The Power of Natural Language Understanding (NLU) Taxonomies
Data is everything in digital advertising, and most businesses struggle to find great sources. With the vast amount of data available, it’s crucial for businesses to be able to effectively collect, organize, and analyze it in order to make informed decisions about their advertising campaigns. However, the end of third-party cookies has had a significant impact on data collection for businesses, making it more challenging to gather the information needed to effectively target advertising campaigns.
Third party cookies have long been used to track and collect data on consumer behavior and preferences, but with their demise, businesses have had to find new ways to gather this information. One solution that has emerged in response to this challenge is the use of audience cohorts. These solutions use a combination of first-party data, machine learning, and other technologies to create groups of users with similar characteristics and behaviors. The issue is that current behavioral cohorts are too generalized, leading to broad data taxonomies that fail to deliver targeted results.
What are Advertising Taxonomies?
Advertising taxonomies are a system of classification used to organize and categorize different types of advertising campaigns, providing a framework for targeting the right audience, aligning with the specific goals of a business and analyzing the performance of the campaigns to optimize results.
Taxonomies typically include a hierarchical structure, with broad categories at the top level and more specific subcategories underneath. They can also be based on attributes such as target audience demographics, product or service categories, and campaign objectives. This allows for more granular targeting of advertising efforts to specific consumer segments, products or services, and campaign goals.
Taxonomies are essential for keeping track of the different advertising campaigns and efforts, as well as for analyzing the performance of these campaigns to evaluate their effectiveness and identify areas for improvement. They also help in budgeting and forecasting by providing insights on how best to allocate resources.
The Problem with Traditional Advertising Taxonomies
Advertising taxonomies are often overly generalized and lack specificity, which can hinder their ability to deliver effective results for businesses. The problem with generalized taxonomies is that they do not take into account the nuances and unique characteristics of individual products or services or the audiences seeking them. This can lead to a one-size-fits-all approach that does not effectively target specific consumer segments or address the needs of a particular business.
Furthermore, generalized advertising taxonomies are often based on broad and generic categories, such as “consumer goods” or “automobile services”, which do not provide enough information to effectively target the right audience. This can result in wasted ad spend and a lack of ROI. It’s important for businesses to be aware of the limitations of generalized advertising taxonomies and to consider more specific alternatives in order to achieve better results.
What are Natural Language UNDERSTANDING (NLU) Taxonomies?
Natural Language Understanding (NLU) Taxonomies are frameworks used to categorize and structure the meaning of words and phrases in natural language text. This technology allows for the analysis and understanding of human language, making it possible to identify the intent behind written or spoken language.
These taxonomies use mathematical techniques from the field of probability theory to classify and categorize words and phrases based on the likelihood of them appearing together in a given context on the path to purchase. The probability of certain words or phrases appearing together in a given context is determined through the use of NLU algorithms and statistical models. These models analyze the co-occurrence of words in large amounts of data, and use this information to assign probabilities that specific cohorts will visit a page at a specific point on their path to purchase.
The Power of NLU Taxonomies
The application of NLU Taxonomies in the advertising industry has become increasingly important in recent years, as advertisers aim to better target their audiences and create more effective campaigns. By using NLU Taxonomies, advertisers can analyze customer behavior, preferences and purchasing patterns, allowing them to better understand the needs and desires of their target market.
Advertisers can use NLU Taxonomies to categorize product and service offerings, to understand consumer language and preferences, and to classify advertising messages based on tone, sentiment and intent. For example, an advertiser could use NLU Taxonomies to identify the emotional impact of a particular ad campaign and adjust it accordingly to better resonate with their target audience.
This is how NLU taxonomies ultimately enable the compression of a buyer’s journey, as brands are present at the right moment with precise targeting and relevant content.
NLU Taxonomies can also be used in the optimization of search engine advertising. By categorizing keywords and phrases, advertisers can improve the relevance and performance of their search ads. This can lead to higher click-through rates, better conversion rates and ultimately a higher return on investment for advertisers.
In conclusion, the use of Natural Language Understanding Taxonomies is becoming an increasingly important tool for advertisers to better understand and engage with their target audiences. By providing deeper insights into consumer behavior and preferences, NLU Taxonomies can help advertisers create more effective and targeted campaigns, resulting in improved marketing ROI.
The Core Differences Between Natural Language Understand (NLU) & Natural Language Processing (NLP)
| Basic | Natural Language Understanding | Natural Language Processing |
| Purpose | Focuses on understanding the meaning behind the human language. | Concerned with processing and analyzing natural language text |
| Approach | Utilizes machine learning algorithms to categorize and structure the meaning of words and phrases. | Utilizes a combination of linguistic and computational techniques to perform tasks such as text classification and sentiment analysis |
| Output | Produces a categorization or understanding of the meaning behind language | Processed and analyzed text data |
| Applications | Applied in fields such as advertising and customer service, where understanding human language is key | Used in a wider range of applications including language translation, text-to-speech, and sentiment analysis |
Conclusion
As great data continues to drive successful campaigns and data privacy remains at the forefront of digital advertising, NLU taxonomies are emerging as a dominant solution. Businesses need more targeted solutions that reduce ad-friction, increase efficiency and do so with the highest privacy, attention and brand safety standards. Vatic is at the forefront of this industry shift, providing an alternative to generalized taxonomies.
Vatic’s proprietary technology makes advertising seemingly invisible by predicting with unprecedented accuracy where a user will be, what they want to see and how they want to see it. Whether you’re on the buy side and looking to tap into high-intent audiences without cookies while reducing ad-fraud and wastage, or on the sell side looking to make your inventory work smarter with real-time predictive intelligence insights. Vatic is delivering predictive intelligence for a better internet.
Book a demo with us today to see exactly how predictive intelligence can take your campaigns to a new level.