MARKETING
Want to know how AI-powered Payroll Software can Transform Your Business?
Payroll management is an essential element of any modern organization. Due to the complex process of data, it is difficult to keep the payroll on the right track, even for the most sophisticated organizations.
Artificial intelligence disrupts traditional payroll functions by effectively managing payroll data to eliminate opportunities for error through human effort. Because the payroll agenda requires absolute accuracy, orderliness, and streamlined processes, AI has become a lifeline for companies struggling with payroll management.
Automation is an important application for artificial intelligence and machine learning technologies. With the help of AI, companies optimize global tasks such as payroll and time tracking.
AI-Powered Payroll Software
Payroll software should also address employees who can use the software to update their personal information with access to their pay statements. It is useful that they also receive automatic notifications when there is a payday.
Payroll Software combines their payroll software expertise with their data security expertise and says the new application will bring speed and efficiency to small business owners.
The software also comes with live chat support in the app, including regular updates to keep up with changes in payment rules and changes in line.
Changing data-intensive payroll
AI has the potential to completely disrupt traditional processes, and more and more organizations are realizing their potential later. Almost every internal process, including HR and payroll processing, benefits from AI automation and data analysis capabilities.
Effective payroll management requires routine processing of a lot of data. At the same time effective coordination of other aspects of human resource management.
Here are some ways to improve your AI rewards:
- Faster and more accurate classification of employees for wage calculation and inclusion in the tax zone
- Improved interactivity, connectivity, and query resolution through artificial intelligence chat chats
- Extensive compliance with a dynamic control environment
- Faster and more accurate analysis of collected data to create new payroll strategies
- Better data-making opportunities are supported by statistics and forecasts
- Faster integration/relief of employees in the organization’s payroll system upon entry/exit
With the help of artificial intelligence-driven automation and analysis, HR and salary-specific functions have been transformed from administrative functions into strategic decision-making agents for modern organizations.
Solving urgent business problems
Artificial intelligence enables HR organizations to provide new knowledge and services on a large scale without increasing volume or cost. Persistent problems, such as having human resources to implement a business strategy and allocating financial resources to it, can be addressed through judicious use of AI solutions.
Aggregation and verification of data:
Gathering, organizing, and retrieving much of the information that is part of traditional payroll processing requires many hours of manual effort, much of which is spent transferring data between human capital management systems and human capital management systems. applications, usually through static Excel tables.
This approach means that it can be very easy to load obsolete, inaccurate, or incomplete data into processing systems – if lending teams are unwilling to spend time sorting and repairing. Errors can then be caught, but at this stage, errors have already led to delays, which require time and effort from staff. However, automation can streamline data management in two ways. First, using application programming interfaces (APIs), organizations can schedule automatic data transfers between their key operating systems and payroll software to eliminate time spent manually uploading and downloading.
Second, linking automatic transfers to rules-based data verification systems means that payment data can be automatically deleted, both before and after processing. This reduces the need for costly iterations and results in more accurate data for analysis and better reporting.
Solving wage barriers using AI
Payroll professionals are always at their fingertips due to the lack of a documented payroll management system within the organization. Even in companies with well-documented systems, there are many problems due to poorly managed processes. It is known that those skilled in the art have difficulty tracking staff costs. And that’s just the tip of the iceberg.
Global organizations are beginning to understand the problems with their traditional payroll management system and want to invest in artificial intelligence technology to address critical issues.
Process flow and prediction:
Too often, people think that there is only one “right” way to do things – that’s the way things are always done. Even when looking for new systems, companies always focus on applications that can be configured to reflect their existing and often more complex processes, instead of seizing opportunities – or improving their business.
Another problem is that manual processes cannot be obtained, controlled, or measured in any systematic way. It is also very difficult to compare their performance compared to others in a process improvement initiative. This means that many organizations have a blind spot in the payroll and are unable to detect patterns or detect disruptive trends.
However, once the payroll tasks are automated, it also collects relevant data for benchmarking and trend analysis. By regularly analyzing this information, paying professionals can identify potential areas where artificial intelligence processes can be automatically “corrected” by immediately identifying any inconsistencies in the data, identifying questionable patterns, and automatically implementing new, corrective rules over time.
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MARKETING
YouTube Ad Specs, Sizes, and Examples [2024 Update]
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!
MARKETING
Why We Are Always ‘Clicking to Buy’, According to Psychologists
Amazon pillows.
MARKETING
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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