The Gist
- AI-native content discovery. Vector search enables semantic understanding, going far beyond keywords to find meaning in customer queries and media assets.
- Faster, fresher insights with RAG. Retrieval Augmented Generation (RAG) lets CMOs deliver real-time, accurate responses without retraining AI models.
- Strategic database integration. Combining traditional and vector databases helps marketers create scalable, responsive systems that power dynamic personalization.
As artificial intelligence reshapes marketing technology, vector search has emerged as a critical capability for 2025 data strategies. For marketing leaders, understanding how this technology impacts content discovery, customer experience and operational efficiency has become essential.
We will take a look at the basics behind vector search, and what opportunities marketers have for implementing it.
Table of Contents
Understanding Vector Search in the AI Era
What Makes Vector Search Different From Traditional Search
Vector search is derived from the need for generative AI. Vectors are needed in generative AI applications because they embed complex meanings in a compact fixed-length array of numbers. An array is the “vector” in vector databases.
Vector databases are specialized database systems that store vector embeddings – vectors containing numeric data that represent text, images, video or audio. The media is converted into high-dimensional mathematical vectors so that comparing and searching for information in those vectors happens rapidly.
Traditional databases rely on manually inserting keywords into file metadata. The methodology is suitable in many instances but can be slower to make comparisons and provide information in a query. In contrast, the high dimensionality of vectors counters the design of traditional text-based indices.
What Impact Vector Search and Databases Bring to Marketing Teams
How Vector Databases Enhance Large Language Models
Because of this, vector databases introduce strategic changes in how data is stored and indexed. The dimensionality of vector databases provides additional information for LLMs to process when a prompt is activated. LLMs have been accurately trained on data, but the information based on that data can change over time. Vector databases supplement the trained data with up-to-date information.
This is where Retrieval Augmented Generation (RAG) comes into play.
Retrieval Augmented Generation is a query process that relies on vector databases to infuse large language models with more relevant, up-to-date responses. When a marketing team needs their AI system to access current product information, marketing campaign data or customer insights, RAG uses vector search to find and retrieve the most relevant information from their company’s database before generating a response.
For example, a customer service AI assistant supported by a LLM using RAG could instantly access the latest product specifications, pricing and promotional details through vector search, ensuring its responses align with current marketing campaigns. This combination of vector databases and RAG helps marketing teams maintain consistency across AI-powered customer touchpoints while keeping the information fresh without retraining the entire AI model.
Improving Search Accuracy With Semantic Matching
The quality of semantic understanding is raised with vector search. Vector search enables matching based on meaning rather than just keywords. This opens the door for a variety of improved indexing on media and content. Metadata on frequently asked media can be restructured and utilized. Content tagging and categorization strategies are revised. All of this leads to reexamined ways that related content is linked and discovered.
For example, a marketing team using vector search could automatically match customer support queries with relevant content across multiple channels, reducing response times and improving customer satisfaction. This same technology can power product recommendations that understand the context of a customer’s journey, not just their keyword searches.
Related Article: Are Marketers Prepared for AI Search Disruption?
Strategic Implementation Considerations
Technical Considerations for Implementing Vector Search.
This altered tech paradigm means organizations need to explore how implementing specialized vector databases will impact their existing data systems. They need to identify the business value of vector embeddings within their operations and see how their overall stack is improved. The arrival of vector databases alters the computational and storage requirements for embedding generation.
Vector search also raises the need to alter data quality tactics. Quality control for vector search systems includes validating semantic relationships and updating embedding models. These ensure that generative AI relying on these models offers reliable responses. Consistent data cleaning and normalization are also necessary.
Growing Industry Investment in Vector Search Tools
The race to incorporate AI has made vector databases a hot topic. Companies have been investing their tech budgets into database development, seeking how to integrate vector search capabilities into their existing SQL or NoSQL databases.
More solutions to manage vector databases and offer vector search services are being launched to market, with many increasingly mentioning potential cost savings. Amazon, for example, just launched OpenSearch Vector Engine, a real-time vector search platform that creates indexes on vectorized content. It can configure indexes on billions of vectors in a given query while optimizing its runs within memory-constrained environments.
Amazon claims in its news memo that OpenSearch can run vector search at a third of the cost while providing the capability for “ low-cost, accurate vector search that responds in low hundreds of milliseconds.”
Other databases have added features. MariaDB has just begun to offer vector database services in its most recent version, 11.7. The feature arrives after a number of organization changes to keep MariaDB competitive in the vector database marketing, including a private equity buyout from K1 Investment that took MariaDB private last fall.
The Shift Toward Semantic and Hybrid Search
Vector search has raised the profile for semantic search. The shift to semantic search requires new approaches to query understanding and processing, as well as quick strategies for converting user inputs into vector representations and for handling hybrid searches that combine vector and keyword matching.
This means marketers should consider the infrastructure that sets computational resources for real-time embedding generation, scalable storage solutions for vector data and optimized performance for similarity searches.
The Enduring Role of Traditional Databases
All of this excitement for vector search does not mean traditional databases are not valuable in supporting customer experience. Traditional databases excel at exact matches among data fields, in which you need to find records that precisely match your criteria, such as a segment of customers aged 25-30 who live in Rhode Island.
This means traditional databases work well with exact requests within structured data queries, such as complex joins across multiple tables and aggregation tasks that compute sums, averages, and other statistics across numeric fields.
Where the Vector Is Pointed Toward
Building a Balanced Data Strategy With Vector and Traditional Databases
As marketing teams plan their 2025 data strategies, they’ll need to balance traditional and vector databases implementations in their operations.
As marketing teams plan their 2025 data strategies, they’ll need to balance traditional and vector databases implementations in their operations. While traditional databases remain crucial for precise segmentation and analytics, vector search enables the intelligent content discovery and personalization that modern marketers are seeking.
Marketing leaders should begin by identifying high-impact use cases where vector search could enhance customer experience – whether through improved content recommendations, more intuitive search functionality, or dynamic personalization stemming from vector search capabilities.
Marketers have an opportunity to develop a hybrid strategy that leverages both traditional and vector databases to create more responsive information systems that can scale with growing AI capabilities.
Organizations that successfully integrate vector search into their data infrastructure will be better positioned to deliver the personalized, context-aware experiences that will define marketing success in 2025 and beyond.
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