AU2020323296A1

AU2020323296A1 - Optimising paid search channel internet campaigns in an ad serving communication network

Info

Publication number: AU2020323296A1
Application number: AU2020323296A
Authority: AU
Inventors: Paul Forest
Original assignee: Liquid Ai Pty Ltd
Current assignee: Liquid Ai Pty Ltd
Priority date: 2019-07-26
Filing date: 2020-07-24
Publication date: 2022-01-06
Also published as: WO2021016655A1

Abstract

A system collects signals from a first paid search campaign of a paid search channel across a communication network. The first paid search campaign has a first set of phrases having brand-specific keywords. An empirical behavioural signature is determined from the signals collected, the behavioural signature having conversion metrics for the first set of phrases for each of a plurality of attributes measured from signals collected. A second paid search campaign is then served using the paid search channel across the communication network. The second paid search campaign has a second set of phrases having generic phrases which do not comprise brand-specific keywords. Targeting of the second paid search campaign is optimised according to the behavioural signature wherein bid amounts for the generic phrases are adjusted according to the conversion metrics for the first set of phrases for each of the plurality of attributes.

Description

Optimising pa id search channel internet cam paigns in an ad serving commu nication network

Field of the Invention

[1 ] This invention relates generally to optimising internet campaigns in an ad serving network.

Background of the Invention

[2] Companies and individuals wishing to increase volume and/or quality of web traffic to a web page may implement campaigns using paid search channels served over the Internet.

[3] A paid search channel is a form of digital marketing where search engines display ads in search engine results pages (SERPs).

[4] Paid search generally works on a pay-per-click model (PPC) and wherein various ad formats, include text ads, are shown at the top or bottom of organic search results. Ads may also be displayed on display, video serving, shopping or app networks.

[5] Ad campaigns comprise ad groups which target phrases by bid amounts and other parameters. Signals are collected to measure performance metrics including click, impressions, click-through rate (CTR), average cost-per-click (CPC), average position, cost/conversion and conversion rate.

[6] Various methods have been proposed to optimise these performance metrics, including US 9,235,570 B2 (PARK et al.) 12 January 201 6 [hereinafter referred to as D1 ] which discloses optimization of paid search campaigns using signals collected from organic search channels.

[7] D1 teaches determining a page rank associated with keywords of an organic search campaign and determining a relationship between the organic search campaign and a paid search campaign. Specifically, D1 determines whether a paid search conversion rate of keywords within a paid search campaign affects an organic search conversion rate of the keywords within the organic search campaign when a bid price of the keyword in the paid search campaign is increased. [8] The present invention seeks to provide a way to optimise paid channel performance metrics, which will overcome or substantially ameliorate at least some of the deficiencies of the prior art, or to at least provide an alternative.

[9] It is to be understood that, if any prior art information is referred to herein, such reference does not constitute an admission that the information forms part of the common general knowledge in the art, in Australia or any other country.

Summary of the Disclosure

[10] Embodiments provided herein relate to optimisation of paid search channel performance metrics across a communication network by collecting signals from a first paid search campaign of a paid search channel across the communication network. The first paid search campaign comprises a first set of phrases which comprise brand specific keywords.

[1 1 ] A behavioural signature is determined from the signals collected which comprises conversion rates for the first set of phrases for each of a plurality of attributes measured from the signals collected.

[12] A second paid search campaign is then served using the paid channel across the communication network which comprises a second set of phrases comprising generic keywords which do not comprise brand specific keywords.

[13] The targeting of the second paid search campaign is optimised according to the empirical behavioural signature. Specifically, bid amounts for the generic keywords are adjusted according to the conversion rates for the first set of phrases for each of the plurality of attributes.

[14] As such, the targeting of phrases comprising generic keywords is optimised using the empirical behavioural signature observed from online behaviour signals of brand loyal customers and using phrases comprising brand specific keywords which have relatively higher conversion rates.

[15] Other aspects of the invention are also disclosed. Brief Description of the Drawings

[16] Notwithstanding any other forms which may fall within the scope of the present invention, preferred embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

[17] Figure 1 shows an ad serving system in accordance with an embodiment;

[18] Figure 2 shows a system for optimising internet campaigns in the ad serving system in accordance with an embodiment; and

[19] Figure 3 shows a process for optimising internet campaigns in the ad serving system in accordance with an embodiment.

Description of Embodiments

[20] Figure 1 shows an ad serving system 100 comprising an ad server 101 in operable communication with a plurality of electronic devices 108 across a communication network 107.

[21 ] The ad server 101 comprises a processor 102 for processing digital data. In operable communication with the processor 102 across a system bus 104 is digital storage 1 15. The digital storage 1 15 is configured for storing digital data including computer program code instructions. In use, the processor 102 fetches these computer program instructions and associated data from the storage 1 15 for interpretation and execution for the implementation of the functionality provided herein.

[22] The ad server 101 further comprises a data interface 103 for sending and receiving data across the communication network 107.

[23] The storage 1 15 may comprise an ad serving application 105 and a plurality of ad campaigns 106.

[24] When the electronic devices 108 requests content from across the communication network 107, the ad server 101 serves ads in accordance with the ad campaigns 106. The user may interact with an ad displayed by digital display of the electronic device 108 which is recorded as a signal by the ad server 101 .

[25] The system 100 may comprise a search network 1 10 comprising a search engine 109 wherein the ads appear within organic search engine results therefrom. The system 1 00 may further comprise a display network 1 12 wherein ads are injected by way of client-side code into webpages served by third-party web servers 1 1 1 . The system 1 00 may further comprise a video serving network 1 14 comprising a video server 1 13 wherein ads are displayed overlaid the video content serve thereby. Further networks may be included including shopping, app networks and the like.

[26] Figure 3 shows a process 128 of optimising paid search channel campaigns across the communication network 107 described with reference to Figure 2 which shows the communication network 107 having a paid search channel (typically a PPC paid search channel) served by the ad server 1 01 .

[27] The process 1 28 comprises collecting signals 1 21 at stage 1 29 from a first paid search campaign 1 1 9 of the paid search channel 1 18 across the communication network 1 07.

[28] The first paid search campaign 1 19 comprises a first set of phrases 1 16 comprising brand specific keywords. A phrase may comprise one or more keywords. The term“brand specific” as used herein should be construed as keywords of phrases including generally recognisable brand trademark keywords such as“Nike”,“Adidas” and the like. For example, the brand specific phrases 1 16 may comprise keywords such as “Nike shoes”, “Nike leather shoes” and the like. These brand specific keywords are used to target brand loyal customers on the Internet for the collection of the signals 121 .

[29] Organic search results or display networks ads are displayed by the user electronic devices 108 by the ad server 1 01 and signals 1 21 are collected by the ad server 101 accordingly, including when users interacts with the ads, such as by clicking on an ad.

[30] At stage 130, a behavioural signature 123 is generated according to the signals 121 collected. The behavioural signature 123 comprises conversion metrics for the brand specific phrases 1 1 6 for a plurality of attributes 1 25.

[31 ] The conversion metrics 122 may comprise number of clicks, click-through rate (CTR), average cost-per-click (CPC), cost/conversion and/or conversion rate. [32] The attributes 125 may comprise user specific attributes including postcode, gender, age, income bracket and device type. The attributes 125 may further comprise time period attributes including hour of the week.

[33] In an embodiment, conversion metrics 122 for the 2386 postcodes in Australia, two types of genders, six age groups, six income groups, three types of devices and 168 hours of the week may be determined from the signals 121 to generate the empirical behavioural signature 123. These combinations afford highly granular conversion metrics 122.

[34] For example, where the conversion metric 122 comprises conversion rate, for a“Nike leather shoes” brand-specific phrase 1 16, the behavioural signature 123 determined from signals 121 may represent:

a. Gender: 80% male, 20% female

b. Age: 5% 18-24, 15% 25-34, 35% 35-44, 20% 45-54, 15% 55-64, 10% 65 or more

c. Income bracket: 5% Top 10%, 20% 1 1 -20%, 30% 21 -30%, 20% 31 -40%, 15% 41 -50% and 10% Lower 50%

d. Hour of week: 0% hour 1 , 0.2% hour 2, 2.2% hour 3 etc

e. Postcode: 0.6% 2000, 0% 2001 , 0.2% 2002, 0.1 % 2003 etc

f. Device type: tablets 30%, mobile phones 50% and desktop computers 20%

[35] At stage 131 , targeting 126 of a second paid search campaign 120 comprising generic phrases 1 17 is adjusted according to the behavioural signature 123 and, at stage 132, the second paid search campaign 120 is served across a communication network 107.

[36] The generic phrases 1 17 include generic phrases which do not include brand specific keywords. For example, the generic phrases may include“leather shoes with laces”,“leather shoes size 12” and the like. Generally, the generic phrases 1 17 may be more numerous than the brand specific phrases 1 16 and comprise“longer-tail” phrases as compared to the brand specific phrases 1 16. Long-tail keywords are keywords or key phrases that are more specific, and usually longer than more commonly searched for keywords.

[37] The targeting 1 26 of the second paid search campaign 120 is optimised by adjusting bid amounts for the generic phrases 1 17 according to the conversion metrics for the brand specific phrases 1 1 6 for each of the plurality of attributes 125.

[38] For example, using the aforedescribed example, the following bids adjustments 127 may be made:

a. Gender: +7% male, -3% female

b. Age: -7% 1 8-24, 15% 25-34, +7% 35-44, +5% 45-54, 0% 55-64, -5% 65 or more

c. Income bracket: -7% Top 1 0%, +5% 1 1 -20%, +7% 21 -30%, +5% 31 - 40%, -5% 41 -50% and -5% Lower 50%

d. Hour of week: +0% hour 1 , +0% hour 2, + 1 % hour 3 etc

e. Postcode: +2% 2000, 0% 2001 , + 1 % 2002, +1 % 2003 etc

f. Device: tablets +0%, mobile phones +1 0% and desktop computers -0%

[39] As is apparent from the above, the spending distribution of each of the attributes 125 is adjusted to mimic the conversion rates determined from the first paid search campaign 1 1 9. A bid adjustment 127 may be made for each conversion metric 123 provided sufficient information is provided in relation to which conversion metric 123.

[40] In embodiments, the phrases 1 1 6, 1 1 7 may be divided into categories such as “leather shoes”,“running shoes” and the like. In accordance with this embodiment, bid amounts for generic phrases 1 1 7 of a category are made according to the conversion metrics to the brand specific phrases 1 16 of the same category. For example, for conversion metrics 122 for brand specific phrases 1 16 of a“leather shoes” category, such as“Nike leather shoes” may be used for the bid adjustments 127 of generic phrases 1 1 7 of the same category, such as“leather shoes with laces”, “leather shoes size 12”and the like.

[41 ] Generally, a brand specific phrase 1 16 and a generic phrase 1 17 is assigned to the same category if the brand specific phrase 1 1 6 includes a subset of the generic phrase 1 17, such as“leather shoes”. For example, a brand specific phrase 1 16 comprising“Nike leather shoes” and a generic phrase 117 comprising“leather shoes size 12” will be assigned to the“leather shoes” category.

[42] In embodiments, the bid adjustments may be made such that the sum of the bid adjustment for each attribute 125 is 0.

[43] The ad server 101 or software application interfacing therewith via API may be configured for automatically adjusting the bid amounts in this way. The ad server 101 or the software application may continually make the bid adjustments 127 in substantial real time depending on the signals 121 received.

[44] Bid adjustments 127 for each generic phrase 1 17 may be adjusted until the bid adjustment reaches a maximum or minimum threshold.

[45] In embodiments, further signals may be collected for the second paid search campaign 120 and wherein the bid adjustments 127 are adjusted further to align the conversion metrics 122 of the first paid search campaign 1 19 with the second paid search campaign 120.

[46] The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the invention. However, it will be apparent to one skilled in the art that specific details are not required in order to practise the invention. Thus, the foregoing descriptions of specific embodiments of the invention are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed as obviously many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. It is intended that the following claims and their equivalents define the scope of the invention.

[47] The term“approximately” or similar as used herein should be construed as being within 10% of the value stated unless otherwise indicated.

Claims

1 . A computer implemented method comprising:

collecting signals from a first paid search campaign of a paid search channel across a communication network, the first paid search campaign comprising a first set of phrases, the first set of phrases comprising brand-specific keywords,

determining an empirical behavioural signature from the signals collected, the behavioural signature comprising conversion metrics for the first set of phrases for each of a plurality of attributes measured from signals collected ;

serving a second paid search campaign using the paid search channel across the communication network, the second paid search campaign comprising a second set of phrases, the second set of phrases comprising generic phrases which do not comprise brand-specific keywords; wherein targeting of the second paid search campaign is optimised according to the behavioural signature wherein bid amounts for the generic phrases are adjusted according to the conversion metrics for the first set of phrases for each of the plurality of attributes.

2. The method as claimed in claim 1 , wherein the conversion metrics comprise number of clicks, click-through rate (CTR), average cost-per-click (CPC),

cost/conversion or conversion rate.

3. The method as claimed in claim 1 , wherein the attributes comprise a user specific attribute.

4. The method as claimed in claim 3, wherein the user specific attributes comprise a postcode attribute.

5. The method as claimed in claim 3, wherein the user specific attributes comprise a gender attribute.

6. The method as claimed in claim 3, wherein the user specific attributes comprise an age attribute.

7. The method as claimed in claim 3, wherein the user specific attributes comprise an income bracket attribute.

8. The method as claimed in claim 3, wherein the user specific attributes comprise a device type attribute.

9. The method as claimed in claim 1 , wherein the attributes comprise a time period attribute.

10. The method as claimed in claim 9, wherein the time period attribute comprises hour of the week.

1 1 . The method as claimed in claim 1 , wherein the attributes comprise attributes comprising all of postcode, gender, age, income bracket, device type and hour of the week attributes.

12. The method as claimed in claim 1 , wherein the first set of phrases and the second set of phrases are allocated to categories and wherein the targeting of the second paid search campaign is adjusted by adjusting bid amounts for second set of phrases according to conversion metrics of first set of phrases of the same category.

13. The method as claimed in claim 12, wherein phrases of the first and second set of phrases are assigned to the same category if the brand specific keywords of the first set of phrases includes a subset of the words of the generic keywords of the second set of phrases.

14. The method as claimed in claim 1 , wherein the bid adjustments are made such that the sum of the bid adjustments is zero.

15. The method as claimed in claim 1 , wherein at least one of the ad server and a software application accessing the ad server via an API automatically adjusts the bid adjustments.

16. The method as claimed in claim 15, wherein the ad server or the software application adjusts the bid adjustments continually in substantial real-time.

17. The method as claimed in claim 1 , wherein bid adjustments for each generic keyword is adjusted until the bid adjustment reaches a maximum or minimum threshold.

18. The method as claimed in claim 1 , wherein further signals are collected for the second paid search campaign to determine further conversion metrics for the second paid search campaign and wherein the bid adjustments are adjusted further to align the conversion metrics of the first paid search campaign with the conversion metrics of the second paid search campaign.

19. The method as claimed in claim 1 , wherein the paid search channel is a pay per click (PPC) search channel.

20. The method as claimed in claim 1 , wherein the paid search channel serves ads in organic search results.

21 . The method as claimed in claim 1 , wherein the generic phrases are be more numerous than the brand specific phrases.

22. The method as claimed in claim 1 , wherein the generic phrases comprise longer long-tail phrases than the brand specific phrases.

23. A system comprising an ad server serving ads for a paid search channel across a communication network and wherein the system is configured for:

collecting signals from electronic devices from a first paid search campaign of the paid search channel across the communication network, the first paid search campaign comprising a first set of phrases, the first set of phrases comprising brand- specific keywords,

determining an empirical behavioural signature from the signals collected, th e behavioural signature comprising conversion metrics for the first set of phrases for each of a plurality of attributes;

serving a second paid search campaign using the paid search channel across the communication network, the second paid search campaign comprising a second set of phrases, the second set of phrases comprising generic keywords which do not comprise brand-specific keywords; wherein targeting of the second paid search campaign is adjusted according to the behavioural signature wherein bid amounts for the generic phrases are adjusted according to the conversion metrics for the first set of phrases for each of the plurality of attributes.