Connect with us

MARKETING

Most important vendor selection tips

Published

on

Most important vendor selection tips

For the past 20 years at Real Story Group, we’ve helped hundreds of enterprises make good martech stack decisions, and over that span, I’ve been fortunate to advise a wide array of technology selection teams. I saw a lot of things go well, and many things go…not so well.

Oh, the lessons we learned!

The most important finding, though, was that spreadsheet-heavy, waterfall-based selection methods just weren’t cutting it anymore. So we started counseling a more agile approach grounded in modern concepts around user-centered design, empirical testing, iterative adaptation, and cross-team participation. In short: design thinking.

Eventually, my colleague Jarrod Gingras and I encapsulated those lessons in a book, “The Right Way to Select Technology” (Rosenfeld Media), from which I’ll share some of the most important lessons and tips here.

Most important lessons

Before getting into specific tips, let’s review three meta-lessons.

Advertisement

1. Tell < show < test

Vendors love to talk about what their technology can do and will readily discuss case studies. They will show what their platforms can do as well, but this typically entails canned demos, and it falls to you to map their relevancy to your needs. You need to perform hands-on testing no matter the toolset before making any final decisions. In other words, never skip a bake-off.

2. The biggest-name vendors often have the most technical debt

Some of the most prominent martech vendors today have been around for some time, and their systems — including those they acquired — are getting long in the tooth. To cover for this, they get very aggressive about marketing, sales and, uh, “analyst relations.” They become less enthusiastic about in-depth technical and functional vetting. That doesn’t mean you should exclude big-name suppliers; just that you should not short-cut any diligence. And never allow yourself to get bullied.

3. Get clear about stack-fit

Martech stacks are evolving to meet the needs of an omnichannel world, and vendor strategies have shifted accordingly. This is a time fraught with both significant gaps and overlaps in your stack. For any new or replacement platform, get clear about where those services “fit” in the larger picture.


Get the daily newsletter digital marketers rely on.

Advertisement


Top tips

Our book publishers encouraged us to conclude each chapter with a series of practical tips, and we ended up typing out more than a hundred all told.

For those that prefer quick reads, here are some of my all-time favorite tips:

  • Be sure to articulate the costs and impact of doing nothing at all in any business case. In martech, stasis can become more costly than change.
  • Never exclude diverse IT / DataOps stakeholders (systems, security, development, architecture, data analysts) from decision making: they represent critical interests and expertise.
  • Conversely, never abdicate decision making just to IT, and place a businessperson to chair decision-making bodies. This promotes alignment with enterprise objectives.
  • Take a candid measure of your internal abilities and resources, and gauge your organization’s appetite for risk as well as cutting-edge methodologies: know thyself before trying to change.
  • Always start with the customer user experience and work your way back into enterprise systems, rather than vice-versa.
  • Pay more attention to developing human-centered business scenarios than “checklist” requirements.
  • In any RFP/tender/demo, ask “how” questions instead of “what” to better illuminate the inner workings of the toolset.
  • Allocate time and resources in proportion to the criticality of this technology to your overall business success. If this is a “platform” in your MarTech Mall, then you’ll want to pay serious time and attention. Boutique supplier? Not so much…
  • Give yourself and the vendor enough demo time, typically a full day.
  • Avoid overly complex scoring methodologies to rate vendors, typically quite unscientific; instead, rank them according to your business objectives.
  • Adopt and modify a “SWOT”-based decision analysis to fit your culture.
  • Price and contract negotiations are an iterative process that you should start as early as possible.
  • Never buy licenses for a potential future need, no matter how good a deal is proffered; instead, drag the buying process out over time: buy only what you need, when you need it, and in the order that you need it.
  • Don’t underestimate “strategic intangibles” in considering overall fit. In particular, I encourage you to focus on the customer ecosystem around any platform. Do customers meet up in person or virtually? Vibrancy here is the best measure of future viability.
  • Services firms can be critical to your success, so evaluate them with the same care and test-based approach that you vetted the core technology.

Hopefully, this gave your selection team enough food for thought to modernize the way you go about decision-making. If you’d like to see all the tips, find them in this series of posts.

Good luck. And ping me on LinkedIn if you have any questions.

Real Story on MarTech is presented through a partnership between MarTech and Real Story Group, a vendor-agnostic research and advisory organization that helps enterprises make better marketing technology stack and platform selection decisions.

Advertisement

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


About The Author

Tony Byrne is founder of Real Story Group, a technology analyst firm. RSG evaluates martech and CX technologies to assist enterprise tech stack owners. To maintain its strict independence, RSG only works with enterprise technology buyers and never advises vendors.

Advertisement


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.”

Advertisement



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.”

Advertisement



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

Advertisement



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