How to Streamline This Process With AI – InstantFollowerz

The future of brand tracking is here — and it’s powered by AI.

Brand monitoring is an essential marketing strategy for measuring brand performance, customer loyalty and market positioning.

Traditionally, companies rely on surveys, panels and market research to collect this data. But these methods can be slow, often taking weeks or months to provide insight, making it difficult for businesses to adapt to market changes in real time. Brand monitoring can also be expensive and time-consuming, making it out of reach for smaller teams with limited budgets.

AI is a potential solution that offers more affordable, faster and more cost-effective results. But what practical marketing applications does AI have for brand tracking – and how accurate is it?

In a recent Marketing against the grain episodesKieran and I used HubSpot as a test case to explore how generative AI tools like ChatGPT and Claude can simplify brand tracking. By comparing AI-based insights with our internal company data, we also assessed how well AI can match traditional tracking methods and its potential for wider use.

Brand tracking capabilities using artificial intelligence

AI offers a more efficient way to monitor and evaluate brand performance, providing faster insights, with greater flexibility. Here, Kieran and I explore three practical applications.

Understand why customers choose your brand over your competition.

AI is not just about quantitative analysis; also helps traders understand the qualitative ‘why’ behind customer decisions by analyzing online customer feedback, reviews and discussion forums.

When we got the AI ​​to analyze why customers choose HubSpotidentified key themes such as ease of use, integration capabilities and customer support. These findings closely matched our internal data, demonstrating AI’s ability to rapidly extract accurate insights from public platforms.

This offers a valuable window into customer behavior, allowing marketers to improve brand messaging and shape acquisition strategies around the attributes that resonate most with their audience.

Estimate your NPS score.

Net Promoter Score (NPS) is a key indicator of customer loyalty and brand satisfaction — but is often expensive and time-consuming to measure.

While AI is not (yet) a complete replacement for NPS surveys, it can provide quick, informal assessments aggregating online feedback and analysis customer sentiment. This helps marketing teams regularly monitor customer satisfaction and make timely adjustments between formal NPS assessments.

In our experiment, we asked the AI ​​to estimate HubSpot’s NPS using online data. The AI ​​produced a range of results that were surprisingly close to our actual numbers, complete with detailed reasoning, demonstrating The potential of AI as well an effective proxy for traditional NPS tracking.

Measure assisted brand awareness.

Aided awareness, or how familiar consumers are with a brand when prompted by a name or logo, is a key metric for assessment of brand visibility and competitive positioning on the market.

Traditionally, this involves hiring research firms to create and conduct extensive research, but AI again offers a faster and more affordable alternative by analyzing publicly available data and consumer sentiment.

In our experiment, we used AI to assess HubSpot-assisted awareness within our target market segment — companies with 200 to 2,000 employees. Interestingly, the two models produced slightly different results, with Claude offering a more accurate estimate compared to ChatGPT-4.

This discrepancy highlights the value consulting multiple AI models for a more rounded picture of your company brand awareness.

Tactical tips for optimizing AI for brand tracking

AI is great – but not perfect. Thinking about how you implement and manage your AI marketing tools maximizes the value AI brings to your brand monitoring strategy.

Here are five helpful tips to ensure you get the best results.

1. Make precise queries for accurate AI results.

The quality of the AI ​​output is directly related to how well you structure your request. Clearly define your target audience, goals and context to help AI generate more focused and actionable insights.

2. Monitor external values ​​and know when to validate.

Set yours AI agents to flag deviations and notify you when results deviate from expectations. This helps determine when you should invest in resources such as manual analysis or additional research to confirm findings.

3. Integrate AI with your existing tools and internal data.

Improve contextual accuracy integrating your AI marketing tools with internal data — like sales calls, SOCIAL MEDIA interactions and website analytics—to capture more personalized AI insights that reflect your brand’s unique context and positioning.

4. Regularly evaluate and update your AI tool.

AI models are constantly evolving, so it’s important to confirm that you’re always using the latest version. Check and update yours regularly AI tools to ensure they are aligned with your marketing team and business goals, giving you the most effective results over time.

5. Build your marketing AI ecosystem now.

“AI will be exponentially better in 12, 18, 24 months,” says Kieran. therefore, it’s time to build your marketing AI infrastructure now, so you’ll be well-positioned and agile enough to integrate future AI enhancements as they become available.

Adopting AI in brand tracking empowers your team to respond more quickly to changes in the market and customer behavior, while future-proofing your AI marketing strategy. To learn more about AI for brand tracking, see the full episodes Marketing Against the Grain below:

This blog series is in partnership with Marketing Against the Grain, a video podcast. He digs deeper into the ideas shared by marketing leaders Kipp Bodnar (HubSpot’s CMO) and Kieran Flanagan (SVP, Marketing at HubSpot) as they uncover growth strategies and learn from prominent founders and peers.