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WTF ISHYPER-PERSONALIZATION

Table of contents

2. Introduction

3. The machine learning revolution

4. Building a profile

6. Timing is everything

10. Frequency capping – with finesse

12. Hyper-personalization in the age of GDPR

13. Shining a light into the black box

14. Conclusion

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Jane wants to buy a new smart TV.

Her journey begins on her smartphone, browsing the sites of a few big-box retail outlets and TV manufacturers. As she traverses the digital landscape, she types keywords like “smart TV,” “streaming” and several famous brand names.

After picking up on these signals, one brand starts retargeting her with display ads across numerous websites, including some that are completely unrelated to retail or electronics. The aggressive tactics alienate Jane, and ultimately she buys a competing brand’s product.

John, another would-be smart TV buyer, follows a similar journey across platforms and devices. This time, the aforementioned retailer has implemented hard frequency caps; John only sees its ads a couple of times, and only on sites directly related to electronics. But when John goes to make his purchase, he changes his mind and decides to buy a streaming stick instead.

The retailer employed entirely different tactics for John and Jane. Both failed.

Why?

The obvious answer: John and Jane are different people. Jane craved a less invasive ad experience, while John needed more aggressive persuasion. It’s become increasingly easy to hyper-target consumers with ads related to their browsing and transaction history. But as we saw with John and Jane, even hyper-targeted messages aren’t always effective, because different people react to different ad experiences...well, differently. Hyper-targeting often fails to get hyper-personal.

But WTF is hyper-personalization? Read on to find out.

Introduction

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Like John and Jane, today’s consumers are active across a vast array of platforms and devices. They visit countless webpages; click on countless products; and complete countless unique transactions. Marketers can glean their locations, their local weather conditions, the times of day they tend to make purchases, and more.

The promise of machine learning is that it allows marketers to ingest these data points and isolate the handful that are actually indicative of a propensity to buy a product. But first, the data has to be diverse and abundant. “The utilization of any technology like this is only going to be as good as the information that we’re putting into it,” said Bruce Williams, general manager of performance media at digital agency 360i.

Predictive capabilities are a key differentiator between hyper-targeting and hyper-personalization. “You can understand if there are certain patterns [of behavior],” explained Bruce Williams, general manager of performance media at digital agency 360i. “What are some of the frequencies that are going to be important for people? And outside of just repeat, replenishment-type products, what are going to be some of the correlated products that are going to be interesting to somebody based off of their past purchases?”

When marketers and their technology partners build machine learning models, the model needs to know how specific consumers are likely to behave next.

It’s more than just a game of “likes.”

The machine learning revolution

Hyper-targeting

Hyper-personalization

utilizes customer data (e.g., demographics or behavioral history), but doesn’t necessarily incorporate sophisticated predictive modeling.

uses machine learning to analyze thousands of data points and establish detailed patterns of behavior, enabling accurate predictions as to how customers will respond to specific ads and campaign tactics.

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Through their browsing and transactional behavior, e-commerce shoppers leave detailed cross-channel information about what content and products they’ve engaged with in the past — not to mention how they’ve responded to previous ad campaigns.

“What we’ve really seen emerge over the last couple of years is the utilization of a lot more deep, real-time data that gets to the core elements of individual or consumers’ intent,” said Williams. First, he said, there’s the behavioral element: “A search, or the information engagement that a retailer might see on a website — those are the core signals of what the individual’s interest is at any given moment in time.”

Then there’s context — for instance, environmental or weather factors that might be influencing what a person is searching for.

But there’s another element that marketers — and even tech pros — often overlook: how consumers have responded to the specific elements of past display campaigns, including the performance of each individual ad unit.

Building a profile

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A/B testing is nothing new, but marketers can miss the opportunity to dive much deeper. Experts can build out their machine learning models to assess the granular, incremental performance of every individual element in a display campaign.

For instance, machine learning models can be built to assess:

This is the kind of granular information that marketers need to work into their machine learning models to build sophisticated profiles and enable predictive capabilities. And with each subsequent campaign, marketers can accumulate more data and further enrich personalization.

In this way, marketers can eschew past failures and emulate past success, targeting consumers with relevant ads that actually move them through the funnel in ways that have proved successful in the past. History repeats itself — but sometimes it needs a nudge in the right direction.

which platforms display ads were placed on.

how consumer actions compared across different websites, across different apps, and across different devices.

how many consumers took a specific action or actions (a click, a purchase, etc.) in response to a specific unit being displayed.

how those actions compared across various demographic and location-based segments.

how effective the ads were at specific stages in the marketing funnel.

and crucially: which actions each individual user took — and how these actions compared to what they’ve done in the past.

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Building predictive profiles doesn’t stop at analyzing which ads, products and product categories customers have engaged with in the past. Nor does it stop at transactional history, or at assessing interests and demographics. Marketers and technologists also need to ask: When will each individual ad bring about the ideal customer action?

According to Oscar Garza, svp of media activation for the digital agency Essence, a failure to understand this dynamic has often led advertisers down some grossly misguided segmentation strategies. “You could target moms who drive vans in Vermont,” he said “But really what you should be looking for is people who are looking to buy a car today. Then subsequently, you look for people that might be looking to buy a car in six months, and invest in those people so they’ll be ready to buy the car in that time.”

Timing can mean several different things, of course, and there are countless ways to get it wrong. Imagine, for a moment, a mid-30s amateur musician. He’s got scraggly

hair down to his waist, a tattoo of Ozzy Osborne on his left bicep and calloused fingers as rough as rawhide.

His personality and interests are just as stark in the digital world. He voraciously consumes music news and reviews on his laptop, smartphone and tablet. He’s spent a king’s ransom on concert tickets through ticketing websites. He’s bought a wardrobe’s worth of Black Sabbath and Metallica t-shirts through direct-to-consumer retail sites.

Already we see a rich assemblage of data, dating back years, pertaining to his content consumption habits and buying history. But there’s another crucial piece of transactional data: He purchases a new electric guitar about once every two years — a much faster time-frame than most guitar buyers.

Timing is everything

Hyper-targetingreaches customers with content that’s tailored to their interests or behaviors, but may stop short of reaching them at the moment when they’re most likely to be influenced by advertising.

Hyper-personalizationanalyzes signals such as transaction history and the timing of past purchases to serve ads when the’re likeliest to be effective.

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The keys to good timing:

• Don’t advertise a product immediately after a customer has purchased it.

• Keep track of how long customers tend to wait before buying another of the same item; start targeting them shortly before that time-frame has fully elapsed.

• Pay attention to predictive signals of intent — such as websites visited or related transactions — that the consumer has historically demonstrated before making certain purchases.

• Deploy targeted ads during the times of day when the consumer is most likely to be influenced.

• Make sure the right ad is displayed during the right stage in the marketing funnel.

Here’s where things get interesting: A guitar retailer has partnered with a technology provider to bring machine learning into the equation, and the model has done a whole lot of things right. First, it’s analyzed the musician’s digital history to acquire a firm grasp of his browsing patterns and transactional behavior.

Moreover, the model stopped targeting the musician with ads immediately after his most recent guitar purchase. There’s no better way to alienate a consumer (not to mention waste resources) than continuing to target a customer after they’ve already bought an item.

Next, the model picks up on the fact

that the musician has been browsing for new guitar picks, which he’s often done in the past right before buying a new guitar. It’s an unmistakable signal of intent.

The model concludes, correctly, that now is the time to act. It starts small, targeting him with a single display ad. Crucially, it does so while he’s browsing his most frequently-visited music criticism site, guaranteeing contextual relevance. “Environment really matters,” says Edwin Wong, svp of insights and innovation at Vox Media. “Content activates people emotionally and activates what they want to do in the real world.”

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The keys to good timing:

So the musician continues clicking on ads, first on his laptop and then on his smartphone. He’s targeted more and more aggressively as he visits a variety of other music sites. He moves farther and farther down the funnel, and the content of the ads changes appropriately at each stage.

The ads are hyper-relevant to his interests and purchase history. They’re contextually relevant. They’re getting engagement. Everything seems to be on track. But when the musician finally visits an e-commerce site to purchase his guitar, he spots a different display placement — this one advertising new

speakers — from the same retailer. He clicks on the ad out of idle curiosity.

He glances at the product page, then notices that it’s 2:00 AM and goes to bed. The next day, he decides to go to a brick-and-mortar shop and buys a guitar from a different brand.

The retailer did most things right, but its machine learning model made one fatal mistake: displaying the wrong ad at precisely the wrong stage in the funnel, and at exactly the wrong time of day.

A hyper-personalized machine learning model wouldn’t have made those

mistakes. Rather, it would have factored in a couple of other data points: namely, the musician’s late stage in the marketing funnel (he was already a hair’s breadth from buying a specific product) and the time of day when he’s made similar purchases in the past (usually before midnight).

All it takes to lose a conversion is a single misstep.

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The keys to good timing:

Thou shalt not irritate the customer. It’s just about the first commandment of display advertising. When it’s violated, abandonment and negative brand association await.

It’s no secret that some display placements can irritate people. And retargeting can reap the lion’s share of the blame, especially when the same ad haunts users from platform to platform without variation. “I always joke that I wish I’d gone into ad product design because that pair of pants that follows me around the internet keeps reminding me that I’m too big to buy the sizes available,” said Vox’s Wong.

Frequency capping – with finesse

Retargeting:A digital advertising technique in which a user who views an ad on one website is shown the same or related ads when visiting a subsequent site or sites.

Frequency capping:A digital marketing technique in which an advertiser sets a maximum number of times that a user can be shown a particular ad.

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Most digital marketers have decided that frequency capping is the best solution — and indeed it’s a crucial tool. The problem is, most marketers have taken a cookie-cutter approach, setting a blanket frequency cap for all users.

That’s a big problem: Some consumers are more receptive to aggressive targeting than others. Indeed, some consumers won’t convert without it.

Essence’s Garza gave the example of frequent web users versus infrequent users. “People who have used the internet for a long time, especially

on desktop, tend to ignore ads, so their ‘tolerance’ or frequency when it comes to a particular conversion rate is different than someone that might be more receptive to ads early,” he said. “So there’s a class of people where 2 or 3 ads might be enough to get this person to really consider your product, and another that might scroll by an ad 40 times before taking a look at it.”

It’s Jane and John all over again. One customer might sour on a brand after viewing three ads. Another might be fine with five. Yet another might not buy the product without seeing 20. Too few marketers have

taken the time to figure out which customer is which.

There’s a solution here, and it all comes back to analyzing behavioral data. How has each individual customer responded to ad targeting in the past? Has Jane abandoned the funnel after being retargeted twice? Has John made purchases after being retargeted 12 to 15 times? Were they more responsive to seeing a succession of different ads, or to seeing the same ad multiple times?

This data exists in abundance — but too few marketers are using it.

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More than a year in, the digital marketing industry is still feeling its way through a post-GDPR reality. The regulatory environment will only become stricter in January, when California’s similar Consumer Privacy Act goes into effect.

Stricter regulation has certainly disrupted some business models, but many industry pros argue that they’re having a salubrious effect. Indeed, some of the players that have been most negatively impacted are lower-quality performers that leaned heavily on personal user data.

“You’ve seen a separation of the experts from the amateurs,” said Michael Horn, chief data officer at the digital agency Huge. “Companies that should already have been caring

about consent and privacy and ethics are now facing shame that they weren’t already paying attention.”

Display advertisers are particularly well-positioned to weather the regulatory storm. As counter-intuitive as it might sound, the most effective hyper-personalization tactics don’t require personally identifiable information like names, email addresses or IP addresses. Rather, the best hyper-personalized display campaigns are all about detecting patterns and correlations tied to behavior and context.

Marketers can analyze what websites or product pages a consumer has been visiting and when; how frequently they’ve clicked on which ads; which types of ads have led

to engagement or conversion; and countless other data points without targeting known identities. So long as they’re conducting themselves ethically, display advertisers have less to fear than most.

Consumers in today’s regulatory landscape are coming to expect ad experiences that don’t invade their privacy. Display advertisers would be wise to operate within the spirit of the rules even when they don’t risk running afoul of the law. That means serving users relevant ads that don’t feel invasive, creepy or alienating. Ultimately, the marketers who reach the heights of hyper-personalization will triumph over those who stop at hyper-targeting.

The keys to good timing:Hyper-personalization in the age of GDPR

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There’s one last point that needs addressing: Hyper-personalized marketing relies on machine learning to do a whole lot of work. Some marketers, understandably, are wary of ceding so much control to AI. Can marketers really rely on a non-person to do the work of personalization? Will they even be able to understand how the AI is making its decisions?

Welcome to the dreaded black box.

To solve this, marketers need to bring AI-related conversations to the forefront. “It’s quite heartening and healthy that there’s much better awareness of things like algorithmic bias, and data supply chain issues, like privacy, quality, et cetera,” said Horn. “[The industry] is having interesting conversations around

accountability and transparency, and about how to get those things baked into the technology itself.”

Effective machine learning models aren’t simply switched on and given free reign. They’re created by humans, their parameters are set by humans, and those parameters can be changed by humans at will. The key is to make sure the humans in question are intimately familiar with the brand and can relentlessly keep track of what’s working and what isn’t.

Some advertisers may choose to bring their machine learning experts in-house; others may choose to enlist third-party specialists; and others may do a bit of both. However they handle it, marketers must make sure that the specialists they rely on understand

the goals of the business as well as anyone else on the team.

Marketers need to know which data and parameters are being factored into their machine learning models. They need to maintain an understanding of how their machine learning-driven tactics are actually affecting their engagement and conversion metrics. They need to know which actions their customers have been taking in response to seeing specific ads, on specific platforms, on specific devices, and at specific times of day. And to make this understanding possible, they need tech specialists who can communicate the information clearly.

The keys to good timing:Shining a light into the black box

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Hyper-targeted display advertising has gotten a bad rap — often for good reason. Time and again, advertisers fail to grasp that consumers are unique individuals. They browse differently, they buy differently, and they respond to ads differently depending on innumerable factors.

The key is building a predictive model that uses rich and abundant data, and that accurately assesses how specific users tend to respond to specific targeting tactics. It’s a lot to keep track of, to be sure. But today’s machine learning tools are sophisticated enough to make it possible.

That’s fortunate, because hyper-targeting can only take marketers so far. They need to make things personal.

Conclusion