Attributing Online Conversions to Offline TV Ads
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Solution Overview
Problem
Measuring the effectiveness of TV advertisements is challenging due to limited methods for attributing online activities to offline events, making it difficult to optimize ads for target demographics and track conversions accurately.
Innovation Solution
A system and method that includes a network location server, lead recognition server, offline event database, and attribution modules to associate client device requests with offline events, calculate lift in client device requests, and attribute visitors to specific ad spots based on demographic and geographic data, enabling accurate attribution of online activities to offline advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical models are used to attribute conversions to TV ads, then conversion attribution is attempted, but measurement precision is insufficient
Solution Approach 1:
The patent introduces intermediary elements (tracking URLs, promotional codes, phone numbers) that serve as mediators between TV advertisements and consumer actions. These intermediaries enable direct tracking of conversions while maintaining measurement precision and reliability by creating a verifiable link between ad exposure and consumer response.
Solution Approach 2:
The system performs preliminary actions by embedding tracking mechanisms (tracking URLs, promotional codes, phone numbers) into TV advertisements before they are aired. This allows for accurate conversion tracking from the outset rather than attempting to retroactively attribute conversions using imprecise statistical models.
2Measurement precision
If linking keys are embedded in ads for tracking, then conversion tracking is enabled, but the method has limited application since only a small fraction of customers use the tracking devices
Solution Approach 1:
The patent implements multiple tracking mechanisms (tracking URLs, promotional codes, phone numbers) that serve different customer behaviors and preferences. This multi-functional approach ensures comprehensive tracking coverage by capturing conversions across various customer interaction patterns rather than relying on a single tracking method.
Solution Approach 2:
The system uses diverse intermediary elements that naturally integrate into different customer journeys. Instead of requiring customers to actively engage with a single tracking device, multiple intermediaries are embedded throughout the ad and conversion process, increasing the likelihood that customers will encounter and use at least one tracking mechanism.
3Loss of information
If viewer panels are used to understand TV ad viewers, then some viewing data is collected, but the sample size is too small (less than 0.022% of population) to provide reliable insights
Solution Approach 1:
The patent enables self-service tracking where consumers naturally provide measurement data through their own actions (visiting tracking URLs, redeeming promotional codes, calling tracking phone numbers). This eliminates the need for small voluntary viewer panels, as the tracking system captures data from actual consumer behavior at scale rather than relying on a tiny self-selected sample.
Solution Approach 2:
The system uses tracking intermediaries (URLs, codes, phone numbers) as mediators that capture viewer information naturally as part of the conversion process. This approach collects comprehensive viewer and conversion data from the actual target population rather than relying on a small, non-representative viewer panel sample.
Data Source
AI summary
Systems and methods are provided for determining a quantity of network location visitors that are likely generated or encouraged by specific offline events. A corresponding number of leads may then be attributed to and associated with those specific events. Ongoing conversion activity of those visitors may be tracked and associated with the offline events. Conversions of those visitors may be attributed entirely or partially to one or more specific offline events. The effectiveness of each offline may then be evaluated based on aggregate lead and conversion information.