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Rethinking the marketing planning process for an agile world

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Rethinking the marketing planning process for an agile world

The whole premise of agile marketing is exactly that:

  • To be agile in our delivery of marketing assets, campaigns, educational content and product launches.
  • To generate conversations within our industries around topics of interest to our clients that would surface the next big thing we’d embark on.

The traditional annual marketing plan is a waterfall approach that does not support fast experiments. It talks about the “4Ps” — product, price, promotion and place — instead of focusing on the customer, their needs, and the fantastic way we are going to solve them. In today’s martech world, we cannot afford to be constrained with an annual marketing plan — and the good news is that we don’t have to be. 

Old habits, bad habits

I have witnessed time and again, throughout my career as a marketer, how marketing teams spend weeks crafting their annual marketing plans, only to see them forgotten in an obscure file somewhere in someone’s laptop. The frequently expressed frustration is: “We spend all this time working on a marketing plan and then we don’t look back at it all year” Who’s fault is it?

I used to adopt the main objectives in my product marketing plan as my goals for the year, actually entering them in the “system” and therefore, I felt a little reassurance that I had not worked for nothing. I’m sure I’m not the only one. 


Catch up on the Agile Marketing Navigator series!


New habits, better habits

As I evolved my thinking into an agile marketing framework, I moved away from the traditional marketing plan and more into thinking about “chunks of work” that would support the overall goal of the company with the product I’m managing. It has been my experience that, in fact, marketing plans that endure are living documents that get updated and evolve. That allows for flexibility and – the devil is in the details here – the plans should be stored in a place where teams can go in and periodically update them to reflect any major changes. 

Here are five things to tackle that will evolve your marketing plan and make it agile:

  1. Keep the market assessment and SWOTs in mind and review them quarterly.
  2. Review your value proposition, positioning statement, unique selling prop, and customer-centric benefits and revisit them throughout the year.
  3. Create Epics (user stories that span more than one sprint) for all your main marketing goals and objectives and add stories underneath the Epic that will become your backlog of deliverables.
  4. Note how many items or stories you have in your backlog that are unassigned.
  5. Keep your plan short in time and scope. In an agile mindset, planning through December 31 when it is only January 2nd is not only frowned upon — you won’t be able to keep that kind of planning up. If you do agile correctly, by end of Q2, your plan will have changed. And that’s okay.

When you think about agile and how to apply it to marketing, looking at the marketing plan and adapting it to be more agile will set marketers up for success as we start the planning season. Let’s start it with the right mindset. We know how to move fast — good marketers know that well. We’ve done it all our lives; we have mastered the art of fast-pace, change, and adaptation. We just have some lingering “old habits” we need to kick to the curb and we’ll be stellar.

Not only stellar, but we’ll be able to cope the work that needs to be done because it will be nicely placed in the multiple iterations that we’ll create on, say, fortnightly periods. We can measure, we can adapt, we can fail fast and move on, and we can conduct so many more experiments. The difference is that, with agile marketing, we’ll have visibility not only into the marketplace but within our organization. We’ll stop being the outbound marketing gals and guys, and be true movers and shakers within our organizations. 

Stacey Ackerman’s Agile Marketing Navigator series continues next week.


Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.


About The Author

Rethinking the marketing planning process for an agile world

Giannina is a product marketing manager at 3M Company. She currently manages the commercialization of new and established healthcare software products and creates brand and marketing content for healthcare organizations of all sizes. She’s a marketing veteran with 20 years of experience covering a wide range of industries, mostly with tech/software companies. Ever since she stumbled upon the concept of agile marketing back in 2015, Giannina became a strong proponent and early adopter of Agile Marketing and Scrum methodologies. She has subsequently led divison’s marketing team to adopt agile methodologies and practices. She is a Licensed Scrum Product Owner; holds a Bachelor’s Degree in international marketing from Rochester Institute of Technology (RIT) and additional advertising and communications degrees from Instituto Peruano de Publicidad – in Lima, Peru, her home country. Giannina lives in Maryland with her husband and two kids.

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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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