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How to tell an effective data story: Tips from Nancy Duarte

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How to tell an effective data story: Tips from Nancy Duarte

Because data storytelling is becoming a timeless skill in an increasingly data-driven profession, we’re resharing this helpful article from last spring’s MarTech Conference.

“Data doesn’t speak for itself, it needs a good storyteller,” said Nancy Duarte, CEO of Duarte Inc., the largest communications firm in Silicon Valley. Her keynote launched the second day of MarTech and gave all those who listened a sense of what the data world is missing – effective communication.

“Some have said data is the new oil,” Duarte explained. “The findings [from the data], though, will stay buried without the help of a communicator.”

The soft skills gap

Job opportunities for data scientists and analysts have thrived in recent years. (Statistician and data scientist were two of the top jobs of 2020.) At the same time, an even greater demand for soft skills (writing, problem-solving and the like) has emerged.

Duarte cited soft skills as the number one skills gap identified by LinkedIn’s Talent Insights tool. They found that 1.6 million jobs remained opened for those with soft skills, nearly a million of which called for oral communication skills.

A separate study by Burning Glass Technologies showed that demand for soft skills was greater for analytics-enabled jobs than the average demand across all occupations.

“This conveys that employers want their data-wranglers to know how to communicate well,” Duarte said.

This gap presents an opportunity for career advancement for individual data scientists. This is because, as Duarte suggests, “most data roles are becoming less about statistics themselves, and more about making sense of the data and applying it to business use.”

How to tell an effective data story Tips from Nancy
Nancy Duarte presenting at MarTech

Communicating problems and opportunities from data

Data scientists and analysts explore data all day. But for others in the organization to make use of the data, it has to be explained.

“Communicating about data is difficult for some people,” Duarte said, “because when you’re analyzing the data you’re going to find one of two things in it. You’re either going to identify a problem or an opportunity in the data.”

To address the problem or opportunity requires action.

“The ability to identify the action and communicate it moves [the data scientist] from being an individual contributor to a strategic advisor,” she explained. “As you build this muscle, you’ll become a more trusted data storyteller.”

With trust, the data scientist can inspire others and become a leader. But this only happens by learning how to tell an effective story. Duarte calls this “getting traction from your data.”

5 ways to get traction from your data

To make an impact on an audience, the presentation has to mean something. For Duarte, meaningful communication is achieved by these five strategies:

  1. Visualize data for your audience. The visuals are a part of a holistic strategy to present the important parts of a study to the audience. This blend of elements should be tailored to the audience’s preferences. But generally, the most important data should be made visually striking so that it stands out. Context should also be included, and in no way does this give permission to hide or cherry pick data.
  2. Structure insights as a story. Telling a story opens up neural pathways in the audience that gets everybody more engaged, triggering more memories and sense perceptions than dry charts and graphs. Structure the data, and the narrative around the data, using a basic three-act format. Start with the problem or opportunity found in the data, and work your way through the supporting data (in Act 2) to get to the actions that will resolve the conflict.
  3. Choose the best action. The verbs, or actions, are the most crucial part of speech in data-related stories. The verbs represent the recommended actions that result from the data insight. These verbs should be performance-driven, so that the higher-ups can identify and accept them as valuable recommendations.
  4. Attach data to something relatable. As Duarte pointed out, people have common knowledge and experience tied to basic measurements. They know basic units of time, and space. They can picture the size of a football, or of a football stadium. Translating important figures into these basic, relatable units will communicate the magnitude of the problem or opportunity.
  5. Humanize the data. Remember who you’re talking about, and also make sure to remind your audience. This allows the audience of the presentation to visualize an outcome based on what the data indicates or recommends. For instance, a company wants to grow its volume of customers or revenue. But to reach that goal, it’s better to think about the specific groups of customers and how they’re being served.

Know your audience, know your role

Humanizing the data makes for a more compelling story, but it also reminds the analyst why they are telling the story in the first place.

“Often in many roles when we’re working in data, we have to dispassionately and analytically look at data,” Duarte said. “We need to suspend our bias, let our critical thinking rule all of the analysis. But once that step is done and the data is all analyzed, we sometimes forget the power in getting to know the humans that generated that data.”

As an analyst, the data one is looking at corresponds to a company or the customers they are serving. This promotes the analyst into the role of a mentor, a leader.

Once the roles are clarified, a better story can be told, because the ground has been laid to grow and develop an empathetic connection. Only through empathy and relatability can the storyteller truly “create a sense of awe in the audience.”

This piece originally posted on March 18, 2021.

About The Author

2022 Predictions Data strategy and privacy
Chris Wood draws on over 15 years of reporting experience as a B2B editor and journalist. At DMN, he served as associate editor, offering original analysis on the evolving marketing tech landscape. He has interviewed leaders in tech and policy, from Canva CEO Melanie Perkins, to former Cisco CEO John Chambers, and Vivek Kundra, appointed by Barack Obama as the country’s first federal CIO. He is especially interested in how new technologies, including voice and blockchain, are disrupting the marketing world as we know it. In 2019, he moderated a panel on “innovation theater” at Fintech Inn, in Vilnius. In addition to his marketing-focused reporting in industry trades like Robotics Trends, Modern Brewery Age and AdNation News, Wood has also written for KIRKUS, and contributes fiction, criticism and poetry to several leading book blogs. He studied English at Fairfield University, and was born in Springfield, Massachusetts. He lives in New York.


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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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