CN113256330A - Information delivery effect attribution method and device - Google Patents

Information delivery effect attribution method and device Download PDF

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CN113256330A
CN113256330A CN202110548351.6A CN202110548351A CN113256330A CN 113256330 A CN113256330 A CN 113256330A CN 202110548351 A CN202110548351 A CN 202110548351A CN 113256330 A CN113256330 A CN 113256330A
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information
users
subset
time period
offline
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刘甜
张博洋
崔波
杜睿桓
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Beijing Wodong Tianjun Information Technology Co Ltd
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Beijing Wodong Tianjun Information Technology Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0242—Determining effectiveness of advertisements
    • G06Q30/0245—Surveys
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location

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Abstract

The invention discloses an information delivery effect attribution method and device, and relates to the technical field of computers. One embodiment of the method comprises: determining an experimental group and a control group; respectively grouping the experimental group and the comparison group according to exposure behaviors of the experimental group and the comparison group before the offline information release and during the offline information release to obtain a first subset, a second subset, a third subset and a fourth subset; and calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first subset, the second subset, the third subset and the fourth subset. According to the embodiment, the influence of the online channel and the offline channel on the user is fully considered by respectively analyzing the users influenced by different advertisements, and the attribution can be accurately carried out.

Description

Information delivery effect attribution method and device
Technical Field
The invention relates to the technical field of computers, in particular to an information delivery effect attribution method and device.
Background
It is common and effective to evaluate the effectiveness of advertisements using double-insertion methods. When the advertisement is applied to the experimental group (managed group), the increase of the purchasing power cannot indicate the effect of the advertisement, because the purchasing power naturally increases under the condition of not applying the advertisement, and if the purchasing power is greatly increased, the increase of the purchasing power is very obvious. It is desirable to obtain a value for purchasing power without applying an advertisement. The double difference method may provide this value by finding a population sufficiently similar to the experimental group, which may be referred to as a control group. Thus, the increase in purchasing power over time Δ t after application of an advertisement in the experimental group can be compared to the increase in purchasing power Δ c without exposure to the advertisement in the control group, and the effectiveness of the advertisement can be expressed as Δ t- Δ c. However, due to the popularization of mobile phones, users affected by offline advertisements are inevitably affected by online advertisements, and if the advertisement effects are calculated by neglecting the mutual effects, the calculation is obviously inaccurate. Such as placing an offline advertisement in a cell, passing users may be recorded by the app as having their exposure to the offline advertisement, and among these users, some may have searched the brand and be exposed to the online advertisement, and some may not. And the simple double difference method is used for calculating users with different exposure degrees by using the same method, the proportion of the online advertisements and the offline advertisements in purchasing power is the same, and the influence of different channels on each other is not considered, so that the problems of unreasonable and inaccurate attribution results are caused. Moreover, it can only calculate whether there is an advertisement effect, but cannot calculate the advertisement effect under the line and the advertisement effect on the line independently, and the attribution is inaccurate. After two advertisement effects are mixed up, if the offline advertisement is not effective actually, but the offline advertisement is considered to be effective by mistake because the online advertisement is effective, the offline advertisement is put on the screen according to the same strategy, and the input-output ratio of the real advertisement fee is maintained at a lower level in the future, so that the resource waste is caused. Ad attribution refers to the assessment of the value of the contribution of different contact points (advertisements) to achieving a conversion Goal (GMV) for different marketing channels experienced by a user's trip over a particular period of time. Gmv (gross merchandisc volume) means total volume of a transaction within a certain period of time. Within the e-commerce website definition is the website deal amount, which actually refers to the amount of the order taken, including the paid and unpaid portions.
Disclosure of Invention
In view of this, the embodiment of the present invention provides an information delivery effect attribution method and apparatus, which can find a control group for an experimental group affected by advertisements of different degrees; respectively calculating the advertisement effect aiming at users with different exposure behaviors; the putting effect of the online advertisement and the offline advertisement and the superposition effect of the online advertisement and the offline advertisement when the online advertisement and the offline advertisement are simultaneously applied are respectively calculated, and accurate attribution results can be obtained.
In order to achieve the above object, according to an aspect of the embodiments of the present invention, there is provided an information delivery effect attribution method, including:
the server determines users of the experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
the server judges whether the experimental group users have online exposure experiences in a first target time period and a second target time period, and groups the experimental group users according to a judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and the server calculates the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first set, the second subset, the third subset and the fourth subset.
Optionally, the process of determining, by the server, the users in the experimental group based on the geographical locations of the offline information placement positions and the action tracks of the users includes: the server judges whether the minimum distance between the user and the offline information release position is smaller than or equal to a first threshold value or not according to the action track of the user and the geographic position of the offline information release position; and if so, determining that the user has the experience of exposure at the offline information release position in a first target time period, and taking the user as an experimental group user.
Optionally, the server obtains the action track of the user according to the following process:
the server acquires an action track of the user in the first target time period through a target application program; or
The server acquires the action track of the user in the first target time period through the image information acquired by the target camera equipment; and the distance between the target camera equipment and the offline information release position is smaller than or equal to a second threshold value.
Optionally, the step of determining, by the server, the comparison group of users based on the geographic location of the offline information placement location and a preset similarity condition includes:
taking the offline information release position as an experimental release position, and determining a comparison release position according to the geographic position of the experimental release position and the similarity limiting condition;
and determining a control group user based on the control putting position.
Optionally, the process of determining the control group comprises: and taking the offline information release position as an experiment release position, and determining a contrast release position according to the geographic position of the experiment release position and the similarity limiting condition.
Optionally, the similarity limitation condition includes: and the Euclidean distance between the experimental throwing position and the comparison throwing position is within a preset interval.
Optionally, the target application includes an application for delivering the information online;
the similarity limitation further comprises one or more of the following:
the difference value of the room prices between the cell where the experimental release position is located and the cell where the comparison release position is located is within a first preset range;
the difference of the number of people acquired by the target application program between the cell where the experimental release position is located and the cell where the comparison release position is located is within a second preset range;
the difference of the number of registrants on the application program for putting the information on the line between the cell where the experimental putting position is located and the cell where the comparison putting position is located is within a third preset range;
and the difference of the total information conversion data before the information line is released between the cell where the experimental release position is located and the cell where the comparison release position is located is within a fourth preset range.
Optionally, the first subset of users have both an under-line exposure experience and an over-line exposure experience for a first target time period and no under-line exposure experience and over-line exposure experience for a second target time period;
a second subset of users having only an offline exposure experience, no online exposure experience, and no offline exposure experience and online exposure experience for a second target time period;
a third subset of users having only an inline exposure experience, no offline exposure experience for the first target time period, and no inline exposure experience and no offline exposure experience for the second target time period;
the fourth subset of users have no under-line exposure experience and no over-line exposure experience for the first target time period and no under-line exposure experience and no over-line exposure experience for the second target time period.
Optionally, calculating, according to the information conversion data of the first set, the second subset, the third subset, and the fourth subset, a contribution degree of the offline information placement to the user conversion behavior, a contribution degree of the online information placement to the user conversion behavior, and a contribution degree of the offline information placement and the online information placement to the user conversion behavior together includes:
calculating a first difference value between the information conversion data of the users of the fourth subset in the first target time period and the information conversion data of the users in the second target time period, and taking the first difference value as a natural growth ratio;
calculating a second difference value between the information conversion data of the users of the third subset in the first target time period and the information conversion data in the second target time period, calculating a third difference value between the second difference value and the natural growth rate, and taking the third difference value as an on-line information delivery growth rate;
calculating a fourth difference value between the information conversion data of the users of the second subset in the first target time period and the information conversion data of the users of the second subset in the second target time period, calculating a fifth difference value between the fourth difference value and the natural growth rate, and taking the fifth difference value as an offline information delivery growth rate;
calculating a sixth difference value between the information conversion data of the users of the first subset in the first target time period and the information conversion data in the second target time period, calculating a seventh difference value between the sixth difference value and the natural growth ratio, and taking the seventh difference value as a combined growth ratio of online information delivery and offline information delivery;
and determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the online information delivery increase ratio, the offline information delivery increase ratio and the combined increase ratio.
Optionally, determining, according to the natural growth ratio, the online information delivery growth ratio, the offline information delivery growth ratio, and the combined growth ratio, a degree of contribution of the offline information delivery to the user conversion behavior, a degree of contribution of the online information delivery to the user conversion behavior, and a degree of contribution of both the offline information delivery and the online information delivery to the user conversion behavior includes:
calculating the contribution degree of the offline information delivery to the user conversion behavior according to the sum of the information conversion data of the users of the first subset and the users of the second subset in a second target time period and the offline information delivery increase rate;
calculating the contribution degree of the online information delivery to the user conversion behavior according to the information conversion data of the users of the first subset in a second target time period and the online information delivery increase rate;
and calculating the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the users of the first subset in the second target time period and the combined growth ratio.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an information delivery effect attribution device including:
the determining module is used for determining users of the experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
the grouping module is used for judging whether the experiment group users have on-line exposure experience in a first target time period and a second target time period, and grouping the experiment group users according to the judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and the attribution module is used for calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first set, the second subset, the third subset and the fourth subset.
Optionally, the determining module is further configured to: judging whether the minimum distance between the user and the offline information release position is smaller than or equal to a first threshold value or not according to the action track of the user and the geographic position of the offline information release position; and if so, determining that the user has the experience of exposure at the offline information release position in a first target time period, and taking the user as an experimental group user.
Optionally, the determining module is further configured to: acquiring an action track of a user in the first target time period through a target application program; or acquiring the action track of the user in the first target time period through image information acquired by the target camera equipment; and the distance between the target camera equipment and the offline information release position is smaller than or equal to a second threshold value.
Optionally, the determining module is further configured to: taking the offline information release position as an experimental release position, and determining a comparison release position according to the geographic position of the experimental release position and the similarity limiting condition; and determining a control group user based on the control putting position.
Optionally, the similarity limitation condition includes: and the Euclidean distance between the experimental throwing position and the comparison throwing position is within a preset interval.
Optionally, the target application includes an application for delivering the information online;
the similarity limitation further comprises one or more of the following:
the difference value of the room prices between the cell where the experimental release position is located and the cell where the comparison release position is located is within a first preset range;
the difference of the number of people acquired by the target application program between the cell where the experimental release position is located and the cell where the comparison release position is located is within a second preset range;
the difference of the number of registrants on the application program for putting the information on the line between the cell where the experimental putting position is located and the cell where the comparison putting position is located is within a third preset range;
and the difference of the total information conversion data before the information line is released between the cell where the experimental release position is located and the cell where the comparison release position is located is within a fourth preset range.
Optionally, the first subset of users have both an under-line exposure experience and an over-line exposure experience for a first target time period and no under-line exposure experience and over-line exposure experience for a second target time period;
a second subset of users having only an offline exposure experience, no online exposure experience, and no offline exposure experience and online exposure experience for a second target time period;
a third subset of users having only an inline exposure experience, no offline exposure experience for the first target time period, and no inline exposure experience and no offline exposure experience for the second target time period;
the fourth subset of users have no under-line exposure experience and no over-line exposure experience for the first target time period and no under-line exposure experience and no over-line exposure experience for the second target time period.
Optionally, the attribution module is further to:
calculating a first difference value between the information conversion data of the users of the fourth subset in the first target time period and the information conversion data of the users in the second target time period, and taking the first difference value as a natural growth ratio;
calculating a second difference value between the information conversion data of the users of the third subset in the first target time period and the information conversion data in the second target time period, calculating a third difference value between the second difference value and the natural growth rate, and taking the third difference value as an on-line information delivery growth rate;
calculating a fourth difference value between the information conversion data of the users of the second subset in the first target time period and the information conversion data of the users of the second subset in the second target time period, calculating a fifth difference value between the fourth difference value and the natural growth rate, and taking the fifth difference value as an offline information delivery growth rate;
calculating a sixth difference value between the information conversion data of the users of the first subset in the first target time period and the information conversion data in the second target time period, calculating a seventh difference value between the sixth difference value and the natural growth ratio, and taking the seventh difference value as a combined growth ratio of online information delivery and offline information delivery;
and determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the online information delivery increase ratio, the offline information delivery increase ratio and the combined increase ratio.
Optionally, the attribution module is further to:
calculating the contribution degree of the offline information delivery to the user conversion behavior according to the sum of the information conversion data of the users of the first subset and the users of the second subset in a second target time period and the offline information delivery increase rate;
calculating the contribution degree of the online information delivery to the user conversion behavior according to the information conversion data of the users of the first subset in a second target time period and the online information delivery increase rate;
and calculating the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the users of the first subset in the second target time period and the combined growth ratio.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; a storage device, configured to store one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the information delivery effect attribution method according to the embodiment of the present invention.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer-readable medium having a computer program stored thereon, wherein the computer program is configured to implement the information delivery effect attribution method of the embodiments of the present invention when executed by a processor.
One embodiment of the above invention has the following advantages or benefits: because the users who collect offline information release positions are used as experimental groups, and a comparison group with higher similarity is searched according to the geographic position; grouping the users of the experimental group and the control group according to the exposure behavior of the users, and calculating information conversion data of each group of users; according to the information conversion data of different users, calculating the contribution degree of offline information release to the user conversion behavior, the contribution degree of online information release to the user conversion behavior and the contribution degree of offline information release and online information release to the user conversion behavior together, so that a comparison group can be found for experiment groups influenced by different degrees and different channels; respectively calculating information releasing effects aiming at users with different exposure behaviors; the launching effect of the online information launching and the offline information launching are respectively calculated, and the superposition effect of the online information launching and the offline information launching when the online information launching and the offline information launching are simultaneously applied can obtain accurate attribution results.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of an information delivery effect attribution method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a sub-flow of an information delivery effect attribution method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an experimental group and a control group of the information delivery effect attribution method according to the embodiment of the present invention;
fig. 4 is a schematic view of a main flow of an information delivery effect attribution method according to another embodiment of the present invention;
fig. 5 is a schematic diagram of main modules of an information delivery effect attribution apparatus according to an embodiment of the present invention;
FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of an information delivery effect attribution method according to an embodiment of the present invention. The method may be applied to a server. As shown in fig. 1, the method includes:
step S101: determining users of an experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
step S102: judging whether the experimental group users have online exposure experiences in a first target time period and a second target time period, and grouping the experimental group users according to a judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
step S103: and calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first subset, the second subset, the third subset and the fourth subset.
In this embodiment, the information of offline impressions and the information of online impressions may be advertisement information.
In the first step of this embodiment, users in the experimental group and users in the control group are obtained. The on-line information is released and the exposure is large, a large number of exposure users exist, and the comparison group which is similar to the users as far as possible is not easy to find for the users, so that the experimental group and the comparison group are determined by releasing the off-line information. The offline information release is fixed on the information release position, so that the user passing through the information release position can be collected through the action track of the user or the camera equipment near the offline information release position. Specifically, as shown in fig. 2, the process of collecting the user who passes through the information slot by the action track of the user includes:
step S201: and acquiring the action track of the user in the first target time period. The action track of the user can be obtained through the target application program. The target application may include an application that places the information online, or may include an application that is unrelated to the information.
Step S202: and judging whether the user has the experience of exposure at the offline information release position within a first target time period or not according to the action track and the geographic position of the offline information release position. Specifically, whether the minimum distance between the user and the offline information release position is smaller than or equal to a first threshold value or not can be judged according to the action track and the geographic position of the offline information release position; if so, determining that the user has an experience of exposure at the offline information placement site within a first target time period.
Step S203: and if the user has the experience of exposure at the preset offline information release position in the first target time period, taking the user as an experimental group user.
In the present embodimentAnd if the distance between the user and the offline information release position is less than or equal to the first threshold value d, the user is determined to be exposed to the offline information release position, and if the distance is greater than the first threshold value d, the user is determined not to be exposed. The user who obtains in this way is when having opened the application program app that is relevant with information of putting through the off-line information place of puttingrelativeAnd other application apps that are not related to offline informationirrelevantAnd (4) the user who logs in. Specifically, as shown in fig. 3, d is 300 meters, and an experimental group user set T, T is obtained { x | Eu (x ═ x |)pos,Tpos)＜300m,x∈apprelative∪appirrelevantX is user, xposAs the latitude and longitude of the user,posand (4) putting the longitude and latitude of the offline information of the experimental group. The distance is positively correlated to whether the user has seen the advertisement, the closer the distance, the more likely the user has seen the advertisement, the distance may be set between 50 meters and 500 meters, taking into account the error in app positioning, but the user may not be captured if the distance is too small and the app daily activity is low. This embodiment requires the user to download the target application and allow the target application to obtain the positioning information.
The process of collecting the users passing through the information release position according to the camera equipment near the offline information release position comprises the following steps: and determining a user with offline exposure experience in a first target time period according to image information acquired by the target camera equipment, wherein the distance between the target camera equipment and the offline information release position is less than or equal to a second threshold value. For example, an image pickup apparatus within 10 meters of the offline information placement position is set as the target image pickup apparatus. The image data is obtained through the target camera equipment, real users who see the information are obtained through a face recognition technology, and user names of the users are related.
After the experimental group is obtained, in order to obtain a control group as similar as possible, it is necessary to select an offline information placement site that can be used as a control group according to the geographical location. For convenience of description, the offline information release positions corresponding to the experimental groups are referred to as experimental release positions for short, and the offline information release positions corresponding to the comparison groups are referred to as comparison release positions for short. In this embodiment, the geographic position of the release position can be determined according to the experimentAnd setting similarity limit conditions, and determining a contrast delivery position. Specifically, the Euclidean distance between the experimental release position and the comparison release position is within a preset interval. Comparing the longitude and latitude C of the throwing positionposLongitude and latitude T of experimental throwing positionposIt should be satisfied that the euclidean distance between the two is close, but not so close as to contend for the user. Because the offline information placement positions are related to the cells, after the cell information is collected, the information of the cells can be used as the crowd characteristics to measure the similarity of the offline information placement positions, and therefore, the similarity limiting condition can further include the following four optional conditions:
the difference value of the room prices between the cell where the experimental release position is located and the cell where the comparison release position is located is within a first preset range;
the difference of the number of people acquired by the target application program between the cell where the experimental release position is located and the cell where the comparison release position is located is within a second preset range;
the difference of the number of registrants on the application program for putting the information on the line between the cell where the experimental putting position is located and the cell where the comparison putting position is located is within a third preset range;
and the difference of the total information conversion data before the information line is released between the cell where the experimental release position is located and the cell where the comparison release position is located is within a fourth preset range.
The first preset range, the second preset range, the third preset range and the fourth preset range may be the same or different, and the present invention is not limited herein. As an example, the first preset range, the second preset range, the third preset range and the fourth preset range may all be 20%, and then the optional condition is:
(1) the difference of the average price per square meter between the two cells is within 20 percent;
(2) two cell pass apprelative，appirrelevantThe difference of the number of people obtained should be within 20;
(3) two cell in apprelativeThe difference of the registered user number is within 20 percent;
(4) the total gmv for the two cells should be within 20% of the difference within 14 days before the online placement of the ad.
If the information is an office building advertisement or other category of advertisement, then point 1() is not applicable, but users of office building work can still be collected, using points (2) - (4) to get the nature of the office building advertisement. If there is no condition to impose any similarity restriction, at least the above Euclidean distance restriction is implemented as the regional similarity restriction. As an example, the present embodiment is set to be less than 10km and more than 3 km: eu (C) for 3km & ltPos，TPos) < 10km, e.g. 4km apart, as shown in fig. 3. After the experimental group was obtained, in the same manner, a control group user C was obtained: for example, the distance d between the user and the ad slot of the control group is less than 300m, i.e. it is considered that if there is an ad, the set of control group users C, C ═ y | Eu (y ═ Eu) may be exposedpos，Cpos)＜300m，y∈apprelative∪appirrelevantY is user, yposAs latitude and longitude of the user, CposThe longitude and latitude of the offline advertising space are compared. If there are multiple qualified C groups, one of them is randomly selected.
In this embodiment, the number of the similarity limiting conditions is selected to be positively correlated with the similarity, and the more the similarity limiting conditions are selected, the more the control placement site is similar to the experimental placement site.
The second step of the embodiment is to further group the users according to the on-line exposure behavior of the users during and before the off-line information delivery, divide the users in the experimental group into the first subset t1 and the second subset t2, and divide the users in the control group into the first subset c1 and the second subset c 2. The behavior of the lab team's line-down exposure occurred during the information line-down shot, without any line-down exposure behavior prior to the shot. For example, the offline information is delivered for 14 days from 2021 year 01 month 15 to 2021 year 01 month 28, i.e., the first target time period is from 2021 year 01 month 15 to 2021 year 01 month 28. The time period before the release 2021 year 01-14-month 2021 year 01 can be regarded as the second target time period. Setting a second target time period lineSet of top exposure users as O1The experimental group of users not on-line exposed in the second target period is T' { x | x ∈ T ∞ (1-O)1) A control group of users not exposed within the second target time period is C' ═ x ∈ C ∞ (1-O)1)}. Assuming that the set of exposure users within the first target time period is O, T1 ═ x ∈ T '# O }, T2 ═ x ∈ T' # (1-O) }, C1 ═ x ∈ C '# O }, and C2 ═ x ∈ C' # (1-O) }. The users of the first subset t1 have both an under-line exposure experience and an over-line exposure experience during the first target time period and have no under-line exposure experience and over-line exposure experience during the second target time period. The users of the second subset t2 have both an under-line exposure experience and an over-line exposure experience during the first target time period and have no under-line exposure experience and over-line exposure experience during the second target time period. The users of the third subset c1 have both an under-line exposure experience and an over-line exposure experience during the first target time period and have no under-line exposure experience and over-line exposure experience during the second target time period. The users of the fourth subset c2 have both an under-line exposure experience and an over-line exposure experience during the first target time period and have no under-line exposure experience and over-line exposure experience during the second target time period.
After grouping, information translation data for each subset is calculated for the first target time period and the second target time period, which in this embodiment includes the GMV. Gmv (gross merchandisc volume) means total volume of a transaction within a certain period of time. Within the e-commerce website definition is the website deal amount, which actually refers to the amount of the order taken, including the paid and unpaid portions. The general calculation formula is:
Figure BDA0003074451880000141
where x ∈ G, p ═ { pre, cur }, G ═ t1, t2, c1, c2},
Figure BDA0003074451880000142
the cost of user x during P. For example, the cost of the t1 group during pre can be expressed as:
Figure BDA0003074451880000143
the information conversion data of each packet calculated according to the above formula is shown in table 1 below:
table 1:
Figure BDA0003074451880000144
Figure BDA0003074451880000151
the third step of this embodiment is to calculate, through the information conversion data of each subset in the first target time period and the second target time period, a contribution degree of offline information placement to the user conversion behavior, a contribution degree of online information placement to the user conversion behavior, and a contribution degree of offline information placement and online information placement to the user conversion behavior together. For example, since the users of the first subset are exposed to the on-line exposure and the off-line exposure at the same time, the information transformation data of the users of the first subset grows to include natural growth, factors of information on the line, factors of information off the line, and factors resulting from the presence of both information on the line and information off the line. For example, after a user sees an offline advertisement, thinks that the product is used up, and then logs in an application program related to the offline advertisement and then exposes the online information, the online advertisement is a hundred-element and three-element product, and the user thinks that it is cost-effective to buy three pieces and then place a single three-element product, and thus the cost is 100-element. All users in the final experimental group purchase 10000 yuan, wherein 5000 yuan is brought by non-advertising factors, 1500 yuan is brought by online advertisements, 2500 yuan is brought by offline advertisements, and 1000 yuan is brought by the overlapping influence of the two advertisements.
Specifically, the process of calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior, and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together includes:
calculating a first difference value between the information conversion data of the users of the fourth subset in the first target time period and the information conversion data of the users in the second target time period, and taking the first difference value as a natural growth ratio;
calculating a second difference value between the information conversion data of the users of the third subset in the first target time period and the information conversion data in the second target time period, calculating a third difference value between the second difference value and the natural growth rate, and taking the third difference value as an on-line information delivery growth rate;
calculating a fourth difference value between the information conversion data of the users of the second subset in the first target time period and the information conversion data of the users of the second subset in the second target time period, calculating a fifth difference value between the fourth difference value and the natural growth rate, and taking the fifth difference value as an offline information delivery growth rate;
calculating a sixth difference value between the information conversion data of the users of the first subset in the first target time period and the information conversion data in the second target time period, calculating a seventh difference value between the sixth difference value and the natural growth ratio, and taking the seventh difference value as a combined growth ratio of online information delivery and offline information delivery;
and determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the online information delivery increase ratio, the offline information delivery increase ratio and the combined increase ratio.
More specifically, the natural growth ratio, the online information delivery growth ratio, the offline information delivery growth ratio, and the combined growth ratio are calculated according to the following expression (1):
Figure BDA0003074451880000161
where n denotes a natural growth ratio, o denotes an online information delivery growth ratio, f denotes an offline information delivery growth ratio, and x denotes a combined growth ratio.
After obtaining the natural growth rate, the online information delivery growth rate, the offline information delivery growth rate and the combined growth rate, determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the following processes:
calculating the contribution degree of the offline information delivery to the user conversion behavior according to the sum of the information conversion data of the users of the first subset and the users of the second subset in a second target time period and the offline information delivery increase rate;
calculating the contribution degree of the online information delivery to the user conversion behavior according to the information conversion data of the users of the first subset in a second target time period and the online information delivery increase rate;
and calculating the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the users of the first subset in the second target time period and the combined growth ratio.
The present embodiment may further calculate a contribution degree of natural growth to the user conversion behavior according to a sum of information conversion data of the users of the first subset and the users of the second subset in the second target time period and the natural growth ratio.
The impact of splitting ads for the experimental group T. Analysis of the two groups of users, t1 and t2, respectively, is required to obtain the final GMV,
Figure BDA0003074451880000171
the basic GMV is divided into four factors, namely the natural growth, and the GMV is usedbaseExpressed, calculated from equation (2); GMV derived from offline advertising, and only the portion derived from offline advertising, is GMVofflineExpressed, calculated from equation (3); from and only from the part of the online advertising track, using GMVonlineExpressed, calculated from equation (4); GMV that must be produced by seeing two advertisements simultaneously, from GMVinteractExpressed, calculated from equation (5). Finally, GMVCan be split by the four parts, namely GMV ═ GMVbase+GMVoffline+GMVonline+GMVinteract。
Figure BDA0003074451880000172
Figure BDA0003074451880000173
Figure BDA0003074451880000174
Figure BDA0003074451880000175
According to the information delivery effect attribution method, the users affected by different information delivery channels are analyzed respectively, the influence of the online channel and the offline channel on the users is fully considered, and accurate attribution results can be obtained.
Fig. 4 is a schematic diagram of a main flow of an information delivery effect attribution method according to another embodiment of the present invention, in this embodiment, information about offline delivery and information about offline delivery may be advertisement information. As shown in fig. 4, the method is mainly divided into three steps: (1) collecting users of offline information release positions as experimental groups, and searching for a control group with higher similarity according to the geographic position; (2) grouping the users of the experimental group and the control group according to the exposure behavior of the users, and calculating information conversion data of each group of users; (3) and calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of different users.
Fig. 5 is a schematic diagram of main blocks of an information delivery effect attribution apparatus 500 according to an embodiment of the present invention, and as shown in fig. 5, the apparatus 500 includes:
a determining module 501, configured to determine users of an experimental group based on the geographic location of the offline information release site and the action trajectory of the user; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
a grouping module 502, configured to determine whether the experiment group users have online exposure experiences in a first target time period and a second target time period, and group the experiment group users according to a determination result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and an attribution module 503, configured to calculate, according to the information conversion data of the first set, the second subset, the third subset, and the fourth subset, a contribution degree of the offline information impression to the user conversion behavior, a contribution degree of the online information impression to the user conversion behavior, and a contribution degree of the offline information impression and the online information impression together to the user conversion behavior.
Optionally, the determining module 501 is further configured to: the server judges whether the minimum distance between the user and the offline information release position is smaller than or equal to a first threshold value or not according to the action track of the user and the geographic position of the offline information release position; and if so, determining that the user has the experience of exposure at the offline information release position in a first target time period, and taking the user as an experimental group user.
Optionally, the determining module 501 is further configured to: acquiring an action track of a user in the first target time period through a target application program; or acquiring the action track of the user in the first target time period through image information acquired by the target camera equipment; and the distance between the target camera equipment and the offline information release position is smaller than or equal to a second threshold value.
Optionally, the determining module 501 is further configured to: taking the offline information release position as an experimental release position, and determining a comparison release position according to the geographic position of the experimental release position and the similarity limiting condition; and determining a control group user based on the control putting position.
Optionally, the similarity limitation condition includes: and the Euclidean distance between the experimental throwing position and the comparison throwing position is within a preset interval.
Optionally, the target application includes an application for delivering the information online;
the similarity limitation further comprises one or more of the following:
the difference value of the room prices between the cell where the experimental release position is located and the cell where the comparison release position is located is within a first preset range;
the difference of the number of people acquired by the target application program between the cell where the experimental release position is located and the cell where the comparison release position is located is within a second preset range;
the difference of the number of registrants on the application program for putting the information on the line between the cell where the experimental putting position is located and the cell where the comparison putting position is located is within a third preset range;
and the difference of the total information conversion data before the information line is released between the cell where the experimental release position is located and the cell where the comparison release position is located is within a fourth preset range.
Optionally, the first subset of users have both an under-line exposure experience and an over-line exposure experience for a first target time period and no under-line exposure experience and over-line exposure experience for a second target time period;
a second subset of users having only an offline exposure experience, no online exposure experience, and no offline exposure experience and online exposure experience for a second target time period;
a third subset of users having only an inline exposure experience, no offline exposure experience for the first target time period, and no inline exposure experience and no offline exposure experience for the second target time period;
the fourth subset of users have no under-line exposure experience and no over-line exposure experience for the first target time period and no under-line exposure experience and no over-line exposure experience for the second target time period.
Optionally, the attribution module 503 is further configured to:
calculating a first difference value between the information conversion data of the users of the fourth subset in the first target time period and the information conversion data of the users in the second target time period, and taking the first difference value as a natural growth ratio;
calculating a second difference value between the information conversion data of the users of the third subset in the first target time period and the information conversion data in the second target time period, calculating a third difference value between the second difference value and the natural growth rate, and taking the third difference value as an on-line information delivery growth rate;
calculating a fourth difference value between the information conversion data of the users of the second subset in the first target time period and the information conversion data of the users of the second subset in the second target time period, calculating a fifth difference value between the fourth difference value and the natural growth rate, and taking the fifth difference value as an offline information delivery growth rate;
calculating a sixth difference value between the information conversion data of the users of the first subset in the first target time period and the information conversion data in the second target time period, calculating a seventh difference value between the sixth difference value and the natural growth ratio, and taking the seventh difference value as a combined growth ratio of online information delivery and offline information delivery;
and determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the online information delivery increase ratio, the offline information delivery increase ratio and the combined increase ratio.
Optionally, the attribution module 503 is further configured to:
calculating the contribution degree of the offline information delivery to the user conversion behavior according to the sum of the information conversion data of the users of the first subset and the users of the second subset in a second target time period and the offline information delivery increase rate;
calculating the contribution degree of the online information delivery to the user conversion behavior according to the information conversion data of the users of the first subset in a second target time period and the online information delivery increase rate;
and calculating the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the users of the first subset in the second target time period and the combined growth ratio.
The device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
Fig. 6 shows an exemplary system architecture 600 of an information delivery effect attribution method or an information delivery effect attribution apparatus to which an embodiment of the present invention can be applied.
As shown in fig. 6, the system architecture 600 may include terminal devices 601, 602, 603, a network 604, and a server 605. The network 604 serves to provide a medium for communication links between the terminal devices 601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. Various communication client applications, such as shopping applications, web browser applications, search applications, instant messaging tools, mailbox clients, social platform software, and the like, may be installed on the terminal devices 601, 602, and 603.
The terminal devices 601, 602, 603 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 605 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 601, 602, and 603. The background management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (e.g., target push information and product information) to the terminal device.
It should be noted that the information delivery effect attribution method provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the information delivery effect attribution device is generally disposed in the server 605.
It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a sending module, an obtaining module, a determining module, and a first processing module. The names of these modules do not in some cases constitute a limitation on the unit itself, and for example, the sending module may also be described as a "module that sends a picture acquisition request to a connected server".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
determining users of an experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
judging whether the experimental group users have online exposure experiences in a first target time period and a second target time period, and grouping the experimental group users according to a judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first subset, the second subset, the third subset and the fourth subset.
According to the technical scheme of the embodiment of the invention, users who collect offline information release positions are used as experimental groups, and a comparison group with higher similarity is searched according to the geographic position; grouping the users of the experimental group and the control group according to the exposure behavior of the users, and calculating information conversion data of each group of users; according to the information conversion data of different users, calculating the contribution degree of offline information delivery to the user conversion behavior, the contribution degree of online information delivery to the user conversion behavior and the contribution degree of offline information delivery and online information delivery to the user conversion behavior together, so that a control group can be found for experiment groups influenced by advertisements of different degrees; respectively calculating the advertisement effect aiming at users with different exposure behaviors; the putting effect of the online advertisement and the offline advertisement and the superposition effect of the online advertisement and the offline advertisement when the online advertisement and the offline advertisement are simultaneously applied are respectively calculated, and accurate attribution results can be obtained.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (12)

1. An information delivery effect attribution method is characterized by comprising the following steps:
the server determines users of the experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
the server judges whether the experimental group users have online exposure experiences in a first target time period and a second target time period, and groups the experimental group users according to a judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and the server calculates the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first set, the second subset, the third subset and the fourth subset.
2. The method of claim 1, wherein the step of the server determining the users of the experimental group based on the geographical locations of the offline information placement sites and the action tracks of the users comprises:
the server judges whether the minimum distance between the user and the offline information release position is smaller than or equal to a first threshold value or not according to the action track of the user and the geographic position of the offline information release position;
and if so, determining that the user has the experience of exposure at the offline information release position in a first target time period, and taking the user as an experimental group user.
3. The method according to claim 1, wherein the server obtains the action track of the user according to the following process:
the server acquires an action track of the user in the first target time period through a target application program; or
The server acquires the action track of the user in the first target time period through the image information acquired by the target camera equipment; and the distance between the target camera equipment and the offline information release position is smaller than or equal to a second threshold value.
4. The method according to claim 1, wherein the server determines the control group of users based on the geographical location of the offline information placement site and a preset similarity condition, and comprises:
taking the offline information release position as an experimental release position, and determining a comparison release position according to the geographic position of the experimental release position and the similarity limiting condition;
and determining a control group user based on the control putting position.
5. The method of claim 4, wherein the similarity constraint comprises: and the Euclidean distance between the experimental throwing position and the comparison throwing position is within a preset interval.
6. The method of claim 5, wherein the target application comprises an application that places the information online;
the similarity limitation further comprises one or more of the following:
the difference value of the room prices between the cell where the experimental release position is located and the cell where the comparison release position is located is within a first preset range;
the difference of the number of people acquired by the target application program between the cell where the experimental release position is located and the cell where the comparison release position is located is within a second preset range;
the difference of the number of registrants on the application program for putting the information on the line between the cell where the experimental putting position is located and the cell where the comparison putting position is located is within a third preset range;
and the difference of the total information conversion data before the information line is released between the cell where the experimental release position is located and the cell where the comparison release position is located is within a fourth preset range.
7. The method of claim 1, wherein the users of the first subset have both the line exposure experience and the line exposure experience for a first target time period and do not have both the line exposure experience and the line exposure experience for a second target time period;
a second subset of users having only an offline exposure experience, no online exposure experience, and no offline exposure experience and online exposure experience for a second target time period;
a third subset of users having only an inline exposure experience, no offline exposure experience for the first target time period, and no inline exposure experience and no offline exposure experience for the second target time period;
the fourth subset of users have no under-line exposure experience and no over-line exposure experience for the first target time period and no under-line exposure experience and no over-line exposure experience for the second target time period.
8. The method of claim 1, wherein calculating the contribution of offline information placement to user conversion behavior, the contribution of online information placement to user conversion behavior, and the contribution of offline information placement and online information placement to user conversion behavior together according to the information conversion data of the first set, the second subset, the third subset, and the fourth subset comprises:
calculating a first difference value between the information conversion data of the users of the fourth subset in the first target time period and the information conversion data of the users in the second target time period, and taking the first difference value as a natural growth ratio;
calculating a second difference value between the information conversion data of the users of the third subset in the first target time period and the information conversion data in the second target time period, calculating a third difference value between the second difference value and the natural growth rate, and taking the third difference value as an on-line information delivery growth rate;
calculating a fourth difference value between the information conversion data of the users of the second subset in the first target time period and the information conversion data of the users of the second subset in the second target time period, calculating a fifth difference value between the fourth difference value and the natural growth rate, and taking the fifth difference value as an offline information delivery growth rate;
calculating a sixth difference value between the information conversion data of the users of the first subset in the first target time period and the information conversion data in the second target time period, calculating a seventh difference value between the sixth difference value and the natural growth ratio, and taking the seventh difference value as a combined growth ratio of online information delivery and offline information delivery;
and determining the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the online information delivery increase ratio, the offline information delivery increase ratio and the combined increase ratio.
9. The method of claim 8, wherein determining a degree of contribution of an offline information placement to a user conversion behavior, a degree of contribution of an online information placement to a user conversion behavior, and a degree of contribution of both an offline information placement and an online information placement to a user conversion behavior based on the natural growth ratio, the online information placement growth ratio, the offline information placement growth ratio, and the combined growth ratio comprises:
calculating the contribution degree of the offline information delivery to the user conversion behavior according to the sum of the information conversion data of the users of the first subset and the users of the second subset in a second target time period and the offline information delivery increase rate;
calculating the contribution degree of the online information delivery to the user conversion behavior according to the information conversion data of the users of the first subset in a second target time period and the online information delivery increase rate;
and calculating the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the users of the first subset in the second target time period and the combined growth ratio.
10. An information delivery effect attribution device, comprising:
the determining module is used for determining users of the experimental group based on the geographical position of the offline information release position and the action track of the users; the server determines a comparison group user based on the geographic position of the offline information release position and a preset similarity condition; wherein the experimental group of users have a line exposure experience over a first target period of time and the control group of users do not have a line exposure experience over the first target period of time;
the grouping module is used for judging whether the experiment group users have on-line exposure experience in a first target time period and a second target time period, and grouping the experiment group users according to the judgment result to obtain a first subset and a second subset; judging whether the comparison group users have online exposure experiences in the first target time period and the second target time period, and grouping the comparison group users according to the judgment result to obtain a third subset and a fourth subset; the second target time period is a time period before offline information is released;
and the attribution module is used for calculating the contribution degree of the offline information delivery to the user conversion behavior, the contribution degree of the online information delivery to the user conversion behavior and the contribution degree of the offline information delivery and the online information delivery to the user conversion behavior together according to the information conversion data of the first set, the second subset, the third subset and the fourth subset.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
CN202110548351.6A 2021-05-19 2021-05-19 Information delivery effect attribution method and device Withdrawn CN113256330A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113421135A (en) * 2021-08-24 2021-09-21 北京达佳互联信息技术有限公司 Method and device for determining resource delivery control parameters and electronic equipment
CN115049327A (en) * 2022-08-17 2022-09-13 阿里巴巴（中国）有限公司 Data processing method and device, electronic equipment and storage medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113421135A (en) * 2021-08-24 2021-09-21 北京达佳互联信息技术有限公司 Method and device for determining resource delivery control parameters and electronic equipment
CN113421135B (en) * 2021-08-24 2022-03-01 北京达佳互联信息技术有限公司 Method and device for determining resource delivery control parameters and electronic equipment
CN115049327A (en) * 2022-08-17 2022-09-13 阿里巴巴（中国）有限公司 Data processing method and device, electronic equipment and storage medium
CN115049327B (en) * 2022-08-17 2022-11-15 阿里巴巴（中国）有限公司 Data processing method and device, electronic equipment and storage medium

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