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How advertising rates vary by category on Amazon

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how advertising rates vary by category on amazon

It was 10 years ago, Jeff Bezos famously quipped that “advertising is the price you pay for having an unremarkable product.” As it relates to Amazon today, that logic holds as much water as any of these fine-looking sieves found on Amazon itself. In 2019, the retail site is largely a “pay to play” platform when it comes to defending and driving market share growth. This reality necessitates that Amazon sellers commit to advertising spending on the platform, but also do so based on expected conversion rates and search volume in order to preserve profitability while gaining market share.

How we got here

Two contributing factors have driven Amazon to its current state for sellers. First, across the most popular search terms on Amazon the share of total conversions for a given keyword, on average, heavily weight towards the first few results on the search page. This fact may have been true in the past, but its significance today becomes crystal clear when looking at Amazon’s own brand analytics data. Over the month of August, across the top million search terms on Amazon, the top three organic results captured an average of 62% of conversions.

Dovetailing with this behavior are the increasing prevalence of ads on Amazon search pages. Across nearly every popular search term across nearly every product category on Amazon, multiple Sponsored Product listings exist above the fold, along with, to a lesser degree, a Sponsored Brands placement at the very top of the page. Using the organic conversion share as a proxy for user behavior on the search page overall, those top paid placements are capturing a substantial share of total conversions.

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All this being said, when it comes to user behavior on Amazon, not all categories are created equal. Anecdotally, think about how many options you would consider, and how long you would take to shop, for a 2-pack of ketchup bottles online versus a pair of pants. These differences bare out in the underlying data, which I studied as part of my work at Teikametrics.

How advertising varies by category on Amazon

To get this category-specific picture of Amazon advertising, I examined the paid and organic listings present on the first page of results across the top one million search queries on Amazon over the month of August 2019. The search results data for this analysis was captured over the course of the final week of August 2019, and the category segmentation was based on the top category suggestion from Amazon listed in the sidebar for that search.

I focused on top 20 physical goods categories by the number of queries present in the top million search terms. I then examined the number of ads present in the top 10 combined paid and organic results, in addition to any Sponsored Brands placements, on a per term basis, expressing this on a 0 to 100 index. The category labeled ‘100%’ had the most ads in the top 10 results, with all other results being expressed as a percentage of that total.

For context, I also provided the average top three organic conversion share of each term analyzed in the category, as provided by Amazon Brand Analytics, along with the average price of all products on the underlying search results page. The analysis underscores the level of variance across verticals when it comes to the number of ads at the top of a search results page on Amazon. 

Categories more aligned with hobbies, specifically “outdoor recreation,” “arts, crafts and sewing,” and “automotive” had the highest number of ads in the top 10 terms. This is likely due to brand affinity being a major driver of purchases generally in these categories, and many players outfitting across an entire category itself. You may know someone who always buys Coleman or North Face camping products, as one example. For brands, attracting those shoppers and getting them introduced to their brand can create subsequent, related purchases across their wider catalog.

Categories with comparatively lower rates of ads in the top 10 results were more aligned with high consideration, and more accessory-laden categories. These include “toys and games,” “computers and accessories” and “baby products.” In each of these categories, consumers are looking for the “right fit” for their needs based on a wide range of criteria (e.g. age of the child, color preferences, cord length etc.), and may place those needs above sticking with a certain brand, or be more brand agnostic in general.

Both fashion categories stand out, with relatively high ad rates, but lower Sponsored Brand rates. While these are both competitive marketplaces on Amazon, the fact that consumers aren’t as likely to convert on the top results makes those top placements potentially less valuable. These categories are also home to a particularly large number of resellers, who cannot purchase Sponsored Brands placements.

On the other side of the spectrum, both the “beauty and personal care” and “office products” categories have a comparatively high top three conversion share, yet a relatively low rate of ads in the top 10 results. This could relate to slimmer product margins crimping the ability for brands to commit significant budget to ads, but similarly represents a good opportunity for brands in this category to capture more conversions should it be economically viable.

Next Steps

While this analysis captures a moment in time on Amazon, marketers should see this as directionally relevant as they set their strategy for Q4 and beyond.

Sponsored Products and Sponsored Brands advertising is particularly intense across certain categories on Amazon. If you’re a seller in those markets, you need to gain a thorough understanding of which search terms you should target, both from a volume and margin perspective, and be able to bid to value effectively. Remember to not fall into the “magic keyword” trap. Once you have enough data to make an informed decision, you may want to trim down the list of terms you are targeting against, and reallocate budget and adjust bids towards that smaller, higher volume subset, especially during high-traffic periods like Q4.

Conversely, in those few categories with top conversion rates outstripping advertising placement rates, marketers should see this as a market inefficiency they may be able to take advantage of. In these categories, it’s more likely you’ll find relevant, fairly popular search terms where a Sponsored Product ad for your product can rank high on the page, without breaking the bank on a CPC basis.


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


About The Author

AndrewWaber lg
Andrew Waber is the director of insights at retail optimization platform (ROP) provider Teikametrics. In his current role, Andrew manages the analysis, editorial direction and strategy for Teikametrics’ reporting on online retail advertising and the larger online retail marketplace. Prior to his time at Teikametrics, Andrew served as the manager of data insights and media relations at Salsify, the manager of market insights and media relations for advertising automation software provider Nanigans, and as the market analyst and lead author of reports for Chitika Insights, the research arm of the Chitika online ad network. Andrew’s commentary on online trends has been quoted by the New York Times, Re/Code and The Guardian, among other outlets.
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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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