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The ROI of personalized experiences: Process measurements

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The ROI of personalized experiences: Process measurements

This is the third of a three-part series on the ROI of personalization. You can read the first part (audience measurements) here and the second part (content measurements) here.  

After examining how audiences and content are measured in terms of personalized experiences, let’s discuss how brands should approach the process that drives personalization. 

Process measurements require looking at the methods used to personalize, how efficiently they are performed and how they are improved over time.

In this article, we will:

  • Cover three aspects of operationalizing personalization.
  • Do a reality check for those brands that want to go all in on 1:1 omnichannel experiences.
  • Explore the viability of doing this and the cost of not doing it.

Great personalized experiences require alignment across teams

Organizations that are misaligned and have disconnected internal operations will have difficulty providing seamlessly personalized experiences externally to their customers. Let’s look at a few ways this plays out in the real world:

  • Siloed marketing teams, where the “ecommerce team” doesn’t regularly interface with the “email marketing team.”
  • Siloed departments, where marketing, data and engineering all might as well exist on different planets.
  • Siloed product teams where widget A is marketed and supported in a completely different manner than widget B.

To make it even more challenging, some organizations have all of the above. This doesn’t mean you can’t start with some low-hanging fruit. Focus on building bridges where there are the most commonalities and potential benefits.

For instance, if the processes to create mobile app content and email campaigns often overlap, start there. Sure, it won’t provide omnichannel personalization overnight, but you can build consistency and, more importantly, a case for why more coordination and collaboration are needed within the organization.

Breaking down silos and having greater coordination inside your organization is a key step toward creating more holistic and valuable personalized customer experiences.

Dig deeper: Managing the unpredictable: Getting marketing, sales and operations aligned

Testing against hypotheses eliminates anecdotal noise

Most marketers have an opinion about how effective personalized experiences are in influencing engagement and conversions. The challenge is that many of these opinions are anecdotal and what I would call less than scientific. 

To counter this, we need to run true tests, which include:

  • A hypothesis (what our assumption is).
  • A null hypothesis (what must be rejected first to determine that the hypothesis can be true).
  • A threshold of statistical significance that justifies further testing and/or investment in the efforts. 

In other words, welcome back to Statistics 101. 

The best way to determine the effectiveness of your personalization in this way is to do a true A/B test, where the “A” variant provides all users with a generic message/offer/experience and the “B” variant personalizes it. With statistically significant data, you will be able to see if your efforts to personalize

I also recommend that you examine this in a few dimensions. Personalization can be more subtle or more extreme. The cost to deliver — whether that is actual hard costs or time and resources — can vary depending on how extensive that component needs to be personalized. For instance, creating an endless variety of customer imagery can be resource-intensive, while doing database lookups can have a minimal cost once the initial rules are set.

Regardless of how extensively you approach personalization, creating a culture of testing and validation ensures you focus on the right things, cutting through the clutter and anecdotal noise that holds teams back from greater success.

Dig deeper: Why testing is a marketer’s most powerful tool

Feedback loops and continuous improvement

Of course, even rigorous testing is only as good as the process used to incorporate the findings of those tests back into the workstream. This requires a commitment to consistently find ways to enhance and optimize personalization efforts. Two big pieces are a feedback loop and governance over the process.

First, you must create a feedback loop that takes your learnings from your efforts (including your tests) and ensures the people and platforms that rely on them are connected. 

I’ve worked with organizations that were great at measuring and creating in-depth reports of exactly what happened, where and to whom — but had no meaningful way to translate those results into any changes or actions for the next time they needed to do something. 

They had a beautiful library of charts and reports. Yet, their efforts never improved, other than by anecdotal sharing of what made it into reports and what must have been lucky guesses.

Additionally, you need a set of processes to ensure you can change and adapt by incorporating feedback while also not changing too much too quickly. This prevents internal teams — and your customers — from getting confused or frustrated by too much of a well-intentioned thing.

This is where a governance model for your personalized customer experiences will play a role. Remember, it’s not always about moving quickly. Instead, a good governance model:

  • Has transparency and consistency.
  • Moves at the right speed to allow you to adjust your personalization efforts.
  • Avoids too much change that might overwhelm your teams or provide inconsistency to a customer’s experience.

Feedback loops and governance models standardize and systematize your ability to continuously improve the customer experience and, consequently, the ROI that personalization efforts can deliver.

Dig deeper: Implementing agile marketing experiments leads to leadership buy-in

Can lagging organizations catch up to the leaders? 

Some of you may be reading this and thinking that all of this sounds amazing, yet it’s simply not possible in a short timeframe. The leaders in personalized experiences aren’t pausing for the laggards to keep up. 

Large brands may struggle with departmental or product silos. Smaller ones may struggle with the resources and infrastructure required to do all of this well. Setting up the systems and platforms that support personalized customer experience takes investments.

The hard truth is that, despite the challenges, it is imperative for companies that have fallen behind to catch up. Each day that goes by, the gap between the laggards and the leaders continues to expand. The processes, platforms and knowledge from testing — and even missteps — that the leaders gained will only grow more valuable.

In other words, choosing whether to offer more personalization or not isn’t what you should be considering. Instead, it is how you will bridge the gap between you and the competition, all while maintaining profitability and not disrupting either internal (employee teams) or external (customers and partners) audiences.

An iterative, incremental approach is the best and really the only way to do this. A strong prioritization model can help you understand which initiatives will have the biggest impact on the business and your customers while having the most minimal impact on resources.

Measuring the ROI of personalization

Getting a true return on investment from creating and delivering personalized customer experiences requires a holistic view across audiences, content and channels and the processes used to create, manage and continuously improve all of the above.


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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

Greg KihlstromGreg Kihlstrom

Greg Kihlström is a best-selling author, speaker, and entrepreneur, currently an advisor and consultant to top companies on customer experience, employee experience, and digital transformation initiatives as Principal and Chief Strategist at GK5A. He is also the host of The Agile Brand with Greg Kihlström podcast. He is a two-time CEO and Co-Founder, growing both companies organically and through acquisitions, and ultimately leading both to be acquired (one in 2017, and the other in 2021). As a strategist, digital transformation, and customer experience advisor, he has worked with some of the world’s top brands, including AOL, Choice Hotels, Coca-Cola, Dell, FedEx, GEICO, Marriott, MTV, Starbucks, Toyota, and VMware.

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MARKETING

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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