Smart identity resolution practices allow brands to deliver what consumers want—personalized messaging. Research shows repeatedly that individuals expect marketing to target their particular needs and interests. In fact, according to consulting firm McKinsey & Co., 76% of consumers grow frustrated when brands don’t deliver personalized interactions.

What’s more, identity verification is a strong defense against the increasingly pricey threat of consumer fraud, which costs retailers $100 billion annually, research shows. Effective identity resolution practices can curb or prevent that level of abuse altogether.

But just as some consumers demand personalization and others find ways to create multiple identities to exploit brand promotions or return policies, exactly how companies connect the dots to build individual consumer profiles is growing more complicated. Starting this year, Google has begun to limit access to third-party cookies, once an essential ingredient in this work to tie virtual and physical consumer data together. And data privacy regulations, including age limitations on regulated industries such as alcohol and cannabis, means brands must be vigilant about their data quality and linking efforts.

At this crossroads, companies must double down on harnessing best practices to bolster data quality, so they can quickly combat fraud and better serve their customers. That work to proactively address data integrity involves deploying the right tools, including artificial intelligence.

ROBUST IDENTITY RESOLUTION

To build a robust identity resolution strategy, companies are pulling on dozens of disparate strands of information—from social media likes and purchase and shipping history to first-, second-, and until recently, third-party cookies. All those strands center on one thing: data.

When companies rely on bad data, fraudsters can easily take advantage of them, creating fake accounts, for example, to capitalize on a promotion. And it doesn’t take much to strip a brand of millions of dollars. One report details how 4,000 users generated 137,000 fraudulent accounts to abuse a 35% discount deal for first-time customers, costing the company more than $14 million.

Or, in the case of building personalized marketing campaigns with an identity resolution strategy built on unreliable data, a regulated retailer could craft the perfect targeted deals, segmenting discounts based on a consumer’s location and shopping history, but fail to reach the intended audience—or worse, target a shopper that is underage.

For example, a beverage retailer could offer a personalized discount for longtime shopper “Sallie Taylor” to use at her favorite local store on her favorite beverage. But, because of poor identity verification, they could instead send it to “Sally Tailor,” who visited the shop a single time to buy a gift for a friend. Over time, mistakes like this can add up as misdirected messages alienate customers, damage brand reputation, and lead to lost revenue or even fines.

Ensuring the data is right, however, takes work. Coverage is essential; data must be gathered from multiple commercial, private, public, and self-reported sources. Matching strategies must be sophisticated to ensure accuracy.

Maintenance of all that information is critical, too. Those threads of data about longtime customers and potential ones must be constantly scrutinized and scrubbed to ensure they’re up to date. And, of course, risk prevention is required, so the data is secure and verification practices comply with privacy laws.

GOOD DATA, BETTER AI RESULTS

Luckily, as identity resolution efforts grow more complicated, AI is poised to transform this work and help brands strengthen how to verify personas.

Longtime identity resolution tactics have involved matching specific data markers to build a consumer profile—also known as a deterministic approach. But AI-powered tools built with predictive analytics don’t need as many data markers to accurately create profiles of individual customers. Instead, they rely on so-called probabilistic matching, using sophisticated algorithms to uncover trends and correlations to build that profile even when some information is missing.

When it comes to identifying potentially fraudulent activity, AI solutions trained with information from a users’ past behavior can identify unusual activity in real time. With this information, companies can quickly address any potential bad actors and prevent major financial losses.

And AI, along with natural language processing and machine learning algorithms, can connect various strings of unstructured data, such as customer emails or conversations with a chatbot, to pull up insights that inform marketing campaigns.

Going forward, however, harnessing the power of AI makes data integrity more important than ever. For AI tools to deliver accurate predictions and recommendations, they must be trained with precise, real-time, and comprehensive information. Focusing on and deploying effective identity resolution strategies with top-notch data will lay the groundwork to ensure companies can harness the best of what’s to come from AI, proactively address the threat of fraud, and boost customer trust and loyalty.