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How marketers can take steps toward greater personalization

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How to enable greater personalization in a world of impersonal experiences

“We want to be treated as individuals by the brands we do business with,” said Katie Wheeler, senior manager of product marketing at Salesforce, in a recent webinar. “But, it’s hard for companies to treat folks at the individual level because there’s so much data — there are so many platforms and devices.”

“But this is what customers expect — they expect a personalized experience,” she added.

The demand for greater customization is something brands can’t avoid. Seventy-one percent of consumers expect companies to deliver personalized interactions, according to McKinsey’s Next in Personalization 2021 Report. Marketers need to know what level of personalization their organizations are currently providing and what steps need to be taken to improve it.

Assess your personalization readiness

One of the foundational features of personalization is one-to-one interaction, rather than sending the same messaging and providing the same experiences to all customers. However, this can often be challenging for brands.

“A lot of companies invest in all this great technology where customer data is stored,” Wheeler said. “But it’s often siloed in different systems.”

Siloed customer data can open the door to disconnected brand experiences, preventing marketers from understanding their audiences. To enact real-time personalization at scale, brands need to know how their data is stored and how it’s used to create personalized experiences.

brand readiness for personalization
Source: Horizontal Digital and Salesforce

Wheeler recommends marketers implement technology like decision engines to scale up their personalization efforts. These technologies can use AI to track customer behavior, process the data, and then offer personalized solutions in the form of recommended actions, offers, products, etc.

Implement a phased approach

Once marketers know their brand’s level of personalization readiness, they can more easily enact the necessary changes. No matter what amount of personalization you’re dealing with, all marketers can benefit from making changes incrementally.

personalization maturity levels for marketers
Source: Horizontal Digital and Salesforce

“An important reason for starting small is that you may be facing some pushback from stakeholders,” said Matt Wash, senior manager of marketing technology and operations at Horizontal Digital, in the same webinar. “What success can look like in this phase is identifying a single channel of personalization and partnering with the [company] division that is more ready for personalization, finding successes, and proving [that success] to the rest of the organization as you progress.”

Focus on audience segments and business objectives

When enacting a marketing personalization project, marketers should first analyze primary audience data and set up benchmarks for success. Establishing these foundational data points will make it easier to scale efforts in the future, especially when detailed use cases are employed.

“Identify and prioritize use cases to execute,” said Erica Skelly, senior personalization strategist at Horizontal Digital, in the same presentation. “Look at what’s going to drive the biggest business impact with the lowest hanging fruit.”

Getting executives on board with personalization tactics is much easier with use cases that highlight successes. The more data you have, the more specific these can be, which can make it easier to tie them to business goals.


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Expand and connect additional channels

“Learn from the use cases,” said Skelly. “Look at any lift in key metrics … You can also refine or change the use case, making sure you’re enhancing the performance.”

She added, “If possible, connect a new channel to make it a multichannel approach.”

Streaming multiple data feeds from tools like CDPs can help highlight trends from these use cases. Adding these to your martech stack can show marketers which channels are performing best, which channels audience segments prefer, and more.

Skelly also suggests identifying apps, email, or any other connections marketers can use to keep customers within the sales funnel. Then, marketers can test how effective they are at engaging people at key touchpoints.

Leverage insights to create a personalized omnichannel experience

“Utilize the connectivity across the additional channels you’ve identified to bring customers back into the funnel,” Skelly said. “Drive them toward the conversion point to ensure they’re increasing the ROI based on your business objectives.”

This advanced phase of personalization is all about acceleration — scaling up your multichannel expansion, data and trend analysis, and tool integrations. Marketers can then enact more in-depth data measurement tactics such as propensity modeling and last/multitouch attribution.

brand personalization phases
Source: Horizontal Digital and Salesforce

An important thing to keep an eye on during this acceleration phase is your brand’s content. Marketing to audiences on different channels calls for varied types of content, optimized to fit the customer’s context. Marketers should use the insights gained from earlier phases to better structure their personalized messaging in a way that brings people into the funnel.

Wash noted that marketers must also be aware of the “privacy paradox,” which can help keep content/experiences personalized without breaching customers’ privacy: “Everybody wants more personalized experiences,” he said. “But, we also value our privacy and are concerned about the information we’re sharing.”

He added, “That’s something that needs to be constantly assessed when looking for opportunities for personalization.”

Watch this webinar presentation at Digital Marketing Depot.


About The Author

Guide to what you missed at the fall 2022 MarTech

Corey Patterson is an Editor for MarTech and Search Engine Land. With a background in SEO, content marketing, and journalism, he covers SEO and PPC to help marketers improve their campaigns.


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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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