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The Comoto Family of Brands accelerates omnichannel marketing with first-party data

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The Comoto Family of Brands accelerates omnichannel marketing with first-party data

Retail is an ever-changing industry, but the last few years have been particularly disruptive. The COVID-19 pandemic triggered dramatic shifts in consumer behavior that left many retailers struggling to keep up. These factors, combined with the growing influence of Amazon, increasing consumer privacy regulations and deprecating third-party cookies, are only exacerbating the need for transformation in retail that emphasizes customer relationships.

The companies that have been most successful in adapting to these challenges share one critical commonality: they prioritize the collection and use of privacy-compliant first-party customer data as a competitive asset. The Comoto Family of Brands is one such retailer.

In a recent MarTech session, Comoto’s Dana Green joined BlueConic’s Jackie Rousseau-Anderson to discuss how they are using a customer data platform to unify customer data across multiple brands and systems and activate it across channels to deliver more engaging customer interactions.

Putting data at the heart of customer engagement

As America’s largest power sports aftermarket retailer, Comoto is home to Cycle Gear, J&P Cycles and RevZilla.com. With over 150 stores nationwide and e-commerce sites for all three brands, the company manages a complex ecosystem of customer data housed in a multitude of systems.

“Data has always been foundational to our strategies,” said Green, “but it’s easy to get overwhelmed with the amount of information you have.”

That realization led Comoto on an introspective journey to transform how they access and use customer data to unlock the potential of their marketing channels. BlueConic’s customer data platform (CDP) has been a core component in its transformation.

Choosing the right optimization strategy

When it comes to optimizing their e-commerce sites, Green and her team have traditionally relied on Comoto’s UX and Research teams to provide a testing plan based on qualitative customer research. Using BlueConic’s A/B testing and optimization capabilities, the company can marry qualitative and quantitative methods for a more in-depth understanding of its customers.

“When making big updates to our website, we typically have a theory that we’re looking to improve upon. With BlueConic, we can perform A/B testing on our site to validate the research we’ve done with our customers and supplement it with hard data,” said Green.

She noted that even simple A/B tests could produce some big wins. “Our customers have a true enthusiast culture when it comes to riding, but what they shop for often depends on their riding style. So, we decided to test a shop-by-category module on our homepage that resulted in a very positive incremental lift. Just having the ability to provide someone with a custom experience based on the categories they are most interested in is an easy win for us that has a surprisingly big impact.”

Green and her team have since used BlueConic to ramp up their A/B testing efforts. “We have a lineup of things that we want to test at this point. For the most part, whenever we finished a test, it usually begs another question.” But she also cautions others to start small, as tests can get complicated. To tee up tests for success, she recommends:

  • Testing something that’s going to have enough traffic to get a good read on what you’re trying to answer.
  • Making sure you’re clear with your hypothesis and what you’re trying to solve.
  • Defining clear success metrics.

Moving from touchpoints to journeys

Green and her team have also been able to use the learnings from A/B testing as building blocks for the larger, end-to-end customer experience. “The real power we’ve been working on is transitioning to creating lifecycles. So, not just optimizing our site, but making sure we’re connecting that experience with our other channels,” said Green.

The customer lifecycle orchestration capabilities in BlueConic enable Green and her team to move beyond channel-specific campaign workflows and instead orchestrate cross-channel lifecycle marketing programs that are responsive to each customer’s unique journey based on the real-time, unified customer profile data.

“When we’re sending an email — how are we thinking about the experience in which they’re landing on? Or when a paid ad is driving to the website, what can we do to personalize that experience?”

She also noted that sometimes very seemingly simple components, like adjusting to where somebody lives or their primary interest areas, can be a really compelling way to develop a connection with customers.

“We have a blog called Common Tread that features amazing content. The data available in BlueConic not only enables us to understand how and when consumers engage with us on Common Thread, but also tailor our communications based on their individual interests. If they are an adventure rider and we just posted an article on an adventure bike, for example, we can promote the article and introduce them to the Common Thread experience.”

Operationalizing a CDP

Green noted it’s not enough to simply add a CDP to your business infrastructure and expect to immediately reap the benefits. Like any marketing technology, success (or failure) with a CDP often comes down to an ability to effectively manage change within the organization. For Green, education and communication have been key.

“We achieve some of that just by inviting more folks throughout the business to our quarterly reviews on what we’re working on,” said Green. “We used to be set up so our email and onsite teams would meet separately with BlueConic,” she continued. “Now, we meet together so we can work on our combined strategies across both channels. So just making sure to that the communication between the teams is connected has been a really easy, simple win.”

Since the addition of a CDP also fundamentally changes how companies can and should work, Green stresses the importance of alignment on the goals, use cases (immediate priorities and long-term road map), timing and expected outcomes for a CDP implementation across all levels of the organization.

“Our tech team is very busy with a lot of big priorities, which I’m sure a lot of people can relate to,” she explained. The ability to access the unified, actionable data in BlueConic and use it to create compelling experiences on the site without tapping the tech team has been a huge help for us. That way, we can keep moving and grooving and trying new things without being held up when our tech partners are focused on other priorities.”

For others who are embarking on their own customer engagement transformation journey, Green has some advice: “Just make sure that you pick a partner that’s going to listen to your business problems and what you’re trying to achieve as a business. Only then can you truly unlock the full potential of your investment.”


About The Author

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BlueConic, the leading pure-play customer data platform, liberates companies’ first-party data from disparate systems and makes it accessible wherever and whenever it is required to transform customer relationships and drive business growth. Over 350 companies worldwide, including Forbes, Heineken, Mattel, Michelin, Telia Company, and VF Corp, use BlueConic to unify data into persistent, individual-level profiles, and then activate it across customer touchpoints and systems in support of a wide range of growth-focused initiatives, including customer lifecycle orchestration, modeling and analytics, digital products and experiences, audience-based monetization, and more. BlueConic is a global company with offices in the US and Europe.

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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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