Connect with us

MARKETING

3 ways to do segmentation in Google Analytics 4

Published

on

3 ways to do segmentation in Google Analytics 4

If you’re not using segments with your Google Analytics (GA) data yet, there’s a missed opportunity for new insights and data activation that you can start using immediately.  In analytics, an analysis will always begin with a high-level overview of the data. However, the real magic starts when delving deeper — looking into groups within your audience and turning data into stories with segmentation. 

There are features in the new version of GA that allow for actions and quick analyses that weren’t available before. It’s much simpler to report on audiences, and even if a website or app only has basic tracking, there are options to create advanced and in-depth segments based on out-of-the-box metrics. 

The best part? It only takes a few steps, yet the segmentation can have an impact beyond reporting for remarketing and personalization in other tools.

Read next: GA4: What marketers need to know for a successful transition

Why segment in GA4?

Out of hundreds of thousands (or millions) of data points, getting to know your customers is a needle-in-the-haystack situation. The total audience will never be made up of identical users in their behavior, engagement and decision-making. Segments allow you to isolate a subset of data based on user attributes across:

  • Demographics.
  • Content interest.
  • Behavior.
  • Traffic. 

Some examples of questions that you can answer with segmentation are:

  • When people find the site naturally through organic search, which content areas are they most likely to browse?
  • What do the highest spenders engage with while on the site or app?
  • Which age range is most likely to sign up for emails and notifications?
  • Which personalized experience resonates better with the group of people who have added high-value items to their cart but didn’t check out?
  • Are subscribers more active on mobile or the desktop experience?

At its foundation, segmenting is about understanding, planning and targeting.


Get MarTech! Daily. Free. In your inbox.


In GA4, information is in the form of dimensions and metrics. Every measurement is an event, whether it’s a page view, start of a session, video play, lead form, or timer. 

When segmenting your audience, you’ll have to choose what to base the segmentation on. A good start is to evaluate what’s already tracking and what you have to work with in the Lifecycle Reports collection.

You can create a set of rules to design conditions about who and what to include in a particular group. There’s more than one to explore and each has its appropriate use case: 

  • Comparisons.
  • Explorations.
  • Audiences. 

You can think of them as a funnel from broad exploration, to visual analysis, to taking action. This process is an effective way to plan how you will start your segmentation strategy. 

Quick tip. Check out the configuration area for a list of events and dimensions to see what you can potentially leverage for your conditions.

Comparisons

The simplest one and the highest level of exploration is called Comparisons. These additions are available throughout the interface at the top of the screen as you navigate through the reports — from overviews to specific elements like Pages and Screens. If you’ve used Google Analytics in the past, this is similar to the older functionality.

You can add up to five conditions to include or exclude groups from the reports within the slider. That’s not much of a limitation since all the scorecards, visualizations and table rows are replicated for each segment. 

It’s best to add these just a few at a time so the reports are still easily readable. For example, if I’m curious to compare devices and see user retention over time, I’ll start by building a segment with device condition and apply. It’s okay to see the mobile device as part of the whole since it is a comparison to this default All Users segment, but I want first just to see mobile on its own and then a comparison of mobile vs. desktop instead of mobile vs. All Users. 

Though this is a valuable tool, Comparisons aren’t meant to be for main reporting purposes or in-depth insights. They’re temporary and provide a quick way to spot trends and gather ideas for further segmentation with Explorations and Audiences. 

Explorations

Explorations is another option in the data visualization and interactive reporting feature. This reflects the stage of analysis where there are more specific reports to pull and questions to solve. The segment builder has more advanced conditions, and explorations can have up to 10 segments. 

Since Exploration reports are easily shareable, it’s an effective way to collaborate. These custom reports also offer automated anomaly detection that will apply a statistical model to your data and visually show you when there’s unexpected behavior.

Audiences

The most impactful segment tool in Google Analytics 4 is the Audiences feature. It’s more dynamic than Comparisons and Exploration. Because Audiences collect rather than calculate data, so it will only start including users from the time of creation. 

There’s an option for membership and the ability to fire an event when a new user joins (which makes big opportunities for conversions!).

Audiences can even be combined with a method that was previously out of reach for most marketers — predictive analytics. Now, GA4 users can build segments easily in the interface around aspects like “most likely to be a top spender.” Audiences become a dimension of their own, so the segments of users will be available to use in reports in the interface, Data Studio, or any platform you’re using to build visualizations and dashboards with GA4 data. 

Audiences will also automatically be available in the linked Google Ads account and can be used in other Google Marketing Platform integrations, including Google’s A/B testing tool, Optimize.

Wrapping up

Ultimately, getting to know the characteristics and trends of your user groups is one of the essential purposes of Google Analytics. 

By getting familiar with the conditions, the purpose and the use cases of the segmentation tools, you’ll be able to make quicker, focused and informed decisions that affect your organization’s marketing strategy and results.


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


About The Author

Samantha has been working with web analytics and implementation for over 10 years. She is a data advocate and consultant for companies ranging from small businesses to Fortune 100 corporations. As a trainer, she has led courses for over 1000 attendees over the past 6 years across the United States. Whether it’s tag management, analytics strategy, data visualization, or coding, she loves the excitement of developing bespoke solutions across a vast variety of verticals.

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