How To Leverage AI To Scale New Customer Revenue
As Artificial Intelligence (AI) tools get easier to use, they are changing how brands market by making just about everything simpler and faster. This change helps businesses be more productive and efficient, letting them focus on new ideas and growth.
One big way AI is helping is by making it easier to create ad copy. Tools like ChatGPT can quickly make many different versions of headlines and descriptions, saving time and effort, and letting marketers spend more time planning and trying new things.
AI also helps with making ads more personal. Customers today want ads that feel relevant to them, and AI can make this happen by looking at lots of data to create high-quality, personalized ads for each user, making ads more engaging and increasing the chance of getting new customers.
Another critical assistance AI provides is analyzing data and making reports. Usually, this takes a lot of time and work, but AI can quickly look at large amounts of data, find trends, and create summaries. This makes it easier for marketers to make detailed reports and better decisions.
Platforms like Google Ads have built-in AI tools that help with the whole advertising process, automatically creating headlines, descriptions, images, and even videos. This means marketers have everything they need to make great campaigns without investing extra resources, making the workflow smoother and the campaigns more effective.
In short, AI is changing the game for DTC ecommerce by making tasks more manageable, helping with personalized ads, and improving data analysis and reporting. For brands that want to grow new customer revenue, using AI in their paid media strategies is a must. This article outlines a few ways some brands leverage AI to scale new customer revenue and quickly grow their businesses.
AI For Improved Ad Targeting
AI algorithms have significantly improved ad targeting by analyzing customer data, enhancing the efficiency of paid campaigns, and ultimately helping brands scale new customer revenue.
One of the key ways AI enhances ad targeting is through the creation of lookalike audiences. In Google Ads, this feature is currently available in Demand Gen campaigns, which include YouTube and Discovery placements. Marketers can upload lists of purchasers or website visitors, and AI creates lookalike audiences, targeting users with similar behaviors and interests. This allows marketers to reach potential customers in the middle of the funnel effectively.
Google Analytics 4 (GA4) also offers predictive audiences, which can identify users likely to purchase within a specific timeframe. Once imported into Google Ads, these audiences can be targeted in campaigns. This predictive capability uses large amounts of data to forecast user behavior, helping marketers focus their ads on users most likely to convert.
AI also helps segment audiences based on characteristics like age, purchase history, product preferences, and likelihood to purchase. Google’s extensive audience data includes in-market audiences (users deemed likely to purchase a particular type of good or service based on past behavior) and affinity audiences (users grouped by specific interests, such as fashion or home buying).
These pre-segmented audiences make it easier for marketers to align their targeting with specific goals and find new customers who resemble their existing audience.
Despite the deprecation of cookies, AI and machine learning enable platforms like Google to continue providing valuable audience insights. By leveraging data from services like Gmail, Chrome, Android, and YouTube, Google can understand user behavior comprehensively, which is crucial for effective ad targeting.
Tools like Triple Whale further enhance these strategies by integrating data from multiple platforms, allowing marketers to see how many new customers they are driving from specific campaigns and understand the cost per acquisition by platform. With this insight, brands can optimize their marketing efforts across different channels to maximize their return on investment.
AI To Create Personalized Ad Content
AI is crucial in creating highly personalized ad content that resonates with target audiences, leading to increased conversion rates. A wide range of AI tools are available to help develop specific ad or email copy that captivates an audience.
These tools analyze various data points to tailor the content to the interests and behaviors of potential customers. For example, Google and Meta have integrated AI features that dynamically select headlines and description line combinations based on users’ past behaviors, optimizing the ad content to capture users’ attention effectively.
One of the standout features is the ability of AI to create landing pages that automatically update with the keywords users search for. Tools like Optimonk can generate landing pages matching the specific keyword from a user’s search query, ensuring that visitors are greeted with content that matches their interests. This tailored approach significantly increases the likelihood of conversion.
Within Google, Responsive Search Ads (RSAs) utilize AI to dynamically select and combine headlines and descriptions based on what has historically performed well. As more data is gathered, the ads become increasingly effective at engaging users.
Furthermore, Google’s Dynamic Search Ads (DSAs) automatically generate headlines based on the content of the search and the landing page for a seamless user experience.
Additionally, Google analyzes website content, past creative efforts, and account keywords to generate ideas for new headlines and descriptions. This saves marketers time and leverages proven strategies to drive engagement. These AI-generated suggestions are often highly effective, as they are based on extensive data and analysis.
Even though AI can create compelling ad copy quickly, it’s still important for marketers to review and refine the content. While AI can generate numerous variations and themes, a human touch ensures that the final copy aligns perfectly with the brand voice and campaign objectives.
Dynamic Bidding Strategies with AI
Google’s AI-driven bidding strategies automatically adjust bids in real time based on various data points, such as past performance, budget, user behavior, bid value, ad rank, and more. This approach allows advertisers to efficiently reach their target audience with the right message at the right time, thereby scaling new customer revenue.
Unlike manual CPC bidding, which requires continuous adjustment for specific ad groups or keywords, automated bidding removes the guesswork. Google Ads uses AI to set bids based on the likelihood of an ad resulting in a click or conversion, helping businesses achieve specific performance goals. This automation saves time and ensures that bids are optimized to meet business objectives.
Furthermore, Google’s Smart Bidding leverages AI to tailor bids for each auction. It considers a wide range of signals, such as device, location, time of day, remarketing list, language, and operating system, capturing the unique context of every search. This “auction-time bidding” ensures that each bid is optimized to meet advertiser goals.
Seasonal adjustments are another area where AI-driven dynamic bidding strategies shine. AI can adjust bids to become more aggressive during high-demand periods, such as Black Friday or holiday sales, capturing increased traffic and conversion opportunities.
AI Chatbots and Customer Engagement
AI-powered chatbots transform customer engagement by responding instantly to questions and concerns when visitors arrive on a landing page. These chatbots are crucial in streamlining how brands acquire new customers and scale new customer revenue.
One of the key advantages of AI chatbots is their ability to engage with customers 24/7, offering immediate assistance and personalized experiences. By leveraging algorithms and natural language processing, chatbots can interact with visitors in real time, answering inquiries, providing product recommendations, and resolving issues efficiently. This real-time interaction helps maintain customer interest and prevents potential leads from abandoning the site due to unanswered questions.
For businesses, this means that chatbots can act as an extension of their customer service team, handling routine queries and freeing up human agents to focus on more complex issues.
On landing pages, chatbots can provide valuable support by addressing common questions, guiding users through the buying process, and offering personalized recommendations based on browsing history and preferences.
Moreover, AI chatbots can gather and analyze data from customer interactions to provide insights into user behavior and preferences. This data can be used to refine marketing strategies, improve product offerings, and personalize future interactions.
For example, Google’s AI-powered chatbots can be integrated into ad campaigns to ensure visitors who click ads and land on a website receive immediate and relevant responses. This integration helps capture leads and nurture them through the sales funnel. Additionally, chatbots can be programmed to follow up with visitors, providing personalized offers or incentives based on their previous interactions, thereby increasing the likelihood of conversion.
AI chatbots also play a vital role in handling post-purchase inquiries, ensuring that customers receive timely support for any issues or questions they might have after making a purchase. This continuous engagement helps build trust and loyalty, encouraging repeat business and positive word-of-mouth.
In short, brands leveraging AI chatbots can ensure a seamless and satisfying customer experience, ultimately leading to higher conversion rates and improved retention.
Predictive Analytics and Forecasting with AI
Within Google, AI tools provide powerful forecasting models that help businesses understand and predict demand for specific products and search terms. These models analyze historical data and current trends to forecast when demand will likely increase, allowing marketers to allocate their budgets more effectively.
For example, if a particular keyword category is expected to surge next month, marketers can adjust their budgets to capitalize on this increased interest.
Google’s forecasting tools also assist in optimizing budget allocation and return on investment (ROI). By inputting a list of keywords into the forecasting tool, marketers can see projected spending levels and expected CPA (cost per acquisition).
This information is crucial for developing strategic plans, not just for peak periods like Black Friday but for new campaigns as well. These insights allow marketers to toggle budget settings and see potential outcomes, helping them to make informed decisions that maximize their advertising spend.
Google Trends data is also a critical component of these forecasting models. By analyzing search patterns over the past twelve months and projecting future trends, Google can provide insights into when users will likely start searching for specific products.
For instance, search activity typically increases in the weeks leading up to Black Friday. Using this data, brands can build awareness and engage potential customers well in advance, positioning themselves to take full advantage of the heightened interest.
AI’s ability to predict market trends and consumer demand goes beyond just identifying when to increase budgets. It also helps understand which products are likely to be popular and which customer segments are most likely to convert.
AI-driven predictive analytics within Google provides brands with the tools they need to accurately forecast market trends and consumer demand. These insights are instrumental in strategic planning, helping businesses optimize paid media campaigns, allocate budgets more effectively, and scale smoothly.
Leveraging AI to Scale New Customer Revenue
As AI continues to evolve and become more accessible, its role in scaling new customer revenue has never been more critical. By automating and optimizing various aspects of marketing, AI enables brands to streamline their processes, increase efficiency, and focus on growth and innovation.
From ad creation and personalized targeting to dynamic bidding and predictive analytics, AI provides the tools needed to enhance efficiency, engagement, and ROI. By embracing these advanced technologies, businesses can drive sustainable growth and stay competitive throughout the rest of 2024 and beyond.
About the Author: Laura Benoit is a Senior Media Analyst at adQuadrant. Originally from Boston, MA, Laura moved to San Diego in 2021 and enjoys exploring California via beaches, hikes, and burrito shops. A graduate of Emmanuel College, she began her marketing career in higher education before transitioning to digital marketing agencies where she’s been for the past 5 years.
