Author: Franz Malten Buemann

Video ad exchanges are online marketplaces where publishers and advertisers can buy and sell video ad inventory in real-time. Video ad exchanges operate similarly to traditional ad exchanges, but they specialize in video ads, which can include pre-roll, mid-roll, or post-roll ads, as well as in-stream and out-stream ads. Video ad exchanges offer a range of targeting options, including demographic, geographic, and interest-based targeting, allowing advertisers to reach specific audiences with their video ads. They also use real-time bidding (RTB) algorithms to determine the winning bid for an ad impression based on factors such as ad relevance, quality, and bid price. Publishers can use video ad exchanges to monetize their video content by offering ad inventory to potential advertisers. Advertisers, in turn, can use video ad exchanges to reach their target audience with video ads and improve their brand awareness, engagement, and conversions. Some popular video ad exchanges include Google Ad Manager, SpotX, and Tremor Video. Video ad exchanges have become increasingly important in recent years as more and more consumers consume video content online, creating new opportunities for advertisers to reach their target audience with video ads. Everything Publishers Need to Know about Video Ad Exchanges Publishers should consider the following when using video ad exchanges: Video content quality Publishers need to ensure that their video content is high-quality and engaging to attract advertisers to their ad inventory. Advertisers are more likely to bid on ad inventory that appears alongside high-quality video content that aligns with their brand image and values. Ad inventory management Publishers should manage their ad inventory carefully to maximize their revenue potential. This includes setting pricing floors and ceilings, managing ad formats, and optimizing ad placement to ensure that ads are displayed in the most effective locations. Ad formats Publishers should be aware of the different ad formats that are available on video ad exchanges, such as pre-roll, mid-roll, and post-roll ads, and determine which formats work best for their content and audience. Ad targeting Publishers should understand the targeting options available on video ad exchanges and use them to their advantage. This includes demographic, geographic, and interest-based targeting, which can help to ensure that ads are relevant to the viewer and improve ad performance. Ad verification and fraud prevention Publishers should work with their video ad exchange partners to ensure that ads are served to real viewers and that they are not charged for fraudulent impressions or clicks. This includes using ad verification tools and fraud prevention techniques to monitor ad traffic and detect any suspicious activity. Publishers can benefit from using video ad exchanges to monetize their video content and reach a wider audience with video ads. By understanding the different ad formats and targeting options available, managing their ad inventory effectively, and using ad verification and fraud prevention tools, publishers can maximize their revenue potential and improve their ad performance on video ad exchanges. If you are interested in building an ad server for your business, dJAX Adserver can provide you with a comprehensive and customizable ad serving solution. With our ad server technology, you can manage and monetize your ad inventory, optimize ad campaigns, and increase your revenue potential. To get started with building an ad server for your business, you can visit the dJAX Adserver website at https://www.djaxadserver.com From there, you can learn more about our ad server technology and explore our range of customizable solutions to find the one that best fits your needs and budget. To get in touch with our team and discuss your ad server requirements in more detail, you can fill out the contact form on our website or email us at [info@djaxserver.com](mailto:info@djaxserver.com) Our team of ad tech experts will be happy to help you build an ad server that meets your unique business needs and helps you achieve your goals. submitted by /u/Djax-adserver [link] [comments]

We’re big fans of creators here at Buffer. We build products specifically for creators, and many of us working here at Buffer are creators. So we were beyond excited to see Passionfroot (another creator-focused company) using Buffer. Passionfroot simplifies the lives of creators by providing a one-stop-shop for managing sponsorships, collaborations, bookings, and payments. It’s essentially  a storefront for fielding requests and managing back-office operations. But Passionfroot is more than just a tool; it’s a diverse community, with a team of second-generation immigrant founders and half of its angel investors from underrepresented minority groups.The Passionfroot teamAkta, the creator at Passionfroot, is a content creator, brand manager, and community builder, who hosts the Creators on Air podcast and writes the Frootful Creator newsletter. With Buffer, she’s been able to streamline her content scheduling process and grow her following on Instagram and LinkedIn. In this blog post, we’ll take you through Akta’s journey with Buffer. She shares how it has helped not only manage but expand her online presence.Setting Priorities and Facing ChallengesAs a content creator, Akta’s primary challenge is staying consistent across all platforms while delivering valuable content that caters to various creator types.Atka started her career as a dentist before becoming a creatorAkta wanted to set a publishing schedule for her podcast episodes and newsletters to prioritize her work effectively—but first, Akta needed to find a tool to help. When Akta began searching for a social media scheduling tool, she compared various options. Akta acknowledged that there’s a lot of different tools to pick from and finding the right one isn’t easy. She appreciated how Buffer offered a powerful yet free version of the tool to help creators like her figure out if it is right without breaking the bank. After reviewing the tools, Akta ultimately chose Buffer for its value. With it’s powerful free plan and reasonably priced paid plans.“I researched different tools for social media scheduling and Buffer offered the most for its value.”Integrating Buffer into Her WorkflowShe needed a tool that allowed her to batch-create content and schedule it over time, saving her valuable time. Buffer was there to help, however, Akta needed to tweak her workflow first. Akta has integrated Buffer into her workflow to manage her content on LinkedIn and Instagram. The process starts by uploading visuals and writing captions for each post in Buffer. She then chooses the most appropriate date and time to schedule the content, ensuring consistency and avoiding overwhelming her audience.“I try to make sure content is consistently being scheduled and spread out so it’s not overwhelming for audience but we’re still consistently getting a message across.”Using Buffer in Tandem with Other ToolsBuffer is a great social media management tool, but it’s not the only tool available to create and manage content. Akta leverages other tools such as Notion, Beehiiv, Final Cut Pro, and Riverside to create a well-rounded content creation and management workflow.For instance, Akta uses Riverside for recording podcasts, which ensures that the audio quality is always top-notch. Final Cut Pro is her preferred tool for editing podcasts to ensure that they are polished and professional. Beehiiv is the tool she uses to send newsletters to her subscribers, while Notion helps her write articles and create a content calendar to ensure that her content is well-planned and organized.By exploring and utilizing other tools that complement Buffer’s strengths, Akta creates engaging and high-quality content that builds your brand online.Achievements with BufferSince implementing Buffer, Akta has observed significant growth on Instagram and LinkedIn. Consistent content scheduling has helped Passionfroot establish a stronger presence and extend its reach. Additionally, Buffer has reduced the stress of manual posting for Akta, allowing her to focus on other aspects of her work.“We’ve been more consistent on Instagram and Linkedin which has helped us to grow our platforms. It’s also taken a lot of stress off my shoulders for posting.”Akta’s story with Buffer illustrates how a powerful social media scheduling tool can help content creators and brand managers grow their following, create better content, and save time, all without breaking the bank.Thanks to Buffer’s scheduling capabilities and other features, Akta has been able to maintain a consistent social media presence, which has been crucial to Passionfroot’s growth and success. If you’re looking for an affordable and efficient way to manage your content, Buffer might be for you. So, why wait? Get started today →

Do you ever ask yourself why you act the way you do? Like why you venture out during a snowstorm to get an iced coffee? 
For marketers, behavioral insights are not only interesting but key for understanding their audience, going beyond demographic and geographic data.
In order to understand your audience’s behaviors, marketers can implement behavioral segmentation, an analysis that groups prospects and customers into different segments based on how they behave. 
Behavioral segmentation is a part of behavioral marketing, a set of methods to collect and analyze consumer behavior data to segment and target audiences with laser-like precision.
In this post, we will go over:

What is behavioral segmentation
Why marketers need behavioral segmentation
Four types of behavioral segmentation
Behavioral segmentation examples

With behavioral segmentation, marketers can gain a more comprehensive understanding of their audience’s motivations and needs, resulting in better tailoring of products and services to their customers.
Behavioral segmentation is interesting because it intersects with psychology — when we think about how someone behaves and why, we naturally think about their psychological motivations. 
However, behavioral segmentation is distinct from psychographic segmentation. 
Psychographic segmentation is rooted in lifestyles, interests, values, and even personality traits. Behavioral segmentation is about how a customer interacts with a brand and their products or services. 
For example, marketers can segment people based on website behaviors like clicks, page views, social shares, and media plays. 

Why Marketers Need Behavioral Segmentation
Marketers need behavioral segmentation for learning about how prospects and customers are likely to use the business’ products and services and their level of engagement. Behavioral segmentation also fosters cross-functional collaboration between the marketing and product teams, helping them align messaging.
Identify Engaged Users
Behavioral segmentation helps marketers identify the most engaged users. It would be a waste of marketing budget and time to try to engage cold leads. By analyzing behavior like opens and clicks, marketers can cut through the noise and allocate efforts to engaging users who are already interested and likely to convert.
Personalize User Experiences With Your Brand

HubSpot’s Behavioral Targeting tool helps you personalize messages.
Generic messages will not get the goods. Providing personalized experiences will go a long way for your brand. Behavioral segmentation helps you learn about your audience’s needs, enabling you to customize your messaging so that you’re making relevant offers. Tools like HubSpot’s Behavioral Targeting tool can help by enabling marketers to personalize marketing outreach at scale.
Retain Customers
You’ve done all the hard work converting leads into customers. Now how do you retain them? 
Behavioral segmentation helps businesses retain customers because marketers know how to personalize the experience to different users. Tailoring experiences so that users feel like their needs are being prioritized increases customer loyalty.

Four Types of Behavioral Segmentation
1. Purchasing Behavior
Look at a customer’s purchase behavior and transaction history. This provides insight into how and why they decide to convert as well as which stages of the buyer’s journey go smoothly whereas where a prospect may come to a bit of a roadblock along the way. It also gives you an idea of which behaviors are likely to accurately predict a conversion.
2. Benefits Sought
Identify what your customers are looking to get out of your product or service — of all of your features, which do they need most to resolve the challenge(s) that they’re experiencing? What specific benefits do they get out of your product and which of those benefits are most important to them? Determine which of those benefits are influencing their decision to use your product/service most.
3. Buyer Journey Stage
Understanding which stage of the customer journey leads to the most conversions or which stage prospects get hung up on most frequently is beneficial information when predicting behavior and segmenting customers based on those behaviors. However, it’s important to note that using customer journey stages in behavioral segmentation can be difficult because there are so many stages — and within those stages are multiple touch points that contribute to behavior or decision to remain in a stage or move forward to a new stage, of the buyer’s journey. That’s why it’s recommended to use a platform like your CRM or an AI/machine learning tool — they record and track all interactions throughout the buyer’s journey to ensure you’re getting a complete view of your customer’s buyer’s journey behavioral data.
4. Usage
Usage-based segmentation enables you to segment your customers based on how they actually use the product or service, how frequently they use it, how long they use it in a single session, or which features they use most. 
For example, if you sell software, you might choose to segment your customers further into more specific usage categories — for instance, heavy users versus average users versus light users. Then, heavy-user messaging could highlight advanced features and upselling campaigns, while low-usage messaging could encourage more usage by discussing the key program features or how to use them.

Behavioral Segmentation Examples
1. Occasion

In 2020, Taco Bell launched a taco e-gifting service in time for the holidays. (Image Source)
Was it a specific occasion that influenced your customer’s decision to convert? Is a purchase decision based on the time of day or even life stage
Occasion-based segmentation can help cater your messaging to a specific point in time during the year when certain audiences interact with your brand. 
For example, if you’re an online greeting card service, you may get repeat customers around the winter holiday season every year. For those customers, your Q4 messaging could amp up holiday deals and offers.
2. Customer Loyalty

Starbucks Rewards offers customers perks. (Image Source)
Customer loyalty provides a solid look at customer behavior — loyalty relates directly to a customer’s habits, actions, needs, usage, and the timing of their actions.
To use customer loyalty when segmenting customers based on behavior, think about:

Which parts of the buyer’s journey are so delightful that they result in loyalty?
How you currently keep loyal customers feeling delighted.
Which prospects are most likely to become loyal?
Which attributes do your loyal customers share?

3. Engagement

Sephora offers unique benefits for highly engaged customers. (Image Source)
Engagement refers to the type and frequency of interactions you see from certain customers. 
These interactions can include site views, clicks, and social media activity. You might segment your customers based on high engagement versus average engagement versus occasional engagement. 
Highly engaged individuals are those who’ve incorporated your brand into their lives regularly. Average users may engage with your brand or product/ service fairly regularly but may not take advantage of its full potential and capabilities. 
Occasional users may just engage with your brand or product/service randomly based on their specific need but don’t rely on it. For example, Sephora offers a community for BeautyINSIDER members.
Grow Better With Behavioral Segmentation
Behavioral segmentation is critical to your business’ success in identifying and understanding target audiences, and tailoring your marketing strategy accordingly. It helps drive leads, convert them into customers, and increase brand loyalty. 
Be sure to keep behavioral segmentation variables in mind and incorporate a tool like HubSpot for further support throughout the process.

Artificial intelligence technology has come a long way since the days of IBM’s Deep Blue, a computer designed to play chess against humans. Nowadays, AI software can improve existing workflows, predict customer behavior, and do much more.

AI is rapidly shaping the marketing landscape. Your team will need to adapt its tech stack to keep up with the competition.
Let’s look at what AI is and how you can use this technology to save time, improve the quality of your leads and, ultimately, make better sales.
Table of Contents

What is AI?
The Benefits of AI
How does AI work?
The Four Concepts of AI
How to Create Basic AI
AI Use Cases for Marketers

AI can replicate human discernment and make real-time decisions. In other words, artificial intelligence is programmed to think, act, and respond just like a real, live human.
AI is not to be confused with automation. Although both automation and AI use real-time data to perform a function, the mechanics and output are vastly different.
For example, automation requires manual data input to perform a certain task. Using an algorithm, that task will repeat, regardless of what the data says or if there’s an error.
AI, on the other hand, is machine learning. Meaning it requires an input of data. As it processes the data, AI can recognize behavior patterns and errors, then adjust its functions and algorithms as needed.
AI is growing in popularity and can be used across a variety of industries. Let’s take a look at the benefits of using it.

The Benefits of AI

Although AI is not exactly fool-proof, it is pretty close to it. There are many benefits to using AI in your workflow and processes. Here are just a few examples of its benefits.
1. It reduces human error.
Let’s face it. Sometimes people make mistakes. We are only humans, after all. The thing about making a mistake is that we can usually learn from it, process what we have learned, and attempt not to make the same mistake again.
Artificial intelligence operates in the same way. While AI acts and performs like a human, it can vastly reduce human error by helping us understand all possible outcomes and choosing the most appropriate one.
AI uses real-time data to predict alternative outcomes. Using data and predictions, we can better understand our options, the results, and the impacts of those outcomes.
This is particularly helpful in business. Decision-makers can consider all possibilities before moving forward.
2. It helps with research and data analysis.
Another benefit of AI is using technology for research and data analysis. AI technologies are smart and can gather necessary information and make predictions in minutes.
What would usually take a human months of research can now be done in significantly less time.
The data collected by AI and the analysis performed are invaluable. With the information collected by AI, your data analysts are better able to make smarter, more informed decisions in less time.
Use the data collected by AI alongside your data analysts’ work.
3. It can makes unbiased, smart decisions.
With the appropriate data, AI removes bias from decision-making. To get the best, unbiased results using AI technologies, you need to ensure you input the most accurate information and data set.
When AI is given the best data, it can accurately predict outcomes, solve problems, and properly perform its functions without human favor of a particular desired result.
However, if the data you feed your AI programs is flawed, you will likely have a biased outcome.
Be sure to check your data for accuracy to maximize this benefit of AI.
4. It performs repetitive tasks.
Although automation and AI are not the same technologies, AI can act like an advanced version of automation, meaning it can be used to perform repetitive tasks and suggest alternative outcomes.
Using AI to perform repetitive tasks gives your employees more time to work on other more complex matters, like closing a sale or checking in with current clients on your roster to retain customers.
AI can be used to perform a multitude of repetitive tasks. AI can perform tasks in HR, like employee onboarding.
AI can also integrate with a chatbot into your website. Although a chatbot might not provide a human touch when interacting with potential customers, using AI to automate interactions between your company and your clients can jump-start processes and move your clients through your pipeline.
For example, AI can help a would-be customer start a new inquiry and gather important customer information and behavior data. Then, that data can be entered into your CRM for later review.

How does AI work?
AI technology is a complex and extremely useful for businesses. HubSpot has incorporated AI right into its software to augment already existing workflows.
HubSpot’s AI can uncover team performance by monitoring sales calls and providing insight to the team. It can also optimize content or create transcripts of recordings and calls.
If AI is a complex but necessary technology, how does it work?
To put it simply, AI works by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set.
It’s important to understand that AI is not just one algorithm. Instead, it is an entire machine learning system that can solve problems and suggest outcomes.
Let’s look at how AI works step-by-step.
Input
The first step of AI is input. In this step, an engineer must collect the data needed for AI to perform properly.
Data does not necessarily have to be a text input; it can also be images or speech. However, it’s important to ensure the algorithms can read inputted data.
It’s also necessary to clearly define the context of the data and the desired outcomes in this step.
Processing
The processing step is when AI takes the data and decides what to do with it. While processing, AI interprets the pre-programmed data and uses the behaviors it has learned to recognize the same or similar behavior patterns in real-time data, depending upon the particular AI technology.
Data Outcomes
After the AI technology has processed the data, it predicts the outcomes. This step determines if the data and its given predictions are a failure or a success.
Adjustments
If the data set produces a failure, AI technology can learn from the mistake and repeat the process differently. The algorithms’ rules may need to be adjusted or changed to fit the data set.
Outcomes may also shift during the adjustment phase to reflect a more desired or appropriate outcome.
Assessments
Once AI has finished its assigned task, the last step is assessment. The assessment phase allows the technology to analyze the data and make inferences and predictions. It can also provide necessary, helpful feedback before running the algorithms again.
AI is extremely beneficial in business. However, choosing the right AI technology for your business needs is important.

The Four Concepts of AI

Image Source
As previously mentioned, not every type of AI will be appropriate for your business, your processes, or your data set. In fact, there are four main concepts of AI that you should consider.
1. Reactive Machine
Reactive machines live up to their concept name. This type of AI can respond or react to real-time data. However, this AI is limited and can’t store information or build a memory bank.
Because it can’t store memories, the AI can’t use past experience to analyze data based on new data behavior.
Reactive machine technologies are best used for repetitive tasks designed for simple outcomes. Consider using reactive machines to organize new client information or filter spam from your inbox.
2. Limited Memory
Unlike reactive machines, limited memory technologies can store and use information to learn new tasks. A limited memory machine will need pre-programmed data to be set in motion.
Once it has processed that information, it can analyze real-time data to make predictions and observations.
Limited memory technology is the most common AI technology used in business. In fact, this is the technology that makes self-driving cars work.
A chatbot is an example of limited memory technology. Chatbots use pre-programmed data to interact with customers and predict their needs based on their actions and inquiries.
3. Theory of Mind
Theory of mind technology is more advanced than limited memory. Like limited memory, theory of mind technology can store information and make observations based on the real-time data it observes.
This technology is more advanced, though, meaning it can respond to human emotions.
Theory of mind technology must be designed to understand that humans are complex, with individual thought patterns and past experiences that affect how they respond to certain stimuli. Because of this, theory of mind technologies are not yet fully developed.
As it stands now, AI cannot fully respond to people in a human-like manner.
4. Self-Aware
Self-aware technology takes the theory of mind technology one step further. It can process information, store it, use it to inform decision-making processes, understand human emotions and feelings, and is also self-aware on a human level.
In other words, self-aware machines operate like human consciousness and can have their own thoughts and feelings.
Self-aware technology is still a very long way off from being fully developed. But, scientists and researchers are making small strides in understanding how to implement human emotions into AI technology.

How to Create Basic AI

AI does not have to be overly complicated in order for you to benefit. You can use AI to perform repetitive functions that drain your employees of their valuable time — time that could be spent strengthening client relationships or making a sale.
To use AI, consider the processes and workflows you can remove from your employees’ plates. Specifically, think about processes you can automate and will not have to tweak as AI does its job.
Let’s look at the basics of implementing AI in your workflow.
1. Define the problem.
Before you decide to incorporate AI into your workflow, consider the processes your teams use daily that are time-consuming and repetitive.
Does your team spend significant time sorting through data to find contact information for potential clients? Could they use their time better by speaking to potential clients and onboarding new customers?
Take some time to identify time-consuming workflows and make a list. From this list, pick a process that is straightforward and repetitive.
2. Define the outcomes.
AI should enhance your already established processes. After you have made a list of processes and workflows that can benefit most from AI, define the desired outcomes.
For example, AI can gather and sort customer data. But before AI can sort through your potential customer base, you need to tell it what to look for and how to sort the information.
Be sure to clearly define the outcomes of your AI processes. AI works best if you have an end goal in mind.
3. Organize the data set.
Having an extensive, organized data set to input into AI technologies is critical. If you do not already keep your data in a centralized location, it’s best that you do that before implementing AI. You don’t want your program to miss an essential data set because it was housed in a different system.
Use a CRM, like HubSpot’s, to organize your data. You’ll need clean data that the algorithm can read. That way, AI technology can understand the data set and recognize its patterns and behaviors.
4. Pick the right technology.
There are hundreds of AI algorithms to choose from, each performing a task with varying efficiency and quality. It’s important to understand that not every algorithm will suit your data set, problem, or desired outcome.
Spend time researching the best AI technology and choosing the one that best fits your needs. Once you have selected an AI technology, run the data to create a model.
5. Test, simulate, and solve.
Now that you have the appropriate technology and a model of what the data should do, rerun the data to test it. This will allow you to determine any kinks that need to be worked out. Once you’re ready to deploy AI, embed it into your workflows, and let it do its thing!
Now you and your employees have more time for more pressing and valuable matters.

AI Use Cases for Marketers
AI technologies can significantly enhance marketing teams’ performance in various ways.
We already know AI can be used for the chatbots on your customer-facing websites. But there are many other ways to incorporate AI into your marketing game. Here’s how.
Sales Forecasting
Sales forecasting is like looking into a crystal ball. Only this crystal ball predicts the future margins of sales for your company.
Analysts must collect necessary data from various sources to make an appropriate forecast. Then, they’ll sort through the data and customer behaviors, compare it to historical data, and predict future sales.
Data analysts often use automated algorithms to help them sort through historical data and keep track of important new information. This process can take quite a while.
But the good news is it can be sped up significantly with the help of AI technology. AI can store data collected from chatbots, analyze which customers are most likely to make a sale, compare real-time data with historical data, and make predictions and assumptions about future sales.
AI uses predictive analytics and can predict forecasts that are up to 80% accurate.
Targeted Advertisements and Content Personalization
Targeted advertising and content personalization is Marketing 101. Every good marketer knows that to make the most sales, it’s necessary to put your brand in front of the eyes of the appropriate audience. AI technologies take targeted advertisements one step further.
You already know your target audience, but do you know exactly what they do after seeing your company’s ad? The reality is you might have a good indicator of customer behavior, but sometimes you may miss the mark. AI can help you make a better inference.
AI can use predictive analytics to determine customer behavior and potential customers’ actions after seeing your ad. The massive amount of advertising information and customer behavior data gathered by AI can also display the next appropriate ad to your customers.
Lead Generation
In the past, a marketer would need to run several advertisements, collect potential customer data, create a customer profile, establish a contact list, and begin contacting would-be clients. This process would likely take days to complete, cutting into sales time.
AI drastically reduces the time marketing and sales teams spend on lead generation. AI can gather customer data, create customer profiles, and generate a contact list of potential customers most likely to make a purchase.
With the time saved, salespeople can better use their time by contacting qualified leads, establishing relationships with new clients, and making the all-important sale.
Dynamic Pricing
AI isn’t just about saving time for your employees. AI can help maximize profits and margins by enabling dynamic pricing. Dynamic pricing is a marketing strategy many businesses use to adjust the prices of their products based on the current supply and demand.
AI technologies use dynamic pricing models to help predict customer behavior, supply, and demand to alert salespeople when to increase or decrease the price of a product or service.
Enhance your business with AI.
While AI can be a complicated technology, using it in your business doesn’t have to be. Artificial intelligence technologies can significantly improve your workflows by saving valuable time and making more accurate predictions.
Brainstorm with your team to list potential processes to automate with AI software. Then, find the appropriate AI technology that will work best for you and your employees. Start improving your business through AI today.

Welcome to HubSpot Marketing News! Tap in for campaign deep dives, the latest marketing industry news, and tried-and-true insights from HubSpot’s media team.
Newer isn’t always better. At least that was the consensus among New Yorkers after the Partnership for New York rolled out its new logo for New York City last month.
The “We ❤️ NYC” mark debuted in late March and was intended to be a modern update of Milton Glaser’s iconic “I ❤️ NY” logo. The imagery coincides with a new campaign aiming to diffuse the “divisiveness and negativity” stemming from the COVID-19 pandemic.
Image Source
Notable changes to the logo include:

Changing the “I” to “We”
Updating the heart so it appears more like a heart emoji
Replacing the typewriter-style font with a variation of Helvetica to match New York subway signage
Adding the “C” at the end of “NY” so the logo refers specifically to New York City

While the new logo was intended to bring people together, unfortunately, it has succeeded in helping people bond over how much they dislike it. This tweet asking folks to share what they think of the new logo has racked up over 2,200 responses that are overwhelmingly negative.
What went wrong with NYC’s new logo?
For starters, “I ❤️ NY” is a tough act to follow.
The original logo designed by Glaser was introduced in 1977 to reinvigorate tourism and morale in New York after a long economic and social slump. Over four decades, it became a beloved image and catchphrase for both the city and state of New York.

I think the city that currently owns the most iconic branding in the entire world should not rebrand.— Allana Harkin (@AllanaHarkin)
March 20, 2023

Much of the criticism of the new logo is directed at the design itself. Many people have questioned the lack of symmetry (We NYC ❤️?), the emoji-esque heart, and the font choice (Helvetica is very widely used).
These elements make the design look unprofessional and unoriginal which feels off-brand for a city known for being a hub of creativity and rich culture, ultimately causing the attempted rebrand to fall flat.
Elsewhere in Marketing
The latest marketing news and strategy insights.
Deepfakes: The use of AI is causing a rise in realistic-looking fake images. Learn what that means for marketers.
TikTok Ban: Pew Research conducted a study to see how Americans felt about the possibility of a TikTok ban and the results may (or may not) surprise you.
YouTube reports that fan-created Shorts can help some creators and artists double their audiences.
Reel-y? How photos are making a comeback on Instagram.
ChatGPT may be banned in Italy due to privacy and safety concerns.
Twitter continues to face roadblocks in recovering advertising revenue since its sale last year.
Biggest consumer behavior shifts: how consumer habits are changing in 2023.