We’re surrounded by data, from clicks and conversations to transactions and reviews. But most of this valuable data is unstructured, traditional databases weren’t built to handle it.
That’s where vector databases come in. They use a technique called vector embeddings to understand meaning, not just keywords, making it easier to search, analyse, and unlock insights from messy, real-world data.
Click here to download “Vector Databases & the Power of Semantic Search” as a PDF.
Vector Embeddings and Databases
Vector embeddings are numerical representations that capture the meaning behind data, not just the words. AI models convert inputs like text or images into vectors in a multidimensional space, where similar ideas cluster together. For example, “annual revenue report” and “yearly income summary” use different words but share the same intent, and their vectors land close together.
They are built for meaning, not just matching. Unlike traditional databases that depend on exact keywords, they use embeddings to find information based on semantic similarity, retrieving what you meant, not just what you typed.
Vector databases enable context-aware search across unstructured data, helping organisations uncover deeper insights, boost relevance, and make faster, smarter decisions at scale.
Why This Matters: Strategic Business Value
Vector databases aren’t just a backend innovation; they unlock real strategic value. By enabling smarter internal search, deeper customer insight, and more context-aware analytics, they help teams move faster, uncover hidden patterns, and make more informed decisions.
Smarter Search. Teams can find information using natural language, not exact keywords, making internal search faster and more intuitive across functions.
Clearer Customer Signals. Embedding unstructured data reveals recurring pain points and patterns, even when phrased differently, sharpening customer insight.
Stronger Decisions. Vector databases enable deeper, context-aware analysis, surfacing insights traditional systems miss and driving more informed decisions.
Kickstart Your Journey with Vector Databases
Getting started doesn’t mean overhauling your entire data stack. Identify high-impact unstructured data sources, choose a platform that fits your ecosystem, and begin with focused use cases where semantic understanding drives clear user value.
- Identify High-Value Unstructured Data. Assess where unstructured data resides; these sources hold untapped insight and are ideal for vector embedding.
- Select the Right Platform. Evaluate purpose-built solutions and prioritise compatibility with existing cloud environment and API ecosystem to ensure seamless integration.
- Start with Targeted Use Cases. Begin with specific, high-impact applications – such as semantic search for knowledge retrieval, summarising large documents, or enhancing virtual assistants. Focus on measurable outcomes and user value.
Ecosystm Opinion
Vector embeddings and vector databases may sound technical, but their purpose is profoundly human, helping systems understand meaning, context, and intent. As AI adoption accelerates, competitive advantage will belong not to those with the most data, but to those who understand it best. This is how we move from information to insight – and from data to decisions.
Customer feedback is at the heart of Customer Experience (CX). But it’s changing. What we consider customer feedback, how we collect and analyse it, and how we act on it is changing. Today, an estimated 80-90% of customer data is unstructured. Are you able and ready to leverage insights from that vast amount of customer feedback data?
Let’s begin with the basics: What is VoC and why is there so much buzz around it now?
Voice of the Customer (VoC) traditionally refers to customer feedback programs. In its most basic form that means organisations are sending surveys to customers to ask for feedback. And for a long time that really was the only way for organisations to understand what their customers thought about their brand, products, and services.
But that was way back then. Over the last few years, we’ve seen the market (organisations and vendors) dipping their toes into the world of unsolicited feedback.
What’s unsolicited feedback, you ask?
Unsolicited feedback simply means organisations didn’t actually ask for it and they’re often not in control over it, but the customer provides feedback in some way, shape, or form. That’s quite a change to the traditional survey approach, where they got answers to questions they specifically asked (solicited feedback).
Unsolicited feedback is important for many reasons:
- Organisations can tap into a much wider range of feedback sources, from surveys to contact centre phone calls, chats, emails, complaints, social media conversations, online reviews, CRM notes – the list is long.
- Surveys have many advantages, but also many disadvantages. From only hearing from a very specific customer type (those who respond and are typically at the extreme ends of the feedback sentiment), getting feedback on the questions they ask, and hearing from a very small portion of the customer base (think email open rates and survey fatigue).
- With unsolicited feedback organisations hear from 100% of the customers who interact with the brand. They hear what customers have to say, and not just how they answer predefined questions.
It is a huge step up, especially from the traditional post-call survey. Imagine a customer just spent 30 min on the line with an agent explaining their problem and frustration, just to receive a survey post call, to tell the organisation what they just told the agent, and how they felt about the experience. Organisations should already know that. In fact, they probably do – they just haven’t started tapping into that data yet. At least not for CX and customer insights purposes.
When does GenAI feature?
We can now tap into those raw feedback sources and analyse the unstructured data in a way never seen before. Long gone are the days of manual excel survey verbatim read-throughs or coding (although I’m well aware that that’s still happening!). Tech, in particular GenAI and Large Language Models (LLMs), are now assisting organisations in decluttering all the messy conversations and unstructured data. Not only is the quality of the analysis greatly enhanced, but the insights are also presented in user-friendly formats. Customer teams ask for the insights they need, and the tools spit it out in text form, graphs, tables, and so on.
The time from raw data to insights has reduced drastically, from hours and days down to seconds. Not only has the speed, quality, and ease of analysis improved, but many vendors are now integrating recommendations into their offerings. The tools can provide “basic” recommendations to help customer teams to act on the feedback, based on the insights uncovered.
Think of all the productivity gains and spare time organisations now have to act on the insights and drive positive CX improvements.
What does that mean for CX Teams and Organisations?
Including unsolicited feedback into the analysis to gain customer insights also changes how organisations set up and run CX and insights programs.
It’s important to understand that feedback doesn’t belong to a single person or team. CX is a team sport and particularly when it comes to acting on insights. It’s essential to share these insights with the right people, at the right time.
Some common misperceptions:
- Surveys have “owners” and only the owners can see that feedback.
- Feedback that comes through a specific channel, is specific to that channel or product.
- Contact centre feedback is only collected to coach staff.
If that’s how organisations have built their programs, they’ll have to rethink what they’re doing.
If organisations think about some of the more commonly used unstructured feedback, such as that from the contact centre or social media, it’s important to note that this feedback isn’t solely about the contact centre or social media teams. It’s about something else. In fact, it’s usually about something that created friction in the customer experience, that was generated by another team in the organisation. For example: An incorrect bill can lead to a grumpy social media post or a faulty product can lead to a disgruntled call to the contact centre. If the feedback is only shared with the social media or contact centre team, how will the underlying issues be resolved? The frontline teams service customers, but organisations also need to fix the underlying root causes that created the friction in the first place.
And that’s why organisations need to start consolidating the feedback data and democratise it.
It’s time to break down data and organisational silos and truly start thinking about the customer. No more silos. Instead, organisations must focus on a centralised customer data repository and data democratisation to share insights with the right people at the right time.
In my next Ecosystm Insights, I will discuss some of the tech options that CX teams have. Stay tuned!
