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Guarantee trust by handing privacy power back to the consumer

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Today’s data collection and user privacy landscape is a minefield. But it also provides important business advantages for brands and companies to address privacy concerns head-on and establish a foundation of trust with their loyal customer base.

Understanding how customers perceive your company’s data practices in this complicated landscape will help you develop the transparent strategies to build the trust you need moving forward.

A new paradigm for privacy in today’s world

Gone are the days of total privacy when it comes to personal data. Every time you walk out the door, you encounter situations where you’re willingly giving personal information away — when you buy a coffee on the way to work, as you pump gas, or listen to the new song you downloaded onto your Spotify playlist.

These seemingly innocuous actions all pile up together and morph into a personalized, digital persona that helps inform how brands and companies speak and interact with each and every consumer. The majority of people may hate this, but let’s be clear: Everyone signed up for it. Time and time again.

How often have you nonchalantly scrolled through a Terms of Service page only to click “accept” without reading anything at all? How many times have you searched for something on Google, whether to find the nearest grocery store or to look up the cheapest flights for an upcoming vacation? As a consumer society, we’ve granted companies the power to take first-party data, and zero-party data, and all kinds of information whenever they so choose, just by living our day-to-day lives.

The privacy paradox

By now, you’d assume people would be comfortable with giving up their personal data in exchange for the assurance of daily conveniences. However, a recent report shows that only 40% of users trust brands to use their personal data responsibly.

This juxtaposition — when consumers continue to give their personal data away but still don’t trust brands to act responsibly with that information — is called the “privacy paradox.” It sums up most touchpoints we have when interacting with brands and companies.

First used as a term in 2001, the privacy paradox is a dichotomy in how a person intends to protect their online privacy versus how they actually behave online, ultimately not protecting their information. This is usually because of an unwillingness to break convenient habits or behaviors. For instance, taking the required time to read the Terms of Service before downloading an app or signing up for a user platform.

While data privacy has always been a top consumer concern, in recent years it’s become an increasingly high priority with major tech companies like Apple, Google, and others receiving intense pressure to heighten security regulations for personal data use. This has led to recent privacy updates that allow users to opt out of tracking and limit the amount of information brands and companies can now obtain. 

According to a McKinsey survey, one in 10 internet users worldwide (and three in 10 U.S. users) deploys ad-blocking software to prevent companies from tracking online activity. 87% of consumers say they would not do business with a company if they had concerns about its security practices, and 71% said they would stop doing business with a company if it gave away sensitive data without permission.

Consumers are increasingly buying products and services only from brands and companies they trust and believe are both protecting their personal data while also using it to connect with them through hyper personalized and engaging touchpoints.

Read next: Build trust, gain sales

Lead with first-party data transparency

First-party data is perhaps the most simple and ultimately effective personal data to collect from users. This data is information companies collect from consumers through owned digital channels. Examples of first-party data include survey data, purchase history, website activity, email engagement, sales interactions, support calls, customer feedback programs, interests, and general behavior in owned digital channels.

What makes first-party data such an opportune marketing tool is that consumers have willingly given brands this personal data. This makes it reliable and future-proof as long as people have consented for their data to be used by marketers to make contact and engage.

With this information captured, it’s imperative for brands and companies to ensure points of contact with users are effective, personalized, and clearly define how and when their data will be used for their personal benefit. Effective methods include an email or text communication that clearly state a person’s information will be kept private and not sold to a third-party; or that a user can easily choose to opt out of communications with a simple click or two.

Consumers respect brands and companies that emphasize an individual’s right to opt out of sharing data, so offering an easy-to-use consumer data opt-out feature, and being very clear about your intended use of their data, is key to establishing a baseline foundation of trust for future engagement.

Give power back to the consumer

According to the Cisco 2021 Consumer Privacy Survey (2,600 anonymous responses across 12 countries), nearly half of respondents feel they are unable to protect their personal data. They cited that the main reason is that companies aren’t being clear about how they are using peoples’ personal data. As a result, one-third have become “Privacy Actives” and stopped interacting or doing business with traditional companies like retail stores, banks and credit card companies. In addition, 25% have made inquiries to organizations about their data and 17% have requested changes or deletions to this data.

The customer should always come before the data — no exceptions. Smart brands and companies will view privacy and respect for customer data as a potential differentiator rather than a barrier to entry. In addition to the tactics described above to help brands and companies be more candid and open with users, there are also important tactics that are easily implemented to continue building trust and to empower consumers to have a say in the conversation about their own data privacy.

First, every company should proactively send out quarterly or annual user data privacy reports that specifically outline how customer data is being used, as well as the safeguards being implemented to protect that data from potential data leaks or hacks. The company should also provide additional levels of assurance of how information is being used to line up with ever-evolving customer comfort levels.

Additionally, the way in which you provide this type of informative, personalized information is key to whether a consumer will choose to react positively or negatively. A post from Statista shows that 97% of people between 18 and 34 accept conditions without reading them. Additionally, the time needed to read through terms of service agreements for today’s leading online services and platforms can be more than an hour. While consumers should be reading the fine print, it’s clear the majority fail to do so — but still expect brands and companies to offer up ultimate transparency when it comes to their personal data use.

How to communicate privacy information

A solution to help bridge this gap and continue to build trust, instead of lengthy emails and updates, is to deliver this information in more visual forms like an infographic, chart, or video message. Personalized touchpoints with consented consumers could include:

  • Surveys.
  • Quizzes.
  • Personalized emails addressing a customer’s specific needs.
  • Rewards or promos designed specifically for each user based on their personal behaviors.

These are just some of the strategies every company — big or small — should be considering to maintain long-term consumer trust and mutual openness.

The data privacy conversation is sticky and comes with many opportunities for brands and companies to mess up and lose consumer trust. With so many opportunities for failure, it’s imperative for brands and companies to be strategically thinking about the most effective ways they can use consumer first-party data to immediately establish trust, consistently work to maintain consumer relationships, and provide the level of user data and privacy transparency that is ultimately expected in today’s evolving digital age. 


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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Josh has been at the forefront of innovative and inclusive data-led marketing for more than 26 years. During this time, he’s had the privilege of building an independent, data-led CRM agency that works with the world’s leading CPG, Financial, Travel, and B2B brands, companies, and organizations to achieve optimal growth between brands and their customers. An architect of Response Media’s Relationship Marketing System™, Josh leads the agency in facilitating true connections between brands and consumers, using digital marketing to deliver relevancy, meaning, trust, and mutual value at scale.
He has pioneered best-in-class consumer acquisition, brand partnerships, and relationship marketing platforms for some of the world’s largest advertisers and brands, including Procter & Gamble, American Red Cross, The Humane Society, ConAgra Foods, IBM, Disney, and Capital One.



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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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