The rise of streaming services and the proliferation of smart TVs are leading more people to cut the cord and turn to CTV. This switch leads to many advantages, including the ability to target specific demographics. 

The benefits of CTV advertising

With data from streaming services and smart TVs, advertisers can create highly targeted campaigns that reach the right audience based on content and timing. 

As we know, this level of precision is not possible with traditional TV advertising, which relies on wide-ranging demographic groups and broad-stroke targeting.

Another key advantage is the ability to measure campaign effectiveness. With traditional TV advertising, tracking how many people actually saw an ad and whether it impacted purchasing behavior is challenging at best. 

Conversely, CTV technology tracks:

• How many people saw an ad.
• How long they watched it.
• Whether they engaged with it. 

This level of engagement and overall measurement is essential for optimizing campaigns and maximizing return on ad spend.

Dig deeper: How CTV can deliver market research for B2B marketers

CTV brings unique advertising opportunities

The shift we’re seeing in viewing habits has seismic implications for the advertising industry. As of late 2022: 

• Netflix alone has eclipsed 200 million subscribers worldwide. 
• Disney+ now touts more than 152 million subscribers worldwide.
• Domestic-only provider Hulu still has 46 million subscribers.

This rise of CTV reach has also led streaming services to develop new ad formats better suited to the environment.

Interactive ads, for example, allow viewers to engage with the content and learn more about a product or service. These dynamic ad formats are proving to be more effective than traditional linear ads across both measurable engagement and conversion.

The larger reach is also translating into an overall increase in ad spend. Some marketers are shifting budgets away from the iOS and social placements where scale is becoming sparser and re-investing in CTV advertising. 

Right now, CTV advertising is projected to increase by over 14% in 2023, according to the IAB. What that all rolls up to is a CTV ad spend that will likely exceed $26 billion in 2023 and $31 billion in 2024.

So, how is this shift in ad spend to being justified? 

Brands and marketers are seeing that CTV offers unique advertising opportunities including:

Audience targeting

CTV is built on data that allows marketers to build or target specific audiences based on many more factors than other forms of TV advertising, including location, language, content and consumption.

Eyes on ads

Because streaming providers don’t often allow for ad skipping, this equates to a much better ad completion rate, ensuring that all ad content is seen by the targeted audience.

Identity and data insights

Because CTV devices are stitched into home networks, marketers can build anonymized insights based on IP address and other device IDs. As such, identity signals can be aggregated into audience insights for even deeper segmentation and traffic monitoring (i.e., which ads led to a website visit).

The overriding goal is to get more eyeballs on both content and ads. With that, many streaming providers aim to provide subscribers the option to switch from the current subscription model to a “no-cost but fully ad-supported” streaming experience. 

Netflix and Disney+ plan to introduce a fully ad-supported option for their subscribers. While the specifics are still sparse, 64% of CTV viewers polled say they would prefer to watch ads than pay for more content, according to a DeepIntent survey. 

That said, Netflix is entering this area cautiously until they can ensure they won’t lose subscribers accustomed to commercial-free viewing. 

Dig deeper: Brands are betting heavily on CTV advertising

How to get the most out of CTV advertising

Whether the model stays subscription-based or ad-supported (or something in-between), we must continue to adapt to take full advantage of CTV targeting’s benefits. 

With the right strategies and partnerships, we can create data-driven targeted ads to find new customers, build more robust and longer-lasting relationships with existing clients and ultimately drive more brand awareness and sales. 

For marketers looking to get the most out of CTV advertising in 2023, here are four tips to remember.

1. Target fraud-free and premium inventory

Until now, TV advertising offers minimal controls in the open exchanges to monitor where and within what content your ad is shown, leading to a slew of brand safety issues. 

With CTV targeting, you can work within CTV aggregators, allowing them to only show your ads in and around brand-safe inventory and to an audience with a higher propensity to engage and convert.

2. Measurement and attribution

The data and identity-driven backbone of CTV advertising allow for:

• Directly matching sales data with ad exposure data.
• Getting deeper insights on the impact of CTV ads viewed on the path to purchase and overall campaign success.

3. Enriched viewership data

With CTV devices connected to IP addresses and other device IDs in the household, you can overlay viewership data with offline and online data.

This lets you better analyze household make-up and create ad creative and messaging tailored to specific audiences.  

4. Revamping ad formats

With ad completion rates so high for CTV ad formats, marketers can get creative.

Consider campaigns incorporating short but interconnected ads that can be shown throughout the program. Such narrative-style ad campaigns can tell a story across multiple commercial breaks within one show or series.

Get ahead with CTV advertising

While we don’t know how CTV advertising will evolve over the next year or two, it’s clear that this format will be a significant area of growth for brands and marketers.

CTV advertising is set to become an increasingly critical part of the overall advertising landscape for many years to come.

The post 4 tips to get the most out of CTV advertising appeared first on MarTech.

While all these macro changes aim at giving consumers more control over their data, it’s also making data-driven marketing a far trickier business. To stay ahead of the curve, more marketing teams are updating privacy policies, revamping their tech stacks and leaning on clean rooms.

Yet, at best, many midsize marketers still have only a basic understanding of what a clean room is and if it really can be the one-stop shop to solve their problems.

So, what exactly is a data clean room?

At the most fundamental level, a data clean room is a type of privacy-enhancing technology that gives marketers a secure and neutral silo where all consumer data can be stored securely, anonymized and made available for a host of services. 

While clean rooms offer a host of services, the most common use cases include audience segmentation, audience overlap analysis and acting as the conduit to conduct measurement and attribution studies, all without compromising user privacy or sharing personally identifiable information.  

The critical aspect that makes data clean rooms a potentially game-changing tool is clean rooms offer access, availability and data usage, for a host of sources, where strict privacy rules are all governed and enforced by the clean room provider. Data privacy is a crucial driver for any marketers looking to work within this environment.  

All data suppliers within a clean room maintain complete control over their data, which is fully encrypted and anonymized throughout the onboarding process and audience building. 

Another critical element clean rooms allow is maintaining privacy-compliant querying and analytics across multiple activation channels giving marketers a way to create aggregate performance reporting. 

Let’s take a deeper look at two of the most common use cases for a data clean room. 

Dig deeper: Why we care about data clean rooms

Use case: Overlap analysis and second-party data 

Clean rooms provide a secure environment to overlay their customer data with information from other brands. This show what the customers have in common based on user profiles, engagement and conversion metrics. 

With this deeper understanding of the customer base, marketers can create new second-party audiences for activation and further analysis. Because all customer data in the clean room is anonymized, both brands can build targeting lists keyed off it. 

The data can also be overlayed with enterprise data providers. This provides new insights based on key demographics, interest behavior and other valuable data points. It means more opportunities to segment their customer data files and build more personalized ad experiences to boost engagement and conversion.

Dig deeper: How companies are leveraging clean rooms and first-party data as cookies vanish

Use case: Measurement and attribution

Privacy legislation and new restrictions around tracking users are making the most basic measurement and attribution studies difficult to conduct with any confidence.

Clean rooms, in this case, are creating small walled gardens where brands can partner with publishers to look at a consumer’s path to purchase. 

Here’s how it works:

• Marketers combine their conversion level data with publisher impression logs, providing a new lens to see if the 10 million impressions they purchased with a participating publisher yielded the desired KPIs. 
• The publisher works with the marketer to onboard an entire anonymized file of users who were shown the brand’s ad. 
• The clean room — with its centralized identifier — matches and analyzes the data. This gives insights into overall performance and enables better informed decisions regarding campaign efficiency.

Clean rooms also offer customized solutions for doing more with first-party data. Marketers can build, activate and measure performance to align with their data-driven marketing goals. 

Remember: It takes a skilled team with analytical and technical prowess to derive the benefits of clean rooms.  

Dig deeper: Marketers look to adtech and agencies to solve the addressability problem

Key considerations before adopting a clean room

Another critical aspect is the overall state of the customer file from a hygiene and compliance perspective. All data in a clean room is federated, which means multiple data sources function as one file. The more accurate your customer file is, the easier it is to create factual and actionable insights. 

This means marketers must do more to ensure the accuracy of first-party data. They must also see to it that all the data is in one location to create a complete and holistic customer file. Below are key questions to ask clean room providers when deciding which one to use:

• What capabilities do you have to integrate into our existing marketing tech stack?
• Do you have the integrations needed to easily use the data with our tech solutions?
• How much can we control data within the clean room, and who else will have access to it?  
• How easy is the clean room to use? Can we use and benefit from this if we don’t have a data tech team?  

Looking to 2023 and beyond, we’ll see more focus on privacy and further limitations on legacy identifiers. 

Clean rooms will likely grow and grow and play a more significant role. First-party data will become even more valuable, allowing us to make the most of clean rooms’ capabilities.

The post On next year’s to do list: Clean rooms and better data appeared first on MarTech.

• What to advertise to us and when.
• And even what language and imagery are used based on deep insights from past purchase data, website and app visitation, time-on-site, and even our personality types. 

Often, all this hyper-focused personalization requires the use of personally identifiable information (PII). 

What constitutes personally identifiable information (PII)?

If you’re unfamiliar with what constitutes PII, it can be broken down into two categories. 

The first is usually referred to as “linked information,” which is any information that can be used to identify a person or household. Examples of this type of data are rather obvious, including:

• Full name.
• Address.
• Email.
• Phone number.
• Login details. 

Another kind of data is “linkage data” which, when used alone, cannot be used to identify a person. However, when multiple types of linkage data are stitched together, they can be used to glean some PII. Some examples of this data could be first or last name combined with the resting location of your mobile device that can be geo-framed back to a terrestrial address. 

It’s important to note that some linkage data is not considered PII as the identifiers captured cannot be used to identify an individual or customer. This non-PII data includes signals like cookies and device IDs that can be used to create anonymized audiences that, while still targeted, cannot be matched back to any individual or household. 

The gray area

Marketers frequently build propensity models and audiences using anonymized data to serve ads for boosting engagement and driving more conversions. 

Most marketers and big adtech platforms are familiar with anonymized data, including cookies and device IDs, which have been reasonably easy to collect when someone visits websites or downloads an app on their mobile device. 

An ever-changing gray area has emerged between:

• What constitutes anonymous data.
• What data can be combined to derive some form of PII.
• And how much of that data is permissible for advertising. 

The new rules for PII data collection and usage

Focus on consent

The new rules for collecting and using PII data hinge on the notion that peoples’ data belongs to them, not big tech. Of these new rules, the first and quite obviously the most important is around consent. Consumers now have to agree to the collection and use of their information

Dig deeper: Going beyond cookie consent: 3 strategies to achieve data compliance

Adapt to privacy changes through identity resolution

Beyond collection, there is a shift in handling and sharing PII data. The result is vendors now providing clean rooms, consent management platforms and safe havens. 

In the past, audiences were built, and programmatic engines served ads in near real-time. While this still happens, these new players act as gatekeepers, cleansing data and ensuring campaigns only use data in the way consumers have consented to.

Along with this is a nascent new identity ecosystem waiting to emerge if and when we get more iOS restrictions and Google deprecates third-party cookies in Chrome. These changes will add another layer of complexity to an already moving target around the permissible use of data for advertising. 

Dig deeper: What is identity resolution and how are platforms adapting to privacy changes?

The new era of data usage is here

While there is plenty of speculation on how much PII data will be permissible for advertising in the U.S., we can be sure that the free-wheeling days of data collection and brokerage are ending. 

This new data usage era will be anchored in consumer consent and ultimately be decided in a consumer’s ability to trust brands they engage with to make purchases, stream content or socialize online. 

This forecast bodes well for marketers and platforms who have invested heavily in their brands, building and fostering trust with their customers. 

Trust, combined with a material value exchange for access to our data, will be the two main driving forces in the next generation of digital advertising.

The post The moving target of permissible PII appeared first on MarTech.

Data-driven marketing has had a transformational shift not only in how we engage with our customers but, even more importantly, in how we target new prospective customers. But for many, this new era of ultra-sophisticated audience-based targeting is begging more questions than the martech industry can answer. Most pointedly, is today’s reliance on data-driven targeting becoming a surveillance state? 

This recent backlash led to California’s Consumer Privacy Act (CCPA) which went into effect in 2018. More states have since followed, giving them more control over what personal data can be collected, brokered and used for marketing. 

Dig deeper: Why marketers should care about consumer privacy

Sensible vs. sensitive data targeting

As marketers, it’s more imperative than ever to respect a person’s privacy and still utilize all the available data responsibly to create personal ad experiences. With little overall regulation, all types of data are at our fingertips to build cross-channel campaigns that can feel tailor-made for the user. It’s a fine line, though, on which ads will be met with delight and which ads will feel intrusive and even offensive. 

As users of all this tech, we know all too well when marketers overstep. That line depends heavily on what’s being sold and how personal the marketer makes the ad experience. A gut check on your data strategies can quickly unveil how personal or behavioral data may inadvertently target a minority or potentially stigmatized group. 

Suffice it to say, if you’re selling pet food, you can likely create some hyper-targeted and personalized ads without tripping the sensitivity trigger. On the other hand, if you’re targeting people with ailments, new or prospective moms or even plus-sized clothing buyers, it’s critical to take a close look at:

• What data is being used.
• How those audiences are modeled.
• How you’re differentiating your messaging to existing customers versus prospective buyers. 

Since it’s never a cut-and-dry answer, here are four suggestions for navigating sensitive data.

1. Steer clear of potentially stigmatizing data

Ad targeting prospective customers based on ailment data, LGBTQ+ or racial background can put us in an all too obvious danger zone. However, it’s just as crucial to be aware of targeting audiences that could be stigmatizing or just too personal. Some more obvious examples of these audiences could include religion, political affiliation, mental health, military status or even data that reveal personal or financial hardship. 

Martech platforms have removed the most sensitive audiences over the past few years. Yet, many ad targeting platforms still contain this data in less conspicuous derivations. For instance, you can no longer target by race in Meta’s properties but can still target BET Awards viewers. 

One way to avoid crossing the line from sensible to sensitive targeting is to review the audiences Meta has removed over the past few years and see if any of your data strategies could touch a sensitivity nerve for your customer or prospect.

2. Data usage for customer vs. prospect targeting

Collecting data on your customers open all sorts of innovative and clever insights that can be used for targeting. With that comes the responsibility to use personal data carefully when building audiences and personalized recommendations. 

They may be your customer, but be cognizant that some data-driven recommendations can be interpreted in a way that may make your customers uncomfortable or even find offensive. 

A big-box retailer learned this the hard way when they relied too heavily on programmatically generated ads and inadvertently served personalized ads for weight-loss products to plus-size apparel buyers. No surprise that the backlash was swift. Be aware of how you use data across the customer journey to avoid inadvertently putting consumers on the back foot.

For prospect targeting, it’s even more critical to be judicious about how personally identifiable data is used. A good rule is to stay close to demographic and publicly available audience data. 

As in life, it’s true in advertising that brands get one chance to make a good first impression. An overly personal ad with a new prospect can feel like a stranger asking or assuming more about the user than they are prepared to share.

Overstepping with new prospects will not only result in lower ad engagement but can quickly trigger a negative brand bias that will be a long road to winning that trust back.

3. Be clear about why you’re collecting data

It’s always a good idea to be open with both your customers and prospects on:

• What is the benefit to them of sharing their information with you.
• How you plan to protect and use their data. 

Consumers are wise to data usage now. It’s crucial to let them know if the data will only be used for product recommendations or for tailored ads and/or personalization. Most importantly, if you utilize a retailer or data cooperative, let consumers know that portions of their data may be shared with other similar marketers for products and services they may be interested in. 

A critical piece of data collection is also giving your customers an easy way to opt out of having their data used for some or all of the marketing services.  

Dig deeper: Going beyond cookie consent: 3 strategies to achieve data compliance

4. Don’t forget traditional data gathering

With the deprecation of third-party cookies and ever-evolving restrictions on data sharing for iOS devices, it’s even more essential now to look to tried-and-true ways to capture user data. 

Whether it be collecting email addresses at checkout or developing a rich content strategy for your brand that incentivizes your customers or prospective customers to subscribe to ‘member only’ content or newsletters. 

Another way to gather new data is to work with other brands that have a high customer affinity for your brand and build second-party data assets to send direct mail or target across display or social media where the likelihood of them engaging with your brand and ideally purchasing is higher than off the shelf audience selections. 

A great example of this is seeing premium fitness brands including Lulu Lemon, and even boutique brands like Vuori, partner with Equinox to merchandise and market their products with luxury-minded fitness consumers.  

Maximize your ad targeting strategy without overstepping

Audience-driven targeting is ever-evolving. The data scientist that gave us the early tools to do data-driven targeting relied on complex programmatic data modeling with the promise to reach people with the right product, at the right time with the right message. 

What we’ve learned since is that while this promise may finally be possible, it’s up to us to decide which of those levers to pull and which ones to push back so we don’t overstep and always keep our marketing and messaging securely in the comfort zone of our customers.  

The post 4 tips for navigating sensitive customer data appeared first on MarTech.

While it’s difficult to conjure any sympathy for these social media giants, marketers and smaller app developers are seeing their entire businesses being upended by these changes, as Facebook is often a primary channel for audience targeting. 

All is not lost and there are ways to help you navigate these new changes.

iOS still dominates ad spend market share — here’s why

It’s helpful to look back at the impact of Apple’s App Tracking Transparency (ATT) privacy features on marketers’ ability to target iOS users. 

iOS’s market share for ad spend was 34% in April 2022, down 4% from April 2021, when Apple implemented iOS 14.5, according to a report from mobile ad-tech firm Adjust. In the latter half of 2021, the iOS market share dipped below 30% by October 2021.

Many marketers and industry experts forecasted that marketers would shift ad spending to Android targeting to compensate for the loss. But new data shows that from April 2022 to the third quarter of this year, that shift to more Android targeting isn’t yielding the results they were getting from iOS device targeting. 

Dig deeper: Study finds iOS 15 is inflating email open rates

Now, marketers are moving ad spend back to targeting iOS devices, even with the limited scale and higher CPMs. According to Adjust, this new gain in market share can be traced back to conversion data, where even the limited number of opted-in iOS users compared to Android are yielding more revenue through larger average order sizes. 

This notion may not surprise many retail marketers, but it’s important to note that new data backs this up. Even with Apple users making up just 27% of the US market compared to Android’s 72% market share, Android users purchase less at a lower average order amount. While it may seem counter-intuitive, “iOS users are still not only a more lucrative customer base, but they are potentially more cost-effective,” says Thomas Petit, an independent app development consultant. 

So, why do these shifts in market share matter to us marketers? It’s because even with the impending impact of iOS 16, we still need to continue to target iOS devices despite limited reach to take advantage of the considerably larger purchasing power of iOS users. 

Even as the iOS landscape becomes more restrictive and shifts further away from any form of third-party tracking, there are tactics marketers can implement now to better position themselves for success. Here are just a few tips to get the most out of your iOS targeting.

4 ways marketers can navigate and succeed in the storm of iOS privacy changes

1. Prioritize and personalize your first-party data

Some levels of personalization and contextualization will be harder to achieve amid all the new iOS changes. However, it doesn’t need to be an impossible task. Now more than ever, marketers need to rely on their first-party data and make the most of personalization based on declared location, setting up multi-language campaigns, and moving toward more video advertising.

Another vital tool is to take another pass at your CRM segmentation, focusing on audiences that yield long-term value rather than short-term profit. Also, look at trusted third-party data companies that offer CRM enrichment services to help you learn more about your customer base. This additional data, including household income and the presence of children, will open up new ways to segment your first-party data and uncover new products and services that will resonate with them based on their household make-up and life stage.

2. Prioritize conversion events

While conversion-level data is a marketer’s primary tool to assess performance, iOS limitations within Facebook make this more and more difficult. It can also be helpful to flip this around and watch for other specific events in your Facebook Performance Measurement data. 

For example, if you’ve only been collecting post-purchase data, integrate other signals like last-click conversion events in both Facebook and Google Analytics. These additional signals can capture new data that can be incorporated into more meaningful path-to-purchase patterns.

3. Unlock more data with UTM tracking

iOS changes have given marketers no shortage of blind spots to navigate when it comes to campaign performance, but Google Analytics can help you navigate around this issue. 

When advertising on Facebook, use UTM parameters on your site’s URLs. As a result, any data tracked by Facebook will populate in your Google Analytics. UTM tracking allows you to backfill user insights and create additional info on your target audience – where they are from, links they click on, and more.

4. Optimize goals on Google Analytics 

Once you have set up UTM tracking, create additional goals in Google Analytics aligned with essential conversion actions tracked on Facebook. This tweak can offer some new insight to see how these other goals fit within your performance metrics. Any decrease in conversions will now be visible, which can help you optimize, improve, and create a better strategy for future campaigns.

The big picture  

We all know that iOS privacy changes will continue to become more restrictive. Still, marketers can use the data they have to build new levers and additional lenses to further optimize and meet their KPIs. 

A winning recipe involves pivoting away from the easier short-term ad targeting goals, dedicating more focus and tools to look at the long game, and building more holistic goals centered around customer lifetime value.

When you invest in higher value, long-term customers and prospects, you’ll reap the benefits, including lower long-term ad spend, less competition in long-tail audience targeting, and more personal and fruitful relationships with existing customers. 

The post 4 tips to navigate the advertising impact of iOS privacy changes appeared first on MarTech.

As digital marketers, we know that insight is the key to personalization. In lieu of browser and device data, forward-thinking marketers are testing other insight-rich sources to build audience profiles that don’t rely on traditional bread crumb trails. I caught up with a few marketers to see what tools and techniques they are implementing to stay ahead of the game. 

1) CDPs and identity solutions 

Customer data platforms (CDPs) and identity graphs build a single view of a user, including explicit and implicit interests and preferences. This singular identity stitches together a host of signals to deliver a 360-degree view to power personalization without third-party cookies.

Working with an established CDP or identity platform keeps all the identifiers related to a customer in one place, including personally identifiable information (PII) like usernames and phone numbers, as well as non-PIIs signals like first-party cookies and publisher IDs. Marketers can leverage these CDPs or identity graph databases to build omnichannel views for customers and prospects, enabling them to create personalized ads and messaging across various touchpoints.

Marketers who work with CDPs or identity platforms can capture data from over a hundred touchpoints, and build a unified view across their entire CRM to drive personalized messaging. Using advanced analytics and modeling, marketers can create a variety of personalization scenarios based on different channels, intent signals, and propensity scores for each user. And connecting the ad identifiers using a virtual ID allows for not only converged addressability but also helps to drive cross-channel personalization.

2) Second-party data 

Another way to get around the loss of third-party cookies is to start building second-party data. This type of incremental audience data is created when a marketer combines their data with another brand or publisher data set to yield new insights and audiences beyond what is available in their own CRM or subscriber database.

The advantages of building substantial second-party audiences allow a marketer to expand their consumer data pool and, more importantly, provide access to more relevant consumer data than marketers would get with third-party cookies or data. Because second-party data involves combining similar yet disparate data sets, the yield is high on actionable insights. It will almost always perform better than a marketer who pays for aggregated third-party data.

This strategy is most useful for more prominent brands or marketers who have built an extensive database of customers. Finding a willing partner might not be easy for small businesses or newer companies that haven’t had the chance to build up their own first-party data. To make this strategy work, you must find a partner to share data with you and then disclose the relationship on your website if you share your customers’ data with another company. Building these second-party audiences has become a cornerstone service for data clean rooms or cloud service providers, including Infosum and Snowflake.

Dig deeper: Why we care about data clean rooms

3) Contextual advertising

For years, we’ve seen contextual targeting touted as an alternative to cookies. This approach focuses on the content consumed — the context of the blog post, video, or other content the person is engaging with — rather than personal information.

As a result, there’s little to no risk around data privacy. Yet, digital marketers can still offer highly personalized content and ads.

While contextual advertising is nothing new to marketers, what has changed is that AI is now used by more advanced providers that can get granular with contextual targeting. Marketers have a continuum of targets they can build personalization around, including metadata, titles, related keywords, comments, and more. By mining this information and looking for signals, marketers gain in-depth insights into their customers that are used for cross-channel personalization and messaging.

This ever-evolving world of contextual advertising and personalization may require marketers to brush up on their skill sets and learn more about how it works today and how it can be leveraged not only for addressability but as a tool for personalization. And, unlike older contextual marketing models that relied heavily on keywords, new contextual targeting tools rely on natural language processing and image recognition.

These more recent algorithms can also grasp the sentiment of pages and apps with unprecedented speed and reliability. Altogether, this enables marketers to display personalized ads in an environment that is both highly relevant for their potential customers and safe for their brands. 

4) Location and interest-based targeting 

Remembering that high-quality intent data for personalization can be captured offline is more important than ever. Where your customers and prospects go or hang out regularly can be equally crucial for insights and personalization opportunities. 

Location data companies like Safegraph, Simple.fi and Factual create rich audience profiles based on pre-determined points of interest and stitch them to their ID, or into cookie-free IDs like UID, for cross-channel and personalized targeting. These companies often have thousands of locations mapped, including quick-serve restaurants, airports, retail stores and golf courses, to name a few. 

Real-world insights from location data can drive personalization using explicit information, including the type of store or location visited, to inferred demographic, affluence and other information to allow for an additional lever to use when developing personalization models.  

In much the same way location-based data provides a slightly more “meta” approach to personalization, interest-based advertising bundles website visitors into broad content topics based on a visitor’s behavior. The most talked about of these interest-based targeting and personalization platforms is Google’s most recently proposed concept, Topics, which replaces its initial strategy, Federated Learning of Cohorts (FLoC). The idea behind Topics is that the browser learns about users’ interests as they surf the web and shares their top interests with participating websites for advertising purposes. All this happens behind their walled garden by categorizing the websites a user visits into a limited set of around 350 broad topics, such as gym-goers or sports car enthusiasts. 

When a user visits a website that supports the Topics API, the browser will choose up to three topics on their device from their most frequent topics in the last three weeks and share them with this website. The website and its advertising partners can then use these topics to determine which type of personalized ad to display. 

While the jury is still out on Topics, Google claims that Topics is more private and offers greater transparency and user control than FLoC and cookie-based targeting. Still, many specifics of the concept are yet to be released. 

5) Better first-party data for personalization

If you want to truly deliver personalized experiences, you need to know who your users are, and an email address is a great first step to building out their profile.

Amp up your user registration. Utilize all the touchpoints site where exchanging information for newsletter sign-ups, cart check-out, discount codes or loyalty programs. ‍

Build more robust customer profiles. Start small but capture as much information as you can about your customers. Integrate additional data collection touchpoints. Follow up with new email subscribers with quick buttons to capture preference data to better target content and products.‍

Engage with email and SMS marketing. Make the most out of email and text message to drive up customer engagement. Send personalized offers and content to users based on their behavior on your site and follow up personalized SMS for special sales, promotions, and discounts.

All in all, the demise of the third-party cookies and the constraints on device-level data doesn’t mean an end to ad personalization; marketers will be utilizing a host of alternate data and IDs to drive cross-channel personalization. Combined, these new tools and tactics will allow marketers to continue having personalized conversations with their customers and prospects. 

The post 5 paths to post-cookie personalized advertising appeared first on MarTech.

Fortunately, technology companies are investing heavily in alternate identity solutions to be the backbone of future attribution and measurement platforms. In fact, according to the American Marketers Association, over $2 billion is slated to be invested by a host of players to help solve the impending measurement conundrum as cookies and device data are all but phased out.

Identity and measurement bracing for impact

The real impact won’t be felt until 2023 when Google starts phasing out cookies. But, even now, changes led by Apple across both devices and Safari are putting marketers on their back foot as they scramble to decipher attribution and conduct effective measurement studies.

Success in this new unchartered world of measurement will rely on identity and measurement companies to work together to understand channel mix contribution better and devise new tools and methods to associate and attribute conversion data accurately. This already daunting challenge is poised to get exponentially more difficult as the channel mix expands, and tracking signals look less like a spoke and wheel and more like a spider’s web.

In this new web-like framework without cookies, marketers will depend heavily on identity and measurement companies to map, ingest and correctly assign credit to all the different modes and channels in a marketer’s advertising arsenal. At the core of all this, marketers, identity providers, and measurement teams are huddling to find news ways and new IDs to identify, track and make sense of every channel’s contribution to a marketers internal and external channel media mix.

Dig deeper: What is identity resolution?

Overcoming identity and measurement obstacles

Right now, that aspect is becoming increasingly opaque with severe limitations for deciphering when a particular person has viewed an ad, let alone assigning the correct attribution by channel. As part of that $2 billion dollar industry investment, though, a whole host of long-standing measurement players and new entries are making headway. 

Prohaska Consulting, a New York-based digital advertising consultancy, is leading the charge to create a landscape of attribution and measurement companies. Working with Prohaska, the below graphic is a snapshot of a larger measurement landscape the firm will be releasing later this quarter. 

While Technology companies like those listed here are peddling fast to come up with alternate solutions for measurement and attribution, it’s important to note that according to a recent IAB research study, only 34% of marketers are currently delving into and testing new measurement strategies. The fatigue over this ever-changing identity and measurement landscape is real.  

What marketers can do now to get ahead of the changes

Simplify models. To start, marketers should steer clear of creating multiple models for assigning credit to outside channels and a separate model to give credit within their organization. For you, the marketer, this translates to conducting a sort of marketing mix audit to ideally identify and stop buying from external channels if fair credit for that placement can be found internally.

This can be a hard sell if your team, like most, is focused on meeting overall KPIs and accurately assigning internal credit as part of the same goal. No one wants to potentially upset the apple cart, even if it’s not working optimally.

Align teams and channels. Further, it’s critical to align all the teams on the definitions of internal versus external channels. For instance, is there a clear and agreed-upon understanding within your divisions to separate video orders on a desktop from video orders viewed on a mobile device? Successful marketers are working hard across silos to get their divisions aligned and aiming to synchronize reporting to capture the most accurate attribution model possible.  

Another way marketers are taking charge of their measurement strategies is increasing the investment in data. This data-driven mindset helps build more accurate attribution and measurement models and utilizes more consumer data and modeling to create a more complete picture of your converting audiences.

Audience profiling. Moreover, a data-rich approach better equips marketers to assess and assign the lifetime value (LTV) of a consumer better.

This deeper dive into audience profiling and enrichment is proving to deliver more actionable insights on which audience profiles makes one conversion type more valuable from a lifetime value perspective.

Bidding. Data helps accomplish this by taking into consideration short-term and long-term LTV and targeting those customers or prospects appropriately. Further, these insights related to LTV can be used to train bidding algorithms that favor internal attribution over external channels yielding a higher overall LTV, and less waste on media spend.  

By adopting a more standardized, holistic attribution and measurement strategy, marketers can land on best practices that will set them up for even more success as the Identity and Measurement companies bring more sophisticated solutions to market. Until then, marketers who get their marketing mix house in order now will have a running start as we all head into the murky measurement waters of 2023.

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We’ve got Google’s ever-impending deprecation of third-party cookies set to take effect in 2023; Apple’s decision to phase out its mobile identifier for Advertisers (IDFA) to track users for targeting, personalization and attribution; and most recently, Google’s announcement that they are planning to follow Apple’s lead and pull the plug on targeting across Android devices.

Those changes, paired with new state-by-state legislation for consumer privacy, force advertisers to rethink almost everything they know about digital marketing.

What do these changes mean to you?

So, what do all these changes mean to you, the marketer? Well, let’s look at the numbers. According to current data from StatCounter.com, Chrome makes up around 65% of the total share of consumer browsing, followed by Safari at roughly 19%. Together, that makes up almost 85% of browser usage that will all but go dark for everything from audience building and retargeting to personalization and multi-touch attribution. According to mobile analytics company Flurry, the stats are now equally challenging for mobile ad targeting, with only 18% of Apple users opting in for app-level tracking.

Fortunately, the adtech wagons are circling and pedaling fast and coming to market with a host of privacy-compliant identifiers allowing marketers to target prospects, personalize ads and conduct measurement studies. This new identity landscape is changing daily, with newcomers, consolidations and integrations happening everywhere. For the marketer who wants a birds-eye view of the leading players, I’ve laid out who they are and how they are building their identity graphs.

Graph Key:

The challenge with a siloed identity landscape is that many proprietary identifiers work well within their own environments but face challenges when connecting to other identifiers for activation or measurement. This gap in the advertising supply chain of identifiers has led to a new crop of identity players developing interoperable IDs with the promise to serve as the translation layer to bring together and unify disparate identifiers that a marketer may be used for targeting, personalization and measurement. While Unified ID 2.0 has taken the lead position in this race, the jury is still out on how well it and other connected IDs will put you, the marketer, back in the driver’s seat. 

While the identity landscape is bound to keep changing in the coming year, Marketers can get their houses in order and be ready for future changes. The below checklist outlines the top identity-centric moves to consider in 2022. 

Get acquainted with clean rooms

Clean rooms were launched as a secure data hub where marketers can store their data and create second-party audiences with cross-over brands or publishers. Clean rooms have grown into a more extensive and robust set of tools for brands and agencies to maintain privacy and compliance while housing and unifying multiple data types. Clean rooms are also evolving to leverage their ability to join datasets and create various input/output integrations to power end-to-end marketing. This can include all applications in the supply chain, from segmentation, activation, measurement and overlap analysis to reach and frequency analytics and consumer journey analysis. 

As the clean room market has grown, differentiation between the types and functionality of Clean Rooms is emerging. 

• Neutral providers: The landscape has grown, and many specialties of pure-play providers are in the space, such as LiveRamp and Infosum.
• Walled garden: Amazon Marketing Cloud, Facebook Advanced Analytics; while you can enrich your own first-party data within their walls, they are not interoperable and require extra data science support to analyze results.
• Inside platform: Cloud storage businesses such as Snowflake and some other marketing companies like Epsilon are also offering clean room services as part of their larger technology stack.

Across the above buckets, the functionality of Clean Rooms is growing as well. Publishers are soliciting the help of clean rooms to empower marketers to connect their first-party data to impression logs, audience segments and user attributes to deliver more prosperous, more actionable consumer insights. 

Additionally, clean rooms have stepped into the customer journey analysis game. They are giving comprehensive and accurate data about their consumer’s interests and behaviors while not revealing personally identifiable information from tapping into publisher data to deliver better experiences for consumers and more effective campaign performance.

Dig deeper: Why we care about data clean rooms

Build or license a consent management platform

While privacy is at the root of this shift in overall identity management, many marketers may still be exposed to risk as more states follow California’s CCPA/CPRA regulations and require all marketers to get explicit consent for targeted marketing. While outsourcing this to a consent management platform (CMP) may be one route, you can follow the federal and various state guidelines to ensure you have the proper notice in place. At a high level, these include the one-click ability to opt out of targeting marketing, a clear statement whether data is sold, the option to give permission to share data, and a data ethics policy.

Beyond the new legislation, a consent management strategy is about building trust with your customers, and at the root of that is giving them transparency and choice. CMPs empower marketers with safety protocols to ensure accurate data and consent are a part of every customer record. Consent solutions enable customers to see and control the data you have collected. Marketers can use these tools to show their customers that privacy matters. Transparency is crucial and builds even more trust with your customer base.  

Manage your data in a CDP/EDP

Another way to get a jump start on the changing identity landscape is to standardize your customer data and unify all your complex customer journeys to simplify personalization, increase customer engagement and manage customer lifetime value. A CDP will unify offline and online customer touchpoints, stitching together actionable customer profiles and activating data across relevant content and audiences. 

Some marketers may need to look beyond CDPs and utilize Enterprise Data Platforms (EDPs) for a more robust solution. Unlike CDPs, EDPs offer more robust features, including real-time APIs from Facebook and Amazon to Google and TikTok, to name a few. An EDP’s real-time data streaming feature offers first-party data tagging, a proprietary identity graph and a data backbone for customer data enrichment and audience modeling.

Looking ahead to the impending changes in identity, marketers can take comfort knowing that while the big privacy reset seems chaotic now, a host of new tools is afoot to help us all navigate a post cookie world. And while much of the changes are out of our control, there are things marketers can do now to ease into the transition and make sure we don’t lose track of our most important asset, our customer data and the relationships it allows us to build.

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