San Francisco’s Fairmont Hotel survived the earthquake of 1906, Tony Bennett singing “I Left My Heart in San Francisco” in the Venetian Room, a starring role as a hotel in the series “Hotel” and a tiki bar with a real fake rainstorm … and, on February 22nd, about 1,500 gliterati of the digiterati descending with their day-old shadow and fast talk to talk talk about data data.

The event was Acxiom LiveRamp’s #RampUp16, transported this year from San Jose to Nob Hill. It’s an insider’s event with a guest list reading like a palimpsest of the ad tech Illuminati. I moderated a panel on the topic of … well, data. It’s an insider’s event because LiveRamp is an insider’s system, a kind of infrastructure luminary in this deep region of technology.

So what does LiveRamp do?

The short answer is: Onboarding. Data onboarding. Marketing data onboarding.

Does that help?

As it turns out, “onboarding” is one of those terms — others are DMP, attribution and machine learning — that are more often used than really understood. On its face, it sounds straightforward enough: onboarding takes customer records from various places and matches them somehow. But where these records come from, how they’re matched and what you do then isn’t so obvious.

Well, friends, I’m here to help.

Although it sometimes seems so, LiveRamp isn’t the only platform that does onboarding. A general definition of “onboarding” could mean simply moving information from one place to another and organizing it, and many platforms do this sort of thing: every DMP and tag management system, for example.

More direct competitors of LiveRamp include marketing service providers like Conversant, Neustar and Experian. There are also mobile cross-device players like Tapad and NinthDecimal and upstarts like Circulate that provide some similar services.

But the market is smaller than it looks. Some of LiveRamp’s rivals actually use bits and pieces of LiveRamp itself, either as a service or as direct resellers. For example, Oracle does its own onboarding but can use LiveRamp to synch up cookies (we’ll get to this). Neustar may resell LiveRamp if a customer asks. In the microverse of marketing data onboarding platforms, LiveRamp is most definitely the plata di tutti platas.

So what does it do?

LIVERAMP EXPLAINS ITSELF

LiveRamp is very good at motivating the problem. Its own marketing prose leans toward melodrama as it bemoans “puddles of customer data” that are — you can see this one coming from Fresno — “in silos” … but all your efforts are “hamstrung” because you can’t “seize the opportunities that are sitting right in front of you” [bold theirs].

What’s sitting right in front of you is the opportunity to do some better:

  • CRM retargeting
  • Lookalike modeling
  • Cross-channel marketing and attribution
  • Site optimization
  • Dynamic creative optimization

All of which are worthy things to do. How? LiveRamp has written its own “5-Minute Primer to Data Onboarding,” which sums up the situation like this:

“It sounds simple, but the mechanics are pretty intense …”

Amen. Both the 5-minute primer and the longer “15-Minute” version are big on motivation and general case studies … but somewhat California slim on details. As are the numerous one-sheets describing its uses (CRM retargeting, lookalike modeling, etc.). These all show a three-step process (onboarders are big on three- or four-step processes), like so:

  1. Upload your data [i.e., send it to onboarder]
  2. Onboarder anonymizes and matches it
  3. Segments are sent to your marketing platforms

That’s onboarding alright, but still a bit abstract.

To complicate the situation further, Acxiom LiveRamp does two different things, only one of which is onboarding. It also does something called “Customer Link,” which turns out to be what its parent company, Acxiom, has been doing since 2009 under the name AbiliTec.

LiveRamp describes these two offerings — Onboarding and Customer Link — as working through four-step processes, like so:

Onboarding

  1. You send LiveRamp (let’s start calling them LR) customer data files — these are sent via dashboard or SFTP and can be millions of rows long
  2. LR anonymizes the data — removes personally identifiable info (PII)
  3. LR matches customer records to devices and digital IDs — this is the magical step where your customers are located in LR’s large web of partner data, which includes Acxiom data
  4. LR delivers segments to marketing applications and media platforms

Okay for now. Let’s roll along to:

Customer Link

  1. You send LR customer data files — in this case, they focus on importing data from your “marketing platforms, CRM database, sales transaction system or third-party data providers”
  2. LR anonymizes the data
  3. LR matches customer records to “Customer Links” — this is the step where each record is mapped to an Acxiom AbiliTec Link [these are unique IDs Acxiom builds for individuals, households, locations and other things marketers like]
  4. LR sends you back your file from #1 with (i) PII removed and (ii) unique Customer Link added as a column

So the difference between LR Onboarding and LR Customer Link appears to be:

  1. Data sources — for onboarding it starts with you; for customer links, it starts with partners
  2. Matching — for onboarding your customer records are matched to devices and “digital IDs”; for customer links, to AbiliTec links

The implication here is that onboarding is a way to take offline customer records and map them to online customer records; and that customer links is a way to take online customer records and map them to offline IDs. They’re complementary activities going in two directions.

So far, it’s clear that both onboarding and customer linking have a similar aim:

  1. You take a bunch of files from a bunch of different systems that contain something that identifies your customer — not the same thing in all cases (or you wouldn’t need LR), but something
  2. You send these files to your onboarder
  3. The onboarder takes the thing that identifies your customer … anonymizes it … and maps each of those customers to some combo of unique Acxiom IDs, mobile device IDs, or cookies provided by partners, who are marketing platforms or media companies

What do you get out of it? A chain of links: this customer record and that customer record and that browser and that device and that Adobe Analytics ID and that Yahoo cookie all belong to the same person.

SPOILER ALERT: The live heart of the onboarding & linking platform is MATCHING. And amid all the anonymizing and hashing and so on it, it’s easy to overlook the fact that this match is done the only way it can be: using a piece (or pieces) of originally personally identifiable information. How do you match your PublisherX cookie and your OptimizelyID and your Google AdID and your LoyaltyCardIDZ to the same person? Well, when they signed up for whatever, the customer probably gave you their name and address, zip code, email address, phone or some combination thereof. It’s this thing — the email, most often, or the phone — properly anonymized, that is the magic match provider.

To work well, this kind of onboarding requires a large network of partners, who participate in this ID swapping based on hashed PII. These partners are heavy on the media players (including publishers and ad tech platforms) that have very large “cookie pools” (that is, browser cookies that have been linked to other browser cookies). LR has 200+ partners you’d recognize. It’s an asset.

So what do you — the hard-working marketer — care about matching all these different customer IDs to a person (or household)? It will let you:

  • Know more about each individual customer since the different files are consolidated
  • Perform better segmentation and general insight generation than you otherwise could
  • Send better targets and targeting ideas to your marketing and ad platforms

Because I’ve outstayed my bloggers’ welcome, I will promise you yet another Part 2 which will give TMI on the process used by a different onboarding platform, as well as a look at AbiliTec’s own 17-year history, which is actually pretty fascinating.

I’m @martykihn 🙂

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