Report: What is data onboarding, and why is it important to marketers?
Management consulting firm The Winterberry Group says this is the first such independent study.
Data onboarding is one of those many processes in digital marketing that is, essentially, a black box.
To help shed light into this particular black box, management consulting firm Winterberry Group has released its first report on data onboarding in the US, “The State of Consumer Data Onboarding: Identity Resolution in an Omnichannel Environment.”
Winterberry says this is the first such report outside of onboarding providers themselves, which include Acxiom’s LiveRamp, Experian and Lotame.
The study addresses at some length the definition of data onboarding. Many people, including me, had considered it is more or less how Wikipedia characterizes the practice: “the process of transferring offline data to an online environment for marketing needs.”
Offline data, such as a retailer’s purchasing records, is matched with online data, like online purchasing, social posts or site visits. The match is made through a “match key,” which is an identifier common to both, such as an email address. Then the combined profile — this customer’s offline record, married with her online patterns — is anonymized and given an ID.
In fact, that’s how the report first defines consumer data onboarding:
“Consumer data onboarding (hereafter referred to as ‘onboarding’) refers to the process of linking offline data with online attributes.”
But then the report adds:
“More specifically, it is the matching of two audience (B2B or B2C) data sets: one, a first-party CRM data set belonging to a marketer and the other, a digital data set belonging to a data provider. The match process uses a common identifier or match key to link the records.”
But, Winterberry Senior Managing Director and report co-author Bruce Biegel told me, data onboarding is really about matching personally identifiable information (PII) with non-PII data. Here’s another definition in the report that is more consistent with his explanation:
“Consumer Data Onboarding: The process of matching (temporarily linking) owned consumer data (PII) with consumers’ corresponding digital attributes (such as cookies, IP addresses, device IDs and other identifiers) to create a cohesive and comprehensive identity for more actionable marketing.”
The term doesn’t necessarily mean that one dataset is from offline, he said, nor that the onboarding necessarily matches offline to online. It’s primarily about PII with non-PII. It’s just that most brands have more of their PII data in offline data like sales, where customers use credit cards or sign up to receive a catalog.
After the linkage is made between datasets, the onboarder generates segments for targeting. Data management platforms (DMPs) can also onboard diverse data, but some vendors, like Acxiom’s LiveRail, specialize in onboarding and can provide certain additional functions, like generating offline segments from the matched data.
From the Winterberry Group’s report
In any case, digital marketers need to have a good sense of what onboarding involves, since it is becoming an essential part of data-oriented targeting.
Person-based marketing, where the profile of a specific and identified person (later anonymized) is filled out by other matching datasets, is the gold standard for targeting ads and marketing. This can be particularly useful when you want to reach the same person across different channels and devices. And it can be a critical part of attributing sales, especially since people commonly move between their online and offline selves.
Winterberry, which queried nearly 20 “senior industry experts” and reviewed related financial and other data sources for the report, noted that this part of the marketing industry was around $30 million in total revenues in 2012. This year, it’s expected to reach $250 million and is on target to hit $1 billion by 2020.
In addition to definitions and growth rates, the report covers a brief history, data onboarding’s value to marketers, various kinds of dataset matching, pricing structures and predictions about where data onboarding is heading.
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