Claims:1. An automated system for an intelligent cross platform marketing optimization comprising:
a meta technology abstraction layer (MTAL), wherein the MTAL is an autonomous layer provided in a central application server connected to a plurality of dedicated servers;
a plurality of third party platforms (TPP), wherein the plurality of third party platforms comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms, wherein each third party platform is situated in the dedicated server; and
an interface module, wherein the interface module is an intermediatory interface between the MTAL and the plurality of third party platforms.
2. The system as claimed in claim 1, wherein the MTAL comprises:
an analytical module, wherein the analytical module is connected to the plurality of third party platforms for collecting data pertaining to a plurality marketing parameters;
a trafficking module, wherein the trafficking module is connected to plurality of dedicated servers;
an autonomous optimizer, wherein the autonomous optimizer is connected to the trafficking module and comprises an artificially intelligent optimization engine and a tracking engine;
a predictor, wherein the predictor is connected to the autonomous optimizer for display of effects of an updated key performance indicator (KPI); and
a trigger module, wherein the trigger module is connected to autonomous optimizer to trigger an alert for a sub-optimal campaign quality.
3. The system as claimed in claim 2, wherein the KPI comprises a system level KPI and a channel level KPI, wherein the system level KPIs are comprised of a primary objective, a secondary objective, an overall budget, impressions, reach and a campaign goal, wherein the channel level KPI comprises a secondary objective.
4. The system as claimed in claim 3, wherein the analytical module facilitates a user to enter data pertaining to a new campaign comprising a campaign nature, an audio campaign file, a visual campaign file, a text campaign file, an overall budget and at least one KPI, wherein the entered data is transferred to the autonomous optimizer.
5. The system as claimed in claim 4, wherein the autonomous optimizer maps the entered data with a historical campaign performance database saved in the central application server, wherein the autonomous optimizer optimizes the remaining KPIs on a basis a matched profile in the historical campaign performance database, wherein KPIs from the matched profile are saved as reference and the autonomous optimizer dynamically changes the KPIs of the new campaign to either equal or better the reference.
6. The system as claimed in claim 3, wherein the analytical module facilitates a user to enter data pertaining to a campaign nature, an audio campaign file, a visual campaign file, a text campaign file and an overall budget, wherein the entered data is transferred to the autonomous optimizer.
7. The system as claimed in claim 5, wherein the autonomous optimizer maps the entered data with a historical campaign performance database saved in the central application server, wherein the autonomous optimizer optimizes the channel level KPIs as well as the system level KPI on a basis a matched profile in the historical campaign performance database.
8. The system as claimed in claim 7, wherein the autonomous optimizer tracks best performing KPIs from one or more matched profiles and enter the tacked KPIs to the new campaign, wherein the matched profile outcome is saved as reference and is provided a cumulation score of 1.
9. The system as claimed in claim 8, wherein the autonomous optimizer optimizes the KPIs of the new campaign to achieve the cumulation score of 1 or more.
10. A computer implemented method for an intelligent cross platform marketing optimization comprising:
a) trafficking a new campaign through a meta technology abstraction layer (MTAL) to a plurality of third party platform (TPP);
b) entering at least one user defined KPI value in a system level KPI and a channel level KPI;
c) searching at least one campaign with matching profile with respect to the new campaign and equivalent user entered system level KPI and channel level KPI;
d) setting a plurality of remaining KPIs from the matched profile in the new campaign and initiate the campaign;
e) tracking a campaign performance and determining an achievement of the user defined KPIs;
f) optimizing remaining KPIs and a plurality of campaign metrics on sub-optimal performance of the campaign;
g) triggering an alert for a sub-optimal campaign strength in case the KPIs and the plurality of campaign metrics are fully optimized.
11. The method as claimed in claim 10, wherein the plurality of campaign metrics comprises a performance classification of the TPPs, a bid change, a bidding algorithm change, a cross channel expenditure budget movement, a budget increase and decrease for a campaign over a TPP, a placement change and a targeting adjustment.
, Description:A) TECHNICAL FIELD
[001] The present invention generally relates to a marketing optimization system and particularly relates to an automated system and method for an intelligent cross platform marketing resource optimization allowing a user to distribute a marketing budget.

B) BACKGROUND OF INVENTION
[002] A significant and robust market exists for marketing digital advertisements on various types of personal computing devices, like computers (desktop and laptop), mobile phones and tablets, and traditional browser-based devices operated by a consumer who is the user of the device. Conventional advertisement tracking systems and methods which were built for personal devices rely on device identification systems and methods to create a record in buyer advertising systems representing showing a digital advertisement (“ad”) to a consumer (the “impression”), and on which personal device a consumer, who was exposed to the impression, took some action (the “event”) in response to the impression (e.g., visiting a website, making an online purchase, calling a telephone number in response to the advertisement, to name a few.). These impressions and events are monitored to derive data like a efficiency of platform and quality of advertisement as well as a various other analytical points.
[003] Many prior art developments attempted to better the experience of an advertiser through implementation of the aforesaid monitored data. One of such prior arts discloses a system and method include a customer engagement platform of an enterprise. The customer engagement platform is configured to connect with an audience computer and provide to the audience computer contact information associated with a marketing attribution. The marketing attribution accompanies an impression served to the audience computer. The impression and marketing attribution associated with the contact information are sent to the customer engagement platform during an interaction with the enterprise. Various similar prior arts exist in current market to exploit user interaction and platform performance data for enhancing the advertiser experience.
[004] However, the conventional prior art techniques limit a data monitoring on a single platform and hence marketing optimization is limited to a single platform and such marketing optimization are in efficient due to lack relative bias and cross platform monitoring as well as optimization.
[005] In the view of foregoing, there is a need for an automated system and method for an intelligent cross platform marketing resource optimization to improve a campaign performance on at least one online platform.
[006] The above-mentioned shortcomings, disadvantages and problems are addressed herein, as detailed below.

C) OBJECT OF INVENTION
[007] The primary objective of the present invention is to provide an automated system and method for an intelligent cross platform marketing resource optimization to improve a campaign performance on at least one online platform.
[008] Another objective of the present invention is to provide a system and method to with autonomous self-optimization cycle that initiates from campaign trafficking. This allows a user to attain high optimization even with low campaign information input.
[009] Yet another objective of the present is to provide holistic channel agnostic media optimization engine through provision of a meta optimization layer function above a plurality of demand side platform (DSP) layers.
[0010] These and other objects and advantages of the embodiments herein will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.

D) SUMMARY OF INVENTION
[0011] The various embodiments of the present invention provide an automated system for an intelligent cross platform marketing optimization. The system comprises a meta technology abstraction layer (MTAL), a plurality of third party platforms (TPP) and an interface module. The MTAL is an autonomous layer provided in a central application server connected to a plurality of dedicated servers. The plurality of third party platforms comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Eeach third party platform is situated in the dedicated server. The interface module is an intermediatory interface between the MTAL and the plurality of third party platforms.
[0012] According to one embodiment of the present invention, the MTAL comprises an analytical module, a trafficking module, an autonomous optimizer, a predictor and a trigger module. The analytical module is connected to the plurality of third party platforms for collecting data pertaining to a plurality marketing parameters. The trafficking module is connected to plurality of dedicated servers. The autonomous optimizer is connected to the trafficking module and comprises an artificially intelligent optimization engine and a tracking engine. The predictor is connected to the autonomous optimizer for display of effects of an updated key performance indicator (KPI). The trigger module is connected to autonomous optimizer to trigger an alert for a sub-optimal campaign quality.
[0013] According to one embodiment of the present invention, the KPI comprises a system level KPI and a channel level KPI, wherein the system level KPIs are comprised of a primary objective, a secondary objective, an overall budget, impressions, reach and a campaign goal, wherein the channel level KPI comprises a secondary objective.
[0014] According to one embodiment of the present invention, the analytical module facilitates a user to enter data pertaining to a new campaign comprising a campaign nature, an audio campaign file, a visual campaign file, a text campaign file, an overall budget and at least one KPI. The entered data is transferred to the autonomous optimizer which maps the entered data with a historical campaign performance database saved in the central application server. The autonomous optimizer optimizes the remaining KPIs on a basis a matched profile in the historical campaign performance database. The KPIs from the matched profile are saved as reference and the autonomous optimizer dynamically changes the KPIs of the new campaign to either equal or better the reference.
[0015] According to one embodiment of the present invention, the analytical module facilitates a user to enter data pertaining to a campaign nature, an audio campaign file, a visual campaign file, a text campaign file and an overall budget. The entered data is transferred to the autonomous optimizer which maps the entered data with a historical campaign performance database saved in the central application server. The autonomous optimizer optimizes the channel level KPIs as well as the system level KPI on a basis a matched profile in the historical campaign performance database. The autonomous optimizer tracks best performing KPIs from one or more matched profiles and enter the tacked KPIs to the new campaign. The matched profile outcome is saved as reference and is provided a cumulation score of 1.
[0016] According to one embodiment of the present invention, the autonomous optimizer optimizes the KPIs of the new campaign to achieve the cumulation score of 1 or more.
[0017] The embodiments of the present invention provide a computer implemented method for an intelligent cross platform marketing optimization comprising:
a) trafficking a new campaign through a meta technology abstraction layer (MTAL) to a plurality of third party platform (TPP);
b) entering at least one user defined KPI value in a system level KPI and a channel level KPI;
c) searching at least one campaign with matching profile with respect to the new campaign and equivalent user entered system level KPI and channel level KPI;
d) setting a plurality of remaining KPIs from the matched profile in the new campaign and initiate the campaign;
e) tracking a campaign performance and determining an achievement of the user defined KPIs;
f) optimizing remaining KPIs and a plurality of campaign metrics on sub-optimal performance of the campaign;
g) triggering an alert for a sub-optimal campaign strength in case the KPIs and the plurality of campaign metrics are fully optimized.
[0018] According to one embodiment of the present invention, the plurality of campaign metrics comprises a performance classification of the TPPs, a bid change, a bidding algorithm change, a cross channel expenditure budget movement, a budget increase and decrease for a campaign over a TPP, a placement change and a targeting adjustment.
[0019] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

E) BRIEF DESCRIPTION OF DRAWINGS
[0020] The other objects, features and advantages will occur to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings in which:
[0021] FIG. 1 illustrates a block diagram of an automated system for an intelligent cross platform marketing resource optimization, according to one embodiment of the present invention.
[0022] FIG. 1a illustrates a block diagram of the communication among a MTAL and the third party platforms (TPPs) through the marketing APIs, according to one embodiment of the present invention.
[0023] FIG. 1b illustrates a block diagram of a meta-technology abstraction layer (MTAL) to optimize a plurality of marketing parameters in the system, according to one embodiment of the present invention.
[0024] FIG. 2 illustrates a flowchart of a computer implemented method for an intelligent cross platform marketing resource optimization, according to one embodiment of the present invention.
[0025] FIG. 3 illustrates a sub-routine flowchart of a method for determining a matching profile for a new campaign, according to one embodiment of the present invention.
[0026] FIG. 4 illustrates a sub-routine flowchart for performing multiple optimization cycle by the autonomous optimizer, according to one embodiment of the present invention.

F) DETAILED DESCRIPTION OF DRAWINGS
[0027] In the following detailed description, a reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practiced is shown by way of illustration. The embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that the logical, mechanical and other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense.
[0028] Conventional marketing optimization solutions are primarily focussed and limited to channel level key performance indicators and a campaign algorithm are optimized in absolute due to absence of monitoring and optimization agent for system level KPIs. The absence of such an optimization agent leads to wastage of huge amount investment through by an advertiser or a campaign owner in case a platform is not optimized to provide efficient returns on investment. The present invention majorly addresses this drawback in the market and provides a unique and dynamic solution to optimize a campaign launched over one or more platforms.
[0029] The system for cross platform marketing resource optimization is shown in FIG. 1-1b comprises a meta technology abstraction layer (MTAL) 101, a plurality of third party platforms (TPP) 102 and an interface module 103. The MTAL 101 is an autonomous layer provided in a central application server 104 connected to a plurality of dedicated servers 105. The plurality of third party platforms 102 comprises a demand side platform (DSP), a data management platform (DMP) and a plurality of utility platforms. Each third party platform 102 is situated in at least one dedicated server 105. The interface module 103 is an intermediatory interface between the MTAL 101 and the plurality of third party platforms 102. The MTAL 101 comprises an analytical module 106, a trafficking module 107, an autonomous optimizer 108, a predictor 109 and a trigger module 110 as shown in FIG. 1a. The analytical module 106 is connected to the plurality of third party platforms 102 for collecting data pertaining to a plurality of marketing parameters. The trafficking module 107 is connected to the plurality of dedicated servers 105. The autonomous optimizer 108 is connected to the trafficking module 107 and comprises an artificially intelligent optimization engine and a tracking engine. The predictor 109 is connected to the autonomous optimizer 108 for display of effects of an updated key performance indicator (KPI). The trigger module 110 is connected to the autonomous optimizer 108 to trigger an alert for a sub-optimal campaign quality. The alert is sent to the user over a computer readable program installed over a computing device such as a smartphone, a laptop or a desktop. The alert comprises a data on previously successful similar campaign and the identified shortcomings in the new campaign.
[0030] The system performances an automated data processing tracking and optimization through a computer implemented method as shown in FIG. 2. The method receives a request from a user for a new campaign establishment over a targeted set of TPPs. The user is facilitated to input the campaign related data as well as a plurality of primary and secondary objective which is optional in nature. The analytical module provided in the MTAL then traffics a new campaign to a plurality of third party platform (TPP) (201). On detecting the input of at least one user defined KPI value in a system level KPI and a channel level KPI (202), the autonomous optimizer searches a historical data pertaining to previously run campaigns having similar the parameters as the new campaign (203). On successfully finding at least one campaign with matching profile with respect to the new campaign and equivalent user entered system level KPI and channel level KPI, the autonomous optimizer sets a plurality of remaining KPIs from the matched profile in the new campaign and initiate the campaign (204). On failing to find a matching profile, the autonomous optimizer activates a “short tracking” mode in which the autonomous optimizer sets an average value for other KPIs and start tracking (205). In “short tracking” mode, the optimization happens with in 24-72 hours to reach an optimal KPI value for primary and secondary objectives in shortest period following which the autonomous optimizer enters into a normal tracking mode (206). The autonomous optimizer continuous to tracking a campaign performance and determining an achievement of the user defined KPIs (207) and optimizes remaining KPIs and a plurality of campaign metrics on sub-optimal performance of the campaign in regular intervals (208). After a predetermined round of optimization, an alert is triggered for a sub-optimal campaign strength in case the KPIs and the plurality of campaign metrics are fully optimized (209).
[0031] The plurality of campaign metrics comprises a performance classification of the TPPs, a bid change, a bidding algorithm change, a cross channel expenditure budget movement, a budget increase and decrease for a campaign over a TPP, a placement change and a targeting adjustment.
[0032] For determining a matching profile, a sub-routine is adopted by the system as shown in FIG. 3. The sub-routine method comprises the steps of:
a) Detecting a new campaign at the analytical module (301);
b) Identifying key identification markers (data related to the new campaign) such as field of application, type of campaign (text, audio, image, video), campaign budget and at least one KPI entered by the user such as view through rate (VTR%) (302);
c) Matching the key identification markers in a historical campaign performance database residing in the central application server (303). The historical campaign performance database is collection of previous campaign performances through the system;
d) In case of exact match, filter the KPI values when the matched campaign has turned as successful or optimal performing in terms of target achievement (304-305);
e) In case of similar match, create a similarity index, enter the KPI values when the matched profile has turned as successful or optimal performing in terms of target achievement. Optimize and track the KPI value values and other campaign metrics to achieve new campaigns target i.e. user entered KPI (306-308).
[0033] The autonomous optimizer further tracks the KPI values after the initial KPI value are entered from the matched profile (as shown in FIG. 4). The sub-routine method followed for optimization of the KPI values comprises the steps of:
a) Updating multiple channel level KPI and the system level KPI on identification of one or more KPIs related to a campaign performance (401). The analytical module is prompted to analyse updates;
b) Tracking the campaign performance on various TPPs for the updated KPI values (402);
c) In case of optimal performance, continue with KPI values and campaign metrics (403);
d) In case of sub-optimal performance, update the KPI values and continue the update for predetermined cycles (404);
e) In case of continued sub-optimal performance, update the campaign metrics and return to the initial KPI values (405). After updating, continue the process followed in steps (b)-(d);
f) In case of continued sub-optimal performance, generate a trigger alert for campaign quality optimization (406).
[0034] Detailed synopsis and explanation of building blocks of the business process:
[0035] Campaign trafficking, KPI setting and management:
a) Ability to traffic campaigns from the ATD platform instead of logging and setting up campaigns in individual downstream buying platforms.
b) As different channels have different parameters, field names, UI flow, targeting mechanisms, creative specifications, objective selection, algorithms, a common structure is identified among them (example- Google Ads, FB, Open Exchange DSPs) and push campaign parameters including demographic, geo, audience targeting through the marketing APIs available with each of the platforms. It will still have human intervention at particular channel levels wherever required but major portion of the campaign setup will be done in one click
c) Channel level KPIs- KPIs based on historical data or media objective will be set on individual campaigns with buying metrics like CPM, CPV, CPI, CPA/CPL/CPR, CPC etc.
d) System level KPIs: Overall budget, impressions/reach and a combination of hard goals and soft goals will be set as KPIs at the ATD system level. The underlying buying platforms will be orchestrated- bidding algorithms changed, bid values changed, targeting adjusted, budget moved across buying platforms/channels will be done to achieve the hard goal with some tolerance (user defined or system recommended) and a soft goal to cover the entire marketing funnel KPIs from branding to performance based campaigns. The system would treat the hard goal as the primary objective which won’t be compromised beyond the tolerance and will look to achieve the soft goal also without jeopardizing the primary objective in any manner. The idea behind having two goals is to ensure cost effectiveness, ROAS, objective based campaign optimization wherever needed. Soft goals are not mandatory while hard goals are. All metrices that can be set as hard goals can also be set as soft goals and vice-e-versa. There will be campaign pacing and budget management by ensuring the overall budget is in sync with the underlying channel budgets and at no point exceeds the overall budget assigned for the campaign. Pacing is a measure of delivery of the campaign by measuring the projected spends with actual spends or against the primary delivery objective. There will also be provision to set time frequency at which the goals are to be met. Example- 1000 leads/conversions every week, 50,000 Video Ad Views in a day.
[0036] Rules/Triggers (System-level, Channel level) and actions: There are two ways triggers will be set and implemented. Firstly- semi-automated manner, where the user would be able to put certain KPI values as triggers and associate actions with them. Example: Increasing budget for a particular channel by a factor of 1.5 in case the CTR% is above 0.8% for that channel. Other use cases will be provided in the addendum. Using decision trees these triggers will fire at channel level and only the firing is automated. Over a period of time the meta system will learn the triggers put into the channels, measure performance changes and come up with trigger recommendation at channel levels. Over a period of time the user can disable manual override and choose for a completely automated mode to run campaigns where triggers get implemented and the user is just notified of the changes implemented. The system works either as a completely automated self-balancing, self-optimizing, self-learning system or using an ensemble of human inputs and recommendation systems to achieve campaign objectives.
[0037] Campaign optimization (by constantly taking feedback from the underlying channels):
a) Classification of campaigns in to – High, Mid and Low performing or potentially High, Mid and Low performing ones using classification models in machine learning
b) Bid changes: Change bid values like CPM, CPC, Target CPA/CPR/CPL wherever possible. Example- Possible in Google Ads, Dv360, Verizon, Media Math, Adobe, The Trade Desk etc. Not possible in certain mediums like Facebook. Changes would mean increasing and decreasing the bid by comparing with historical data and predict current values using linear regression techniques by reducing mean errors and arriving at value of estimators by constant learning by various models. It will perform predictive analysis and take decisions based on the likely impact these changes will have on performance and delivery of these campaigns. A copy of the changes is sent to the database to keep a track of positive/negative/neutral impact of these changes on the campaign. For open-exchange DSPs the system changes senses and changes the bid values rather frequently and also develops custom bidding algorithms for optimized campaign spending.
c) Bidding algorithm changes: Most biddable media platforms have inherent algorithms that are deployed for buying media based on client/campaign objective. The campaigns initially go into learning phase and then start optimizing based on the learnings. Any change in bidding algorithms are only recommended after 7-10days and this translates to constant monitoring of the campaigns, human resource bandwidth allocation. The system will have the ability to change the bidding algorithms either by recommending an algorithm change and then based on user input implementing it or choose and algorithm by itself and implement it, notifying the user of a change that has been made and keeping track of the changes in an automated manner, when substantial learning has been obtained at the meta system level (6-12 weeks)
d) Cross channel budget movement, budget increase/decrease: Based on performing channels/buying platforms, the meta layer will first recommend budget changes which will be accepted or denied by the users and over a period of time after sufficient learning system should be able to make those changes on the fly even in case hardcoded triggers are not put in place for the channel or campaign. This will ensure budgets are spent platform agnostically with sole focus on only meeting client objectives and thereby moving budgets to channels which are either performing well or system decides are likely to perform well
e) Site/placement level changes: Identify which supply sources across sites, platforms, devices (Desktop, Mobile, Mac, etc), channels (Web, Mobile Web, Mobile APP etc), Operating systems (iOS, Android, etc)
f) Targeting adjustment: Geo targeting, audience demography targeting
[0038] Planning and buying forecasting: By keeping a track of all campaign spends/learnings, seasonality, sector the system would be able to provide buying and planning forecasting of supply to have pre-campaign understanding of budget estimates which currently either happens in silos or through silos and in a non-integrated manner
[0039] Data Management Platform: At any point of time there will either be a partnership with a global vendor or a proprietary platform for collection, enrichment and activation of 1st party campaign data through online and offline channels for cross channel activation, retargeting campaigns, consumer understanding, audience adjustment, personalization through the buying platforms integrated either through APIs or S2S integration. The ability to infuse 3rd party and 2nd party audience to cross-pollinate with existing 1st party data is also possible.
[0040] Machine learning models: Models will be developed based on historical data and ongoing data to achieve four key objectives- (a) Learn channel level triggers, system level KPIs and recommendation of the same by self-learning (b) Learn which versions of the campaign changes worked better than other and ability to classify potentially performing and non-performing campaigns (c) Take human approval/disapproval of the system level and channel level triggers recommendation as feedback to learn and come up with better optimization suggestions (d) Help supply forecasting and planning
[0041] 360-degree reporting: Data pulled across channels/buying platforms in an hourly, daily, weekly, monthly, quarterly, yearly fashion to give single view of the audience by collating data from all channels
[0042] Data base schema and instances: Three data base instances are to be maintained with distinct objective- (a) Store ongoing campaign data and ability to fetch data on a real time basis for campaign analysis (b) Historic data of all campaign trafficking details (including creatives), campaign data, channel level triggers, system level triggers for serving as a backup and restore mechanism. (c) Execution of various learning models using neural networks, recommendation systems, linear regression and ensemble wherever required.
[0043] Marketing APIs: Open source APIs available used within the available thresholds to fetch campaign data from all underlying channels. These include but are not limited to Impressions, CPM (Cost per mile), CTR %(Click Through rate), Budget spent (Cost), Leads/Conversions/Results, CPA/CPR/CPL (Cost per acquisition, Cost per results, Cost per lead), Views, True Views, Thru plays, 3 sec views, 5 sec views, VTR%( View Through Rate), VCR% (Video Completion Rate), Offsite conversions, onsite conversions, Unique user, reach etc. User of the platform are able to drill down to Campaign level, ad group level, creative level, targeting (age gender, geo) level and affinity audience level.
[0044] The present invention finds application in other business processes also such as:
a) Advertising technology layer to facilitate demand-supply chain automated campaign management and optimization based on quantitative analysis of objectives using semi-automated to fully autonomous meta data technology layer backed by machine learning models
b) Portfolio management (B2C/B2B finance): Single technology layer to manage, grow and sell funds with ability to set clear objectives like stable asset management, monthly/quarterly/yearly fixed returns or variable returns with tolerance but with secondary objective like maintaining high equity ratio, high debt ratio, maintain returns at a specified percentage. This happens in converse true and by setting a certain tolerance at the hard goal or primary objective. AUM can include- stocks (intraday/CNC/Commodities, Futures and options etc.), fixed deposits, savings account, digital gold, bonds, cryptocurrency etc.
c) Real Estate management
d) Logistics management
e) Travel & Hospitality management
f) Healthcare management
g) Food aggregators
h) Cab aggregators
i) Utilities aggregators
j) Agriculture marketplace
[0045] The channel level KPI comprises cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per action (CPA) and cost per click (CPC) based on a historical data over the TPP.
[0046] According to one embodiment of the present invention, the system level KPI comprises a primary objective, a secondary objective, an overall budget, impressions, reach and a campaign goal, wherein the primary objective comprises cost per thousand impressions (CPM), cost per visit (CPV), cost per impression (CPI), cost per acquisition (CPA) and cost per click (CPC), wherein the secondary objective comprises click through rate (CTR%), Video Completion Rate (VCR%), number of leads (lead flow in a designated time period) and a view through rate (VTR%).
[0047] For example - Performance - Level 1 – CPM,CPA/CPL, CPC, CTR%; Level 2- Maintaining lead flow (range/threshold/absolute) number with Time frequency (Daily/weekly/monthly).
[0048] Branding- Level 1- CPM, CPV; Level 2- VCR% (Video Completion Rate), VTR% (View Through Rate), Views, CPC, CTR%.
[0049] The performance and branding levels form primary and secondary objective and both levels can be put in place with a “Boolean &&” operator to satisfy both levels at a time with a certain tolerance of the primary KPI, say 10%. Example: CPM set as Primary KPI and CTR set as secondary KPI. The system will have to meet CPM KPI with a 10% tolerance to try and meet the secondary KPI of CTR%. Level 1 is always compulsory, and Level 1 and Level 2 are switchable.
[0050] It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the claims.