MARKETING
Announcing: The Impact of Local Business Reviews on Consumer Behavior | SEO Industry Report
The author’s views are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz.
A warm welcome to Moz’s first large-scale survey on the habits of local business review readers, review writers and successful owner responses. Our survey uncovered interesting insights and actions local businesses can take to better serve their customers, earn more reviews, and build relationships. Read our free report today to peruse the findings, our thoughtful analysis, and expert commentary from local SEO industry professionals.
We surveyed 1,000+ US residents to assess trending behaviors surrounding reviews and responses and gained a powerful picture of the role this type of online sentiment is playing in consumer journeys, conversions, and most importantly, reputation. Local business owners and their marketers can access the full report today for strategic takeaways like these:
Only 11% of consumers trust brand messaging over public sentiment
In the complete report, you’ll learn that 96% of consumers now read online local business reviews. That’s basically almost everyone accessing this type of content, and the context comes into high relief when you know that just 11% of the public trusts what brands say about themselves over what customers say. Review management deserves serious investment from local businesses because it is the customers who are now writing the most trusted brand narratives.
52% of respondents say their negative reviews stem from false or incorrect online information on assets like local business listings
Local business listing management also needs to sit at the core of your marketing strategy because, without it, negative customer experiences in the real world result in negative online reviews. When neglect of listings leads to incorrect contact information existing around the web, customers are significantly inconvenienced by driving to wrong locations, calling outdated phone numbers, or arriving on the premises outside of accurate business hours.
With over half of customers having written negative reviews following poor experiences like these, it’s clear that location data management is essential to customer service and is key to protecting your reputation.
91% of consumers’ next steps after reading reviews occur in areas completely controlled by the business
Local search can be a powerful connector between brands and consumers, but it can also sometimes lead to feelings of a loss of control. While business owners and marketers can be part of the conversation in big spaces like review platforms and social media, they can’t directly control it.
This is why it should come as such welcome news that the incredibly broad road of review readers lands the majority of customers right back into areas directly controlled by the business. As the next step after reading reviews, 51% of consumers visit your website, 27% go directly to your place of business, and 13% contact you. It turns out that you have significant control over customer experiences along the post-review-reading customer journey.
The top reason customers don’t review your business is because they forget to
As you dig deep into Moz’s complete survey findings, you will come to identify a leading consumer desire for a substantial number of recent reviews. It’s this trend that obliges local business owners to implement review acquisition campaigns so that fresh sentiment is always incoming.
It’s a welcome insight to know that 38% of customers don’t leave you a review because they simply forget to when they have free time. This is the top reason, amongst many, explaining why you likely aren’t receiving as many reviews as you need to. Fortunately, a remedy is within easy reach with follow-through reminders to review your business being helpfully shared with customers via email, text, and print assets. You can get more reviews if you just keep communicating.
62% of negative reviewers would give a local brand a second chance after an owner response solves their problem
As you move through the complete report, you’ll come to see the medium of reviews as a platform for two-way conversations, with the majority of customers who leave a negative review expecting to hear back quickly from the business owner. It’s harder to imagine better tidings than that 62% of your customers are willing to give your company a second chance if your owner response successfully resolves their complaints.
This figure transforms scary narratives surrounding negative reviews into moments within a relationship where forgiveness is likely to follow when real help is given. A complete local search marketing campaign must include ongoing hands-on responsiveness to online customer sentiment.
Come get the keys to running a customer-centric local business
As we’ve learned, reviews are a wide road almost all of your potential and current customers are traveling on. To fully charge your vehicle for best performance on that highway, local business review stats and trends can help you better serve customers by understanding their needs; implement structural fixes within your business based on problems cited by consumers; earn more reviews to improve your local pack rankings and conversions; and build loyal community relationships via two-way conversations.
Reading The Impact of Local Business Reviews on Consumer Behavior will help you prioritize reputation management tasks on the basis of consumer demand and habits. It will give you access to expert commentary from industry leaders including Aaron Weiche, Amy Toman, Crystal Carter, Joy Hawkins, and Mike Blumenthal. And, it will be a resource you can share with multiple stakeholders, be they clients, staff, team members, or company leadership to get buy-in for the considerable work involved in professionally managing reviews. There’s nothing quite like good data to make a great point, so please come take this ride with us!
Read: The Impact of Local Business Reviews on Consumer Behavior | SEO Industry Report
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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