Connect with us

MARKETING

Managing the unpredictable: The inventory conundrum

Published

on

There is a very special name for stuff before it is sold: inventory.

Online retailers want to have enough inventory to meet demand. Having too much is bad, since you must pay to store it, plus there is an opportunity cost in lost sales. Having too little is bad, because it’s hard to deliver instant gratification if you lack the item the consumer desires and that sale is lost.

When COVID-19 happened, manufacturing, shipping and storage all became wild and unpredictable.
Those factors are intertwined and impact each other. Digital marketers and online retailers must sort through this thicket, all the while being wary of the next unpredictable event.

Just how will they do that?

Read next: How logistics and the supply chain impact customer experience

Before and after

The world used to run on “just in time” inventory. Merchandise at rest is a cost, minimized if the stuff sells shortly after it arrives at the warehouse or fulfillment center. Supply chains were taut throughout the system, from parts to manufacturer, from manufacturer to shipping, from shipping to seller, from seller to customer. Merchandise spent more time in motion than at rest.

Just in time inventory still exists…sort of. “There are still companies with [such] predictable supply and demand profiles that just-in-time still works,” said Mark Hart, Chief Operating Officer of Pollen Returns, a pick-up service for e-commerce businesses.

But as companies migrated to online sales, the weaknesses of just-in-time inventory were exposed. Parts could not get to factories. Sometimes factories closed. Finished goods could not always ship. Warehouses might have that item or might not. “There is a running joke that companies are migrated from just-in-time to just-in-case,” Hart said.

So how do online merchants react when their supply chains snap?

One quick fix is to throw money at the problem, basically spending more on air cargo and shipping, explained Matt Garfield is a Managing Director in FTI Consulting’s retail and consumer products practice (FTI is a global business advisory firm). That can still leave vendors waiting for the goods to arrive while staring at empty shelves.

Or they can go big on just-in-case, but “retailers may have swung the pendulum too far,” Garfield said. Merchants report excess inventory — and undertaking promotions to clear it. “Going forward, we expect to see retailers adjust their inventory models to bridge the divide between JIT inventory and current inventory models. Key focus should be ensuring higher holdings of high velocity, common products while reducing holdings (and potentially introducing sales risk) for lower velocity products.”

“Nearly all of the clients we work with are going through a supply chain review, looking for risks, trying to find alternative or additional suppliers,” said Russ Sharer, Chief Sales Officer for the Brooks Group, a sales training and leadership consultancy. While some clients will hold more inventory, “most will qualify additional suppliers…and balance the business between them as a primary risk strategy. As we are seeing today with large retailers, holding inventory is risky if buyer’s tastes change.”

Read next: Our interview with Pollen’s founder, Spencer Kieboom

Use data, not a crystal ball

Online retailers can’t rely on a fortune teller peering into a crystal ball to get inventory right. But
brands have a lot of data to analyze that can aid forecasting. For big companies, this is not usually a problem. Seasonal demand is easy to predict — Christmas and the holidays, back to school, and so on. Beyond that, you need a different approach.

“I’d say that 70-80% of overall revenue can be modeled well.” Sharer said. “The remaining is either too new or has too unpredictable demand. In my experience, it’s a lot easier for companies to model for smooth demand than for peaks and valleys.”

Hart offered a more pro-active approach: Online retailers must take a position on what they want to sell. Basically, an online retailer uses technology to create a demand for specific items, a strategy that ideally makes demand more predictable.

“’What if’ analyses have been a frequent tool for retailers to plan for potential scenarios (both upside and downside) and quantify the impact to inventory holdings.” Garfield said. But you can “only test for scenarios developed or envisioned by the retailer (typically percentage increases/decreases in demand or supply).”

Read next: The Brooks Group on building customer trust while the supply chain is in crisis


Get the daily newsletter digital marketers rely on.


“Forecasting demand is rarely 100% accurate, but there are steps you can take to get your estimates closer to actuals,” said Casey Armstrong, CMO of ShipBob, a third-party logistics firm. Look at historical data and seasonality. Think through planned promotions and anticipated spikes. Optimize stock levels by tracking fast-selling vs. slow-moving items. Decide how much of each SKU to reorder and when.

Merchandisers and marketers do “finger in the air” forecasting, expecting an item to sell when in fact it may not move at all, noted Dave Emerson, SVP for global e-commerce at global freight and delivery firm Sekologistics. “No one gets fired,” Emerson said. “It’s the cost of doing business.”

Emerson recalls one client who got it wrong. They were paying $400,000 per month to warehouse goods that did not move at all — for eight months. “During the pandemic, they took a punt,” he said. Which leads to the psychology of inventory. It’s like a comfort blanket.

“You can put your arm around it to make sure that it’s there. But that costs money,” he said. There must be a better way. It is one thing to take comfort from having inventory. It is another to know when to get the goods — and being able to sell through.

This is the first part of a two-part article.


About The Author

William Terdoslavich is a freelance writer with a long background covering information technology. Prior to writing for MarTech, he also covered digital marketing for DMN.

A seasoned generalist, William covered employment in the IT industry for Insights.Dice.com, big data for Information Week, and software-as-a-service for SaaSintheEnterprise.com. He also worked as a features editor for Mobile Computing and Communication, as well as feature section editor for CRN, where he had to deal with 20 to 30 different tech topics over the course of an editorial year.

Ironically, it is the human factor that draws William into writing about technology. No matter how much people try to organize and control information, it never quite works out the way they want to.

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

MARKETING

YouTube Ad Specs, Sizes, and Examples [2024 Update]

Published

on

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!

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

Why We Are Always ‘Clicking to Buy’, According to Psychologists

Published

on

Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

(more…)

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

MARKETING

A deeper dive into data, personalization and Copilots

Published

on

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.”

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending