How to Improve Your Brand Tracking Strategy With AI

TL;DR

AI-powered brand tracking measures how visible and well-regarded your brand is online and among customers, giving insight into top-of-funnel awareness plus retention and advocacy. Traditional brand health metrics include visibility, share of voice, perception, sentiment, and net promoter score (NPS), pulled from surveys, platform analytics, and public mentions. AI improves this work by analyzing huge volumes of qualitative text (social posts, reviews, forums, survey comments) with more nuance than old keyword-based methods, cleaning and spotting patterns in data, and turning findings into actionable insights.

An effective AI brand tracking strategy focuses on four areas:

• NPS: AI summarizes open-ended survey feedback and links loyalty trends to actions you’ve taken.

• Brand awareness: AI maps where and how often your brand is mentioned, and can forecast awareness trends versus sales.

• Sentiment: AI reads context to classify attitudes more accurately and can segment sentiment by audience type.

• AI visibility: With AI search growing, track how often your brand appears in tools like ChatGPT using specialized visibility platforms that automate prompt discovery and reporting.

To make it work, set clear goals/KPIs, define and gather the right data from the right sources, and iterate on the workflow regularly. Bottom line: integrating AI into brand monitoring speeds up analysis, improves accuracy, and delivers richer insights that strengthen brand positioning.

How to create AI brand tracking strategies

Monitoring brand health gives companies insights into how they’re perceived by both their customer base and their target audience. Artificial intelligence improves many of the workflows in brand tracking and can provide more contextual data.

This guide will explore how AI helps brand tracking and what factors you need to consider when integrating AI with your brand tracking strategy.

What is Brand Tracking?

Brand tracking, or brand health tracking, measures your brand’s performance in terms of online visibility and public perception. The concept of brand health includes metrics like:

• Brand visibility across different marketing channels.
• Share of voice on marketing channels.
• Brand perception on those platforms and within your customer base.
• Brand sentiment is shown on those platforms and in public online spaces.
• Net promoter score in your customer base.

This process involves gathering data from multiple sources, including platforms’ internal analytics, open sources, and surveys administered to your customers.

The end goal is understanding how your brand is perceived, doing competitive analysis, and forming a strategy to improve your position on the market.

Why is Brand Tracking Important?

Most other metrics give you an understanding of the middle of the funnel. Traffic data and sales data let you understand how leads come to your website, how they convert, and where people drop off from the funnel.

Brand tracking helps you form a better understanding of the top of the funnel and beyond — the retention and advocacy stage. Here are the benefits you can garner from brand health tracking.

• Understand where leads can come in contact with your brand. This can help you either double down on the platforms where your presence is strong or invest in the underperforming ones.
• Understand what people think about your brand. This can help you improve brand messaging on and off your web assets.
• Understand how your current customers regard your brand. This helps you strategize to improve customer retention and stimulate word-of-mouth marketing.

Adding brand tracking to your marketing analytics activities gives you a much fuller understanding.

How AI Improves Traditional Brand Tracking Strategy?

Artificial intelligence enhances many aspects of modern marketing. Here is how it can help improve brand tracking.

Qualitative text data analytics

Large language models are trained on large datasets of human-written text to be able to understand input and generate human-like texts. This makes AI exceptionally good at analyzing qualitative data.

Where we relied on simple algorithms to make sense of large amounts of text before, AI now takes over with much better results. LLMs can go through any text data you have gathered, like:

• Mentions of your brand on blog pages.
• Social media conversations.
• Customer reviews on private and public platforms.
• Open-ended survey answers.
• Conversations with customers.

They can then summarize it, analyze it for customer sentiment, and for trends that appear in that data.

Business analytics

Machine learning can improve every step of the business analytics process. It can help you:

• Clean up data.
• Explore data and identify patterns.
• Process the findings and create actionable insights.

Natural language processing also brings you the ability to interact with data sets through a chat with an AI. Some AI tools offer this feature, where you can simply ask the AI about your dataset instead of going through it manually.

Brand position monitoring

Ultimately, better analytical capabilities lead to a better understanding of your brand’s position on the market. AI can help you find more context and nuance in what people are saying about your brand, whether it’s in a survey or on Reddit. It can also analyze the data you’ve gathered to produce insights that will help shape your brand awareness strategy.

AI Brand Tracking Strategies

AI can improve brand tracking strategy across four major metrics. Here’s how it works:

 

Metric	Why track it	Data sources	How AI helps
NPS	Find insights into customer loyalty.	Surveys of existing customers.	Processing open-ended questions.
Brand awareness	Understand top-of-the-funnel marketing.	Surveys of the target audience, mentions online.	Analyzing text data, general analytics applications.
Sentiment	Understanding the attitude towards your company.	Surveys of existing customers, mentions online.	Analyzing text data, general analytics applications.
AI visibility	Understanding your visibility in AI search.	AI visibility tools.	Finding prompts to track, insight generation.

 

NPS

Net promoter score (NPS) is a metric that shows how likely your current or returning customers are to recommend your business to their friends and family.

Source: SE Ranking

Based on the responses to the NPS survey, you can judge:

• How many people are likely to recommend your business?
• How many people are dissatisfied with your business?
• The ratio between the two groups.

This data is instrumental to predicting business growth and understanding the number of people who are likely not to become repeat customers. If you ask customers for more context than the basic ranking, you can also understand their reasoning behind the answer.

Data sources

Net promoter score data comes from surveys administered to existing customers either as a pop-up or through an email marketing campaign. At the bare minimum, the survey consists of a simple ranking from 0 to 10, but you can add an open-ended question to understand why users give their rating.

How AI can help

NPS is a pretty straightforward metric that doesn’t require further analysis on its own. What AI does instead is help with analyzing the context of the score.

As AI excels at text analysis and summarisation, it’s ideal for making sense of open-ended questions that you might have in your survey. AI can summarize the most common issues people have with your business and judge the sentiment of the replies.

You can use AI-based analytics to find correlations between NPS and other metrics or strategic decisions you took in the past. Use the findings to improve your brand positioning or products to increase customer loyalty.

Brand Awareness

Brand awareness is a measure of how well your target audience knows your brand.  Awareness is the first goal of the sales process because it brings familiarity and trust, which are crucial for making a decision to buy from your brand later on.

Tracking brand awareness allows you to understand how well your top-of-the-funnel marketing performs and evaluate your brand’s position on the market.

Data sources

There are two main options for tracking brand awareness. The first one is conducting a survey of your target audience. You can do that by either doing it yourself and using paid ads to draw in people to the survey, or by contracting a company that specializes in this.

The other method is analyzing publicly available data from:

• Social media
• Forums
• Blog posts
• Search trends

This mostly measures unaided brand awareness — how often people talk about your brand unprompted. It also serves as an indicator of how likely users are to discover your brand online in places that you don’t control.

How AI can help

AI can help you make sense of your brand mentions online and provide a summary of where your brand is mentioned and in what contexts. This can save hours of work you’d spend on analyzing brand mentions manually.

AI-assisted predictive analytics can help you find trends between brand awareness and sales figures, as well as project the brand awareness trajectory.

Sentiment

Brand sentiment is a measure of how well your business is perceived by your target audience. You can form an understanding of it by analyzing brand mentions to find out what emotions are expressed. 

Measuring brand sentiment helps you understand the general attitude towards your company and products and their reputation on the market. This can lead to improving your PR strategy, customer support workflows, or the product.

Data sources

A large part of brand sentiment data comes from monitoring online public places, like:

• Social media conversations.
• Online forums.
• Review websites.

This can gauge brand sentiment in your target audience as well as existing customers. If you want to measure sentiment towards your brand within the customer base, an NPS survey can help, as well as conducting a larger survey with open-ended questions.

You can also measure sentiment in support conversations with existing customers with the help of AI call analytics.

To measure brand sentiment in people who aren’t your customers, you might need to hire a marketing agency to do a focus group survey.

How AI can help

Most traditional brand trackers use algorithmic sentiment analysis solutions that rely on looking through text for a list of words with positive, negative, and neutral values assigned to them, which disregards the context.

AI simplifies and improves the process. It can process survey answers and online comments in bulk and understands the whole text with its context instead of looking for specific words. It can determine the sentiment behind that data, judge the overall sentiment score, and break down the sentiment into customer segments.

AI visibility

With 700 million people using ChatGPT weekly and Google leaning more heavily into AI search features, visibility in AI tools is becoming more important for brand awareness.

Source: Google

 

Even when your mentions there don’t generate traffic, they will improve brand awareness as your company’s name will be shown to multiple users. The same goes for mentions on AI engines like ChatGPT or Claude.

AI visibility is the measure of how often and in which positions your brand gets mentioned on AI search. AI brand monitoring can give insights into what your current position is, how you compare to competitors, and how you can improve your position to build more brand awareness.

Data sources

Tracking AI visibility manually is virtually impossible, as you’d need to run hundreds of potential prompts through multiple LLMs and record the number of brand mentions in each result. You can simplify the process of data collection with an AI visibility tool by SE Ranking. This AI tool will help you find the right prompts to track, track them across multiple AI engines, and analyze the results.

How AI can help

The AI built into visibility tools can help you generate clear insights from the visibility metrics and inform your process of content creation to write content that ranks well in AI search.

Key Considerations in Brand Monitoring

Even with advanced tools, brand health tracking needs a strategic approach to work well. These three points are essential for well-functioning AI brand tracking efforts.

Goal setting

The first thing you’ll need to do is set up goals and KPIs for the AI brand tracking efforts. Based on your larger business goals, decide which metrics you want to track, and define what success means for your company.

Depending on what you need and what your current situation is, it might look something like:

• Increase share of voice on key platforms by 20%.
• Increase brand awareness in the target audience by 15%.
• Move the NPS score in the core customer base segment by 2 points.

Data collection

Before you start collecting data, you’ll need to define what data you need to collect and where. Look at:

• What is your target audience?
• What is their preferred way of being contacted?
• What online places do they frequent?
• What questions do you need to ask them?

From there, start designing the surveys and building the monitoring infrastructure.

For the surveys, you’ll need a reliable way to distribute them. Mostly, it will either take the form of calling your customers or distributing an automated email campaign. For surveys of people who aren’t your customers yet, you might need to contract a specialist.

For monitoring social media and other websites for brand mentions, you’ll need a scraper tool specialized in that task.

Fine-tuning the workflow

A perfect analytics system is rarely built on the first try. More often than not, it’s achieved through iterations. Make frequent reviews of your AI brand tracking workflow, look for areas that produce poor results, and find ways to improve the workflow.

Closing Thoughts About AI Brand Tracking

AI brand tracking can improve the speed and accuracy of measuring brand health. AI with NLP capabilities can analyze text better than previous tech. It can summarise feedback and brand mentions, find commonly discussed themes, and extract customer sentiment faster. Machine learning in analytics can improve brand visibility tracking by finding better insights from trends in data.

You do need to find ways to integrate AI with your existing brand tracking tools or use new AI-first tools. The investment pays for itself because you’ll receive more accurate data and find better insights to improve brand positioning.

Our Go-To Tool for Smarter Brand Visibility Monitoring

If you’re ready to put AI brand tracking into action, you don’t have to piece together a dozen tools to do it. At Simplified SEO Consulting, we rely on SE Ranking because it’s versatile enough to support the full picture of brand health, from traditional search to the fast-growing world of AI search. In one platform, we can track rankings, monitor brand and competitor visibility, audit websites, research keywords and backlinks, and measure AI visibility across major AI engines.

Want to see what your brand looks like through both human search and AI search? Start a free SE Ranking trial and explore the AI Visibility Toolkit alongside the core SEO suite. It’s the same stack our team uses to spot visibility gaps, uncover new prompts and content opportunities, and turn brand insights into measurable growth.

About the Author

 

Kateryna Boiko is a Content Specialist at SE Ranking, a company that developed a robust toolkit for SEO and GEO. Passionate about digital marketing, Kateryna consistently stays up-to-date with the latest industry news and AI trends, sharing her expertise through blog articles. She also loves reading contemporary fiction books and learning languages.