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Solve Attribution Woes: Adjust Your Settings & Expectations for a More Comprehensive Marketing Strategy

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solve attribution woes adjust your settings expectations for a more comprehensive marketing strategy

Very rarely in my PPC life do I bring up the subject of attribution with clients, colleagues, or industry friends without seeing a look of pain cross a face that may have been perfectly congenial a moment ago. Much teeth-sucking and drink sipping ensues when the difficult topic of attribution enters the discussion.

We all fear we aren’t properly attributing our conversions to each platform, be it paid or organic. Namely, this frustration stems from 3 main factors:

  1. The customer journey is more complex than ever before. Customer journeys are not linear, between multiple devices, long sales cycles, and mere impressions (view-throughs) that may or may not have encouraged the user to convert. Facebook and YouTube now have brand-lift studies to close some of the gap, but the cost for these kinds of prove-the-brand-is-improving tests is often beyond the financial reach of smaller brands.
  2. There are more attribution platforms, both free and paid, offered in the digital marketing space. And we have no idea which one has the true data. We are at full saturation and everyone has a solution, including the new Amazon Attribution Beta, and Facebook Attribution, which became available to all advertisers in October 2018 after testing for a year in beta. Third-party attribution vendors crowd the market too, and marketers have decision fatigue.
  3. Getting any attribution source to play nice and line up with another seems like an impossible task, in a world of walled gardens. In the /r/PPC subreddit, it’s common to see cries for help every week regarding two reporting sources misaligning – most commonly, Google Ads and Google Analytics failing to align.

So what can we do to make more educated attribution choices? There are a few main things every marketer must take into account.

1: Pick Your Windows Wisely

Aligning your attribution with the truth starts with the windows you choose in each ad platform. A conversion window is a defined period of time in which a publisher can claim that a click or impression led to a conversion (be it a lead, app install, purchase, or otherwise.) You can set your conversion windows in every single ad platform except Google Analytics, which has reports specifically built for comparing windows.

The Google Analytics Time Lag report is a good place to start if you want to understand how long it takes a user to move from consideration to conversion:

Google Analytics Conversion Time Lag copyThe Google Analytics Time Lag report counts number of days between first touch and conversion

You can use the Path Length report in Google Analytics and segment by specific goals:

Google Analytics Conversion Path Length copyThe Google Analytics Path Length report counts the number of interactions a user takes before converting

Which window do you choose? 30-day impression, 7-day click? 7-day impression, 1-day click? There are several ways to find out! Your window will depend on:

  1. The Nature of your Business
    • You’ll want to pick longer windows for your conversion settings when your products are more expensive, high-consideration products such as software as a service, home remodeling, etc. Comparison shoppers take their time. This is where tracking different movements of users from trial to paid subscription, email signup to quote request are vital so you can track the entire journey of the user. Each movement – from a potential customer learning about your brand to putting money in your pocket, must be tracked in all the platforms you can, from Facebook Analytics Event Source Groups to training salesmen to properly label leads in your CRM software.
    • You’ll want to set your windows to a short period of time if most of your customers are buying with their gut. This is true for those random products you buy from Instagram without much thought. Pony-Os Instagram ads, I’m looking at you! (I swear, it felt like a good purchase in the 7 minutes it took for me to consider it, toss it in my cart, and purchase it!) If your windows are short, you’ll want to align them with the settings of each and every platform you use, as well as your reporting software.
  2. You’ll want to consider the purpose of the advertising channel. Are you advertising for a conversion result, or a lift in brand awareness? For example:
    • Search tends to be a low-funnel channel and results in more direct conversions due to search intent.
    • Social channels tend to suffer from misguided budget cuts, due to marketers not recognizing that these channels are often first-touch or awareness-based. For example, we have a B2B client who runs LinkedIn campaigns to grow brand awareness among a highly specific, professional audience. Just having these high-quality audiences visit their site is improving the quality of their retargeting audiences and will be worth the investment in the long run. But by no means do we treat these campaigns as a conversion-producing, direct channel.
Pony-O low consideration productPony-Os are the fastest I’ve ever gone from watching an Instagram video ad to tossing my money at an advertiser. If you have a product with a short window like this, consider changing your attribution windows to more accurately reflect your buyers!

2: Learn How Different Platforms Attribute Conversions Differently

For Google Ads, the Attribution Playbook is a good place to start. Google also is helpful enough to provide an attribution tool that allows you to compare different search attribution models before taking the plunge and adjusting your conversion attribution settings:

Google Attribution Modeling comparisons

If you haven’t picked through the Google Attribution modeling tool in a while, you’re missing out. You can model cross-device activity, paths and time-lags (similar to what you’ll find in Google Analytics), and first and last click analysis, among other handy tools to slice and dice your data.

Most marketers agree that “Last click” or “Final click” attribution does not even begin to tell the truth and it is no longer recommended. Industry leaders agree, and this Invoca blog on how Google last-click attribution leads marketers astray clearly lays out the reasons why.

It’s easy to look up how each platform uses attribution modeling. A quick search turns up these resources:

3: Appreciate Lag & LTV when Testing a New Channel or Campaign

One of the biggest mistakes that marketers make is deciding a strategy isn’t working too soon. When testing, make sure you have a specific statistical significance you’re shooting for or even a time period in which you’re willing to stick it out and test. If you need a refresher, this post on calculating statistical significance from our own Carrie Albright is a great place to start! Once you have concrete goals, it will make your analysis a lot easier, although patience is always needed when testing any new channel or initiative.

4: The Source of Truth is Beyond the Platforms: It’s in Your Sales Data

This should go without saying. But I’m going to say it anyway! Your salespeople are sure to know more about lead quality than your marketing team. Train your team to gauge lead quality in their CRM. If you’re an e-commerce company, use internal resources to understand revenue and lifetime value. It is vital to have complete clarity between each marketing dollar spent and trendlines of success in your company.

As an agency, Hanapin is always pushing to get more internal information and reporting transparency because if leads do not lead to revenue, we want to know about those failures as quickly as possible. The same for successes – Have regular meetings between all teams to make sure your marketing dollar is balanced between first-touch and bottom-funnel, brand and non-brand. The ultimate source of truth will be money in your pocket. For new clients, often the process of clarifying attribution is working hard to ensure all tracking flows smoothly from campaigns into whatever system is being used to measure success, be it Bizible, HubSpot, Marketo, Salesforce, Pardot, Shopify, BigCommerce, or any number of propriety systems.

The Best Time to Fix Your Attribution was Yesterday. The Second Best Time is Today

We are having more conversations with our clients about attribution every day. This is natural. The rise of automated systems within platforms (Google’s automated bidding settings, Facebook’s mysterious way of using their algorithm to find potential customers) is going to depend on your attribution settings being correct. So if they aren’t correct, fix them today. Look at your attribution windows. Check your settings. Talk to your agency, and get your sales reports in line.

The marketing stack is more complex today than it was yesterday. But there is no time like the present to evaluate your attribution within and without your digital marketing platforms. Review often, and review thoroughly. And make use of absolutely free tools like Facebook Attribution, which uses advertisers in similar verticals and products in the same price points to inform your attribution choices, and Amazon Attribution – they’re free and comprehensive, why not use them?

I hope this blog has given you some places to start auditing your own attribution settings and systems to cut through to the truth and pave the way for a more informed marketing strategy.

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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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