called the average purchase cost or average use cost).

[0061] It is preferable to use purchase data obtained within the company to determine the increased number of purchases, but when targeting brands other than the company's own, it is preferable to derive it using a consumer purchase probability model based on consumer surveys. As a consumer purchase probability model, for example, the "Gamma Poisson recency model" shown in Figure 6, which analyzes recency data asking survey subjects about the time of their last purchase, can be used. These models have already been studied, and existing probability models are sufficient for the probability model used in this disclosure, so a detailed explanation will be omitted here.

[0062] Next, calculate the average cost per use, which can be calculated from the response data of the consumer survey.

[0063] In this disclosure, sales contribution refers to the lift amount for each measure, obtained by multiplying the estimated number of lifts by users for each measure, the increase in the number of lifts, and the average unit price per use calculated from the survey.

[0064] 18 is a diagram illustrating an example of a calculation result of the sales contribution amount for each factor, corresponding to at least one embodiment of the present disclosure. The model of the present disclosure may be capable of estimating the effect of measures → factors → sales (increase in penetration rate × number of times × unit price) by combining the content analyzed by measures ( FIG. 17 ) and the content analyzed by factors ( FIG. 18 ).

[0065] FIG. 19 is a diagram showing an example of the calculation results of the sales contribution amount through each factor when the measure is a television commercial, corresponding to at least one embodiment of the present disclosure. The lift amount due to the television commercial directly estimated using the model of FIG. 17 (measure → sales) was approximately 582 million yen. On the other hand, the model of the present disclosure shown in FIG. 19 shows that the total lift amount from the measure (television commercial) → factor → sales is approximately 1.273 billion yen, which is different from the lift amount in FIG. 17. There may be discrepancies between the directly estimated model and the model estimated from the assistance via factors.

[0066] By analyzing measures → factors → sales, you can understand the structure of communication by analyzing in detail how measures contributed to sales through certain factors.

[0067] As one aspect of the second embodiment, an analysis program that can more accurately estimate marketing results can be provided.

[0068] In the second embodiment, the "terminal device," "server device," "cause," "total number of contacts," and "number of unique contacts" may adopt the contents described in the first embodiment to the extent necessary.

[0069] In the second embodiment, "average purchase price" refers to, for example, the total amount spent to acquire a product or service divided by the quantity owned or the number of times it is provided. "Degree of sales contribution" refers to, for example, the degree of contribution to sales growth, and may be expressed as a number or a percentage. "Reach" refers to, for example, the actual number of people who came into contact with a specific website or brand over a certain period of time, or the percentage of that number relative to the total number of internet users, also known as the number of contacts. "Causal inference" refers to, for example, the idea of ​​statistically inferring causal relationships, i.e., the relationship between causes and the resulting effects, from input data and output data. "Propensity score" refers to, for example, a statistical balancing method used to estimate causal effects by adjusting covariates in observational studies where random assignment is difficult and various confounds are likely to occur.

[0070] In the second embodiment of the present disclosure, the analysis system 1 includes all the functions, but is not limited to this. For example, the functions may be distributed between the server device 10 and the terminal device 30.

[0071] The contents described in the above embodiments can be appropriately designed by those skilled in the art by rearranging them.

[0072] [Notes] The above-mentioned embodiments have been described in such a manner that a person having ordinary skill in the art to which the invention pertains can carry out the following invention.

[0073] [1] An analysis program executable on a server device in an analysis system comprising a terminal device and a server device connectable to the terminal device via communication, the analysis program causing the server device to function as: a first effect estimation means for estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of contacts with each of the predetermined causes and the number of unique contacts with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0074] [2] The analysis program described in [1], wherein the survey results include respondent attributes and average purchase price, and the server device further functions as a sales contribution estimation function that estimates the degree of sales contribution for each attribute based on each of the causes using the number of contacts of the specified cause, the second effect, the average purchase price, and an average number of purchases estimated from a specified probability model of consumer purchases.

[0075] [3] The analysis program described in [2], wherein the specified cause includes details related to measures and details related to factors, and the sales contribution estimation function estimates the degree of sales contribution for each attribute using the second effect estimated for each measure content and the second effect estimated for each factor content.

[0076] [4] The analysis program according to [1] or [2], further causing the server device to function as a contact number estimation means for estimating the number of contacts that have come into contact with a predetermined cause from the survey results.

[0077] [5] The analysis program according to [1] or [2], wherein the first effect estimation function estimates the first effect using causal inference.

[0078] [6] The analysis program according to [1] or [2], wherein the first effect estimation function estimates the first effect using a propensity score.

[0079] [7] The analysis program according to [1] or [2], wherein a Gamma Poisson recency model is used as the predetermined consumer purchase probability model.

[0080] [8] A server device on which the program according to [1] or [2] is installed.

[0081] [9] An analysis system comprising a terminal device and a server device connectable to the terminal device by communication, the analysis system comprising: a first effect estimation means for estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0082]

[10] An analysis program executable on a terminal device in an analysis system comprising a terminal device and a server device connectable to the terminal device via communication, the analysis program causing the terminal device to function as: a first effect estimation means for estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of contacts with each of the predetermined causes and the number of unique contacts with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0083]

[11] A terminal device having the program according to

[10] installed therein.

[0084]

[12] An analytical method executed in a server device of an analytical system comprising a terminal device and a server device connectable to the terminal device by communication, the analytical method comprising: a step of estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a step of correcting the first effect using the total number of contacts with each of the predetermined causes and the number of unique contacts with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0085]

[13] An analytical method executed in an analytical system having a terminal device and a server device connectable to the terminal device by communication, the analytical method comprising: a step of estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a step of correcting the first effect using the total number of contacts with each of the predetermined causes and the number of unique contacts with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0086]

[14] An analysis program that causes a computer device to function as: a first effect estimation means that estimates a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a second effect estimation means that corrects the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be compiled from the survey results, and estimates a second effect.

[0087]

[15] A computer device having the analysis program according to

[14] installed therein.

[0088]

[16] An analytical method comprising the steps of: estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and correcting the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

[0089] According to one embodiment of the present disclosure, it is useful to provide an analysis program that can more accurately estimate marketing results.

[0090] 1: Analysis system 10: Server device 20: Communication network 30: Terminal device

Claims

1. An analysis program executable on a server device in an analysis system comprising a terminal device and a server device connectable to the terminal device via communication, the analysis program causing the server device to function as: a first effect estimation means for estimating a first effect, which is the effect of a specified cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of contacts with each of the specified causes and the number of unique contacts with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

2. The analysis program of claim 1, wherein the survey results include respondent attributes and average purchase price, and the server device further functions as a sales contribution estimation function that estimates the degree of sales contribution for each attribute based on each of the causes using the number of contacts of the specified cause, the second effect, the average purchase price, and the average number of purchases estimated from a specified probability model of consumer purchases.

3. The analysis program described in claim 2, wherein the specified cause includes details related to measures and details related to factors, and the sales contribution estimation function estimates the degree of sales contribution for each attribute using the second effect estimated for each measure content and the second effect estimated for each factor content.

4. An analysis system comprising a terminal device and a server device connectable to the terminal device via communication, the analysis system comprising: a first effect estimation means for estimating a first effect, which is the effect of a predetermined cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of people who came into contact with each of the predetermined causes and the number of unique people who came into contact with any of the predetermined causes, which can be compiled from the survey results, to estimate a second effect.

5. An analysis program that causes a computer device to function as a first effect estimation means that estimates a first effect, which is the effect of a specified cause, from the results of a consumer survey, and a second effect estimation means that corrects the first effect using the total number of people who came into contact with each of the specified causes and the number of unique people who came into contact with any of the specified causes, which can be compiled from the survey results, and estimates a second effect.

6. A computer device comprising: a first effect estimation means for estimating a first effect, which is the effect of a specified cause, from the results of a consumer survey; and a second effect estimation means for correcting the first effect using the total number of people who came into contact with each of the specified causes and the number of unique people who came into contact with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

7. An analytical method comprising the steps of: estimating a first effect, which is the effect of a specified cause, from the results of a consumer survey; and correcting the first effect using the total number of people who came into contact with each of the specified causes and the number of unique people who came into contact with any of the specified causes, which can be compiled from the survey results, to estimate a second effect.

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