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The Differences in B2C & B2B Marketing and How You Can Succeed at Both

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The Differences in B2C & B2B Marketing and How You Can Succeed at Both

Both B2B and B2C industry models have experienced exponential digital growth. Many opportunities exist to go either from B2B to B2C or vice-versa, as the many available channels have made it more possible as eCommerce evolves. There is a wide variety of marketplaces, tools, and solutions to make B2C and B2B marketing processes easier.

Despite that, balancing B2B and B2C marketing can be difficult. Each model requires completely different target audiences, messaging, and business approaches, so there’s a lot to juggle to apply both successfully.

What is more critical than any of these things, the fuel that can be used to drive B2B and B2C marketing simultaneously is organized product data. After all, product data is what will attract and inform both B2B and B2C audiences, be enriched to disseminate the right message, as well as propel transactions, be it long or short sales cycles.

That being said, let’s first address the main differences between B2C and B2B marketing when it comes to managing product data before discussing how you can implement both.

How B2C Marketing Works

B2C marketing aims to garner the attention of individual consumers, usually end-users of a product. The B2C business model focuses on attracting a high volume of customers to make small-scale buys. Think of a CPG brand trying to sell as many paper towel rolls as possible. As a result, while customer experience continues to be important, there is less of an emphasis on involved relationships. They remain transactional.

The main goal of a B2B marketing strategy is to attract and convert as many consumers as possible. The target audience is based on a larger-scale market, which allows for more opportunities but also requires narrowing in on several buyer personas.

B2C messaging focuses on emotion, to drive revenue with more views, clicks, shares, likes, and of course, impulse buys. Marketing copy has to be more straightforward and resonate with the audience.

The major channels for marketing are social media, Facebook, Instagram, chatbots, and retargeting campaigns.

Product data is essential for B2C: Product specs must be accurate to attend to fast-paced decisions. In addition, teams need the infrastructure to be able to update marketing content promptly.

The goal of managing product information properly is efficiency; to sell more products fast. Delivering product data fast drives an excellent customer experience and can result in repeat buyers. Product pages must include concise, yet comprehensive product descriptions, giving shoppers the right information in seconds for well-made purchase decisions. Speed and accuracy are the pillars of B2C sales.

How B2B Marketing Works

B2B companies sell to other businesses or third-party retailers, that will then market to consumers (or in some cases, other retailers). With multiple pricing tiers or custom pricing, B2B websites’ main objective is to offer educational content and an account dashboard for clients to manage higher order volumes. The longer sales cycles require continuous communication, so relationships are critical to success.

The main goal of B2B marketing is to generate leads while maintaining these strong customer relationships. For this, brand consistency is the cement that keeps customers trusting. Because of the narrower pool of potential customers, B2B companies need to focus on a specific niche and match that profile with the right messaging.

With marketing, logic overshadows emotion. To grow the B2B buyer’s confidence, a robust comprehensive set of information is critical, especially when paired with personalization: speaking directly to and delivering content customized to the potential customer.

Important B2B marketing channels include LinkedIn, email, messenger, SMS, SEO, and paid ads.

B2B buyers do extensive research before making a purchasing decision. Companies need to compare services between competitors to find the right product that a) aligns with their long-term goals and b) matches their specific needs. To address that, product specifications must be accurate, standardized, and credible. Companies will see product data before they ever engage with you.

Moreover, it’s important to deliver product data in detail, in longer forms than that of B2C marketing, to build interest while communicating your products’ value in-depth.

B2B customers require education-focused content sprinkled with industry jargon, rational benefits, and an extensive set of technical specifications, attributes, safety data, and other files.

The Differences between B2C and B2B Marketing

In short, B2C marketing focuses on straightforward, emotion-evoking messages for the average consumer, highlighting benefits and solving pain points. B2B marketing drives business by appealing to logic, emphasizing long-term advantages for buyers regarding cost, time, or resources.

How to Succeed at Both B2B and B2C

First of all, why go down both avenues? Here are just a few reasons:

  1. There are more retail opportunities at your disposal; strategize ways to sell to more by targeting specific personas in either camp.
  2. When you go from B2C to B2B, the higher AOV per customer boosts revenue. When you’re B2B and start selling to consumers, you can have a near-automatic system of receiving sales with digital self-checkouts.
  3. Adding a channel can be fairly straightforward, especially with a PIM/DAM solution.

Success Starts at Product Data

While differences are clear, B2B and B2C do share common ground. For instance, product data serves as the essential foundation for reaching and informing the right audiences in B2B and B2C marketing.

In order to succeed at both, you must know both your B2B vs B2C audiences, create the websites, manage the right channels, and deliver relevant messaging to each respective channel. To get this right, using an automated product data management system will help in the following ways.

Optimize product data

For more efficient marketing measures, you need to optimize your product data while retaining accuracy. Storing assets and data in one source, for instance, gives you access to all necessary content: attributes, specs, documents, photography, descriptions, and more. A database with good taxonomy, like PIM, improves findability so marketers can find products in less time. While you publish products to B2C sites in less time, you can also have a branded portal ready for B2B buyers to access marketing data in a self-serve manner.

Enrich product data

For marketing purposes, initial manufacturer data must be enriched to represent the product well to emotional buyers. It’s necessary to construct rich, accurate content that is compelling. Enrichment is what drives engagement, improves SEO, and provides readable product information for both B2B and B2C audiences. It helps to deliver custom data to specific B2B leads, while simplifying the B2C on-store decision-making process.

Improve product data management processes

The best way to make quality product data accessible to all parts of your business is by centralizing it. One source of standardized product data reduces costs on both B2B and B2C fronts. Reduce costs by standardizing your information from the beginning system, and with better information transmission with trade partners and retail channels.

Reduce complexity, hone success

Simultaneously managing B2B and B2C can be complex, but it doesn’t have to be. With a PIM tool, you can have product data organized and ready to go, comprehensive, etc. Sift through highly organized product data easily to publish to either B2B or B2C channels. Despite the requirements, each one needs, set yourself up to add a new avenue whenever you’re ready.


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MARKETING

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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