MARKETING
8 Ways To Leverage AI To Improve Lead Generation
In today’s digital age, businesses are constantly seeking innovative ways to improve their lead generation strategies. Traditional methods can be time-consuming and may not always yield the desired results. However, with advancements in artificial intelligence (AI), businesses now have the opportunity to enhance their lead generation efforts and drive better outcomes. In this article, we will explore eight key ways to leverage AI to improve lead generation and propel your business forward.
Personalized Content Recommendations
AI-powered algorithms have the ability to analyze vast amounts of data to understand user preferences and behaviors. By leveraging AI, businesses can deliver personalized content recommendations to potential leads, increasing engagement and conversion rates.
AI algorithms can analyze a lead’s browsing history, social media activity, and other relevant data points to suggest content that aligns with their interests and needs. This targeted approach ensures that leads receive content that resonates with them, enhancing the overall customer experience and increasing the likelihood of generating quality leads.
Chatbots for Instant Engagement
AI-powered chatbots have revolutionized customer engagement by providing instant and personalized interactions. When integrated into lead generation strategies, chatbots can engage with website visitors, answer queries, and gather relevant information. Chatbots can use natural language processing to understand and respond to user inquiries, providing a seamless and efficient user experience.
By automating initial interactions, businesses can capture leads’ contact information and qualify them based on their responses. This not only streamlines the lead generation process but also ensures that leads receive prompt assistance, enhancing their overall experience with your brand.
Natural Language Processing for Lead Qualification
AI-powered natural language processing (NLP) techniques can help businesses automate lead qualification processes. NLP algorithms can analyze and extract information from leads’ responses, such as email inquiries or form submissions, to determine their level of interest and qualification.
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By automating lead qualification, businesses can save time and resources while ensuring that only the most qualified leads are pursued further. NLP can help categorize leads based on their intent, sentiment, and specific criteria, enabling businesses to prioritize follow-up actions and improve the efficiency of their lead generation efforts.
Predictive Lead Scoring
Lead scoring is a critical aspect of AI lead generation, as it helps businesses prioritize and focus their efforts on the most promising leads. AI-powered predictive lead scoring takes this process to the next level by using machine learning algorithms to analyze historical data and identify patterns that indicate lead quality.
These algorithms can analyze a wide range of data points, such as demographic information, past interactions, and purchase behavior, to predict a lead’s likelihood of converting. By leveraging AI for lead scoring, businesses can allocate their resources more effectively and focus on leads with the highest potential, improving overall conversion rates.
Automated Email Campaigns
Email marketing continues to be a powerful tool for lead generation. However, manually managing email campaigns can be time-consuming and prone to human error. AI-powered solutions can automate various aspects of email marketing, such as email scheduling, personalization, and segmentation.
AI algorithms can analyze lead data to determine the most appropriate time to send emails, personalize email content based on individual preferences, and segment leads into targeted groups for more relevant messaging. By automating these processes, businesses can optimize their email campaigns, deliver personalized experiences to leads, and increase the chances of converting them into customers.
Voice Search Optimization
With the increasing popularity of voice assistants and smart speakers, optimizing lead generation strategies for voice search is becoming essential. AI can help businesses adapt their content and SEO strategies to align with voice search queries. AI-powered algorithms can analyze voice search patterns and understand the intent behind queries to provide relevant and accurate information.
By optimizing content for voice search, businesses can increase their visibility in voice search results and capture leads who prefer using voice assistants for information retrieval.
Intelligent Lead Scouting
AI can also be leveraged for intelligent lead scouting, which involves identifying and targeting potential leads that match a specific set of criteria. AI algorithms can analyze large amounts of data from various sources, including social media platforms, business directories, and public records, to identify leads that meet predefined characteristics.
This approach helps businesses identify new and untapped markets, discover leads that may have otherwise gone unnoticed, and expand their reach. By using AI for intelligent lead scouting, businesses can uncover new opportunities and increase their chances of finding high-quality leads.
Data Analytics and Insights
AI-driven data analytics tools provide businesses with powerful insights into lead generation strategies. These tools can analyze vast amounts of data in real-time, uncovering patterns, trends, and correlations that human analysts may overlook.
AI algorithms can identify the most effective channels for lead generation, analyze customer behavior, and provide actionable recommendations for improving lead conversion rates. By leveraging AI-powered analytics, businesses can make data-driven decisions, optimize their lead generation efforts, and continuously improve their strategies based on actionable
insights.
Leveraging AI can significantly enhance lead generation efforts and drive better results for businesses.
By using AI to deliver personalized content recommendations, implementing chatbots for instant engagement, utilizing NLP and voice search optimization, leveraging predictive lead scoring and scouting, automating email campaigns, and utilizing AI-driven data analytics, businesses can optimize their lead generation strategies, improve conversion rates, and ultimately drive business growth.
Embrace the power of AI and unlock its potential to transform your lead generation efforts into a more efficient and effective process.
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.”