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The formula for calculating martech ROI

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The formula for calculating martech ROI

I run a MarTech company and everyone on our team clearly understands the importance and value of MarTech and yet, when I or someone else makes a pitch for a new addition to our stack, the first question my co-founder asks is “what’s the return on investment (ROI) if we buy that product?” It seems we’ve been discussing ROI a lot lately and because we’re a relatively small team a discussion usually suffices.  A discussion won’t suffice as we scale, we need to bring more discipline and structure to the process so this week I’ve been researching (aka googling) how others approach this challenge. I didn’t find anything specifically related to MarTech but did find a number of articles1 related to calculating ROI for IT technology which were helpful. 

For products where there is a tangible cost benefit in the form of new revenue, measurable productivity improvements, or cost savings, a straight-forward ROI calculation is workable.

The ROI formula

((Gain – Cost)/Cost) x 100 = ROI%

Some notes about leveraging this formula:

  1. Time period: Three years is the most common period for calculating gain. 
  2. Costs: Costs should include all expenses to implement and manage the product for the three-year period not just the monthly or annual subscription costs. This includes training expenses. 
  3. In calculating the gain and costs it’s important to consider the trajectory of both if you expect to add more product users over the three-year time period. 
  4. An initial ROI calculation is a best estimate, the only way to validate the ROI is to implement the product and revisit your assumptions regularly over time. Measuring actual ROI will provide data that will be valuable in projecting ROI for new products that are similar in structure or value. 
  5. Document your detailed assumptions, it’s the only way you’ll be able to revisit the calculation. 
  6. Set standards where possible. For example: If you are calculating productivity savings you want to make sure that everyone in the organization is using the same hourly rate for each job function.
  7. If you are not sure how to approach this calculation, ask your vendor for help. They should understand the value they bring to your environment and anecdotal data from other customers.  
  8. Some of these calculations will be complicated due to multiple quantifiable benefits. For example: When I look at my product, value can be quantified by reduced technology expenses, productivity gains, cost avoidance, and a host of additional minor elements. If you can get to a desired ROI without quantifying every single element then good enough. The more complicated and the more variables the harder to maintain. Focus on the elements with the biggest impact. 

For some products it’s virtually impossible to quantify the ROI which has got me thinking about how to qualitatively assess the products in my stack and the overall stack itself.

In a previous life, I was involved in an angel investment group and one of the most difficult tasks in funding early-stage startups was to assign a value (valuation) to a company. There are at least eight different formulas (probably more) for calculating valuation but they all are calculated using company financials. In an early-stage venture, company financials are a best guess so any calculation done against those is going to be flawed.  For that reason, most of the angel community relies on a combination of looking at valuations for similar companies and some form of qualitative assessment, the most common being The Berkus Method. The Berkus Method identifies five critical risk factors — idea, team, prototype, relationships/build-on-demand, and sales — and an investor assigns a dollar value to each based on the company’s progress in each area to reach a final valuation number. 

A ‘Berkus Method’ for martech

We need a Berkus Method equivalent for marketing technology, a method that provides the ability to quickly assess the value of the products we use and the stack overall. Instead of assigning a cash value to each component, the idea would be to assign a rating. I’ve been thinking about the key components and have come up with the following as a first draft:

  1. Satisfies the use case for which it was acquired.
  2. Extensible to support additional use cases.
  3. Integrates with other products in the stack.
  4. Ease of deployment and use.
  5. Data contributor.
  6. Data source.
  7. Contributes to driving revenue and customer lifetime value.
  8. Contributes to lowering customer acquisition costs.
  9. Contributes to creating a positive customer experience.
  10. Contributes to customer engagement.
  11. Enables new marketing capabilities.
  12. Enables new marketing channels.
  13. Supports data compliance requirements.
  14. Enhances security.
  15. Critical to marketing.

For each component, the user would assess contribution on a scale (1 to 5 or 1 to 10) and then total the assessments and divide by the number of components rated.  Not every component would be relevant to every product so the number of components rated would be variable product to product. Are these the right components? Should there be more or less?

I’d love some help from our MarTech community in refining this idea, finalizing the component list and thinking through how to extend this to create an overall stack value. Please reach out directly with your thoughts.  Have any of you created something like this or an alternative within your organization that you would be willing to share? With more and more money being spent on marketing technology now is the time to jump on this before we are under or pressure to reduce technology costs.


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Investing in new business tech: How to calculate ROI;

Top benefits of calculating ROI for technology investments;

Calculating ROI on Information Technology projects;

7 Tips for How to Calculate ROI Percentage for Technology Investments.


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


About The Author

Heres how startups and small companies should build their marketing

Anita Brearton is founder and CEO of CabinetM, a marketing technology management platform that helps marketing teams manage the technology they have and find the technology they need. A long-time technology marketer, Anita has led marketing teams from company inception to IPO and acquisition. She is the author of the Attack Your Stack and Merge Your Stacks workbooks that have been written to assist marketing teams in building and managing their technology stacks, a monthly columnist for CMS Wire, speaks frequently about marketing technology, and has been recognized as one of 50 Women You Need to Know in MarTech.

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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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