MARKETING
The 7-Step Process for Making Logical Decisions
Psychology tells us that emotions drive our behavior, while logic only justifies our actions after the fact. Marketing confirms this theory. Humans associate the same personality traits with brands as they do with people — choosing your favorite brand is like choosing your best friend or significant other. We go with the option that makes us feel something.
But emotions can cloud your reasoning, especially when you need to do something that could cause internal pain, like giving constructive criticism, or moving on from something you’re attached to, like scrapping a favorite topic from your team’s content calendar.
There’s a way to suppress this emotional bias, though. It’s a thought process that’s completely objective and data-driven. It’s called the rational decision making model, and it will help you make logically sound decisions even in situations with major ramifications, like pivoting your entire blogging strategy.
But before we learn each step of this powerful process, let’s go over what exactly rational decision making is and why it’s important.
What is Rational Decision Making?
Rational decision making is a problem-solving methodology that factors in objectivity and logic instead of subjectivity and intuition to achieve a goal. The goal of rational decision making is to identify a problem, pick a solution between multiple alternatives, and find an answer.
Rational decision making is an important skill to possess, especially in the digital marketing industry. Humans are inherently emotional, so our biases and beliefs can blur our perception of reality. Fortunately, data sharpens our view. By showing us how our audience actually interacts with our brand, data liberates us from relying on our assumptions to determine what our audience likes about us.
Rational Decision Making Model: 7 Easy Steps(+ Examples)
1. Verify and define your problem.
To prove that you actually have a problem, you need evidence for it. Most marketers think data is the silver bullet that can diagnose any issue in our strategy, but you actually need to extract insights from your data to prove anything. If you don’t, you’re just looking at a bunch of numbers packed into a spreadsheet.
To pinpoint your specific problem, collect as much data from your area of need and analyze it to find any alarming patterns or trends.
Example:
“After analyzing our blog traffic report, we now know why our traffic has plateaued for the past year — our organic traffic increases slightly month over month but our email and social traffic decrease.”
2. Research and brainstorm possible solutions for your problem.
Expanding your pool of potential solutions boosts your chances of solving your problem. To find as many potential solutions as possible, you should gather plenty of information about your problem from your own knowledge and the internet. You can also brainstorm with others to uncover more possible solutions.
Example:
Potential Solution 1: “We could focus on growing organic, email, and social traffic all at the same time.”
Potential Solution 2: “We could focus on growing email and social traffic at the same time — organic traffic already increases month over month while traffic from email and social decrease.”
Potential Solution 3: “We could solely focus on growing social traffic — growing social traffic is easier than growing email and organic traffic at the same time. We also have 2 million followers on Facebook, so we could push our posts to a ton of readers.”
Potential Solution 4: “We could solely focus on growing email traffic — growing email traffic is easier than growing social and organic traffic at the same time. We also have 250,000 blog subscribers, so we could push our posts to a ton of readers.”
Potential Solution 5: “We could solely focus on growing organic traffic — growing organic traffic is easier than growing social and email traffic at the same time. We also just implemented a pillar-cluster model to boost our domain’s authority, so we could attract a ton of readers from Google.”
3. Set standards of success and failure for your potential solutions.
Setting a threshold to measure your solutions’ success and failure lets you determine which ones can actually solve your problem. Your standard of success shouldn’t be too high, though. You’d never be able to find a solution. But if your standards are realistic, quantifiable, and focused, you’ll be able to find one.
Example:
“If one of our solutions increases our total traffic by 10%, we should consider it a practical way to overcome our traffic plateau.”
4. Flesh out the potential results of each solution.
Next, you should determine each of your solutions’ consequences. To do so, create a strength and weaknesses table for each alternative and compare them to each other. You should also prioritize your solutions in a list from best chance to solve the problem to worst chance.
Example:
Potential Result 1: ‘Growing organic, email, and social traffic at the same time could pay a lot of dividends, but our team doesn’t have enough time or resources to optimize all three channels.”
Potential Result 2: “Growing email and social traffic at the same time would marginally increase overall traffic — both channels only account for 20% of our total traffic.”
Potential Result 3: “Growing social traffic by posting a blog post everyday on Facebook is challenging because the platform doesn’t elevate links in the news feed and the channel only accounts for 5% of our blog traffic. Focusing solely on social would produce minimal results.”
Potential Result 4: “Growing email traffic by sending two emails per day to our blog subscribers is challenging because we already send one email to subscribers everyday and the channel only accounts for 15% of our blog traffic. Focusing on email would produce minimal results.”
Potential Result 5: “Growing organic traffic by targeting high search volume keywords for all of our new posts is the easiest way to grow our blog’s overall traffic. We have a high domain authority, Google refers 80% of our total traffic, and we just implemented a pillar-cluster model. Focusing on organic would produce the most results.”
5. Choose the best solution and test it.
Based on the evaluation of your potential solutions, choose the best one and test it. You can start monitoring your preliminary results during this stage too.
Example:
“Focusing on organic traffic seems to be the most effective and realistic play for us. Let’s test an organic-only strategy where we only create new content that has current or potential search volume and fits into our pillar cluster model.”
6. Track and analyze the results of your test.
Track and analyze your results to see if your solution actually solved your problem.
Example:
“After a month of testing, our blog traffic has increased by 14% and our organic traffic has increased by 21%.”
7. Implement the solution or test a new one.
If your potential solution passed your test and solved your problem, then it’s the most rational decision you can make. You should implement it to completely solve your current problem or any other related problems in the future. If the solution didn’t solve your problem, then test another potential solution that you came up with.
Example:
“The results from solely focusing on organic surpassed our threshold of success. From now on, we’re pivoting to an organic-only strategy, where we’ll only create new blog content that has current or future search volume and fits into our pillar cluster model.”
Avoid Bias With A Rational Decision Making Process
As humans, it’s natural for our emotions to take over your decision making process. And that’s okay. Sometimes, emotional decisions are better than logical ones. But when you really need to prioritize logic over emotion, arming your mind with the rational decision making model can help you suppress your emotion bias and be as objective as possible.
Editor’s note: This post was originally published in July 2018 and has been updated for comprehensiveness.
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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