If the current state of data privacy feels like a bit of a trainwreck to you, you’re not alone. In the US, we seem to be on our way to having 50 states all with their own data privacy laws before anything gets passed at the federal level. This lack of decision is affecting adaptation and innovation in the adtech industry. Many companies in the tech space have been frustrated because there is too much uncertainty and without precise rules and parameters, there is no clear path forward.

This article will explain what could be on the horizon for data privacy regulations, the changes technology companies are making, and what advertisers can do to stay compliant during the next few years of uncertainty.

What Is Creating Uncertainty in Data Privacy Regulations?

There are five main factors creating uncertainty in the direction of data privacy.

1. New data privacy regulations continue to be created that muddies the water for companies operating across U.S. states and global regions. Example: Ebbie Yazdani, federal policy director at TechNet, told privacy fellows at the National Press the cost of state regulations. “If we allow a patchwork of state laws to continue building, and/or if Congress is unable to fully preempt state laws, there’s going to be a tremendous cost to America’s economy and technology leadership,” Yazdani said, citing a study that said if 50 states enacted their own privacy laws, it would cost $1 trillion for the economy over 10 years.

2. Existing regulations are adding amendments to add further restrictions. California already amended the CCPA with the CPRA and existing federal authorities are extending their reach. Similarly, as lawsuits occur, they add new definitions to regulations, like the Schrems lawsuits have in the EU. Example: The HHS (which regulates HIPAA) provided guidance that would make pseudonymous data PHI under HIPAA when collected on sites run by covered entities. 

3. Lawsuits are starting to create havoc with added debate around how to enforce the regulations globally. Additionally, the private right of action in certain state privacy bills increases the likelihood of class action litigation. Example: Lawsuits and fines aren’t just happening in reaction to data breaches. In September 2022, Ireland’s Data Protection Commissioner (DPC) fined Instagram for violating children’s privacy under the terms of the GDPR. The long-running complaint concerned data belonging to minors, which was made more public when users upgraded their profiles to business accounts to access analytics tools. Meta is fighting the lawsuit, in disagreement with how the fine was calculated. 

4. The U.S. federal government is struggling to figure out its role. There have been attempts at federal privacy legislation, but they have not been successful. And existing federal bodies are extending their authority to try to cover this gap. Example: The federal government has made multiple attempts to pass federal privacy acts. The most notable is the American Data Privacy and Protection Act (ADPPA) which has failed to be passed due to concerns over conflict with the California Privacy Act (CPA). 

5. Technology doesn’t have a clear path forward. Companies that collect digital data and create products around data are disagreeing on the appropriate path forward since there isn’t a clear future state for privacy regulations. Example: Google Chrome continues to delay cookie deprecation and enhance their Topics approach in Sandbox, even with concerns from outside parties.

Throughout all of this uncertainty, data usage is under a microscope from the state Attorney Generals and EU Privacy Regulators. Every company collecting, analyzing, and using data is vulnerable to being targeted with a lawsuit and non-compliance fees.

Will a U.S. Federal Law Soon Usurp State Regulations?

At the end of the first quarter of 2023, there were five states with signed data privacy laws and another 19 are in the works—some of which have multiple active bills. It is expected that around four or five of these will get passed in 2023, with the rest to follow in 2024 and future years to come. 

There have been a few attempts at the federal level to establish new data privacy regulations. Multiple federal leaders, including Sen. Amy Klobuchar (D-Minn.), have introduced federal data privacy laws without success. Sen. Suzan DelBene (D-Wash.) proposed online data privacy legislation that would give consumers control over their data and the Federal Trade Commission the right to enforce it. 

The most well-known recent attempt has been the American Data Privacy and Protection Act (ADPPA). The ADPPA was a United States proposed federal online privacy bill that, if enacted into law, would have regulated how organizations keep and use consumer data. Unfortunately, advocates of California’s CCPA felt the ADPPA wasn’t strong enough to protect Californians. House Speaker Pelosi aligned herself with the Californian advocates, issuing a statement that the ADPPA, “must continue to protect Californians — and states must be allowed to address rapid changes in technology.” 

The American Data Privacy Protection Act (ADPPA) has bipartisan support and is the closest the U.S. has come to passing a comprehensive consumer data privacy law. But it hasn’t passed yet. In 2023 Rep. Kathy McMorris Rogers is expected to reintroduce the bill.

Should the ADPPA at some point get passed into law, it is important to understand the differences between it and the CCPA. The ADPPA goes significantly beyond the CCPA in placing the burden of protecting information on those that process it instead of the individuals who generate it. Here are some of the key differences:

• It spells out and limits the allowable uses of data.
• It requires privacy and algorithmic impact assessments.
• It extends civil rights protections online, protecting against manipulation.
• Provides the individual a much broader right to sue.

The ADPPA would be the first comprehensive privacy legislation to extend civil rights protections to discrimination in the use of personal information and require all businesses and nonprofits to incorporate privacy by design when assessing their use of algorithms.

But will it pass anytime soon?

There is significant polarization on this topic that is creating paralysis. With more states having their regulations come into effect in the near future, it appears that the federal government will look to the experience the states have to form a template for how this will work at the national level. Meaning, we may be years out from seeing a national regulation to clarify and simplify data privacy compliance.

What Is the ePrivacy Regulation Relative to GDPR?

The General Data Protection Regulation (GDPR), is the European Union’s data privacy regulation that has been in effect since 2018 and is widely considered to be the most strict regulation globally. 

The ePrivacy Regulation has also recently been proposed in the EU. This regulation creates a consent standard for cookies. The regulation also offers some ground rules for when data may be processed, with, for example, the use of pseudonymized or anonymized data. This regulation will be applicable to traditional electronic communication service providers, such as mobile and landline telephone operators, but will also cover Internet instant messaging and VOIP apps (email, apps, etc.), as well as machine-to-machine communications such as the IoT.

While GDPR only applies to the processing of personal data, ePrivacy regulates electronic communication even if it concerns non-personal data. Also, in the case of cookies, ePrivacy generally takes precedence. It is expected that the ePrivacy Regulation will take effect sometime in 2023, and there will be a 24-month transition period.

The US and EU signed an agreement toward the end of 2022 that regulates the collection of data between the US and EU governments on each other’s citizens called the EU-U.S. Data Privacy Framework. This replaces previous agreements (Privacy Shield and Safe Harbor), which had been overturned by the Court of Justice of the European Union (CJEU) because of concerns around EU government surveillance of EU data subjects without controls. The new agreement imposes limitations and safeguards on access to data by US intelligence agencies and establishes an independent and impartial redress mechanism to handle and resolve complaints from Europeans concerning the collection of their data for national security purposes.

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How Is Adtech Changing to Account for Data Privacy?

Google

Google continues to push out its third-party cookie deprecation in Chrome. It has now been delayed until the second half of 2024. Google is trying to create the appearance that the Privacy Sandbox is viable so they can meet their commitments to the UK CMA. 

The World Wide Web Consortium (W3C) recently said Google’s Topics ad platform is not good for privacy so they aren’t working on it. The W3C Technical Architecture Group (TAG) raised a series of concerns from their early design review of the Topics API. The TAG representative wrote that Google’s proposed Topics API fails to protect users from “unwanted tracking and profiling” and maintains the status quo of “inappropriate surveillance on the web.” Their representative commented, “We do not want to see it proceed further.”

Browser engine developers Webkit and Mozilla also have spoken out negatively about Google’s approach, warning of privacy deficiencies. Mozilla went so far as to say that Topics is “more likely to reduce the usefulness of the information for advertisers than it provides meaningful protection for privacy.”

While Google works to come up with a workable solution, companies are still able to make use of third-party cookies from Chrome users. While that is good news for some, the delay is also postponing development efforts for alternative solutions and keeping data privacy as a continued concern for advertisers working with this data.

TrustPid 

Meanwhile, major European Telecoms have gotten permission to create their own advertising platforms instead of losing business to big tech. Orange, Vodafone, Telefonica, and Deutsche Telecom are creating TrustPid, a joint advertising platform. The development of their own “privacy first” advertising platform has been in the works for two years. It will allow the four businesses to develop a competitor to major tech companies including Amazon, Apple, Google, and Meta. This is being developed in accordance with the GDPR and the upcoming ePrivacy Regulation so it may very well become a privacy template for future advertising platforms.

US companies are following this closely as EU regulators have been saying for years that the only way to pull info from the terminal equipment of a user for ad targeting is via consent. But the regulations make consent incredibly difficult to obtain. If TrustPid has figured out a way to make this happen, the world will be interested to see what they are doing that the EU regulators like.

Universal IDs (UID)

EU Privacy Regulators are encouraging tech companies to move toward needing consent for any UID. However, they have a big problem with the way Apple is implementing this. Apple’s App Tracking Transparency policy forces app developers to display an additional (Apple-designed) prompt to request permission from end users for the developer to “track” the user, even when the user has already consented to the sharing of its data through the developer’s own consent tool. Concerns that this removes data-based competition and consumer choice across the Apple ecosystem have triggered investigations by multiple European authorities. 

Bottom line? For now, obtaining consent is king. But there still isn’t a clear path for the future. 

What Can Advertisers Do to Protect Themselves among the Uncertainty?

Eventually, the U.S. federal government will come through with legislation that clears up the confusion amongst the state laws. And eventually, Google will finally deprecate third-party cookies. However, both of these major changes are likely a few years out.

In the meantime, advertisers need to ensure they are working with data providers who are compliant with active data privacy regulations. Understanding the main aspects of the US state-level regulations is helpful to validate third party data providers are privacy-compliant. Look for data partners that have multiple options for handling consent (both opt-out and opt-in). They will also have plans in place for handling data requests and privacy lawsuits. 

Key aspects of privacy-compliant third-party data providers:

• They have a broad definition of personal information that includes pseudonymous IDs. The regulations have a broad definition of personal information that now includes pseudonymous IDs. Note: Every ad tech company has a unique ID which is still processing personal data so make sure they are handling this appropriately. 

• They have clear opt-out and opt-ins as needed. A privacy-compliant provider will provide clear opt-out and will have opt-in for sensitive data such as health, religion, etc.

• They have a way to handle data subject access requests. Pseudonymous data is hard to verify when a request comes in. A privacy-compliant provider will have already figured out how they are doing this. 

• They have a plan for handling a private right of action. Attorney Generals have the ability to sue to enforce laws and it is important to know that your data provider has a plan to address and show compliance before a formal lawsuit is filed.

Making sure your data partners are working within the data privacy regulations today and into the future is critical. It is better to plan for the highest restrictions now before it’s too late and the only way to do that is to not do it alone, but to work with a privacy-first data partner like ShareThis. 

The post The Rapidly Changing Future of Data Privacy and How to Prepare for It appeared first on ShareThis.

When executed well, people-based marketing enables brands to reach the right people with the right content at the right time and place. 

So how can you get started on this ambitious endeavor? Here are some steps to create a people-based marketing strategy. 

Step 1: First Things First, Get Organized with Your Data

Compiling data from multiple marketing channels, including email, social, paid, organic, sales, and events is extremely difficult. That’s why it’s so important to organize this information as much as possible in one central location. Besides, having a good place to pull in all of your data makes it easier to analyze, find gaps, or enhance it.

Two of the most popular tools that can help you collect, centralize, and manage your customer data are customer relationship management (CRM) software and a customer data platform (CDP).

CRMs are the older of the two. These tools generally collect data from interactions customers and prospects have with a company. 

CDPs offer more robust capabilities than CRMs. CDPs can integrate large volumes of user-level data from multiple sources—including CRMs—to create a bigger picture of consumers than CRMs and offer stronger insight into customer journeys. 

While both can segment audiences for campaign targeting, CDPs generally offer more sophisticated levels of segmentation, especially if they can run machine-learning models. 

Step 2: Get Real with the Cleanliness of Your First-Party Data

Data is only useful when it’s accurate, up-to-date, and has integrity. 

Start small by checking the data you own, which is called first-party data. This data includes personal identifiable information (PII), which consumers share with their implied or active consent, like email addresses, physical addresses, phone numbers, and usernames.

First-party data also includes information you collect yourself through means like: 

• Customers’ purchase histories
• Digital interactions consumers have on your websites or mobile apps
• Conversations users have with a call center or chatbot
• Participation in a loyalty program

Ensure you are collecting first-party data in the best way possible before expanding to more tools and data sets.

Step 3: Determine the Holes in Your First-Party Data 

Data fuels people-based marketing, but it’s about the right kind of data, not the quantity. Analyze the data you have to figure out what is actually useful. 

Then, determine which additional types of data could help you achieve your objectives. After all, in this cookie-depreciating world, first-party data is generally not enough to build holistic customer views.

For example, a retail brand can use its own transaction data to know if someone bought a swimsuit, but it may be missing context for the sale. Online behavioral data from a data provider could offer clues: If they’re researching activities in Hawaii, there’s a good chance they’re planning a trip. Being able to connect the dots – to understand the why behind the purchase – can help the brand tailor future offers, like for sunhats and sandals.

Step 4: Fill In the Holes and Strengthen Consumer Profiles

As you can see, third-party data providers can help you fill the gaps of what you don’t know about your customers. 

Third-party data includes the granular data points that external companies can collect in real time about what individual users are doing across verticals, industries, and categories.

Examples of third-party data include:

• Interest data based on online behavior like sharing content, searching for information, clicking on content, and viewing web pages
• Location data
• Purchase history
• Demographic data
• IDs for devices that individuals use including tablets, connected TV, mobile, and desktops

Incorporating additional data should be relatively seamless at this stage since you’ve built such a strong foundation in the previous steps.

Step 5: Run Reports to Continuously Analyze Your Data

Any information you’re gathering that pertains to audience devices, locations, channels, time, interests, and intent should provide actionable insights. These learnings are likely to change over time, even over short periods of time, so the process of analysis must be dynamic.

Creating up-to-date, regular reports on data that you are ingesting as a marketer can not only help you segment customers but also:

• Keep a pulse on trends
• Learn which channels and strategy tactics are working
• Guide your content creation
• Understand where to prioritize your time and efforts to get the best results

Use Your Rich Consumer Data and Analysis to Put “People” First 

You can ultimately leverage your data to drive conversions and build loyalty by:  

• Reaching the right prospects and consumers
• Sending audiences the right content 
• Hitting them at the right time 
• Targeting audiences at the right place

How to Reach the Right Customers and Prospects

This is where your segmentation shines and you can spend your media budget efficiently.

An organic grocery store in Los Angeles, for example, can find young local moms who have researched ways to incorporate healthy foods into the diets of their young eaters and have purchased organic vegetables in the past.

How to Send Audiences the Right Content 

Having a 360-degree view of customers across touchpoints can help you understand what they’re interested in and where they are in their buying journey. This intelligence can inform which offers, ad copy, website content, recommendations, and other kinds of messages are most likely to resonate and be welcomed by your prospects and customers.

Personalization is paying off. According to a recent study, nearly 80% of surveyed business leaders said consumers spend more (34% more on average) when their experience is personalized.

How to Hit Audiences at the Right Time

Timing is everything in life, and that includes when you’re engaged in people-based marketing. The best time to send offers and hook someone is when their interest has peaked. 

Your interest data can help you determine this pivotal point. For the grocery store’s segments, for instance, this could be when moms are researching specific products.

You should also communicate during the time period of the day that targets have consistently demonstrated the most engagement and would most likely be receptive to your messages.

How to Target People at the Right Place

For digital consumers, “place” translates into “device.” And, people don’t usually limit themselves to a single device, including when they’re on a purchase journey.

You need the ability to seamlessly reach targets on any device they’re currently using. Device IDs can aid in this cross-device targeting. 

Use Your Data to Determine Next Steps

Your data can help you continue to nurture targets down the pipeline until there is a sale. Then you can employ upsell or cross-sell strategies.

Data can also help you understand when the interest of targets has declined. You could learn when it’s time to direct resources toward more potentially profitable audiences.

Conclusion

People-based marketing is challenging because you need high-quality data from a range of sources, easy-to-use tools that can help you manage the data, and a good strategy for reaching targets both effectively and efficiently with powerful content.

But as reliance on the third-party cookie fades away, this marketing approach will only become more attractive. That’s why it’s a great time to create a well-rounded people-based marketing strategy with partners that can help you fill in your data gaps like ShareThis.

The post People-Based Marketing: 5 Steps to Get You Started appeared first on ShareThis.

In today’s fast-paced digital marketplace, staying ahead of the competition is a top priority. This means, as a media buyer, you’re always on the lookout for ways to improve your targeting so you get the most out of your ad dollars. 

Chances are, you’ve used lookalike modeling to optimize your ad spend. With lookalike modeling, you can use your first-party data to create an expanded audience of people who are more likely to convert or engage. But without a full understanding of how lookalike modeling works, you may not be leveraging the full capabilities of this powerful machine-learning-based tool. 

What Is Lookalike Modeling?

With the help of machine learning techniques, lookalike modeling takes a data set—referred to as a “seed set”—of your existing customers and uses this to create a new, larger audience of potential buyers who share similar attributes and behaviors.

These shared attributes and behaviors mean you now have a highly relevant pool of people who are more likely to be interested in your products or services. 

Behind the Scenes: How Lookalike Modeling Works

Now that we know the general definition of lookalike modeling, it’s time to look at what’s going on “under the hood”. By understanding how lookalike modeling works, you’ll be better able to optimize your advertising budget and reduce inefficient ad spend. 

While each programmatic platform will have its own methods, there are a number of elements common to the lookalike modeling process overall:

Data Collection

Your “ideal customers” seed set is built using first-party data. You most likely won’t need to include all or even most of your first-party data to create your seed set—in fact, each programmatic platform has different limitations on what data points they can match to your first-party data to find ideal customers in their network. But any first-party data you don’t use can still be useful during the pre-seed stage, for filtering and honing in on who your most valuable customers are. 

The seed set data you use will depend on your campaign objectives. For example, the data used to build a seed set for a campaign targeting people most likely to purchase Product A might consist of a list of customers filtered down by behavioral or purchase history information related to Product A, such as recent purchases, repeat purchases, and the action of adding the product to their carts. 

Data Analysis

Because the attributes (and behaviors) you choose to focus on will form the basis of your lookalike audience, you’ll want to create audiences with attributes that are a good fit for your campaign objectives. Programmatic platforms will then use a number of techniques, including machine learning algorithms, to help analyze your seed set data and identify patterns and similarities in these attributes.

For example, suppose your objective is to raise brand awareness. In that case, the platform might identify patterns in attributes and behaviors related to your broader seed set’s interests, social media behavior, and brand affinity. For a conversion campaign, however, patterns in purchase history, online shopping behavior, product affinity, and in-platform conversions would most likely be better predictors. And with both objectives, certain demographic and geographic attributes could be relevant.

You’ll also need to balance quality versus scale when choosing the attributes you want to be analyzed and used for your lookalike model. More attributes will mean a more highly targeted audience, but you’ll likely end up with a lookalike audience that’s much smaller. 

Lookalike Model/Audience Creation

In this stage, the programmatic platform uses machine learning techniques to build and train the lookalike model. This model includes the identified seed attributes and behaviors which can then be used to create a custom audience of people who share similar attributes and behaviors.

Once created, your lookalike model isn’t set in stone. Instead, the programmatic platform will continue to refine it, based on any new data or variables as they arise or are added. The platform may also adjust or modify its proprietary machine learning algorithms from time to time to reflect any changes in the target audience—for example, inflation may mean customers become more price-conscious, causing a shift in their buying behavior. 

Machine Learning & Lookalike Modeling

Machine learning drives the lookalike modeling process, and every programmatic platform has its own proprietary machine learning algorithms. 

Several types of machine learning techniques are commonly used in lookalike modeling. The following are simplified explanations of some of the most popular techniques:

• PU Learning: PU learning (positive-unlabeled learning) works with data that contains only positive examples and unlabeled examples. Your seed set contains positive examples (for example, customers who add items to their cart). PU learning uses these positive examples to identify unlabeled examples in the programmatic platform’s data that are similar to the positive examples. 

• Gradient Boosting Machines (GBMs): Using an iterative process, GBMs identify target market attributes by using decision trees to predict outcomes (for example, whether a person likes to cook), detect errors, and create new decision trees that correct those errors. These decision trees typically have a single root node from which other nodes branch out, and the user sets a maximum depth, based on the objectives and the dataset. 

• Logistic Regression: Logistic regression is a machine learning technique that predicts the probability of an event occurring (for example, renewing a subscription) by identifying patterns between audience characteristics and the desired attribute (the subscription renewal).

• Random Forests: As its name indicates, the random forests technique uses several decision trees to make predictions based on customers’ characteristics (for example, whether someone will click on a link). It then takes the overall consensus of these decision trees to form a final prediction.

• Neural Networks: Just like the human brain, neural networks use interconnected nodes (“neurons”) to perform complex calculations on input data consisting of customers’ characteristics (ie age, gender, location). The result is a probability score that predicts the likelihood of the desired attribute (for example, engagement in a social media post) based on these characteristics.

• Support Vector Machines (SVMs): SVMs work by finding the boundary (known as a hyperplane) that best separates customers known to exhibit the desired attribute (for example, an interest in sports) from those who do not. 

• K-nearest Neighbors (KNN): KNN uses proximity to make predictions. The “k” refers to the number of nearest neighbors, based on their distance (or similarity) to the target attribute, used to make the prediction (for example, whether a customer will fill out an online form). 

Pros and Cons of Lookalike Modeling

Lookalike modeling offers advertisers several pros:

• can expand limited first-party data
• improved ad performance
• efficient targeting
• cost-effective customer acquisition
• better conversion and lead generation

On the flip side, though, are the cons:

• lack of diversity in seed set and machine learning algorithms data can lead to biases
• requires a quality static seed set data
• does not incorporate real-time first-party data to capture behavioral or preference changes
• cannot identify untapped audiences

Tap into the Potential of Lookalike Modeling

With the power of lookalike modeling, you can build an audience of potential buyers who share attributes and behaviors similar to your most valuable customers—increasing the likelihood of conversions. 

You can also improve the cons of lookalike modeling by enriching your first-party data with ShareThis data. Our advanced data science capabilities use the latest best-in-class techniques to enrich your seed audience, making all the difference when it comes to lookalike modeling. Get in touch with us to learn more. 

The post Look under the Hood of Lookalike Modeling appeared first on ShareThis.

Gartner estimates that by the end of this year, 65% of people across the world will have their personal data protected by privacy regulations. This is great news for consumers, but a difficult challenge for businesses trying to operate using data.

There are now over 120 data protection and privacy regulations globally. And many more are in the works. This can be very confusing for companies with global operations to navigate. It is critical for advertisers to understand these regulations to ensure they can remain compliant and confidently select privacy-compliant data providers and partners.

This comprehensive guide will explain the most heavily restrictive data protection laws so advertisers can better understand what they can and can’t do with first-party data.

Non-compliance with Data Privacy Comes at a High Price

There are two main risks when it comes to data privacy:

1. Losing business because users have lost trust in the way you handle their data.
2. Getting fined by regulatory bodies for non-compliant data handling.

Both of these risks come with costs that can severely hurt a business. For instance, Brazil’s data protection law can claim 2% of revenue a company generates in Brazil and the European Union’s GDPR can claim 4% of global company revenue. While many of these laws are still new, there are many companies that have already been fined significant amounts of money. 

Quick facts on big GDPR fines:

• The largest fine to date is the GDPR fine to Amazon in 2021 of $877 million. 
• Meta (formerly Facebook) got hit in 2022 with a fine of $433 million. 
• The biggest GDPR fines also include Google, which has been hit twice with fines totaling $65 million. 
• The GDPR has fined an estimated 1,000 companies in the past 2 years with cumulative fines of $1.25 billion.

For those businesses only operating in the United States, there are state-by-state regulations to contend with. The first active regulation was California’s CCPA, which fines $7,500 for each individual consumer data privacy violation. Ten businesses have been fined to date, but the total amounts have not been disclosed. What is clear is that regulations like the CCPA can cause a nightmare of inquiry resolution and additive fees once consumers identify a problem.

Companies need to take precautions to avoid non-compliance penalties. This is especially true for marketers who advertise utilizing a lot of data to identify target audiences and place ad contracts. This makes it critical for advertisers to clearly understand the most stringent policies and the important questions to ask internally and externally to ensure compliance.

Notable Data Privacy Regulations

The most talked about regulations today are the ones that have been passed into law with grace periods or ones that are actively passing out fines. The European Union’s General Data Protection Regulation (GDPR) is one of the strictest and highest publicized. Brazil’s data protection law (LGPD) came out after GDPR and is the most significant regulation in South America. In the United States, California’s Consumer Data Privacy Act (CCPA) was the first to be passed with Colorado’s Privacy Act and Virginia’s Consumer Data Protection Act (CDPA) close behind. Here’s the breakdown of these active regulations that are setting the precedent for the rest of the world.

General Data Protection Regulation (GDPR)

• Date law passed: April 14, 2016
• Date went into effect: May 25, 2018
• Definition: The GDPR requires companies to ask for some permissions to share data and gives individuals rights to access, delete, or control the use of that data. The GDPR does allow for opt-in permission where the user actively subscribes to receive emails or newsletters by providing their email address and sometimes their name and other personal information. Note, the UK also has its own version of the GDPR: UK GDPR
• Enforcement: Individual data protection authorities (DPAs) from the 27 EU member states enforce the GDPR. DPAs are independent of the government. Their role is to investigate complaints, provide advice on data protection issues, and determine when the GDPR has been breached.
• Penalties: For especially severe violations, the fine can be up to 20 million euros, or up to 4% of their total global revenue of the preceding fiscal year, whichever is higher. For less severe violations there are fines of up to 10 million euros, or up to 2% of their global revenue of the preceding fiscal year, whichever is higher. 
• What this means for advertisers: The GDPR applies to any company that processes the personal data of EU citizens or residents, or offers goods or services to them. Any company that fits this description needs to adhere to these seven principles:
  1. Lawfulness, fairness, and transparency — Processing must be lawful, fair, and transparent to the data subject.
  2. Purpose limitation — You must process data for the legitimate purposes specified explicitly to the data subject when you collected it.
  3. Data minimization — You should collect and process only as much data as absolutely necessary for the purposes specified.
  4. Accuracy — You must keep personal data accurate and up to date.
  5. Storage limitation — You may only store personally identifying data for as long as necessary for the specified purpose.
  6. Integrity and confidentiality — Processing must be done in such a way as to ensure appropriate security, integrity, and confidentiality (e.g. by using encryption).
  7. Accountability — The data controller is responsible for being able to demonstrate GDPR compliance with all of these principles.

It is also important for advertisers to be aware of strict new rules about what constitutes consent from a data subject to process their information.

• Consent must be “freely given, specific, informed, and unambiguous.”
• Requests for consent must be “clearly distinguishable from the other matters” and presented in “clear and plain language.”
• Data subjects can withdraw previously given consent whenever they want, and you have to honor their decision. You can’t simply change the legal basis of the processing to one of the other justifications.
• Children under 13 can only give consent with permission from a parent.
• You need to keep documentary evidence of consent.

Brazil General Data Protection Law (LGPD)

• Date law passed: August 14, 2018.
• Date went into effect: September 18, 2020.
• Definition: LGPD governs the processing of personal data with the purpose of protecting the fundamental rights of freedom and privacy for individuals. Any organization, irrespective of where it is located, that has customers or clients in Brazil, needs to be in compliance with LGPD. This means that it is not just Brazilian citizens whose personal information is protected, but any individual whose data has been collected or processed while inside Brazil.
• Enforcement: A part of this law included the creation of a federal independent regulatory authority, The Autoridade Nacional de Proteção de Dados (ANPD). Their role is to interpret and enforce the LGPD and act as the national supervisory authority.
• Penalties: Administrative sanctions may be applied by authority in case of violation of LGPD. Among the sanctions, there are notices and fines, that may vary from 2 percent of the company’s turnover in Brazil in its last fiscal year, limited in total to 50,000,000.00 (fifty million reais) per infraction. There is also the possibility of a daily fine to compel the entity to cease violations.
• What this means for advertisers: Any business that collects information on users while they are in Brazil needs to ensure compliance with these standards, including obtaining unambiguous consent to use their personal data. If you follow the opt-in consent rules for GDPR, you will be compliant with the LGPD.

California Consumer Privacy Act (CCPA & CPRA)

• Date law passed: June 28, 2018 the CCPA was passed. The CPRA is an amendment to the CCPA and was passed on November 3, 2020.
• Date went into effect: The CCPA went into effect Jan 1, 2020. The CPRA amendment went into effect on Jan 1, 2023.
• Definition: This landmark law secures new privacy rights for California consumers, including:
  • The right to know about the personal information a business collects about them and how it is used and shared;
  • The right to delete personal information collected from them (with some exceptions);
  • The right to opt out of the sale or sharing of their personal information; and
  • The right to non-discrimination for exercising their CCPA rights.

In November 2020, California voters approved Proposition 24, the CPRA, which amended the CCPA and added new additional privacy protections that began on January 1, 2023. As of January 1, 2023, consumers have new rights in addition to those above, such as:

• The right to correct inaccurate personal information that a business has about them; and
• The right to limit the use and disclosure of sensitive personal information collected about them.
• Enforcement: The California Attorney General has complete authority to enforce the CCPA. That said, the CPRA amendment granted the California Privacy Protection Agency full administrative power, authority, and jurisdiction to implement and enforce the CCPA; the Attorney General retained enforcement powers. 
• Penalties: Violations of the CCPA are subject to enforcement by the California attorney general’s office, which can seek civil penalties of $2,500 for each violation or $7,500 for each intentional violation after notice and a 30-day opportunity to cure have been provided.
• What this means for advertisers: Businesses that are subject to the CCPA have several responsibilities, including responding to consumer requests to exercise these rights and giving consumers certain notices explaining their privacy practices. 

Virginia Consumer Data Protection Act (VCDPA) 

• Date law passed: March 2, 2021
• Date went into effect: January 1, 2023
• Definition: The VCDPA  gives consumers the right to access their personal data and request that it be deleted by businesses. It also requires companies to conduct data protection assessments related to processing personal data for targeted advertising and sales purposes. The law even contains some restrictions on the use of de-identified data, or data modified to no longer directly identify individuals from whom the data were derived. 
• Enforcement: The Virginia Attorney General is the only entity that can enforce the VCDPA and seek damages. 
• Penalties: Violations can result in civil penalties of up to $7,500 per individual consumer violation. In addition, businesses can be held responsible for the reimbursement of the office’s reasonable expenses, which include attorney’s fees.
• What this means for advertisers: Virginia’s act is not much different from the CCPA, but it is important to note that it does not specify company revenue. Meaning, this applies to any size company processing data.

A comprehensive list of regulations in countries throughout the world can be found here.  

New Data Privacy Regulations Passing in 2023

More data privacy regulations are in the works, but here are some of the big ones going into effect in the near future.

The Colorado Privacy Act (CPA)

• Date law passed: July 7, 2021
• Date goes into effect:  July 1, 2023
• Definition: The Colorado Privacy Act provides Colorado residents with the right to opt out of targeted advertising, the sale of their personal data, and certain types of profiling. Starting July 1, 2024, controllers will need to honor user-selected universal opt-outs for targeted advertising and sales. Colorado residents also have the right to access, correct, and delete their personal data as well as the right to data portability. Controllers will generally have 45 days to respond to consumer requests.
• Enforcement: Compliance with the CPA is exclusively enforced by the Colorado Attorney General or the district attorney. 
• Penalties: Under the CPA, violations are subject to civil penalties under the Colorado Consumer Protection Act, which provides for civil penalties of no more than $20,000 for each violation from an individual consumer.
• What this means for advertisers: As another consumer protection law, this will be additive to the activity from California and Virginia. That means that if a company is accused of a violation in one state, that may quickly spill over into other states with similar regulations.

The Connecticut Data Privacy Act (CTDPA)

• Date law passed: May 10, 2022
• Date goes into effect: July 1, 2023
• Definition: The CTDPA provides Connecticut residents the following enumerated rights:
  • The right to access personal data that a controller has collected about them.
  • The right to correct inaccuracies in their personal data.
  • The right to delete their personal data, including personal data that a controller collected through third parties.
  • The right to obtain a copy of their personal data in a portable and readily usable format that allows them to transfer the data to another controller with ease.
  • The right to opt out of:
    • the sale of their personal data;
    • the processing of personal data for the purposes of targeted advertising; and
    • profiling that may have legal or other significant impacts.
• Enforcement: The Connecticut Attorney General has exclusive authority to enforce the CTDPA.
• Penalties: Up to $5,000 per violation. In addition to civil penalties, the Attorney General can also pursue litigation for additional damages.
• What this means for advertisers: Companies who collect and process personal data of Connecticut residents must:
  • Provide notice regarding the types of personal data they process, the purpose(s) for processing, whether and why they share personal data with third parties and information about how consumers can exercise their various rights (e.g. access, deletion) over their personal data.
  • Limit collection of personal data to what is adequate, relevant, and reasonably necessary for the specific purpose(s) for which the data is processed (also known as “data minimization”).
  • Obtain consent before processing a consumer’s sensitive data.
  • Respond to requests to exercise consumer rights granted under the CTDPA.
  • Conduct assessments before processing personal data in a manner that presents a heightened risk of harm to consumers (called “Data Protection Assessments”). This includes processing personal data for the purposes of targeted advertising, sale, or profiling, and processing sensitive data.

Utah Consumer Privacy Act (UCPA)

• Date law passed: March 24, 2022
• Date goes into effect: December 31, 2023
• Definition: Consumers are provided four main rights under the UCPA:
  • Right to access – the right to confirm whether a controller is processing their data and the right to access that personal data.
  • Right to delete – the right to have the company delete their personal data that they provided to the controller.
  • Right to data portability – the right to obtain a copy of their personal data in a format that is readily usable.
  • Right to opt out of certain processing – the right to opt out of the processing of their personal data for the purposes of targeted advertising; or the sale of personal data.
• Enforcement: The Utah Attorney General has exclusive authority to enforce the UCPA.
• Penalties: Companies have 30 days once notified to fix the violation. If it is not fixed within that time period, the attorney general can impose penalties and damages up to $7,500 per violation.
• What this means for advertisers: This act is easier on businesses than those from California, Virginia, and Colorado. As long as a business is compliant with those stricter regulations, there should be no change when this law goes into effect.

Federal Data Protections in the US

While consumer data privacy is being handled mostly on a state level in the US, there are many federal-level regulations that apply to certain industries that also need to be adhered to.

Children’s Online Privacy Protection Act (COPPA)

COPPA imposes certain requirements on operators of websites or online services directed to children under 13 years of age, and on operators of other websites or online services that have actual knowledge that they are collecting personal information online from a child under 13 years of age.

Health Insurance Portability and Accounting Act (HIPAA)

The Health Insurance Portability and Accountability Act of 1996, commonly known as HIPAA, is a series of regulatory standards that outline the lawful use and disclosure of protected health information (PHI). HIPAA compliance is regulated by the Department of Health and Human Services (HHS) and enforced by the Office for Civil Rights (OCR).

Gramm Leach Bliley Act (GLBA)

The Gramm-Leach-Bliley Act requires financial institutions (companies that offer consumers financial products or services like loans, financial or investment advice, or insurance) to explain their information-sharing practices to their customers and to safeguard sensitive data.

Fair Credit Reporting Act (FCRA)

The Fair Credit Reporting Act protects information collected by consumer reporting agencies such as credit bureaus, medical information companies, and tenant screening services. Information in a consumer report cannot be provided to anyone who does not have a purpose specified in the Act. Companies that provide information to consumer reporting agencies also have specific legal obligations, including the duty to investigate disputed information. In addition, users of the information for credit, insurance, or employment purposes must notify the consumer when an adverse action is taken on the basis of such reports. 

Questions to Ask Data Providers to Ensure Data Privacy Compliance

When working with data providers and partners, it is now more important than ever to ensure that they are handling personal data appropriately. Here are the critical questions to ask to confirm new data providers will stay compliant with current and upcoming regulations.

1. Is there a separate Privacy team in your company that drives privacy compliance? Or is privacy built-in by design?
2. What documentation do you maintain on the data you’re collecting, such as consent,  how it’s used, where it’s stored, which employee is responsible for it, etc.?
3. What security measures do you have in place for your data storage?
4. How are you handling the opt-in vs. opt-out requirements imposed by regional privacy regulators?
5. What are you doing to address recent US State Laws?
6. What are you doing to address recent GDPR regulations?
7. How do you ensure you handle sensitive data in a privacy-compliant manner, especially sensitive health or religious data?
8. Are you planning on getting a privacy audit/review for an eventual certification this year? If so, which organization are you planning to work with on this?

Asking these questions will give a clear picture of how serious the data provider is about data protection and privacy as well as how prepared they are to demonstrate compliance with international regulations. Partnering with data providers like ShareThis, who put data privacy at the forefront of their business practices, will allow advertisers to worry less about data privacy compliance and instead focus on advertising success.

The post The Era of Data Privacy Laws: A Comprehensive Guide appeared first on ShareThis.

Since the disruption of cable TV, the streaming space continues to get more and more confusing with new channels and formats popping up constantly. Less than a year ago, we were discussing the new opportunities for advertising in CTV and OTT. Fast forward to now and there has been an explosion of new entrants and even more niche channels for advertisers to be more targeted with their spend.

This is great news, as it has created new opportunities to more directly and cost-effectively reach target audiences. Especially with more ad-based services and channels entering the market like Netflix’s lower-tier ad service and free services like PlutoTV gaining popularity. But this also poses difficulty for advertisers trying to wade through the clutter to find the best ways to reach their target audiences. This article will explain the various streaming categories and confusing acronyms so that marketers can better understand how to navigate this landscape for advertising.

What Are CTV, OTT, VOD, Linear TV, and FAST in Marketing-Speak?

CTV, OTT, VOD, and Linear TV are all types of television, but they differ in how they are delivered and consumed. Here’s a breakdown of each:

What Is CTV?

CTV stands for Connected TV, which refers to televisions that are connected to the internet and allow users to stream content directly from the internet.

Examples of CTV providers include:

• Smart TVs with built-in streaming capabilities, such as Samsung, LG, and Vizio.
• Streaming devices such as Apple TV, Roku, Amazon Fire TV, and Chromecast.
• Gaming consoles such as Xbox One and PlayStation 4, which also have built-in streaming capabilities.

What Is OTT?

OTT stands for Over The Top, which refers to the delivery of television content via the internet, without the need for a traditional cable or satellite subscription.

Examples of OTT providers include:

• Streaming platforms such as Netflix, Hulu, Amazon Prime Video, Disney+, and HBO Max.
• Live TV streaming services such as Sling TV, PlayStation Vue, YouTube TV, and AT&T TV Now.

What Is VOD and AVOD?

VOD stands for Video On Demand, which refers to a service that allows users to select and watch video content at their own convenience, rather than at a scheduled broadcast time. AVOD is Video On Demand with Advertising and is typically a free service due to the commercial ads that play throughout the primary content. VOD providers are the companies that offer video content in the on-demand format. VOD providers can be standalone platforms, or it can be a feature offered by other types of providers like streaming services, cable, and satellite providers.

Examples of VOD/AVOD providers include:

• Streaming platforms such as Netflix, Hulu, Amazon Prime Video, Disney+, and HBO Max.
• Live TV streaming services such as Sling TV, PlayStation Vue, YouTube TV, and AT&T TV Now.
• Social media platforms like YouTube, TikTok, Facebook Watch, and Twitch.
• Cable and satellite providers such as Comcast and DirecTV, which offer on-demand content as part of their packages.
• Online rental and purchase platforms such as iTunes, Google Play, and Vudu.

What Is Linear TV?

Linear TV refers to traditional television, in which programming is broadcast at a scheduled time and must be watched live or recorded for later viewing. Linear TV providers are the companies that offer television content that is scheduled to be broadcasted at a specific time. They provide a programming schedule that viewers must tune in to watch live, or record for later viewing. Linear TV providers typically require a subscription to access their content, and they may also offer on-demand content as part of their packages.

Examples of Linear TV providers include:

• Traditional cable and satellite providers such as Comcast, DirecTV, and Dish Network
• Broadcast television networks such as ABC, NBC, CBS, and Fox
• Public television stations such as PBS
• Specialty networks such as ESPN, HBO, and AMC
• International networks such as BBC and CCTV

What Are FAST Channels?

FAST (Free Ad-Supported Streaming TV) Channels are a feature that allows users to watch live TV channels on streaming platforms. They use automation technology to scan and index live TV channels, making them available to be streamed on-demand. This allows users to watch live TV channels, just like they would on traditional cable or satellite TV, but through streaming platforms.

FAST channels are different from traditional Linear TV channels as they are not scheduled and are available to watch anytime, anywhere, and also don’t require a cable or satellite subscription. They can be accessed through streaming devices or smart TVs that support the technology.

Examples of streaming platforms that offer FAST channels:

• Pluto TV, Sling TV, YouTube TV, and Philo are just a few platforms that offer a combination of live and on-demand content, which makes them more flexible and convenient for users.

Still confused? In short, CTV and OTT are the way of watching, VOD is the way of consuming and Linear TV is the way of scheduling. FAST channels are the combination of Linear TV and VOD to provide users with VOD content in a Linear TV format.

How Does Advertising Work on These Streaming Platforms and Channels?

Advertising on CTV, OTT, VOD, Linear TV, and FAST channels is done in different ways, depending on the platform and the type of content being viewed. CTV and Linear TV are more traditional, offering 30-second spots. The opportunities for targeting are limited to geographies and some demographics based on the content. In comparison, OTT, VOD, and FAST channels have more advertising options and increased targeting capabilities. They provide more detailed information on their viewing audiences and allow in-content placements. Additionally, ads can be placed in specific channels or within specific content to target specific audiences.

In general, advertising on streaming platforms allows for more targeting, measurement, and flexibility than traditional linear TV advertising. Advertisers can better understand the audience, and deliver more relevant and engaging ads. Because of this, advertisers are moving their marketing dollars away from traditional linear TV avenues and instead looking to the advanced targeting potential of streaming TV. 

With effective targeting and better measurement, this trend will certainly continue. Modern marketers will be experimenting and shifting ad spend in this direction and will continue to adapt to the opportunities that arise as streaming TV platforms evolve.

The post Decoding the Streaming Alphabet Soup: Let’s Unravel the Acronyms. appeared first on ShareThis.

Marketers no longer need to be convinced about the benefits of taking a data-driven approach to their work. A recent Salesforce survey found global marketers are relying on an average of 15 different data sources, up from 10 the previous year. In fact, 82% employ “declared interest/preferences data” and 77% use “inferred interest/preferences data.”

As a marketer, you naturally only want to use reliable data to enrich the data you already own.

After all, using poor-quality data can lead to big problems. You can fail to get an accurate 360-degree view of your customers, for example, leading you to target the wrong people with your ads or the right people but with the wrong products. Promising campaigns can look like failures, and ultimately, the money you invest in data is wasted.

Though data quality may seem like an abstract concept, it’s actually not. You can consider a number of measurable characteristics to determine whether or not the deliverables of a data provider can help you reach your goals.

What Are the Characteristics of Data Quality Measurement?

The data integrity company Precisely defines five traits that are integral to data quality:

• Data accuracy: Is the information correct in every detail?
• Data completeness: How comprehensive is the information?
• Data reliability: Does the information contradict other trusted resources?
• Data relevance: Do you really need this information?
• Data timeliness: How up-to-date is the information? 

You can assess these characteristics in some very practical ways when you’re testing data.

• Accuracy: Did the data provider give correct enrichment fields, like are the categories assigned correctly? If the data includes names and addresses, are they spelled correctly?  

• Completeness: Are there a number of empty values in fields that are relevant to your use cases? It would be a red flag if you see a lot of blanks in columns for postal codes for certain geolocation, for instance.

• Reliability: Can you cross-validate the data with your existing data assets, like transaction data? For example, if a data provider claims a group of users is sports fans, transaction data should bear that out. After all, sports fans generally purchase more sporting goods than consumers who aren’t interested in sports. 

• Relevance: Is the data properly created? If you request only data that pertains to customers in the U.S., you shouldn’t receive data about customers in Europe.

• Timeliness: Does the data have a lag? For some brands, data that is more than 96 hours old has already gone stale and become useless. Ensure the timeliness of the data suits your purposes. 

Providers have ways to confirm they’re producing high-quality data. They can use natural language processing (NLP), for instance, to measure if their categories are assigned correctly. They can set up data quality checkpoints for data deliverables with their partners. They can also receive anomaly detection alerts for irregular data that comes through their sources.

Asking providers about these and other measures they take to deliver quality data can help you choose the right partners.

An Additional Gauge of Data Quality

Achievements and credentials around data quality can help you feel comfortable about working with a data provider.  ShareThis is proud of the recognition they’ve received in this area. 

ShareThis is one of only six data providers to have received the IAB Tech Lab’s Data Transparency Label. The label offers a clear view of our audience segments, including where our original data was sourced, how we constructed the audience, and the topline description of each audience.

Our company has also been awarded the Neutronian Certification Badge following a comprehensive audit of the processes and procedures we use to build our audience segments. The certification covers five main categories of data quality with 10 to 15 attributes evaluated in each category. These categories are:

• Consent and compliance
• Sourcing transparency
• Dataset characteristics
• Methodology and processing
• Performance

In addition, Neutronian releases a quarterly Data Privacy Scores report where ShareThis consistently ranks as a top data provider. 

Conclusion

Here’s the bottom line: Since quality data can lead to good decision-making, it’s always a good decision to ensure a third-party provider’s data is of the highest quality.

In addition to having an eagle eye on measurable characteristics while testing data, you can ask providers about their quality control and check for achievements and credentials they’ve received for producing quality data.

The post Testing Data? Ask These Questions to Evaluate Data Quality appeared first on ShareThis.

Want to score a game-winning touchdown for your brand this Super Bowl Sunday? You’ll be going head-to-head against your competition—which means your brand needs to stand out in the weeks-long marketing blitz that forms the core of the Super Bowl season for marketers across the nation. 

And if you’re aiming to score big, you need a game-winning play in your marketing game day playbook. The good news is, you already have one: behavioral data. Using behavioral data to drive your Super Bowl marketing campaigns is the game changer that lets you do more to leverage one of the biggest marketing events in America today.

Why Super Bowl Sunday Is a Marketer’s Dream

Super Bowl Sunday has long been a major advertising event for the big brands, with people just as likely to watch the Big Game for the ads as they are for the iconic gameplay moments. In fact, 42% of Super Bowl viewers say they’re there specifically for the ads—a rarely voiced consumer opinion that highlights the marketing opportunity provided by this one big sporting event that takes place every February. 

And while few brands today have the marketing budget for the $7 million 30-second Super Bowl primetime ads, you don’t need a big budget to get your brand’s offensive line in gear for the Big Game. 

Here’s why: No matter what niche or industry your brand is in, a certain portion of your target audience will be watching the Super Bowl. Take the 2022 Super Bowl stats, for example. The estimated audience of over 208 million viewers was made up of more than just avid football fans. In a survey leading up to Super Bowl Sunday last year, 80% of casual NFL fans and 20% of non-NFL fans said they planned to watch the game. 

This means the big event is a prime marketing opportunity for your brand, no matter what demographics make up your target market. And behavioral data provides you with the key information you need to get your Super Bowl marketing campaigns in front of the game viewers that matter most to your brand. 

Get Your Brand Into the End Zone

Today’s Super Bowl is a star-studded extravaganza that goes beyond mere sport, and chances are, you’ll have avid fans, casual fans, and curious non-fan onlookers alike within each of your marketing segments. 

Tap into behavioral data segments to transform the Big Game into the perfect opportunity for your brand to stay relevant and reach your target market at scale. With the right data, you can easily access the information you need to reach the right audience from among the sea of viewers tuning in to watch the game—a much better use of your team’s time and your marketing spend.

Use Behavioral Data for a Game-Winning Marketing Drive

Using behavioral data as a guide, your marketing team can craft standout Super Bowl creative tie-ins that elevate your message above the noise. For example, Super Bowl seasonal segments can include:

• auto shoppers
• beer buyers
• football fans
• movie enthusiasts
• party hosts
• technology fans
• TV upgraders

Your behavioral data lets you zero in on the things that matter most to your audience—all while tapping into the fun and excitement that forms the cornerstone of viewers’ Super Bowl experiences. 

Personalization for the Win

Personalization is an extremely powerful way to help your brand stand out from its competitors. According to a recent McKinsey survey, 71% of consumers say they expect personalized interactions from companies and brands—and on the flip side, effective personalization led 76% of consumers to consider purchasing from a brand. 

Behavioral data places the personalized marketing approach at your fingertips, providing you with easy access to the what, the where, and the how behind your audience’s why—information that’s key to successful personalization.

Score Big with Real-Time Data

Behavioral data gives you the real-time data you need to tailor your marketing messages for each of the following key Super Bowl marketing campaign moments:

Before the Big Game. When it comes to the Big Game, anticipation can be nearly as fun as the game itself—which means the lead-up to game day is a great time to begin associating your brand with the big event. For example, in the weeks before Super Bowl Sunday, you can:

• optimize your existing content for Super Bowl-related terms
• craft new content to build excitement for the big event
• generate buzz for in-game giveaways and contests
• release teasers for game-day discounts tied into key in-game stats
• create paid ads that leverage pre-game hype

During the Big Game. It’s estimated that 46% of viewers will watch the Super Bowl with a second, mobile screen in hand, giving you the chance to be a part of your market’s game day interactions. For example, on game day your team can keep your brand top-of-mind by:

• monitoring game-related social media conversations for opportunities to authentically include your brand voice
• sharing memes in real time connecting key in-game moments with your products
• offering promotions or giveaways tied to specific game scores or stats
• live streaming game play discussions that tie into your brand
• using fun polls relevant to both game play and your products to encourage fan engagement

After the Big Game. There’s a winning team every Super Bowl Sunday, but even when the game ends, your campaigns don’t have to. For example, you can keep the post-game connection with your brand alive in the following ways: 

• engaging in game-related conversations relevant to your brand through social media, email, and text
• posting content highlighting how important game moments relate to your products
• creating video spoofs of popular Super Bowl ads using your brand
• sharing memes featuring your products that relive iconic game moments 
• live streaming post-game analysis that ties into your products

Identify the Latest Trends

Behavioral data also has the ability to turn your Super Bowl marketing campaigns into the play that keeps on winning. How? By tracking metrics across each of your key Super Bowl campaign moments, you can see what’s resonating with specific market segments. For example:

• What parts of your campaign did your audience interact most with? 
• Where was your audience when these interactions happened?
• What devices did they use to perform these interactions?
• How long and how often did they interact with your campaigns?

These metrics provide you with a goldmine of new behavioral data that can be used to inform future marketing campaigns, personalize brand communications and interactions, and craft marketing messages that stand out from the rest of your competitors.

Conclusion

By using behavioral data from providers like ShareThis, as a key component of your Super Bowl game day playbook, you’ll have the opportunity to score more than just a few field goals. Your data can help you leverage your marketing campaigns during the biggest marketing event in America—so your message stands out from all the noise and gets in front of the viewers that matter the most to your brand. 

The result? With ShareThis behavioral data, you can tap into that touchdown and go for the two-point conversion for the win.

The post Super Bowl Marketing: Score Big with Behavioral Data appeared first on ShareThis.

DEI stands for “diversity, equity, and inclusion,” and chances are you’ve been hearing the acronym everywhere you turn in the digital marketing world. But despite its ubiquity, DEI is much more than just the latest marketing buzzword: Audiences want to see inclusivity in marketing, and brands aren’t just taking notice, they’re also raising the standards of DEI in marketing with some truly inspiring campaigns. 

Inclusive Marketing: No Longer Taking a Back Seat

Today’s demographics and consumer expectations are fueling companies’ prioritization of inclusive marketing. Consider the following stats:

• Age. Even though research shows that less than 10% of all ads portray individuals aged 65 and above, these older adults represent between 15% to 28% of the population in developed nations.

• Gender identity. The Human Rights Campaign’s recent analysis of data from the 2021 Census Bureau Household Pulse Survey indicates that approximately 20 million people in the US identify as LGBTQIA+ (including more than 2 million adults who identify as transgender). 

• Race and nationality. Representation matters: 71% of people in a recent survey conducted by Meta said they expect brands to promote diversity and inclusion in their online marketing, and 59% said they preferred to buy from brands that meet these expectations.

• Social impact. Whether it’s wage inequality, climate change, or racism, the 2022 Edelman Trust Barometer found that consumers believe businesses have the power to create positive societal change. And brands are taking notice: Within the environmental realm, for example, 378 companies spanning 55 industries and 34 countries around the globe have signed The Climate Pledge to reach net-zero carbon emissions by 2040.

When it comes to DEI, the marketing world has come a long way. Even the audience segments you activate for your campaigns can align with your inclusive campaign initiatives. And while there’s still a long way to go, let’s celebrate the DEI marketing achievements of innovative and progressive brands around the world with this list of 13 top inclusive marketing campaigns in 2022:

1. Google’s “Black-Owned Friday” Campaign

In 2020, Google, the US Black Chambers, Inc., and advertising agency BBH joined forces to launch an initiative to turn Black Friday into Black-Owned Friday. And this year, the initiative lives on with “Buying All Black”, a video celebrating the third year of Black-Owned Friday. And there’s more, including a “Black-Owned” icon available for businesses on Google Search and Maps, additional resources for Black-owned businesses, and the ByBlack national business directory.

2. LEGO’s “A-Z of Awesome”

Inspired by the desire to use play to prompt open conversations about gender identity, sexual orientation, and gender expression, LEGO’s A-Z of Awesome campaign celebrates inclusivity, self-expression, and creativity. The campaign invites members of the LGBTQIA+ community to create the A-Z of Awesome alphabet by using LEGO bricks to design each letter, and to tell the world the stories behind their designs. 

3. L’Oreal’s Transformation Initiative

For trans people, hair can be an incredibly transformative means of expressing their identity. But on the flip side, salons aren’t always a safe space for this crucial component of the transitioning process. In recognition of this, L’Oréal Professional Products, in partnership with the True Self Foundation, established the Transformation initiative, a network of beauty salons in Puerto Rico staffed by hair professionals specially trained to create a safe space that meets the needs of people in transition. 

4. Amazon Alexa’s “What Is Love” Campaign

In this innovative campaign, Amazon partnered with Ad Council to ask people of different backgrounds to share what love means to them. The goal? To inspire people to take action through everyday acts of love and build a more inclusive community around them. Today, when you ask Amazon’s Alexa, “What is love?”, you’ll hear the voice of a real person sharing their experience of how an everyday act of love made them feel included. 

5. The Bombas Pride Collection

Homelessness on its own is difficult enough, but for the 40% of homeless teens in the US who identify as LGBTQIA+, homelessness can also restrict your ability to express your true self through your clothing. The Bombas Pride collection addresses this challenge by donating an item of clothing for every item purchased from the collection, with the goal of providing identity-affirming clothing choices to homeless LGBTQIA+ youth. 

6. Dove’s “As Early as Five”

Did you know that 1 in 5 Black children as young as five have experienced hair discrimination? The impact can have lasting effects, with Black children being forced to conform with school hair policies or risk losing educational opportunities. This year, as part of the CROWN Coalition it co-founded in 2019, Dove released the video “As Early as Five” as part of its ongoing initiative to drive change through the passage of laws nationwide to make hair discrimination illegal. 

7. Virgin Atlantic’s “See The World Differently”

When was the last time you felt able to be unapologetically yourself? With its 2022 “See the World Differently” campaign, Virgin Atlantic decided to kick things up a notch after two challenging pandemic years for the travel industry, by championing the right of everyone—from its crew to its passengers—to be who they are. Or, as Lady Blackbird sings so beautifully in the brand’s colorful video ad, “I am what I am.” 

8. Discover Puerto Rico’s Sustainable and Inclusive Vacations

Discover Puerto Rico’s innovative campaigns speak directly to visitors’ desires and expectations for sustainable and inclusive vacations: Whether you’re concerned about the environment and want to experience the world through sustainable travel, or you want a gay-friendly, diverse, and vibrant vacation, the Island of Enchantment has got you covered. 

9. P&G’s “The Name”

“Belonging starts with a name.” But when people treat the proper pronunciation of your name with indifference or bias, that sense of belonging can be shattered. P&G’s video, “The Name” hopes to prevent this kind of othering that so many Asian Americans and Pacific Islanders face daily in the US. And to help prevent pronunciation errors, the brand also offers an innovative name tool to accompany the campaign, so everyone can celebrate their names.

10. Visit Florida’s “Limitless Florida”

For people with disabilities or special needs, travel can come with a whole set of challenges that our more ableist culture often tends to dismiss. Enter Visit Florida’s inclusive “Limitless Florida” campaign, which provides travelers who have accessibility issues with the resources they need to get the most from their visit to the Sunshine State, including a virtual tour to help you practice your trip. 

11. Shan International Foods’ #DifferentYetTogether

No matter what your background, the food of your childhood and your culture often has the power to soothe you when you most need that extra care. And with its #DifferentYetTogether video campaign, Shan International Foods highlights how food can bring people together to embrace their differences. 

12. Men’s Wearhouse: “This is Our Prom”

For its prom promotion this year, Tailored Brands’ Men’s Wearhouse chose to showcase the diversity of today’s teen world in “This is Our Prom,” with the most diverse cast the brand has ever worked with. As CBO Matt Repicky notes, representation not only matters, it’s also necessary for engendering customer trust. 

13. M&Ms “For All Funkind”: Meet Purple

As part of its ongoing “For All Funkind” campaign, M&Ms introduced Purple, its first new character in a decade. The optimistic, quirky, and authentic new candy star represents acceptance and inclusivity, and each time her song “I’m just gonna be me” is streamed, the brand will donate a dollar to the non-profit organization Sing for Hope.

Bonus: J&B Whiskey’s Holiday 2022

Our special bonus video: Published by J&B in December 2022, this video beautifully and eloquently sums up the powerful impact of inclusive marketing. 

Conclusion

With such innovative inclusive marketing to inspire us in 2022, the new year can only bring on even greater strides in how brands around the world promote DEI and social good through their marketing. And with ShareThis’s Data for Good audience segments that are designed around key social issues, you can leverage our most valuable asset—our data—to align with like-minded audiences to create real change in the world. 

The post 13 Inclusive Marketing Campaigns to Inspire Your 2023 Marketing Strategy appeared first on ShareThis.

Great marketing plans are made better with data. As the annual planning season begins, it is important to include data in the process to set achievable goals, appropriate budgets, and ensure the plans will deliver in execution. 

There are five basic steps that most marketing planning processes include: data collection, setting objectives, defining target customers, determining strategy, and setting a budget. Collecting the right data in the first step will ensure good decision-making throughout this process. This article aims to help marketers working on their plans by explaining what data is needed and how that data can be used during each step of the planning process.

Step 1: Data Collection

During planning, data is needed to answer critical questions on customers and marketing performance like:

• What are the attributes of the ideal customer? Tip: Consider demographic info and other attributes that are true for the most loyal and highest revenue customers that also have the least amount of drain on the business from returns, support calls, etc.

• What channels have been most effective in reaching those ideal customers?

• What valid opportunities are there to reach those ideal customers that haven’t yet been tried?

• What segments could we test out based on similarities with our ideal customers?

• Is there something we don’t know about our ideal customer and is there a way we can learn more? 

Not many companies have the data needed to answer those questions so typically a combination of internal and external data sources are needed. Marketers should aim to collect: 

First-party data:

• Internal data collected on consumers/customers from systems like CRM (customer relationship management) or PoS (point of sale)

• Marketing performance from systems like MA (marketing automation), DSP (demand side platform), CDPs (customer data platforms), and website analytics to name a few.

• Sales and support data on ideal customers based on revenue, support needed, loyalty, etc. that might come from systems that marketing does not have access to. 

External data:

• Market data that may help to illuminate trends happening in a target market or to identify which markets to target. This may come from analysts, research houses, or looking at online behavioral trends.

• Target audience data that can provide information on the interests of buying personas. This could come from research houses or online behavioral data.

• Target audience data on their online behavior which can indicate the best channels to reach them, the best messaging to use, and product expansion opportunities. This comes from online behavioral data.

While there is a wealth of information to be found from internal data sources, this data is typically historical. It doesn’t do much to indicate where the market is going, how your buyers are thinking, and where the opportunities are for growth. Look to external data sources to help fill these critical gaps.

Step 2: Set Objectives and Marketing Measurement

Setting objectives and determining how to measure their achievement is typically a top-down exercise in most marketing organizations. Revenue goals are set and marketing is told to figure out a way to meet them. However, it is always smart for marketers to gain an understanding of the gap in expectations versus last year’s outcomes. 

Marketing needs to look hard at the internal data from marketing efforts over the past year to identify areas for growth or performance improvement. It is also critical for marketing to determine what is realistic for the achievement of goals. If executive leadership is expecting marketing to contribute a certain percentage of revenue, it is critical to raise a red flag if those objectives are not achievable with the resources available.

Assuming objectives have been set that are possible, marketing needs to consider what its key performance indicators (KPIs) are that will be measured throughout the year. Marketers can measure a lot of things, but too much information can be a burden in collection and decision-making. Select a clean north star metric per campaign or channel that is actionable. Meaning, the information can be used to make changes that will impact overall performance. Examples include metrics like: website traffic, conversions, leads, and revenue generated.  

Step 3: Redefine and Define Target Markets and Target Customers

The planning process may require refining the ideal customer, defining the ideal customer for a new market, or finding related market segments to expand from the current customer base. To do this, marketers should use a combination of first-party consumer data and third-party data to get insight into who is engaging with the company’s brands and products. 

This can be further refined by analyzing customer attributes found in internal data like spend, loyalty, and support calls to identify an ideal subset for the target audience of marketing campaigns. Additionally, external data can be used to identify the key attributes of this subset like demographics, behavioral trends, interests, and where they go on the internet. This can help demand generation efforts focus on channels where the target audience spends their time. 

For marketers looking to expand their target markets, the ideal customer data set identified can be compared with larger external data sets to find lookalike markets. This type of big data comparison can also expose marketing opportunities in other segments or via new marketing channels that have yet to be explored.

Step 4: Determine Go-to-Market Strategy

A natural flow from defining target markets is to pick the best channels to reach that audience. Third-party behavioral data can identify where the target audience is engaging the most. This information should be compared to internal data of past performance of marketing channels and tactics to validate channel opportunities.

Once channels and tactics are determined, initial marketing messaging can be created. By analyzing external data, target audience behavior or research can be used to determine the pains and interests of the target audience. With that information, personalized messaging can be developed that will best engage the target audiences.

Step 5: Establish Budget

Reflecting on the outcomes of this process, the marketing budget should be allocated accordingly. Just as it was important to check in on the achievability of revenue objectives, it also may make sense to check in on goals. Make sure that the plan is not over or under projecting accomplishing those objectives based on the targeting and tactics proposed in the go-to-market strategy.

Identifying new market or target audience opportunities may also provide marketing with the leverage to make the case for an expanded budget. If that is the case, consider making it contingent on a pilot or experiment where the additional budget is provided if the experiment is successful.

Use Data to Continually Optimize Execution

Once planning is finished, the need for data isn’t. Put a plan in place to measure performance and revise the strategy throughout the year on a regular cadence using measurements from internal and external sources. 

Of course, markets and buyer behaviors change quickly, particularly in consumer markets, so data needs to also drive marketing execution. Getting a continuous stream of external data that can provide insights into buyer behaviors can help companies spot trends early to take advantage of them. Continuous analysis of behavioral insights from third-party sources can truly make or break the achievement of annual objectives.

Looking for behavioral data to better advise your marketing strategy? Look into ShareThis’ data solutions today.

The post Want a Data-Driven Marketing Strategy? Follow These 5 Steps. appeared first on ShareThis.

By Stanley Lin, Director, Marketing, ShareThis and Brett Sanderson, Sr. Director, Product Marketing, Cadent

Travel has felt uncertain since the onset of the COVID-19 pandemic in early 2020. Through 2022, ever-changing travel restrictions, the emergence of several COVID variants, and overwhelmed airports have threatened to ruin countless trips for travelers in the U.S. and abroad. As a result, years of pent-up demand are driving a spike in national and international “spring break” travel with consumers booking flights, lodgings, and tourist activities at a record-breaking pace. In fact, according to Virtuoso, a global luxury travel network, travel sales in 2023 are pacing 47 percent higher than 2019.

As we approach the spring travel season, ShareThis partnered with Cadent to round up the best data-driven marketing strategies for travel and tourism advertisers. 

Preparing for 2023 Travel and Beyond

Audience fragmentation continues to pose a challenge for advertisers. Consumers are increasingly shifting their attention to streaming CTV and OTT apps, while still watching linear programming. As some consumers “cut the cord,” cable and broadcast remain more than half of the Total Usage of Television according to Nielsen’s The Gauge. However, in 2022, a whopping 92% of U.S. households were reachable by CTV programmatic advertising, while the number of CTV users amounted to more than 110 million among Gen Z and Millennials. 

To reach their strategic target audience, advertisers need to activate their brand message across all TV media, including cable, broadcast, indexed TV, addressable STB, and OTT/CTV including FASTs. By engaging the right consumers at the right time, advertisers can drive key performance metrics such as brand awareness and sales lift and quantify their return on investment in terms of business outcomes. 

Cadent Aperture Platform was purpose-built to help advertisers seeking an end-to-end, cross-screen solution by enabling seamless audience activation across all TV media types and providing a holistic view of reporting and measurement. Aperture allows advertisers to securely onboard their first-party data then enrich that audience with trusted third-party data. By leveraging strategic audience segments from third-party data partners like ShareThis, travel advertisers can boost the performance of their CTV campaigns. 

Attract New Travelers with Behavioral Audiences on CTV

The best way to introduce new brands to travel consumers at scale is by creating brand awareness campaigns distributed on CTV. But to do this well, advertisers need to use all the information they can get about their target audience, such as: what they are interested in, how to position an offer, and the best time to make a connection across CTV channels. Unfortunately, most advertisers don’t have data on the behaviors and interests of their target audience.  Luckily, partnering with the right data provider is the ticket to success this upcoming travel season.

ShareThis gathers real-time interest and intent data from travelers’ online activity as they search for vacation ideas and begin to plan their trips. This is the best data to tie into CTV so that campaigns can hit their mark. For example, ShareThis has a spring break travel insights report covering January – March 2022 that revealed that U.S. Spring Breakers in 2022 were:

• Focused predominantly on US trips
• Searching for cost-effective travel options
• Looking to do community service on their trip

A recent study revealed that 93% of those surveyed will take at least one leisure trip over the next 12 months — the highest percentage since the start of the pandemic. But when is the best time to engage consumers with travel offers? An interesting finding from ShareThis data over the past few years is that the highest week for online traffic for people researching Spring Break online is consistently the third week of October, which falls around midterm season for colleges. This could indicate an optimal opportunity for brands to get in front of college students during peak testing periods.

Beyond trends, online behavioral data can be matched up with target audiences to get very specific on the best CTV channels to reach them and the best messaging to engage travelers. Third-party insights are critical to fine-tuning messaging, special offers, and target audiences to engage new travelers and maximize return on advertising. And seeking out this type of data can make a big difference in the success of advertising campaigns this season.

Next Steps for Travel & Tourism Advertisers 

With the rapidly changing economy and our rapidly changing thinking around travel, what will travelers be looking for this year? It is critical to utilize third-party insights to be able to shape the targets and messaging of advertising efforts. This is especially true where video advertising for CTV is involved.

Ready to activate data-driven TV for your spring travel campaigns?

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