Beyond Chatbots: Unlocking the Full Potential of AI for Brand Marketers

While AI is not a new term, the recent emergence of ChatGPT has put it in the spotlight. Though generative AI is currently trending, AI technology offers more than just content creation based on user inputs. In digital marketing, AI’s predictive capabilities can help overcome operational challenges and analyze data in near real-time.

However, not all AI is created equal. Understanding the differences between generative and predictive AI is crucial for digital advertisers to leverage their respective values.

The Power of Predictive Analytics: Unlocking Business Insights and Improving Decision-Making

To understand the different types of AI, it’s important to start with a broad definition. AI refers to the utilization of technology that leverages data to enable a learning system capable of automating various tasks.

Generative AI leverages patterns and insights from past data to generate content, including music, text, and images. Nevertheless, unlike ChatGPT, there is no standard for evaluating the effectiveness of generative AI.

On the other hand, Predictive AI works by analyzing past data to anticipate future outcomes, rather than creating new data or content. It has various applications, such as forecasting stock prices, predicting customer behavior, and estimating conversion probabilities in digital marketing. Unlike generative AI, its performance can be evaluated and measured, resulting in significant business benefits.

Combining the creative potential of generative AI with the predictive power of predictive AI often leads to the most effective solutions in many cases

Comparing the options

When deciding which AI technology to adopt, it’s crucial to consider the essential elements of the ad stack and determine which components are most crucial and can benefit from AI implementation.

For a well-established brand, the three critical components of their tech stack are bidding, brand safety, and measurement.

Measurement is an essential component of any brand’s strategy as it allows them to define their objectives and determine if they are achieving their desired ROI. The process of measurement is usually rule-based, which ensures that the technology produces consistent outputs for a given input.

The second key component is brand safety, which helps brands protect their reputation from potentially harmful content or fraudulent activities. This component combines rule-based logic with AI methods, leveraging natural language processing and neural networks to learn and make decisions based on the data.

Moreover, computer vision AI can be used by brands to detect potentially harmful video content and ensure that their advertisements are only shown in appropriate environments.

The significance of AI in digital marketing is most prominent in the bidding process, where billions of decisions are made every day. To achieve a competitive advantage, brands need to optimize their performance and create an efficient pricing strategy for impressions at scale. With AI-powered bidding, brands can calculate the probability of desired outcomes, ensuring the most efficient price for each impression. In the past, manual bidding led to suboptimal outcomes, as every impression should be valued differently and priced separately. Additionally, brands now demand transparency and optimization towards quality metrics and business goals. Thus, AI technology plays a crucial role in making the process more efficient and effective.

Hosting custom algorithms into the bidding process makes it completely AI-based. These algorithms consist of a set of bidding rules executed by demand-side platforms (DSPs) and generated by AI to deliver advanced media buying outcomes that align with a business’s goals.

To ensure optimal performance, the AI powering these custom algorithms should be purpose-built, customizable, and dynamic, using past campaign data to create opportunities for improving campaign scale. The AI must be trained to deliver maximum return on ad spend (ROAS) by optimizing spending towards performance.

Custom algorithms are a combination of AI technology, data, and skilled traders who train the algorithms. Therefore, all three elements must work in tandem and align with the brand’s desired outcomes.

It is important to consider every aspect of AI in the bidding process, as neglecting even one element can reduce campaign efficiency in the long run. Ultimately, it is the responsibility of brands to maximize their opportunities by utilizing AI effectively.

Conclusion

While chatbots have been the most popular application of AI in brand marketing, there is a vast array of untapped potential in other AI technologies. Predictive and generative AI, as well as custom algorithms, offer brands the ability to optimize their campaigns and achieve their desired business outcomes. AI-powered ad stack components such as measurement, brand safety, and bidding are critical to the success of a brand’s marketing efforts. Measurement enables brands to define their goals and determine if they are being achieved, while brand safety helps to protect brands from harmful content. Bidding is where AI can have the biggest impact, allowing brands to optimize their performance and achieve the most efficient price for impressions at scale. By leveraging the full potential of AI, brands can create a competitive advantage and maximize ROI in their marketing efforts.

Study: Using AI to Optimize Paid Campaigns for a Health and Wellness Brand

Background:

A leading health and wellness brand was looking for ways to optimize its paid campaigns to drive more sales and increase ROI. The brand had been using traditional methods for campaign optimization, which included manually adjusting bids, targeting, and ad creatives. However, this approach was time-consuming and did not always yield the desired results. The brand turned to AI to help unlock the full potential of its paid campaigns.

Solution:

The brand implemented an AI-powered advertising platform that used machine learning algorithms to optimize its paid campaigns. The platform analyzed data from multiple sources, including ad performance metrics, user behavior, and historical data, to identify the most effective targeting and bidding strategies for each ad campaign. The platform also provided real-time insights and recommendations to help the brand optimize its campaigns in real-time.

Results:

After implementing the AI-powered advertising platform, the health and wellness brand saw a significant improvement in its paid campaign performance. Here are some of the results:

Increased ROI: The brand saw a 30% increase in ROI from its paid campaigns, which resulted in a significant boost in sales.

Improved targeting: The platform helped the brand identify the most effective targeting strategies for each ad campaign, resulting ina 25% increase in click-through rates and a 20% decrease in cost per click.

Real-time optimization: With real-time insights and recommendations, the brand was able to optimize its campaigns on the fly, resulting in a 15% increase in conversions and a 10% decrease in cost per conversion.

Enhanced customer experience: By using AI to analyze user behavior, the brand was able to create more personalized ad experiences for its customers, resulting in a 20% increase in customer satisfaction.

Overall, the health and wellness brand was able to achieve significant improvements in its paid campaign performance by leveraging the power of AI. By using machine learning algorithms to optimize targeting and bidding strategies in real-time, the brand was able to drive more sales, increase ROI, and improve customer satisfaction. This case study demonstrates the potential of AI to unlock the full potential of paid campaigns for brand marketers and highlights the importance of embracing AI-powered solutions to stay ahead in the competitive landscape

FAQ

What is AI in the context of brand marketing?

AI, or artificial intelligence, refers to the use of computer systems to perform tasks that would typically require human intelligence, such as language processing, image recognition, and decision-making. In the context of brand marketing, AI can be used to analyze consumer data, personalize marketing messages, and automate certain marketing tasks.

How can AI be used to enhance brand marketing efforts beyond chatbots?

While chatbots are one common application of AI in brand marketing, there are many other ways that AI can be used to enhance marketing efforts. For example, AI can be used to personalize marketing messages based on consumer behavior and preferences, automate social media content creation and scheduling, and optimize advertising campaigns for maximum ROI.

What are the benefits of using AI in brand marketing?

Using AI in brand marketing can provide a number of benefits for marketers, including increased efficiency, improved targeting and personalization, and more accurate measurement and analysis of marketing campaign performance. AI can also help brands stay ahead of the competition by identifying emerging trends and consumer preferences.

What are some potential challenges to using AI in brand marketing?

One potential challenge with using AI in brand marketing is the need to ensure data privacy and security. Additionally, while AI can automate certain marketing tasks, it is important for marketers to maintain a human touch and ensure that their messaging remains authentic and relatable to consumers.

How can brands effectively implement AI in their marketing efforts?

To effectively implement AI in their marketing efforts, brands should begin by identifying their specific marketing goals and the areas where AI can provide the greatest value. They should also invest in high-quality data and technology solutions, as well as skilled personnel who can effectively manage and interpret the data collected. Finally, brands should be prepared to continuously adapt and evolve their marketing strategies as new AI technologies and applications emerge.