MARKETING
The Definitive Guide to Customer Service Chatbots on Social Media
Nowadays, we no longer have to spend hours on hold to get a hold of someone from the help desk or wait until the next business day to hear back from a company about an issue we have raised. Customer service has become fast-paced, thanks to innovations in technology. As a result, customer behavior and expectations have also evolved tremendously.
We have reached the age of sophisticated smart devices, tools, and systems. Take the case of “bots”— a collective term for various modern machines, software, or computer programs that replicate human dialogue by using voice commands, text chats, or both to simulate human conversations that personalize customer service.
Although it’s only in the last few years that chatbots have taken off with users and business owners, they’ve been around for quite a while. This shift in perception of chatbots and conversational interfaces was largely influenced by advancements in machine learning and artificial intelligence, as well as the thriving use of messaging apps and social media marketing.
A Brief History of Chatbots
In the 1960s, MIT professor Joseph Weizenbaum introduced ELIZA, the very first chatbot in the history of computer science. Through substitution methodology and pattern matching that simulate conversation, ELIZA functioned by identifying keywords or phrases in the input and then generating replies using those keywords from pre-programmed responses.
Kenneth Colby, an American psychiatrist, created PARRY in 1972. Modeled after a schizophrenic patient, PARRY is a natural language program that mimics schizophrenia by simulation of human s. It operates by adjusting the weights allocated to verbal inputs, triggering an intricate system of assumptions, attributions, and emotional responses.
Then in 1995, Richard Wallace invented ALICE (Artificial Linguistic Internet Computer Entity). Unlike ELIZA, the ALICE chatbot could handle more complex conversations using natural language processing. It is a universal language processing bot that conducts chats through heuristic pattern matching.
More than a decade later, a Chinese company called WeChat developed a more powerful chatbot in 2009. WeChat earned the respect of users who have been invested since the inception of this popular social media platform. It has evolved into one of the most efficient methods for marketers and corporations to lessen the labor they perform while interacting with clients online.
Cortana then debuted at Microsoft’s Build 2014 developer conference. Incorporated successfully into both Windows 10 PCs and Windows phone devices, this program receives and responds to voice commands using voice recognition and appropriate algorithms.
Meanwhile, Amazon also launched Alexa in 2014, embedding the program into devices such as the Amazon Echo, Echo Dot, Echo Show, and others.
Top 5 Reasons to Invest in Chatbots
1. Offer immediate customer support 24/7
Today, almost all businesses are expected to provide online information and prompt assistance about their products and services. Being able to address inquiries round the clock increases customer satisfaction and loyalty. With efficient chatbots that can access extensive resources, you can deliver timely solutions.
2. Yield simple scalability
The most crucial thing every customer-centric company can prioritize is the ability to scale support. By incorporating cutting-edge chatbots into your customer service, you save yourself from the inconvenience of hiring more support experts and training them. As a bonus, you can gather real-time feedback since chatbots can be configured to capture data from customer interactions.
3. Elicit quicker purchases
Many people want their purchases done faster and easier due to their hectic schedules and fast-paced lifestyles. Customers that seek quick services while shopping can rely on chatbots, as they can keep up with transactional conversations quite well. These advantages of artificial intelligence for customer service allow for a significantly faster shopping experience and, ultimately, serve a larger consumer base.
4. Enhance lead and revenue generation
Improved lead creation, selection, and nurturing are all possible with chatbots. They’re capable of customizing messages based on information provided by consumers or social media platforms. This enables chatbots to ask appropriate questions, supply necessary information, and assist in converting leads to customers.
5. Cut costs on customer service operations
Statistics show that chatbots can help organizations benefit from reduced customer service costs by accelerating response times and attending up to 80% of typical inquiries. That results in saving up to 30% in customer support expenditures. Furthermore, 40% of customers don’t mind at all if a chatbot or a real person assists them so long as the services they receive match their requirements.
Best Practices in Using Chatbots to Strengthen Social Media Strategy
1. Create customized experiences
A chatbot’s algorithm keeps track of what users write and post on social media and then customizes the consumer experience. Chatbots can craft tailored content and personalized messaging by pulling up collected data from previous preferences and purchases.
Whether it’s on your website, Facebook messenger, or Twitter Direct Message, you can leverage your chatbot’s features to promote customer engagement and satisfaction.
2. Design a content structure
Identify the kind of content that best suits your customers as they engage in chatbot conversations. Here, you could work on the most frequently asked questions first.
Consider the type of inquiries your customers are likely to ask your chatbot so you can build the necessary processes to deliver them the most appropriate and helpful response. Then check with your staff to gain more perspective of your customers’ common concerns.
3. Develop a distinctive voice for your bot
Working on a unique personality for your chatbot makes for a human experience that represents your brand’s style and values. Brainstorm with your creative team to set an ideal and consistent voice for your chatbot.
You could even assign it a name like other companies do to remain transparent with clients and keep them informed that their queries are being handled by a bot.
Get Started with Social Media Chatbots
When traditional marketing efforts fail, consider making use of AI-powered chatbots to facilitate your social media management. Regardless of your preferred branding strategy, turning on the chatbot technology pays dividends by delivering better results.
Industry-leading companies are well underway in integrating state-of-the-art chatbots into their social media platforms—and it’s high time for you to re-evaluate your customer service approach, as well.
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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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