Trend Detection in Online Advertising
A method for trend detection in online advertising, the method comprising using at least one hardware processor for: receiving current performance data associated with a current online ad entity; determining a class with which the current online ad entity is associated, by applying a clustering algorithm to one or more attributes associated with the current online ad entity; fetching historical performance data associated with one or more historical online ad entities associated with the class; and comparing a behavior of the current performance data with a behavior of the historical performance data, to detect an abnormal trend in the behavior of the current online ad entity.
1 . A method for trend detection in online advertising, the method comprising using at least one hardware processor for:
receiving current performance data associated with a current online ad entity;
determining a class with which the current online ad entity is associated, by applying a clustering algorithm to one or more attributes associated with the current online ad entity;
fetching historical performance data associated with one or more historical online ad entities associated with the class; and
comparing a behavior of the current performance data with a behavior of the historical performance data, to detect an abnormal trend in the behavior of the current online ad entity.
2 . The method according to claim 1 , wherein the current performance data comprises one or more time series of one or more performance parameters, respectively.
3 . The method according to claim 2 , wherein the one or more time series comprise two or more time series, and wherein the one or more performance parameters comprise two or more performance parameters, respectively.
4 . The method according to claim 1 , wherein the historical performance data comprises one or more time series of one or more performance parameters, respectively.
5 . The method according to claim 4 , wherein the one or more time series comprise two or more time series, and wherein the one or more performance parameters comprise two or more performance parameters, respectively.
6 . The method according to claim 1 , wherein the current online ad entity and the historical online ad entity are each selected from the group consisting of: an individual ad, a set of ads, a campaign and a set of campaigns.
7 . The method according to claim 1 , wherein the one or more performance parameters are selected from the group consisting of: impressions, clicks, click-through rate (CTR), conversions, return on investment (ROI), revenue per click, cost per impression, cost per click, revenue per impression, reach and frequency.
8 . The method according to claim 1 , wherein the current performance data is of a time window equal in length to a time window of the historical performance data.
9 . The method according to claim 1 , further comprising using the at least one hardware processor for transmitting a command to an advertising platform, to affect a monetary parameter pertaining to the current online ad entity,
wherein the command is based on the detected abnormal trend.
10 . A method for trend detection in online advertising, the method comprising using at least one hardware processor for:
receiving performance data associated with an online ad entity, the performance data comprising at least two performance metrics;
detecting an interrelation between the at least two performance metrics over time;
comparing the interrelation with a rule set characterizing behavioral trends associated with the two performance metrics; and
based on the comparing, indicating that one of the behavioral trends has been identified.
11 . The method according to claim 10 , wherein each of the at least two performance metrics comprises a time series.
12 . The method according to claim 10 , wherein the at least two performance metrics comprise at least three performance metrics.
13 . The method according to claim 10 , wherein the online ad entity is selected from the group consisting of: an individual ad, a set of ads, a campaign and a set of campaigns.
14 . The method according to claim 10 , wherein the at least two performance metrics are selected from the group consisting of: impressions, clicks, click-through rate (CTR), conversions, return on investment (ROI), revenue per click, cost per impression, cost per click, revenue per impression, reach and frequency.
15 . The method according to claim 10 , further comprising using the at least one hardware processor for transmitting a command to an advertising platform, to affect a monetary parameter pertaining to the online ad entity,
wherein the command is based on the identified one of the behavioral trends.
16 . A computer program product for trend detection in online advertising, the computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to:
receive current performance data associated with a current online ad entity;
determine a class with which the current online ad entity is associated, by applying a clustering algorithm to one or more attributes associated with the current online ad entity;
fetch historical performance data associated with one or more historical online ad entities associated with the class; and
compare a behavior of the current performance data with a behavior of the historical performance data, to detect an abnormal trend in the behavior of the current online ad entity.
17 . The computer program product according to claim 16 , wherein the current performance data comprises one or more time series of one or more performance parameters, respectively.
18 . The computer program product according to claim 17 , wherein the one or more time series comprise two or more time series, and wherein the one or more performance parameters comprise two or more performance parameters, respectively.
19 . The computer program product according to claim 16 , wherein the historical performance data comprises one or more time series of one or more performance parameters, respectively.
20 . The computer program product according to claim 19 , wherein the one or more time series comprise two or more time series, and wherein the one or more performance parameters comprise two or more performance parameters, respectively.
21 . The computer program product according to claim 16 , wherein the current online ad entity and the historical online ad entity are each selected from the group consisting of: an individual ad, a set of ads, a campaign and a set of campaigns.
22 . The computer program product according to claim 16 , wherein the one or more performance parameters are selected from the group consisting of: impressions, clicks, click-through rate (CTR), conversions, return on investment (ROI), revenue per click, cost per impression, cost per click, revenue per impression, reach and frequency.
23 . The computer program product according to claim 16 , wherein the current performance data is of a time window equal in length to a time window of the historical performance data.
24 . The computer program product according to claim 16 , wherein the program code is further executable by the at least one hardware processor for transmitting a command to an advertising platform, to affect a monetary parameter pertaining to the online ad entity,
wherein the command is based on the detected abnormal trend.
25 . A computer program product for trend detection in online advertising, the computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to:
receive performance data associated with an online ad entity, the performance data comprising at least two performance metrics;
detect an interrelation between the at least two performance metrics over time;
compare the interrelation with a rule set characterizing behavioral trends associated with the two performance metrics; and
based on the comparing, indicate that one of the behavioral trends has been identified.
26 . The computer program product according to claim 25 , wherein each of the at least two performance metrics comprises a time series.
27 . The computer program product according to claim 25 , wherein the at least two performance metrics comprise at least three performance metrics.
28 . The computer program product according to claim 25 , wherein the online ad entity is selected from the group consisting of: an individual ad, a set of ads, a campaign and a set of campaigns.
29 . The computer program product according to claim 25 , wherein the at least two performance metrics are selected from the group consisting of: impressions, clicks, click-through rate (CTR), conversions, return on investment (ROI), revenue per click, cost per impression, cost per click, revenue per impression, reach and frequency.
30 . The computer program product according to claim 25 , wherein the program code is further executable by the at least one hardware processor for transmitting a command to an advertising platform, to affect a monetary parameter pertaining to the online ad entity,
wherein the command is based on the identified one of the behavioral trends.