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Why data-driven decision-making is the foundation of successful CX

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Why data-driven decision-making is the foundation of successful CX

Without actionable data, customer experience strategies are doomed to fail. Lisa Loftis, Principal of Customer Intelligence Solutions at SAS, discussed some interesting CX findings from Futurum Research in her presentation at our MarTech conference.

“One of their most significant findings was that the future of CX is in real-time data collection analysis and being able to tune these activities so that you can proactively meet and exceed customer requirements,” she said.

She added, “In our philosophy, data does not change the organization — decisions do.”

Marketers have a responsibility to add more data into their decision-making processes, especially given the technologies available. Marketing automation platforms have made decision-making more effective by streamlining tasks that used to take up much of marketers’ time.

“Automating decisions is not a new focus for marketers and CX leaders,” she said. “The issue is that the pandemic-induced digital behaviors that we’ve been talking about have ratcheted up the importance of automating decisions in CX.”

Here are some reasons why successful CX strategies require data-driven decision-making.

Data adds customer context

Data drawn from analytics and CRM systems can provide marketers with much-needed context to make better campaign decisions. What’s more, these tools can create the foundation brands need to automate these choices going forward.

“You can begin to understand how relevant the company is to the customer,” Loftis said. “Do they have products? Do you have products that they want or need and how do they feel about their past interactions with you? This information falls almost exclusively in the CRM category, and it can be used to understand things like segment behavior and offer personalization.”

chart showing how data adds customer context for decision-making
Source: Lisa Loftis

She added, “We can start to understand what motivates an individual and what their influence value can be. The data that makes up personal context comes from a mix of the third-party purchased information and social media activity.”


Why brands must embrace responsible marketing practices

Automation determines the next best actions

Loftis provided an anonymous case study of a large bank that used automated decisioning, helping illustrate the benefits of automation. She described how the campaign yielded significant benefits for this bank, generating 6 million leads annually and 80,000 to 100,000 new accounts per year in marketing ROI over 100% in the first few years.”

determing customer next best actions with marketing automation
Source: Lisa Loftis

She also laid out the process by which successful marketing teams work with these technologies: “Marketing groups generate individual targeted lead lists which are submitted to a central decision engine. The engine uses a combination of predictive analytics and machine learning, business rules, and predetermined constraints to develop a list of potential offers for each customer.”

She added, “So when the customer…visits an included channel, the channel contacts the decision engine for a list of possible offers.”

Marketers would be wise to vet their chosen automation system, ensuring its decisioning process aligns with organizational goals. When deployed correctly, these technologies can optimize customer offers in real-time to provide the best possible CX.


Why data driven decision making is the foundation of successful CX

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Streaming data fuels decisioning

Many organizations have turned to data streaming, a solution designed to address data pipeline issues, recognizing its value in the decisioning process.

“Streaming data isn’t new, but the way that we handle it has changed pretty significantly in the last few years,” Loftis said. “In effect, it was a smaller version of the data warehouse, another data silo, and it existed for one reason: to store data.”

“The problem was that there was almost always a lag in the process,” she added.

Data streaming can help marketers capture and aggregate large quantities of customer data, which can be used to fuel automated marketing processes. This is also all done in real-time to ensure customers enjoy seamless experiences.

“To meet customer expectations, streaming data has to be analyzed and acted upon as soon as it comes into the stream, not hours later,” said Loftis. “The data and analysis results can always be stored for later usage if the nature of the actions does not call for real-time delivery. But the digital engagement models today mean that we have to apply analytics to the data as it is moving through the stream.”

She added, “The goal of a true streaming data platform is to apply high-end analytics directly to the data.”


Snapshot: Marketing automation

For today’s marketers, automation platforms are often the center of the marketing stack. They aren’t shiny new technologies, but rather dependable stalwarts that marketers can rely upon to help them stand out in a crowded inbox and on the web amidst a deluge of content.

HubSpot noted late last year that marketing email volume had increased by as much as 52% compared to pre-COVID levels. And, thankfully, response rates have also risen to between 10% and 20% over their benchmark.

To help marketers win the attention battle, marketing automation vendors have expanded from dependence on static email campaigns to offering dynamic content deployment for email, landing pages, mobile and social. They’ve also incorporated features that rely on machine learning and artificial intelligence for functions such as lead scoring, in addition to investing in the user interface and scalability.

The growing popularity of account-based marketing has also been a force influencing vendors’ roadmaps, as marketers seek to serve the buying group in a holistic manner — speaking to all of its members and their different priorities. And, ideally, these tools let marketers send buyer information through their tight integrations with CRMs, giving the sales team a leg up when it comes to closing the deal. Learn more here.


About The Author

1640828540 338 Why brands must embrace responsible marketing practices

Corey Patterson is an Editor for MarTech and Search Engine Land. With a background in SEO, content marketing, and journalism, he covers SEO and PPC to help marketers improve their campaigns.


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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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