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7 Critical Factors You Must Consider When Choosing RPA Tools

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Robotic Process Automation is a technology that makes it easy for businesses to build, deploy and manage bots that can replicate humans interacting with digital systems and software. These bots can perform structured and pre-defined tasks such as filling out a form, processing a financial transaction or sending messages.

The core purpose of robotic process automation is to automate mundane and repetitive tasks so that your employees don’t waste their time on those tasks and focus on more value-driven activities with automatic employee monitoring software. Yes, a human first has to define the workflow for a bot for it to work but once done, it can perform most tasks automatically.

Advantages of Robotic Process Automation

Here are some of the advantages of robotic process automation:

  • Optimal resource utilization
  • Save time
  • Reduces cost
  • Minimize errors
  • Increases business capacity

Disadvantages of Robotic Process Automation

Some of the disadvantages of robotic process automation are:

  • Requires monitoring and maintenance
  • Not capable to extract information from unstructured datasets
  • Can not automate complex tasks
  •  The time-consuming and costly setup process

In this article, you will learn about seven critical factors you must take into account when choosing robotic process automation tools for your business.

Before discussing factors you should consider when buying robotic process automation tools, it is important to understand that every robotic process automation tool has its own format and does not offer any kind of portability. This means that there are no standards so the one size fits all formula does not apply here.

Make sure that the robotic process automation software you are planning to buy has all the features you need along with some handy extras. Ask for proof of concept before rollout and only buy the software when you are sure that it is the right choice to meet your business needs.

7 Factors To Consider When Buying RPA Tools

Here are seven factors you must consider when buying a robotic process automation software

1.  Ease of bot setup

Setting up a robotic process automation software can be a daunting challenge for businesses as it can take a lot of time and resources. That is why it is imperative that enterprises invest in robotic process automation tools that are easy to set up and use.

It must also allow a level of customization and let businesses create custom bots for different buyer personas. Developers should be able to call the robotic process automation tool API when writing code for automation.

2.  Low-code capabilities

Gone are the days when only experienced developers could create websites and apps. With the advent of low code tools, anyone can now create an app even with little to no coding knowledge(accounting app, management app, etc.). Low code development lets you drag and drop ready-made components from the tool library and write small code snippets for functions that are not present in the tool library of the tool. Choose robotic process automation tools that offer these low code capabilities.

3.  Machine learning capabilities

As mentioned before, robotic process automation software struggles when it comes to extracting actionable insights from unstructured datasets. Since a major chunk of company data is in unstructured form, it makes robotic process automation tools useless.

That is where the machine learning capabilities of these robotic process automation tools come in handy. With these capabilities, it can parse through documents, find information and return it to users. This can enhance the user experience and boost customer loyalty. Some vendors might give this a fancy name but the functionality remains the same.

4.  Integration with enterprise applications

Another important factor you can not afford to ignore when buying robotic process automation software is compatibility with enterprise applications. At the end of the day, your robotic process automation software’s utility is highly dependent on how these tools can integrate with your existing business application. This is about your data integration with supermetric alternatives and so on.

Its capability to extract data from your existing business applications matters most. Does your robotic process automation tool offer plugins to seamlessly connect it with your database, accounting systems, HR systems, appointment setting services and ERP systems? If yes, then you should certainly consider it as an option if it fulfills all your requirements.

5.  Orchestration and administration

Before these bots can take care of mundane tasks, you will have to first configure them and feed them with the right information as well as a secure credential. This secure credential is usually stored in a credential store. If you want other users to use your bots, you will first have to authorize and authenticate them.

You should also allocate resources for certain bots which trigger when a special event occurs. Once you have set it up, now you have to monitor it so it can work without human involvement. You will have to constantly improve its machine learning capabilities so it does not need human support when performing tasks.

6.  Process and task discovery and mining

Identifying business processes you want to automate and prioritizing them is critical for the success of your robotic process automation implementation. Unfortunately, it is also the most time-consuming part of the process as well.

The more your robotic process automation software lets you mine for processes from system log and construct task flows by observations, the easier it will be for you to implement it and automate your business processes. Look for robotic process automation tools that make task discovery and mining painless.

7.  Scalability

If you are planning to implement robotic process automation throughout the organization, you will bump into scalability issues. The best way to resolve these scalability issues is to implement them in the cloud, in containers or via virtual machines. If the orchestration component can allocate extra bots when needed, solving the scalability problem is not a problem.

At the end of the day, the success and failure of your robotic process automation rest on identifying the best tasks and processes to be automated. Make sure to document every step involved in the process. Never cut corners in testing cycles because it can lead to some missing links in your robotic process automation systems.

What factors do you consider when selecting a robotic process automation software? Share it with us in the comments section below.

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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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