JP6057974B2 - Register device - Google Patents

Description

  The present invention relates to a register device that settles a sales price based on the price of a product.

  Conventionally, for example, a register device (cash register) described in Patent Document 1 below is known.

  This conventional register device includes, for example, a display control unit in which a touch panel display unit capable of touch operation is provided in an inclined shape, a print unit placed behind the touch panel display unit on the display control unit, and the print unit. And a money storage unit for placing the placed display control unit.

JP-A-10-134248 (FIG. 1 etc.)

  In recent years, for example, a register device installed in a store or the like is required to be able to promote sales as well as to settle a sales price based on the price of a product.

  This invention is made | formed in view of such a point, and it aims at providing the register apparatus which can aim at sales promotion.

請 Motomeko 1 register device as claimed, there is provided a cash register to settle the sales price based on the price of the commodity, the correlation of several other commodity classification for one of the commodity classification for each product category from the purchase history of the customer A first calculation means for calculating a strength and calculating an expected sales promotion amount for each product classification based on the correlation prediction using the calculated correlation strength; and a product classification that has not been purchased for a predetermined period or more from a customer purchase history. A second calculation means for extracting and calculating an expected sales promotion amount for each product category based on the actual forecast with respect to the extracted product classification; an expected sales promotion amount calculated by the first calculation means; and a second calculation means And a recommended product category determining means for determining a recommended product category based on the expected sales promotion amount calculated in the above.

The register device according to claim 2 is the register device according to claim 1, wherein the recommended product classification determining means, when issuing a coupon for the recommended product classification, The maximum value for each product category is selected by comparing the value with the expected sales promotion amount calculated by the second calculation means, and the result of multiplying the selected maximum value by the set magnification is the final expected sales promotion amount. To do.

The cash register according to claim 3, wherein, in the cash register of claim 1, wherein the recommended goods classification determining means, when the screen display recommended products classifications, promotional expected amount calculated by the first calculating means The result of multiplying by the set magnification is the final expected sales promotion amount, and the result of multiplying the expected sales promotion amount calculated by the second calculation means by the set magnification is the final expected sales promotion amount .

  ADVANTAGE OF THE INVENTION According to this invention, it becomes possible to recommend an appropriate product according to a customer, and sales promotion can be aimed at.

It is a schematic front view of the register apparatus which concerns on one embodiment of this invention. It is a block diagram of a register apparatus same as the above. It is a flowchart which shows the process outline | summary of a register apparatus same as the above. It is a processing flow figure at the time of calculating correlation strength. It is an example of a customer's purchase history. It is an example of the statistics amount for every goods classification. It is an example of the correlation strength for every goods classification. It is an example of the statistics amount for every goods classification. It is an example of the correlation strength for every goods classification. It is a processing flow figure at the time of calculating the sales promotion expected amount by correlation prediction. It is an example of a customer's purchase history. It is an example of the statistics amount for every goods classification. It is an example of the average value of the expected sales for each product category. It is an example of the correlation prediction sales promotion expected amount for each product category. It is an example of a customer's purchase history. It is a processing flow figure at the time of coupon issue. It is an example of the sales promotion information (including a set magnification) for each product category. It is an example of the result of multiplying the maximum value of both sales promotion expected amounts by a set magnification. It is an example of a sales promotion history (past recommended product history). It is an example of the coupon which a printer means prints and issues. It is a processing flow figure at the time of screen display. It is an example of the screen which a touchscreen display part displays. It is an example of the result of multiplying the sales promotion expected amount by the result prediction by the set magnification. It is an example of the result of multiplying the sales promotion expected amount by correlation prediction by the set magnification.

  An embodiment of the present invention will be described with reference to the drawings.

  In FIG. 1, reference numeral 1 denotes a register device for a cleaning business, for example. This register device 1 is installed at a reception of a cleaning store, for example, a customer membership store, and customers (member customers) are among a plurality of types of products. The sales price is settled based on the price (amount) of the product selected and ordered. The “product” here means a service (service) provided to another person in addition to the product itself.

  As shown in FIG. 1, the register device 1 includes a device main body 3 having a money storage portion 2 that can store money and the like. The apparatus main body 3 is provided with a liquid crystal touch panel display unit 4 that can be touch-operated with a fingertip by a store clerk who is an operator. That is, the touch panel type register device 1 includes a touch panel display unit 4 that is a display unit that also serves as an operation unit (input unit).

  Further, as shown in FIG. 2, the apparatus body 3 is provided with a control means 5 comprising a CPU or the like. In addition to the touch panel display unit 4, at least a storage unit 6, a printer unit (print issuing unit) 7, and a speaker unit (sound generation unit) 8 are electrically connected to the control unit 5.

  Furthermore, the control means 5 has at least the functions of the first calculation means 11, the second calculation means 12, and the recommended product category determination means 13 in addition to the normal register function.

  Here, the storage means 6 includes a plurality of customer information, a purchase history for each customer, a plurality of product information (product name, product classification, price, etc.), sales promotion information for each product classification (each product), and past The sales promotion history etc. are stored.

  The first calculation means 11 calculates the correlation strength of a plurality of other product categories (for example, outerwear and trousers) with respect to one product category (for example, shirts) for each product category from purchase histories of a plurality of customers. Using the calculated correlation strength, an expected sales promotion amount for each product category based on the correlation prediction (expected promotion amount by correlation prediction) is calculated.

  The second calculation means 12 extracts a product category that has not been purchased for a predetermined period (for example, 11 months) from the purchase history of the customer who visited the store (one customer), and each product based on the actual prediction only for this extracted product category The expected sales promotion amount for each category (the expected sales promotion amount based on the actual result prediction) is calculated.

  The recommended product category determination means 13 determines the expected sales promotion amount based on the correlation prediction calculated by the first calculation means 11 and the expected sales promotion amount based on the result prediction calculated by the second calculation means 12 for the customer who visited the store. The recommended product classification related to the product to be recommended (recommended product) is determined.

  The recommended product category determination unit 13, for example, when issuing a coupon for the recommended product category (when causing the printer unit 7 to print and issue a coupon for the recommended product category), the correlation calculated by the first calculation unit 11. Select the maximum value (higher amount) for each product category by comparing the expected sales promotion amount by prediction with the expected sales promotion amount calculated by the second calculation means 12, and select this The result of multiplying the maximum value by a set magnification (a magnification set in advance for each product category, for example, a positive integer including 0, the same applies hereinafter) is used as the final expected sales promotion amount.

  Further, the recommended product category determination means 13 is calculated by the first calculation means 11 when, for example, the recommended product category is displayed on the screen (when the recommended product category is displayed on the touch panel display unit 4 as a display means). The result of multiplying the expected sales promotion amount by the correlation prediction by the set magnification is set as the final expected sales promotion amount, and the final sales promotion is the result of multiplying the expected sales promotion amount by the actual calculation calculated by the second calculation means 12 by the set magnification. Expected amount.

  Next, the operation and the like of the register device 1 will be described.

(1) Process Overview FIG. 3 is a flowchart showing a process overview of the register device 1. As shown in FIG. 3, the control means (control unit) 5 of the register device 1 The expected sales promotion amount is calculated (step 1), the expected sales promotion amount is calculated for each product category based on the actual prediction (step 2), and it is determined whether or not a coupon for the recommended product category is issued (step 3).

  And when it is judged that a coupon is issued, based on two prediction results, it corrects by past sales promotion history and purchase history, and a coupon of a recommended product classification (for example, a single recommended product classification) is obtained by the printer means 7. Issue (step 4).

  On the other hand, if it is determined not to issue a coupon, it is determined whether or not the recommended product category is displayed on the screen (step 5).

  If it is determined that the screen is to be displayed, correction is performed based on the two prediction results based on the past sales promotion history and purchase history, and the recommended product category (for example, a plurality of recommended product categories) is displayed on the touch panel display unit 4 as a message. Display on the screen. If it is determined not to display the screen, the process ends (step 6).

(2) Calculation of Correlation Strength Used for Correlation Prediction The control means 5 of the register device 1 uses the purchase history for each customer stored in the storage means 6 as shown in FIG. Is calculated.

  Specifically, it is calculated as follows. First, it is assumed that there is a purchase history as shown in FIG.

  Then, a process of accumulating the purchase history shown in FIG. 5 for each customer is performed. At that time, the past purchase amount is a seasonal coefficient (a coefficient set in advance, a number greater than 0 and equal to or less than 1, and so on. ).

  For this seasonal coefficient, for example, the processing month and the months before and after it are set to “1”, and the other months are set to “0.5”. Note that the year is not considered and only the month is determined. In other words, the same month in the previous year is “1”.

  Therefore, for example, assuming that the processing date is July 2014, the aggregation of the product category 1 for the customer 1 is as follows.

  First, customer 1 purchases product category 1 four times, 10/1, 1/1, 4/1, and 7/1. In this case, since the seasonal coefficient is “1” only for 7/1 and “0.5” for the others, the sum of the past purchase amounts multiplied by the seasonal index is as follows.

  1000 x 0.5 (10/1 min) + 800 x 0.5 (1/1 min) + 1000 x 0.5 (4/1 min) + 800 x 1 (7/1 min) ⇒ 2200

  When the other product categories 2 to 5 of the customer 1 and the product categories 3 and 5 of the customer 2 are similarly calculated, the resulting statistical amount is as shown in FIG.

  Next, when [Process 2] in FIG. 4 is performed based on the result, the following is obtained.

  In the case of the product category 1, since the customer who purchased it is only the customer 1, the correlation strength of the other plurality of product categories with respect to the one product category 1 remains the ratio of the customer 1 purchased, For example, the product category 2 is 450 ÷ 2200⇒0.20. Similarly, the product category 3 is 500 ÷ 2200⇒0.23, and the product category 4 is 800 ÷ 2200⇒0.36. The same applies to product category 2.

  In the case of product category 3, the customer who purchased it is customer 1 and customer 2. Therefore, for the total amount of 1500 yen for product category 3, the correlation strength of each product category is the total calculation of customer 1 and customer 2, respectively. Since the product category 1 is purchased at 2,200 yen for the total amount of 1,500 yen in the product category 3, the correlation strength of the product category 1 with respect to the product category 3 becomes 2200 ÷ 1500 → 1.47.

  When calculated in the same manner, the calculation result of the correlation strength for each product category is as shown in FIG.

  In this case, since the product category 5 (yukata) has the highest correlation strength between the product category 1 (shirts) and other product categories, generally the recommended product category for customers who frequently produce product categories 1 (shirts). As a result, sales promotion of the product category 5 (yukata) is performed (for example, the recommended product category recommended to the customer may be determined based only on the correlation strength calculated from the purchase history in this way).

  However, if the same tabulation is performed in April of the following year, the seasonal coefficient changes, so tabulation for each customer and tabulation of correlation strength are as shown in FIGS. 8 and 9, respectively (originally July 2014) After that there should be a purchase history, but it is omitted for explanation).

  In this way, due to the change in seasonal coefficient, the correlation strength of product category 1 (shirts) with other product categories will be highest in product category 3 (coats) if it is 4/1 the following year. For customers who give a lot of category 1 (shirts), sales promotion of product category 3 (coat) is performed as a recommended product category.

  Moreover, since the calculation result can be aggregated by combining data between a plurality of stores, the correlation strength is transmitted from the register device 1 of each store to a server connected via a computer network. Then, by counting the data at the server and then redistributing the results, it is possible to use the correlation strength with a higher sampling number and higher accuracy. In this case, the data is stored in a state in which the division of each correlation strength table (FIGS. 7 and 9) is not performed. As a result, when the totals between the stores are added together, the totals can be obtained by simple addition.

(3) Calculation of Expected Promotion Value by Correlation Prediction When receiving an order for a product from a customer who visited the store (when receiving a product), the control means 5 of the register device 1 stores the customer (visit customer) stored in the storage means 6 10), the expected sales promotion amount for each product category based on the correlation prediction is calculated using the correlation strength as shown in FIG.

  Specifically, it is calculated as follows. First, it is assumed that a customer 3 having a purchase history as shown in FIG. 11 visits a store in July 2014 at a store having correlation strength (correlation strength information) as shown in FIG.

  [Process 1] in FIG. 10 in this case performs the same processing as the calculation of the correlation strength, and the statistical amount for each product category of the customer 3 is as shown in FIG.

  Next, in [Processing 2] in FIG. 10, the sales expected from the correlation strength are aggregated for each product category.

  The expected sales of the product category 2 for the customer 3 are obtained by adding up the expected sales estimated from the correlation strength of FIG. 7 to the statistical amount calculated in FIG. That is, the expected sales for the product category 2 for the customer 3 are calculated as follows because the statistical amount of the product category 1 is 400 yen and the statistical amount of the product category 3 is 1000 yen.

  (400 × 0.20 + 1000 × 0.3) ÷ 2⇒190

  When calculated in the same manner, the results shown in FIG.

  Finally, in [Processing 3] in FIG. 10, by subtracting the statistical amount determined in [Processing 1] from the expected sales determined in [Processing 2], the amount can be expected for sales promotion for each product category. The “expected sales promotion amount” is calculated, and the result is as shown in FIG.

  The expected sales promotion amount shown in FIG. 14 becomes a statistically expected amount by performing sales promotion on the customer 3.

(4) Calculation of expected sales promotion amount based on performance prediction The expected sales promotion amount based on the predicted performance of the customer who visited the store is purchased for a product category (product) that has not been purchased by the customer for a predetermined period, for example, 11 months or more. Multiply the amount by the seasonal coefficient. The result is the expected sales promotion amount based on the performance prediction.

  Specifically, for example, a customer with a purchase history as shown in FIG. 15 visits a cleaning store in October 2014.

  In this case, the product categories that have not appeared for more than 11 months from the date of visit are the product category 3 (coat) and the product category 4 (yukata), and these two product categories are extracted from the purchase history of the customer.

  Since product category 3 (coat) is issued in April and October, the seasonal coefficients are “0.5” and “1”, respectively. For this reason, the expected sales promotion amount for the product category 3 (coat) is calculated as follows.

  1000 × 0.5 (4/1 minute) + 1000 × 1 (10/1 minute) ⇒1500

  Similarly, since the seasonal coefficient for product category 4 (Yukata) is “0.5”, the expected sales promotion amount for this product category 4 (Yukata) is 800 × 0.5 (7/1 minute) ⇒400. .

(5) Recommendation Product Classification Coupon Issuance In the setting of the register device 1, for example, when it is set to issue and issue a recommended product classification coupon (printed material) together with a slip, the processing shown in FIG. 16 is performed. Do.

  The specific processing is the result calculated from different customers and different purchase histories. For convenience, when the customers illustrated in (3) and (4) visited the store in October 2014, it was based on correlation prediction. An example is given assuming that the expected sales promotion amount and the expected sales promotion amount based on the actual result are the results calculated in the above (3) and (4), respectively.

  For example, assume that the sales promotion information for each product category is as shown in FIG.

  In this case, first, according to [Process 1] of FIG. 16, the maximum value (higher amount) for each product category is obtained by comparing both results from the results of (3) and (4). When the maximum value is multiplied by the set magnification, the result is as shown in FIG.

  By multiplying the set magnification in this way, it is possible to remove items that do not actually require sales promotion (for example, an admission fee) or to preferentially promote products with a high profit margin. The set magnification can be changed and set individually for each product category.

  Next, according to [Processing 2] in FIG. 16, since this customer has a purchase history as shown in FIG. 15, the product category 1 (shirt) is not subject to coupon issue.

  Further, assuming that there is a past sales promotion history (recommended product history) as shown in FIG. 19, a sales promotion history within one month (within a predetermined period) for the corresponding customer 3 is for product classification 3. For this reason, avoiding the product category 3, the product category 4 having the maximum expected sales promotion amount among the product categories 2, 4, and 5 becomes the recommended product category, and is the object of coupon issue.

  Then, according to [Process 3] in FIG. 16, for example, a coupon as shown in FIG. 20 is printed and the printed record is added to the sales promotion history in FIG.

  In addition, it is not limited to the structure which avoids the merchandise classification related to the sales history within the past month. For example, if the sales history is within the past month with respect to the last calculated sales promotion amount, You may make it multiply x3%. That is, for example, in the case of the next day, since 3% is multiplied, if the expected sales promotion amount is 1000 yen, it is 30 yen.

(6) Screen display of recommended product category In the setting of the register device 1, for example, when the recommended product category is set to be displayed on the screen, the processing shown in FIG. 21 is performed.

  The specific process is a result calculated from different customers and different purchase histories, but for the sake of simplicity, the customer exemplified in (3) and (4) An example will be given assuming that the expected sales promotion amount based on the correlation prediction and the expected sales promotion amount based on the actual prediction are the results calculated in (3) and (4), respectively, when visiting the store.

  In this case, for example, the screen display as shown in FIG. 22 is performed, that is, the message screen which is the recommended product display screen shown in FIG. 22 is displayed on the touch panel display unit 4 as a pop-up screen, for example. Process.

  The message in the first half of the message screen (a product that has not appeared recently) is a message created by [Process 1] in FIG. 21. Specifically, according to [Process 1], the message (4) If the result (expected sales promotion amount based on actual result prediction) is multiplied by the set magnification shown in FIG. 18, the result (final expected sales promotion amount) shown in FIG. 23 is obtained.

  From this result, it is assumed that the maximum value × the set magnification is displayed in the order of product category 3 and product category 4, which is the order in which the effect greater than 0 is expected, and the first half message of FIG. 22 is obtained.

  The message in the latter half of the message screen (the product you want to have ...) is the message created by [Process 2] in FIG. 21. Specifically, according to [Process 2] When the result (3) (expected sales promotion amount based on correlation prediction) is multiplied by the set magnification shown in FIG. 18, the result (final expected sales promotion amount) shown in FIG. 24 is obtained.

  From this, messages are created in the order in which the effect of maximum value × set magnification larger than 0 is expected except for the product classifications 3 and 4 which are the targets in [Process 1], and the message in the latter half of FIG. 22 is obtained.

  The clerk who sees the message screen including the message thus obtained recommends “coat, yukata, trousers” as a recommended product to the customer who visited the store. Moreover, if there exists the coupon issued by said (5), it will be given to a customer.

  According to such a register device 1, it is possible to recommend an appropriate product (recommended product) according to each customer who has visited the cleaning store by issuing a coupon for the recommended product category or displaying a screen for the recommended product category. Therefore, it leads to an order for cleaning, and sales promotion (order reception promotion) can be achieved.

  In addition, the register apparatus 1 is not limited to the thing for cleaning businesses installed in a cleaning store, For example, you may install in stores other than a cleaning store.

  In addition, the register device 1 is not limited to the configuration including the touch panel display unit 4, and may be a configuration including an operation unit (input unit) and a display unit separately, for example.

  Further, for example, the message (recommended product classification) shown in FIG. 22 may be generated as sound from the speaker means 8, and means for outputting the recommended product classification to the customer is arbitrary.

  Further, for example, a part of the slip on which the sales price is printed may be a recommended product classification coupon. In other words, the slip and the coupon may be composed of a single sheet of paper.

  Further, for example, a recommended product category coupon may be periodically transmitted to a customer's e-mail address or the like.

1 Register device
11 First calculation means
12 Second calculation means
13 Recommended product category decision means

Claims (3)

A register device that settles the sales price based on the price of a product,
Calculate the correlation strength of multiple product categories for one product category for each product category from the purchase history of the customer, and use this calculated correlation strength to calculate the expected sales promotion amount for each product category based on the correlation prediction First calculating means for calculating;
A second calculation means for extracting a product category that has not been purchased for a predetermined period or more from the purchase history of the customer, and calculating an expected sales promotion amount for each product category based on the actual forecast for the extracted product category;
And a recommended product category determining unit that determines a recommended product category based on the expected sales promotion amount calculated by the first calculation unit and the expected sales promotion amount calculated by the second calculation unit. Register device. The recommended product category determination unit compares the expected sales promotion amount calculated by the first calculation unit and the expected sales promotion amount calculated by the second calculation unit when issuing a coupon for the recommended product category. select the maximum value for each product category by, cash register according to claim 1, characterized in that this selected maximum value set magnification results final promotional expected amounts multiplied. When the recommended product classification determining means displays the recommended product classification on the screen, the result of multiplying the expected sales promotion amount calculated by the first calculation means by the set magnification is set as the final expected sales promotion amount and the second calculation cash register according to claim 1, wherein that the result obtained by multiplying the set computed promotional expected amount ratio by means the final promotional expected amount.

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