MARKETING
How connecting customer data drives personalized experiences
Karen Naves, SVP of global demand generation at Tealium, recently gave a presentation on the benefits — and necessity — of connecting customer data to marketing initiatives. This process can help brands gain a more complete view of their audiences, allowing for more personalized experiences.
Many organizations use CDPs or data management platforms to collect and activate this data, helping them meet the unique needs of each customer.
Here are some actionable steps Naves recommends marketers take to enhance their personalization by connecting customer data.
Create buyer personas and ideal customer profiles
“Some people say, ‘I’ve got my [buyer] persona, I’m ready to go. We want to go to market,’” said Naves. “But, they’re not ready yet. They need to figure out their ICP [ideal customer profile] as well.”
“They’re different, but they’re both equally as important,” she added.
The buyer persona is the fictional personality marketers create that represents a specific type of user who interacts with their brand. In contrast, the ICP is a description of customers who will benefit from your product or service.
Getting these confused can disrupt personalization efforts, so marketers should set aside time to create accurate versions of each, connecting customer data appropriately.
![How connecting customer data drives personalized experiences differences between buyer personas and ideal customer profiles](https://articles.entireweb.com/wp-content/uploads/2022/05/How-connecting-customer-data-drives-personalized-experiences.png)
“For buyer personas, you’re thinking about creating a better user experience for your customers,” said Naves. “When you’re developing your buyer persona, you’ll consider things like their role within the organization, their title, their responsibilities, as well as some of the challenges they would face.”
Reduce churn with loyalty campaigns
According to Naves, marketers can help reduce customer churn through targeted loyalty campaigns. By rewarding customers after they reach designated tiers, brands can foster engagement.
She offered a fictional example of targeting a gamer who’s signed up for a trial, showing the power of a CDP for driving customer loyalty: “We want to make sure that he doesn’t churn. Using a CDP, we can help prevent it. In this example, we have his website data, his product usage data, and all of the information that he provided to sign up in our CRM database … We’re going to create a [segment] around this gaming data to show that he only plays with 10 characters or less because we noticed in our models that if a person is playing a game and only has 10 characters built into this game, that person will churn.”
“We want to create some kind of reward to encourage him to use more characters, so we would add a subscription offering him one month free if he creates 10 or more characters,” she added.
Marketers and brands can employ loyalty programs like these that fit their audiences and industries. The key is to ensure they’re personalized.
![How connecting customer data drives personalized experiences How connecting customer data drives personalized](https://articles.entireweb.com/wp-content/uploads/2022/05/How-connecting-customer-data-drives-personalized-experiences.jpeg)
![How connecting customer data drives personalized experiences How connecting customer data drives personalized](https://articles.entireweb.com/wp-content/uploads/2022/05/How-connecting-customer-data-drives-personalized-experiences.jpeg)
Looking to take control of your data? Learn about trends and capabilities of customer data platforms in the latest edition of this MarTech Intelligence Report.
Implement personalized cross-sell campaigns
“Cross-selling increases revenue and helps reduce churn,” Naves said. “This is your opportunity to hyper-personalize.”
“You want to connect with your customers in a trusted, real-time way,” she added.
The chance of making a sale to existing customers is 60-70%, while the probability of selling to a new customer is only 5-20%, according to data from Invesp. So, it makes sense to market to these existing customers first, especially through cross-selling.
Marketers can use the data from these existing customers to generate personalized offers for complementary or similar products to those they’ve already purchased, and, ultimately, encourage these customers to remain loyal to the brand.
“That’s why applying these strategies from loyalty and cross-sell perspectives is critical,” she said. “They’re used for maintaining customers, and personalization is the absolute key.”
Customer data platforms: A snapshot
What they are. Customer data platforms, or CDPs, have become more prevalent than ever. These help marketers identify key data points from customers across a variety of platforms, which can help craft cohesive experiences. They are especially hot right now as marketers face increasing pressure to provide a unified experience to customers across many channels.
Understanding the need. Cisco’s Annual Internet Report found that internet-connected devices are growing at a 10% compound annual growth rate (CAGR) from 2018 to 2023. COVID-19 has only sped up this marketing transformation. Technologies are evolving at a faster rate to connect with customers in an ever-changing world.
Each of these interactions has something important in common: they’re data-rich. Customers are telling brands a little bit about themselves at every touchpoint, which is invaluable data. What’s more, consumers expect companies to use this information to meet their needs.
Why we care. Meeting customer expectations, breaking up these segments, and bringing them together can be demanding for marketers. That’s where CDPs come in. By extracting data from all customer touchpoints — web analytics, CRMs, call analytics, email marketing platforms, and more — brands can overcome the challenges posed by multiple data platforms and use the information to improve customer experiences.
MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
![YouTube Ad Specs, Sizes, and Examples [2024 Update] YouTube Ad Specs, Sizes, and Examples](https://articles.entireweb.com/wp-content/uploads/2024/06/YouTube-Ad-Specs-Sizes-and-Examples.jpg)
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
![Why We Are Always 'Clicking to Buy', According to Psychologists Why We Are Always 'Clicking to Buy', According to Psychologists](https://articles.entireweb.com/wp-content/uploads/2024/05/Why-We-Are-Always-Clicking-to-Buy-According-to-Psychologists.pngkeepProtocol.png)
Amazon pillows.
MARKETING
A deeper dive into data, personalization and Copilots
![A deeper dive into data, personalization and Copilots A deeper dive into data, personalization and Copilots](https://articles.entireweb.com/wp-content/uploads/2024/05/A-deeper-dive-into-data-personalization-and-Copilots.jpg)
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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