MARKETING
A 5-step guide to retiring martech tools without disrupting operations
Regularly auditing your martech stack must be part of your organization’s process. During an audit, you will likely identify a handful of tools that can be retired for various reasons.
Perhaps your company has undergone a merger or acquisition, and you have duplicate tools that perform the same function. Maybe other tools in your stack might have added functionality, making a tool redundant. Or the tool just is not utilized enough to justify the cost.
Dig deeper: Are you getting the most from your stack? Take the 2023 MarTech Replacement Survey
Whatever the reason, identifying what tools can be retired is a critical part of a martech audit. But what happens after you’ve made that decision? How do you retire a tool without causing an uproar among users or breaking automations?
Retiring martech software requires a thorough planning process and clean execution. Here are five steps to follow.
Step 1: Identify system integrations and process dependencies
To avoid disrupting your operations, it is critical to identify what other systems and processes depend on the tool you are planning to retire. If you work for a large organization and the tool has been in place for many years, this may be more difficult than it seems. If your organization hasn’t been thorough about documentation, other systems may use API calls or data exports from the tool without you knowing it.
Even more challenging than identifying systemic integrations is specifying manual processes that exist outside the tool but are dependent on it. For example, a report gets sent out of the tool that a different team is manually copying/pasting into another system. These types of manual workarounds happen more often than you might think.
Start talking to everyone and anyone in the organization who has a connection to the tool. If possible, find the individuals involved in the tool’s initial implementation. They may have the institutional knowledge you’ll need to understand the downstream impacts of the tool’s retirement fully.
Dig deeper: Why marketers are replacing foundational martech
Step 2: Assess data migration needs
You may need to account for significant data migration as part of the retirement process. Heavily regulated industries such as financial services or healthcare have mandatory data retention periods (sometimes as long as seven years!).
Document what data needs to be migrated and determine where that data needs to go. If you are moving from one similar platform to another, for example, a marketing automation system, you may migrate the data from your old tool into the new one. Ask your new vendor how other clients have handled their data migration needs. They may be able to offer professional services or point your IT team to solid documentation for best practices they have seen with other clients.
But what happens if you are retiring a tool and not going to replace it? Where should that data go? Work with your IT partners to devise a plan. The data may be stored on a SharePoint site or in a new database. The best solution will consider how accessible that data needs to be and who needs to access it.
If the data only needs to be accessed rarely, it may be sufficient to store it in a database that someone on the technology team can extract once in a blue moon. However, if non-technical users, such as marketers or salespeople, need to access the historical data regularly, you will need a more user-friendly solution.
Step 3: Identify the right time to retire and build a project plan
After assessing your martech stack, you must identify the right time to retire the tool. This could be based on several factors, such as the end of a contract, the completion of a project or the end of a fiscal year. Whatever the reason, ensure that the timing will not disrupt your operations.
Once you’ve set the target retirement date, it’s time to build your project plan. Considering all of the downstream systems impacted and the data migration needs from Steps 1 and 2. You should have all the information you need to build a project plan working backward from your target retirement date.
What happens if the project work to decouple systems or migrate data will push you past your target date? If your target date was based on a contract, negotiate with the vendor to see if you can extend the contract month-to-month to give you the time you need to fully execute your plan rather than get locked into a full-year renewal.
Dig deeper: 4 steps to take before hitting go on your new martech platform
Step 4: Develop a communication plan
Communication is vital when retiring a martech tool. Identify everyone who needs to be looped in, such as employees, agency partners and sometimes external customers. Give them sufficient notice and be transparent about the reasons for retiring the tool. You should also communicate your plan for migrating data to a new tool and what support will be available during the transition.
If you are retiring one tool and replacing it with another, focus your communications on the benefits the new tool provides. Share the training plan so that users know they will be given support to help them learn the new tool.
Try and have the tools overlap for a specific period so that users are given a soft landing where they can start to learn the new tool while still having the safety net of doing things the old way if they are on a hard deadline and struggling with the new tool.
When retiring a tool that will not be replaced, talk to users to understand the features and functionality they depend on and work to build a plan that addresses how those needs can be met through other tools or processes.
Once you have migrated your data from your old tool and trained employees on using a new one, it is time for retirement. The period after the tool is retired is crucial for communication. Be sure to communicate to stakeholders the “wins” from retirement.
What’s the adoption rate of users for the new tool? What sales wins or positive client experience feedback have you heard about the new tool? How many days has it been since the tool was retired that there have been no errors or disruptions to operations? What new investments have been made with the cost savings from retiring the tool?
These are all key questions that can be used as a jumping-off point to remind stakeholders of why the tool was retired in the first place.
Studies have shown that marketing organizations use as many as 91 marketing cloud services, and the bloat in tools can drag down productivity. For many marketers, “less is more” and retiring duplicative or underutilized tools can be a team’s secret weapon.
When done properly with a strong plan, retiring solutions will result in a leaner, cheaper and more effective martech stack.
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Opinions expressed in this article are those of the guest author and not necessarily MarTech. Staff authors are listed here.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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