In the vast landscape of eager consumers, both large and small brands often struggle to locate their target audience effectively. This challenge arises from various factors, including customers not providing actionable information, being unaware of in-market consumers, or losing potential buyers to competitors. The solution lies in leveraging first-party data strategies, empowered by AI and machine learning, to gain valuable insights into in-market customers and optimize campaign performance.
Digital media deployment is essential in contemporary marketing, and brands traditionally relied on third-party cookies to identify potential customers. However, as data becomes more elusive, first-party data emerges as a potent tool. Brands equipped with first-party data can drive personalized messaging, gaining a deeper understanding of their customer base. The caveat, however, is ensuring the accuracy and completeness of this data.
Incomplete or inaccurate first-party data poses challenges for brands, manifesting in various forms such as unknown transactions, conquest opportunities with competitors, and the existence of in-market customers who remain invisible due to lack of engagement. Evolving privacy regulations and data deprecation further complicate matters, making some devices and browsers unreachable for brands.
To address these challenges, identity resolution becomes crucial. Marketing and advertising technologies with built-in identity resolution can fill gaps in first-party data by utilizing multiple data sources, creating a unified customer view. Two key solutions benefiting from identity resolution are Customer Data Platforms (CDPs) and digital media.
Companies utilizing identity resolution need to organize, clean, complete, and enhance first-party data to fuel digital media activation with valuable insights. Similarly, digital media solutions integrated with first-party data and identity resolution provide a more extensive view of customer behavior.
Let’s dive into some examples of first-party data being used in the real-world.
Brand example: An analysis of your customer database might help you identify a key audience segment for a product or service. For example, ATB Financial may have developed the below campaign after an analysis of their customer database led to a realization that young consumers were a key segment for the bank’s “Load & Go” prepaid Mastercard. The campaign targeted 17-19-year-old ATB customers who do not currently use the prepaid credit card product. In addition to the load & go product, the bank highlighted their mobile app and mobile payment capabilities that are perfect for young consumers on the move.
Having direct data from consumers about what they want can help brands deliver powerful messaging, especially when this knowledge is combined with technology to dynamically serve personalization at scale, resulting in compelling, cost-effective marketing communications.
Brand example: Boating and water sport retailer, West Marine, delivers dynamic content to consumers based on their psychographic preferences. In their preference center, the brand directly asks their audience about the types of water activities they enjoy (fishing, sailing, paddle boarding, etc.) and then uses this data to deliver personalized, relevant communications.
Brands can use first-party data to create triggered email campaigns which are automated based on consumer behaviors.
Brand example: Lands’ End uses first-party data to create a comprehensive email program that has achieved a 158% higher conversion rate (purchase per email click) than the retail industry average. Each of the 15+ trigger message types uses insights from subscribers’ email activity, purchase history, browse behavior, and product interests to create personalized, relevant communications, including welcomes, cart abandonment, reactivation and more. In addition, Lands’ End combined first-party data with testing to determine the optimal message frequency and volume for their triggered communications – building a carefully planned message hierarchy and automated threshold rules.
Brands can use first-party data to create personalized messages that resonate with their audience.
Brand example: Brands can create campaigns to automate the upsell process and ensure that they don’t miss an important opportunity to reconnect with customers. HSBC sends a triggered welcome email to new customers. The email is written in a casual, friendly tone and features an important call to action – offering a “financial review” at the new branch as a way to upsell new customers on additional financial services.
First-party data can be applied to lower acquisition costs and boost campaign results by creating lookalike models or similar audiences. Brands can take what they know about their best customers – how they behave, what incentives and offers they respond to, what they’re interested in – and use it (in combination with third-party data) to target new audiences who are most likely to convert.
Brand example: Google Ads, Microsoft Bing Ads, Facebook, and LinkedIn all have features that allow marketers to build lookalike or similar audiences based on first-party data.
Online life insurer, Haven Life, developed a creative Facebook campaign to attract new customers. Using data about their current customers to create a lookalike audience, the company was able to target users who were most like their high-value customers. In addition to placing the ads on Facebook, the brand developed a Facebook Messenger bot to help engage leads and drive them towards completing a quote questionnaire within Messenger, all without needing to leave the “walled garden” of Facebook. During the month-long campaign, the brand saw a 12% lift in completed quote forms at a 23% lower cost per lead than other digital platforms.
In conclusion, first-party data, when harnessed effectively with identity resolution, emerges as a powerful tool for brands to connect with customers in meaningful ways, fostering loyalty and driving business growth.
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.