What is attribution and why do you want it? Attribution is the act of assigning credit score to the advertising supply that almost all strongly influenced a conversion (e.g. app set up). You will need to know where your customers are discovering your app when making future advertising and marketing choices. The Kochava attribution engine is comprehensive, authoritative and iTagPro smart tracker actionable. The system considers all doable factors and iTagPro then separates the winning click on from the influencers in actual-time. The primary components of engagement are impressions, clicks, installs and events. Each element has particular criteria that are then weighed to separate winning engagements from influencing engagements. Each of these engagements are eligible for attribution. This collected device information ranges from unique gadget identifiers to the IP deal with of the gadget at the time of click on or impression, dependent upon the capabilities of the network. Kochava has thousands of distinctive integrations. Through the mixing course of, now we have established which gadget identifiers and ItagPro parameters each network is able to passing on impression and/or click.

The extra gadget identifiers that a network can pass, the extra information is offered to Kochava for reconciling clicks to installs. When no system identifiers are provided, Kochava’s sturdy modeled logic is employed which depends upon IP address and gadget person agent. The integrity of a modeled match is decrease than a system-based mostly match, but nonetheless leads to over 90% accuracy. When a number of engagements of the identical type occur, they are recognized as duplicates to offer advertisers with more perception into the nature of their site visitors. Kochava tracks every engagement with every advert served, which units the stage for a comprehensive and authoritative reconciliation process. Once the app is put in and launched, Kochava receives an set up ping (both from the Kochava SDK within the app, or from the advertiser’s server by way of Server-to-Server integration). The install ping contains gadget identifiers in addition to IP handle and iTagPro reviews the consumer agent of the system.

The information obtained on set up is then used to find all matching engagements based on the advertiser’s settings throughout the Postback Configuration and deduplicated. For more data on campaign testing and gadget deduplication, refer to our Testing a Campaign help doc. The advertiser has complete management over the implementation of tracking events throughout the app. In the case of reconciliation, the advertiser has the power to specify which post-install event(s) define the conversion point for iTagPro product a given marketing campaign. The lookback window for occasion attribution inside a reengagement campaign will be refined inside the iTagPro smart tracker Override Settings. If no reengagement campaign exists, all events will likely be attributed to the supply of the acquisition, whether attributed or iTagPro smart tracker unattributed (natural). The lookback window defines how far again, from the time of install, to contemplate engagements for ItagPro attribution. There are different lookback window configurations for gadget and Modeled matches for both clicks and installs.

Legal standing (The authorized status is an assumption and is not a authorized conclusion. Current Assignee (The listed assignees may be inaccurate. Priority date (The priority date is an assumption and is not a legal conclusion. The applying discloses a goal tracking method, a goal tracking device and iTagPro smart tracker digital tools, iTagPro smart tracker and pertains to the technical field of artificial intelligence. The strategy comprises the following steps: a first sub-network within the joint monitoring detection community, a first feature map extracted from the goal feature map, and a second characteristic map extracted from the target characteristic map by a second sub-network within the joint tracking detection community; fusing the second characteristic map extracted by the second sub-community to the first function map to obtain a fused feature map corresponding to the primary sub-community; buying first prediction data output by a primary sub-network based mostly on a fusion characteristic map, and buying second prediction information output by a second sub-community; and determining the current position and the movement path of the shifting target within the target video primarily based on the first prediction data and iTagPro smart tracker the second prediction info.