How Identity Resolution Platforms Work

Identity resolution platforms often provide a variety of features along with contact matching. An identity resolution platform is where you can maintain all your data and create your customer profiles. An identity resolution platform essentially allows you to understand your audience targeting and campaign personalization.

Most programmatic advertisers know that identity resolution helps simplify identity graphs for better ad targeting. But many people do not know how the platforms work and how targeting can be adversely affected by bad data.

Personalizing content across devices is a key differentiator for consumers that helps drive sales. When marketers can target buyers on an individual basis based on their past activity, it gives them a hand up in moving them towards a purchase. Being able to link customer data across devices to the same individual is critical in this personalization process.

According to a 2021 Deloitte survey, the average household now has 25 connected devices. This flood of devices accessing the internet means that it’s becoming more difficult to identify consumers across their various touchpoints. This is why identity resolution in 2022 is of paramount importance for programmatic advertisers. Here’s a breakdown of how identity resolution works and some of the challenges in doing it effectively.

Defining Identity Resolution

Identity resolution is the process of matching identifiers across all touchpoints, platforms, or channels to build a cohesive, omnichannel view of a consumer – thereby helping brands deliver relevant messaging throughout the customer journey. In short, it means building profiles to understand customers and help nurture them through the buyer journey.

For the majority of advertisers, the case of identity resolution is largely unsolved. In order to accurately segment digital audiences, marketers require a very large quantity of structured data and metadata that can link unstructured data points together to create profiles of users they want to target. However, the problem is that these profiles are difficult to produce due to bad data infiltrating the identity graph.

The Bad Data Problem

In order to resolve identities, you need to chart the connections between various data points with an identity graph. Identity graphs are profile database that aggregate all of the known identifiers linked to a given person or entity. With this graph, marketers can more easily target individuals across their various devices if they’ve been interacting with a specific ad or piece of content. But as marketers’ ability to produce cookies to track identities disappears in light of new privacy laws, these graphs are growing increasingly polluted by junk data.

Targeting ads using bad data causes marketers to waste dollars deploying ads to bots. But outside of just the budget losses, bad data in your identity graph muddles forward-looking data-based strategy decisions. When 76% of marketers use data to drive their decisions, they need to ensure that they’re targeting real people.

Ad fraud is a big driver of this problem. Throughout the years, the ad industry has somewhat thwarted passive fraudsters by developing fraud prevention filters. However as digital marketing has become more valuable, ad fraud has also become more lucrative. Bot traffic, domain-spoofing, and click farms have forced industry leaders to throw significant resources into solving this problem.

To mitigate ad fraud, companies need to align with consumer data providers who prioritize data quality. With quality-scored data and automation tools that identify bad data, marketers can root out the junk and increase their return on ad spend (ROAS).

Unjunking Messy Data Sets

One way to clean up data is by breaking down the data you’re using to target into deterministic vs probabilistic. Probabilistic data is an algorithm-based approach that aims to draw linkages between two data points. While this can be helpful in solidifying identity graphs, probabilistic data is mostly a guessing game.

Conversely, deterministic data is verified data that marketers know connects one identity to another. BDEX uses exclusively deterministic data to resolve identities based on what we know to be true. We believe this is the best way to help get rid of the noise generated by bots and click farms and focus on serving ads to the audiences that produce the best ROAS. 

By cross-analyzing your data with a large, structured set of deterministic data, marketers can aggregate, normalize, and programmatically optimize structural data – giving them more precision in their targeting. Additionally, tools like BDEX ID Check can spot bad or outdated IDs, fraudulent data, recursive ID linkages, and other inconsistencies to make sure all of the data being used to target is as clean as possible.

Looking at all these data points and distinguishing between the real ones and the automated bots can give marketers money back in their ad spend budget and increase the effectiveness of their campaigns. 

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