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Improving website search was key to boosting Paul&Shark’s e-commerce revenue

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Improving website search was key to boosting Paul&Shark's e-commerce revenue

Paul&Shark’s website went live in 2019, but the Italian luxury clothing brand has been around for half a century. “The brand has a long history,” said Giusseppe Miriello, Paul&Shark’s Global Digital Director.

Miriello’s role is focused on growing the digital department which includes e-commerce and will soon include B2C apps and marketplaces.The brand’s inspiration was based on a trip the founder, Paola (Paul) Dini, took to Maine. Dini visited a sailmaker’s shop and saw the name “Paul&Shark” inscribed on a sail from an 18th century ship.

Improving website search was key to boosting PaulSharks e commerce revenue
Image supplied by Paul&Shark.

Said Miriello, “He saw this as a sign of destiny and he decided that this would be the name of the brand. From the beginning, we’ve been working with sustainable materials. Our garments are notorious for being able to withstand windy and wet conditions. Over time, we extended selling our products worldwide and recently to e-commerce.”

As was the case with many retailers, Paul&Shark experienced incredible online growth in 2021 which strained their e-commerce team. A significant pain point was the website’s search function, which was the out-of-the-box tool that came with Magento, their e-commerce platform. Magento was slow at processing the data from their catalog, leading to a poor overall website search experience.

Miriello wanted a website search tool with more flexibility around visual merchandising and the ability to create relevant, timely search results for each shopper. 

Better search & discovery equals improved customer experience

Miriello understood that Paul&Shark needed to fix the website’s search experience. He’d worked with Algolia, an API search and discovery platform, at previous companies.

Algolia was built specifically for people who own their content. It speeds up website search results out of the box and is also highly customizable.  Its visual merchandising feature allows website owners to better control how the search results appear and the order that they appear in.  

Visual merchandising is an approach that retailers use to maximize customer satisfaction. In physical stores, this includes optimizing store layout, such as displaying products at eye-level or placing signs for easy scanning by shoppers while they’re browsing. This helps motivate people to purchase more and stay longer.

“Algolia allows the brand or site owner to use visual merchandising to better control how search results appear and the order that they end up in,” explained Piyush Patel, Algolia’s Chief Strategic Business Development Officer. “It uses business rules like inventory, availability, location geotargeting, and other variables to better target the outcomes that consumers expect. It also uses data like previous purchase history, brand affinity, and product type.”

Searchandising: A modern approach to visual merchandising

“Searchandising” is the process of improving website search functionality to help visitors find what they’re looking for. In e-commerce, website search is crucial to the buying experience for online shoppers. Fully 94% of U.S. consumers quit a shopping session due to poor product search results, according to a recent Harris Poll conducted by Google. 

A good e-commerce search experience includes delivering results quickly, providing customized product listings, and surfacing relevant product recommendations depending on season, region and other factors.

Paul&Shark has about 25,000 CPUs and they’re located in 108 countries, so when the pandemic drove shoppers online, their e-commerce searches increased by a magnitude of 50X in 2021. Their existing website search tool wasn’t robust enough to handle the increase in search volume.

“Magento is very, very slow at processing all the data and it would have been extremely difficult to give an excellent customer experience without a third-party tool that can fetch the data from our catalog and process the search in the appropriate way,” said Miriello.

For example, Miriello wanted to communicate specific styles with specific times of the year. “For that,” he said, “the e-commerce team needed more granular control of relevance and powerful visual merchandising capabilities.” 

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Screenshot of Algolio’s visual merchandising interface. Image provided by Algolia.

Alogolia gives nontechnical users the ability to manage the relevance and ordering of the list of product search results via a drag-and-drop interface. Rules are created manually (e.g., if inventory is higher for one item versus another, you can set a rule to have the higher inventory product show up at the top of the results). 

Miriello used Algolia, not only to improve search relevance, but also to remove the manual processes previously used to build product listing pages. “I took ownership of the implementation,” he explained. “There was a system integrator who did the work of integrating the code, but I created the algorithms both from the searchandising and visual merchandising aspects of setup.”

A faster path to that cotton shirt

Algolia’s technology leverages AI to get shoppers the results they want quickly. Brands have a lot more control about how to incorporate additional data from past purchases and customer shopping behavior, but shoppers don’t have to be logged in to benefit from this feature. Search results can be purely session-based. 

“If I search for organic milk during a session and then search for bananas, it assumes I’m going to want organic bananas,” said Patel. “I’ve shown that I already have a preference. The data makes it easier to deliver relevant results. All of this is aimed at getting the consumer to the outcome they want much quicker without actually having to forge through lots of irrelevant results.”

After implementing Algolia’s search and discovery technology, Paul&Shark experienced upticks across the board: 

  • Use of the search function increased by 39%.
  • Revenue accrued in the first month of implementation increased by more than 15%.
  • Conversion rates rose to almost 10%.

In addition to these tangible results, Miriello realized a surprising benefit from the improved search functionality — customers were using search to find information as well as products.  

“Well, of course numbers are good,” he said. “And since we are planning for two improvements with integration, of course the numbers will get better in the future, but what really stood out is that our customers use search to find information instead of reaching out to customer care. In the midst of their product queries, they’re searching for things like ‘how can I load up my loyalty points, how can I exchange a product, and how many days does it take for delivery?’” 

The information surfaced by the customers’ search queries isn’t necessarily being expressed to customer care. These searches are helping Miriello’s team better identify things that customers want and further streamline search results to score content more appropriately based on customer queries.

Said Miriello, “Any query related to non-commercial items will be driven to FAQs or customer care. This way we hope to improve the customer experience and improve sales with the help of the customer representatives who are experts in product issues.”


About The Author

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Jacqueline Dooley is a freelance B2B content writer and journalist covering martech industry news and trends. Since 2018, she’s worked with B2B-focused agencies, publications, and direct clients to create articles, blog posts, whitepapers, and eBooks. Prior to that, Dooley founded Twelve Thousand, LLC where she worked with clients to create, manage, and optimize paid search and social 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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