Keeping pace with consumers’ digital footprints is exhausting. In the era of short attention spans, digital distractions, and cross-device advertising, effective data onboarding is a top marketing hurdle. Not to mention, flawed data collection and targeting methods yield low match rates and questionable consumer trust.
For brands with years of historical records, these pitfalls hit hard. Moving forward requires data onboarding strategies that capture new, digital touchpoints with all customers, old and new. Doing so builds the foundation to craft targeted messages that connect emotionally and drive action amidst the age of distraction.
At its core, data onboarding is the process of linking offline data, typically residing in customer relationship management (CRM) platforms, with online identifiers to form a cohesive and unified customer profile. The data from CRM platforms may include email addresses, phone numbers, and postal addresses, while online identifiers span from IP addresses and first-party cookies to mobile ad IDs (MAID) and universal IDs.
This combination of data points and signals allows marketers to gain a comprehensive, “joined-up” view of customer journeys and behaviors across various offline and online channels. As consumers seamlessly switch between devices, experiences and platforms, the need for a holistic view is more critical than ever.
Data matching, a fundamental aspect of onboarding, can be achieved through two primary approaches: probabilistic or deterministic matching. The choice between these methods depends on factors such as the quality and quantity of available data, the sites or devices being linked, and the overarching objective of the onboarding process.
Probabilistic matching involves leveraging AI and machine learning algorithmic models to assess the likelihood that offline consumer data corresponds to its online counterpart. On the other hand, deterministic matching aims to identify an exact one-to-one match between offline and online profiles. Often, a combination of these approaches is used, as many CRM datasets may not be large enough for deterministic matching at scale.
A brand can’t implement a successful onboarding strategy, without quality customer data. But for some brands, this in itself can be challenging. Marketers can offer incentives so individuals voluntarily share personal details through channels like account sign-ups, loyalty programs, or subscriptions. However, brands must carefully assess the value and actionability of this data to ensure it aligns with its target customers.
Even when brands possess customer data, activation can be challenging due to its dispersion across various departments, including customer services, e-commerce, marketing, and media buying. Breaking down these data silos and transitioning to a unified Customer Data Platform (CDP) is crucial for ensuring data accessibility throughout the organization. Although this process introduces its own set of challenges, recent advancements in machine learning have simplified tasks such as data transformation and duplication filtering.
Successful data onboarding provides brands and their agencies with significant advantages across a spectrum of channels.
Data onboarding is complicated, and that complexity increases as media channels multiply. Yet it remains a critical component in today’s omnichannel world. Brands today, however, have access to specialized onboarding partners and a growing number of data-savvy agencies that can assist with the heavy lifting. Whether managed internally or externally, the reality is that no brand can afford to ignore the significance of data onboarding in today’s dynamic and omnichannel-driven marketing landscape.
This article was originally written by Lotame’s Chief Growth Officer, Alexandra Theriault, and published in Performance Marketing.