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How Blackcart’s ‘try-before-you-buy’ software is helping Mohala sell sunglasses

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How Blackcart’s 'try-before-you-buy' software is helping Mohala sell sunglasses

How Blackcarts try before you buy software is helping Mohala sell sunglasses
Image provided by Mohala.

“Most eyewear in the world is one size fits all,” explained Mohala founder Ashley Johnson. “It’s leaving out women who have different nose bridges, face widths, and face shapes than the standard face shape. It causes the problems of sunglasses or eyewear sliding down the face, resting on cheeks, leaving imprints, and hitting lashes.”

Headquartered in Hawaii, Mohala Eyewear is a startup with a unique approach to designing sunglasses. The company makes adjustable-frame eyewear with various nose bridges and widths that can be customized to fit different faces. 

In addition to bringing more inclusivity to eyewear, Mohala has a mission to help fund education for girls in Bangladesh, Cambodia, India, Laos, Nepal, Sri Lanka, Tanzania, and Vietnam. For each pair of sunglasses sold, Mohala donates a week of school.

Upon launching the company, Johnson had two challenges to address. First, she needed to let people know about Mohala’s unique product offering. She also needed to help people determine which size frame worked best for them. 

While most people know their dress or shoe size, the concept of sunglasses that fit your specific nose bridge is a new one. Johnson wanted to give her customers the opportunity to try the different nose bridge sizes so they could find the best fit for their face shape. 

Said Johnson, “The biggest advantage brick and mortar stores have over e-commerce stores is that customers can try multiple things on before they pay for it. I needed to figure out how my e-commerce business could get over that hurdle.”

A startup with a problem to solve

Johnson needed a solution that enabled her customers to try her different frame designs and easily return what they didn’t want. At the time of her research, there wasn’t much available for small retailers to facilitate try-before-you-buy (TBYB) programs.  

She initially reached out to Shopify, the platform she uses to host her e-commerce store, but they had nothing that fit this need at the time. Ultimately, Johnson could find only two companies with the TBYB technology she needed. One company turned her away because they were only working with brands doing a certain sales volume. 

Said Johnson, “The other company was Blackcart, which had fundraised and launched during the pandemic. They initially said I was too small, but I convinced the owner that small brands need this service too, so he said he would take a chance on me.”

Blackcart launched in June 2020 and their TBYB technology integrates with top e-commerce platforms including Shopify, Magento, Salesforce, and WooCommerce. But when Johnson reached out to them, they were just starting out. Blackcart’s software gives retailers the ability to ship items to a customer’s home with free and easy returns of what they don’t want. The customer only pays for what they choose to keep, plus a nominal deposit fee when they place their order.

A necessary customer experience

Companies like Warby Parker and (naturally) Amazon have TBYB models. In the former case, shoppers can select 5 frames to test at home. Amazon’s program allows shoppers to select up to 6 different items of clothing to try on at home. 

According to a Forrester study commissioned by Shopify, 57% of consumers say TBYB programs say that the flexibility and convenience of trying new products influences their decision to buy online.

Johnson notes that just like larger retailers, small businesses need the option to implement a TBYB program. She emphasized that being able to try something before you buy it is important to the shopping experience, even (or especially) for e-commerce purchases. 

“I think all customers are going to expect it,” she said. “It would be hard for Mohala’s customers if they wanted to try all three nose bridges. They would have to spend $600 up front and then return things. Now, they pay a $10 deposit and then mail back anything that doesn’t fit.”

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Try before you buy order page. Image provided by Mohala

Most people who use the TBYB feature keep at least keep one pair of glasses and sometimes they keep two. The $10 deposit is a fee that Blackcart recommended Mohala start with. 

Blackcart’s TBYB feature enables Johnson’s small team of five to manage the entire trial process via a separate portal where they can easily process returns. Since it integrates with Shopify, refunds and payments can be processed and reported to Shopify. Mohala fulfils them the same way they fulfill any normal order.

“It’s a very smooth process. Blackcart has continued to upgrade their technology, so it’s just gotten better and better,” said Johnson.

Johnson’s team has monthly meetings with Blackcart to review results of the program, identify opportunities, and discuss any new features (e.g., more robust reporting tools were recently added to help with banking reconciliation).


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Try-before-you-buy boosts sales

Mohala experienced a 12% increase in sales in 2021, the year that they added Blackcart. Part of the increase was due to partnering with Nordstrom at the end of 2021 so that Mohala’s glasses could be sold by the retailer. Over the past two months, they’ve had more revenue from Blackcart than from regular orders. 

“It shows that people prefer to have that try-before-you-buy experience,” said Johnson. “I think it just feels safer. If a customer likes three different colors or styles, but isn’t sure which one is going to look best on them, it’s a safer way to shop.”

Johnson’s advice to fashion and beauty retailers is that they should all have a TBYB component as part of their business model, regardless of their size. There is clearly a need for this technology in the e-commerce ecosystem. 

“I really believe in this software and this customer experience,” said Johnson. “Blackcart has been very willing to work with smaller startups.”

Read next: More case studies by Jacqueline Dooley

Johnson notes that it’s important to determine how the TBYB feature aligns with your offering. When they launched the feature, they didn’t set a three pair requirement so customers could try one pair at a time. “Requiring three pairs to try made more sense within the parameters of the program for us along with the $10 deposit.”

A try-before-you-buy strategy should be determined based on your company’s offering and goals, but Johnson’s biggest advice is simply to “do it.” Implementing this software can give your company a competitive advantage and provide an overall better shopping experience for e-commerce customers.


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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