Key Concepts Every Executive Should Know About One-to-One Marketing

In One-to-One Marketing by Jamie Turner

In order to get one-to-one (1:1) marketing to work for your business, it’s important to understand the key concepts that are involved with the whole process. When you understand the vocabulary behind 1:1 marketing, you’ll understand how to put it to work for your business.

So, with that in mind, let’s take a dive into the concepts you’ll need to understand as you start using 1:1 marketing. What follows are a series of terms that have been adapted from an excellent glossary put together by LiveRamp. (If you would like to read the full LiveRamp glossary, you can do so here:

First things first, it’s a good idea to understand the concepts of cookies, cross-channel marketing, and customer ID codes. By understanding each of these concepts, we’ll have laid the foundation for some of the more complex concepts we’ll address later. 

A cookie is a little piece of data that a website sends through a user’s browser to recognize a device for a future visit. They’re mostly used to relay information about a user’s behaviors and activity on the site. Cookies track things like how many pages they visited, how long they stayed on each page, and which items were in their shopping cart.

Cross-channel marketing is when you reach the same customer on different marketing channels with a single campaign. For example, Michael sees a sponsored post on Instagram letting him know about a new tennis racket from a brand he likes. The next day, he sees a video ad on YouTube for the same tennis racket. The day after that, he sees a banner ad for the tennis racket and decides to click through to the website where me makes the purchase. That’s cross-channel marketing in a nutshell.

When any marketing system interacts with a customer or a prospect, they give that customer a customer ID code which is a unique identifier for that particular customer. The trouble is, when different systems give different codes, you end up with multiple codes for the same customer which is why your targeting software doesn’t know it’s dealing with the same person as your personalization software. 

That’s where identity resolution comes in. Identity resolution is the near real-time process of connecting hundreds of identifiers used by different channels, platforms, and devices. It enables marketers –– along with supporting agencies, technology platforms, data owners, and publishers –– to tie them back to the same person in a deterministic, privacy-safe way for 1:1 marketing targeting, measurement, and personalization.

Okay, now that we have some of those key terms and concepts out of the way, let’s talk about ad targeting and addressability. Ad targeting is a favorite tool of 1:1 marketers because it allows organizations to use what they know about a prospect or customer to serve them the most relevant ads. This can be as simple as cookie-based retargeting, or as complex as using CRM, past-purchase, and interest data to hyper-target ads to specific groups of prospects or customers. 

Addressability is a term that is related to ad targeting, but is somewhat different. In order to interact with a customer on a specific marketing channel, you need a way to reach them. In other words, you need an address. In some cases, this is relatively simple — to send an email, you use an email address; to send direct mail, you use a postal address. But in the online world, addresses are anonymous, tied to devices and changing all the time. This makes it hard to reach the same customer across channels and devices with a consistent message. That’s where identity resolution fits which, as mentioned, ties all the data together to create a deterministic, privacy-safe identifier for each individual.

Okay, let’s shift gears and talk about content and the delivery of ads through various platforms. Over-the-top video (OTT) is content that is viewed on internet-connected devices. Apple TV, Hulu, and Roku all deliver TV content via the internet without requiring users to subscribe to cable or satellite TV. The advantage of OTT is that it gives advertisers the kind of reach they’d get from traditional TV with the accurate targeting they’ve come to expect from their digital campaigns. 

Connected TV (CTV) is a subset of OTT that is delivered through smart TVs or any TV that is internet-enabled. Samsung, Vizio, and other manufacturers have interfaces that allow users to directly log in to the OTT apps to view content. 

Addressable TV is a data-driven TV that enables one-to-one household targeting. It allows marketers to show different ads to different households when they’re watching the same show. It brings the data-driven relevance and personalization that marketers use in their digital campaigns to TV. Advanced TV is slightly different from Addressable TV. With Advanced TV, you can reach people regardless of how they watch TV content, whether it’s via addressable TV, over-the-top, or digital video. Advanced TV channels combine the sight, sound, and motion that makes video advertising so valuable with the broad reach of traditional TV and the targeting and measurement capabilities that marketers have long used in their digital campaigns.

Now that we’ve discussed content and content delivery systems, let’s talk about an important concept called attribution modeling. When a 1:1 marketer uses hyper-targeted advertising to serve ads to prospects and customers, a certain percentage of them actually buy the product or service. But which ad contributed most to that purchase? Was it the TV commercial they saw that drove them to the top of the sales funnel? Or a banner ad they clicked on when they were in the middle of the sales funnel? Or the magazine ad they saw when they were at the bottom of the sales funnel? 

If you’re scratching your head on that one, welcome to the world of attribution modeling. The basic aim of attribution modeling is to figure out which marketing actions or channels contributed most to a certain customer action. Said another way, it’s about using analytics to give credit where credit is due, and knowing how much credit is due to each ad. 

There are a variety of attribution models that 1:1 marketers use. A last click attribution model assigns 100% of the credit for a sale to the last ad the prospect or customer clicked before buying. So, if a prospect saw a social media ad and clicked through to your website, and then saw a paid search ad and clicked through, and finally saw a banner ad and clicked through and then made a purchase, then 100% of the attribution for the sale would go to the banner ad. That’s last click attribution. 

U-shaped attribution gives more credit to the first and last click. Using the example above, a 1:1 marketer might assign 40% of the credit to the first social media ad, and 20% to the next ad (the paid search ad), and then another 40% to the final banner ad. That’s U-shaped attribution. Time decay attribution gives less credit to the first ads the prospect or customer sees and more credit to the last ads the prospect or customer sees. In other words, as time goes on, more credit is given to each ad that was seen leading up to purchase. Conversely, linear attribution gives equal credit to all of the ads a prospect or customer sees prior to purchase. 

Awrighty, then … lots going on here. Now let’s talk about data, specifically data ethics, data hygiene, and data integration. Data ethics is a company’s approach to protecting the data they own and have access to. While complying with the law is the bare minimum, many companies (including LiveRamp, the folks who have the in-depth glossary we mentioned at the top of this chapter) take data ethics even further, establishing data governance practices that encompass what their customers and users consider to be fair and just. 

Today, because of data breaches, laws like the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and countless other news-making events, data ethics is becoming another measure of consumer trust in the brands and companies they support. 

Data hygiene, or data cleansing, is the process of fixing all the little glitches in your data and making sure it complies with your own set of standards. Said another way, data hygiene is all about cleaning up your data. It’s related to data integration in the sense that data integration is the process of combining data sets that live in different applications so you have a unified view of your customer. 

We have a few more terms here, so stay with us. If you understand the concepts and terms in this chapter, you’ll be well on your way towards being a full-fledged 1:1 marketer, so hang in there. Almost done. 

Personally identifiable information (PII) is data that, on its own or when combined with other data, can be used to identify someone. As data-driven marketers become increasingly interested in creating more personalized experiences across all channels, it’s important that this information be handled with care. Many platforms anonymize records so that they’re de-identified so that no PII is transferred to the online marketing platforms. 

Anonymization enables marketers to deliver highly-focused, relevant experiences to audience segments while protecting consumer privacy and considering data ethics. It’s important to note that PII is a U.S.-specific term. In Europe, the term “personal data” is used to encompass PII and a broader set of data not included under the U.S. definition, including social media posts, pictures, transaction history, and more.

Look-alike modeling is a way to expand your audience and extend your reach. It’s where you (and your third-party data provider) analyze your current customer data to come up with a target audience segment of people with similar behavior, demographics, or preferences. If you’re targeting women between the ages of 25 and 34 who have a propensity to buy cosmetics, and you’re using your own first-party data to create your campaign, then you could incorporate look-alike modeling into your process. In other words, you can take your first party data and use it to create a matching profile of people from a third-party data source. Then, you would target the people from the third-party data source because they would have a greater propensity to buy your product or service.

Offline attribution is another concept that is important to know. With online attribution, it’s relatively easy to connect the dots — if a person clicked on a paid search ad to get to your website and then purchased the product or service, then you can attribute the sale to the paid search ad. But offline attribution is incredibly hard when either the customer’s action took place in a physical store, a brand location, or a contact center. Did they buy those socks in-store because of an ad they saw when they were using a news app? Or did they buy them because of a banner ad they saw previously? 

It used to be virtually impossible to connect the dots when someone saw an ad online but then made the purchase in-store. But it’s not impossible anymore. In fact, that’s what 1:1 marketing is all about –– the ability to hyper-target an ad to a specific segment of the population, and then track whether or not the person made the purchase (either online or offline) after having seen the ad. 

Did we say that was easy to do? No, it’s not easy. And it’s not perfect, either. But it is possible and that’s what this book is all about –– targeting, tracking, and calculating the ROI of a 1:1 marketing campaign. 

About the Author: Jamie Turner is an internationally recognized author, professor, and speaker. He and Chuck Moxley co-authored An Audience of One which has been called “One of the most important marketing books of the past decade” and “Essential reading” by executives from around the globe. To download the Introduction and Chapter 1 for free, just click here. No forms to fill out, no e-newsletter sign-ups. Just free stuff. For you!