Connect with us

MARKETING

Your Content Analytics Are Meaningless Unless You Have This [Rose-Colored Glasses]

Published

on

Your Content Analytics Are Meaningless Unless You Have This [Rose-Colored Glasses]

If we can measure it, it must be important. So, is our job to just determine how accurately we can get that number?

Not at all. If any measurement is to mean anything, the first task is to agree on what equals success. It’s one of the unspoken secrets in all of marketing measurement. Agreement on measurement is much more important than the accuracy of the measurement itself.

Agreement on #ContentMarketing measurement is more important than the accuracy of the measurement, says @Robert_Rose via @CMIContent. Click To Tweet

A couple of weeks ago, I had this conversation with a director of marketing at a technology company. We were talking about the accuracy of digital marketing and how senior leadership directed him to be “sharper” (i.e., better) on measuring content marketing’s contribution to the overall marketing strategy.

His first planned initiative was to get into the details of the accuracy of the analytics tools. He wanted to make sure they were generating the right numbers, which were all in line with each other.

I told him getting more accurate data was the least of his challenges. What senior leadership really wanted was an agreement on what the value is.

Agreement matters more than accuracy

Look at TV ratings. They have never been accurate. In the early days, participants in the selected homes listed the shows that they watched and for how long in diaries. Do you think any of them took a wild guess at what they watched on Tuesday? And, up until a few years ago, the representative sample for television ratings was about 20,000 households in the United States. When you consider over 100 million homes in the US have a television, that’s like walking into a basketball arena of 10,000 people and figuring out what everybody wants for dinner by asking two of them.

As I explained to the marketing director: Television advertising isn’t a $60 billion industry because it’s accurately measured. It’s because everybody has agreed to the standard that determines “good” television based on ratings, regardless of their accuracy.”

TV advertising isn’t a $60 billion industry because it’s accurately measured. It’s because everybody has agreed to the standard that determines “good” TV, says @Robert_Rose via @CMIContent. Click To Tweet

The same must be true in your content marketing strategy. You first must define, align, and agree on your objectives and identify the unambiguous measurement of success.

How do you do that? Well, I like to think of measurement as a “design problem,” not an engineering problem, but here is a three-step process that has worked for us:

Step 1: Set your objective

Well-articulated objectives are clear and succinct and use plain language. They also imply or explicitly mention a time horizon to reach them. Objectives are the most important thing to get agreement on.

Set well-articulated objectives with a time horizon to reach them. That’s the most important thing to agree on, says @Robert_Rose via @CMIContent. Click To Tweet

For example, a plainly stated objective might be: After the first year, our content marketing program will generate 30% of the new qualified leads in our demand-generation efforts.”

Setting and agreeing on strategic objectives doesn’t mean they never change, shift, or evolve. It just means you are aligned on the objective.

Step 2: Agree on key results

Now that you have an aligned strategic objective, you need to agree on the second most important thing – the definition of unambiguous success. This is what that marketing director’s senior leadership actually meant by getting “sharper” on how the measurement of content marketing was going to contribute to the business.

Define the key results and (most importantly) agreed upon measurements to determine if the objective has been reached. Again, clarity and simplicity are critical.

To be clear, these key results are not key performance indicators (KPI). Your key results are the definition of the goal. The KPIs, which I’ll get to in a moment, are the measurements to help you evaluate the progress toward those goals.

For example, the objective is to drive 30% of the new qualified leads. That’s a shared purpose, but it’s not defined. No one has agreed on what that means yet. To define what that means, the three agreed-upon key results might be:

  • Increase current qualified lead velocity into sales by 15% as measured by sales-enablement form fills.
  • Increase conversion rates of free trials by 25% as measured by the number of trials created.
  • Decrease cost-per-thousand advertising rate by 20% as measured by average digital CPM rate.

Notice how I used the words “as measured by.” In constructing your key results, you may use hard numbers instead of percentages, or you might not have numbers at all. The key is to all agree on what the unit of measurement will be.

Now, take the time to pause and socialize your strategy. You most likely will have more than one strategic objective made up of multiple key results. Use this approach to achieve buy-in from your senior leadership.

Once you have shared objectives and agreement on how they will be measured, it’s time to care a bit about the veracity of your metrics.

Step 3: Design your measurement metrics

If you’ve gotten this far, you likely realize no single analytics tool is going to give the direct answers you need. Your sales-enablement form-fill information will most likely come from your CRM system. Your conversion of free trials might, literally, be calling up Mary and asking, “How many free trials from this landing page did we have last month?” And your CPM decrease might be a view in Google Analytics or an average across multiple ad-tech systems.

Your KPIs – the first level of your measurement metrics – are likely going to be drawn from a variety of data sources. They can help track your progress toward reaching one or multiple objectives. In the case where multiple numbers make up a KPI, break out another category of identifying all the sources of those numbers.

For example, Content Marketing Award-winning company ServiceNow publishes Workflow Quarterly with the objective of generating leads. One of their KPIs is what they call an “engagement KPI,” a custom metric that combines page views, time on page, and scroll length. The engagement KPI is used to score articles to help ServiceNow evaluate the content’s effectiveness in delivering value to the reader.

Put these things together, and you’ve designed a measurement program that people will agree with.

More than accurate numbers – you know the numbers everyone agrees on.

Just remember that accuracy is how close we are to a standard or truth. But, to determine accuracy, you first need to define what the measurement is attempting to assess. In other words, you must define the standard or truth before accuracy means anything.

Now you’re measuring what’s truly meaningful – the truth we all believe in.

Get Robert’s take on content marketing industry news in just three minutes

https://www.youtube.com/watch?v=videoseries

Subscribe to workday or weekly CMI emails to get Rose-Colored Glasses in your inbox each week.

Cover image by Joseph Kalinowski/Content Marketing Institute




Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

MARKETING

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

Published

on

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!

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

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

Published

on

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

Amazon pillows.

(more…)

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

A deeper dive into data, personalization and Copilots

Published

on

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.”

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending