The future of brand tracking is here – and it’s based on AI.
Brand tracking 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. However, these methods can be slow and often take weeks or months to provide insights, making it difficult for companies to adapt to market changes in real time. Brand tracking 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 accessible, faster and cheaper results. But what practical marketing applications does AI offer for brand tracking – and how accurate is it?
In a current one Marketing against the grain ConsequenceKieran and I used HubSpot as a test case to explore how generative AI tools work ChatGPT And Claude could optimize brand tracking. By comparing AI-powered insights with our own internal company data, we also assessed how well AI competes with traditional tracking methods and what potential there is for broader use.
AI-powered brand tracking capabilities
AI offers a more efficient way to track and evaluate brand performance, delivering faster insights and greater flexibility. Here, Kieran and I explore three practical applications.
Understand why customers prefer your brand over the competition.
AI is not just about quantitative analysis; It also helps marketers Understand the qualitative “why” behind customer decisions by analyzing online customer feedback, reviews and discussion forums.
When we asked the AI to analyze Why customers choose HubSpotCore topics such as user-friendliness, integration options and customer support were identified. These results were largely consistent with our internal data and demonstrated AI’s ability to quickly extract accurate insights from public platforms.
This provides valuable insight into customer behavior and allows marketers to do this Improve brand messaging and shape acquisition strategies about the attributes that resonate most with their audience.
Estimate your NPS score.
Net Promoter Score (NPS) is an important indicator of customer loyalty and brand satisfaction – but measuring it is often expensive and time-consuming.
Although AI is not a complete replacement for NPS surveys (yet), it can provide quick, informal estimates Collect and analyze online feedback Customer sentiment. This helps marketing teams monitor customer satisfaction regularly and make timely adjustments between formal NPS assessments.
In our experiment, we asked AI to estimate HubSpot’s NPS based on online data. The AI returned a range of results that was surprisingly close to our actual numbers, along with detailed justification to clarify this The potential of AI as an effective proxy for traditional NPS tracking.
Measure brand awareness.
Aided awareness, which is how familiar consumers are with a brand when prompted with its name or logo, is an important metric for Assessing brand visibility and competitive positioning in the market.
Traditionally, this involves hiring research companies to create and conduct large-scale surveys, but AI offers a faster and more accessible alternative by analyzing publicly available data and consumer sentiment.
In our experiment, we used AI to estimate HubSpot-powered awareness in a target market segment – companies with 200 to 2,000 employees. Interestingly, the two models produced slightly different results, with Claude providing a more accurate estimate compared to ChatGPT-4.
This discrepancy highlights the value of Consult multiple AI models to get a more comprehensive picture of your business Brand awareness.
Tactical tips for optimizing AI for brand tracking
AI is great – but not perfect. By thinking carefully about how you implement and manage your AI marketing tools, you will maximize the value that AI brings to your brand tracking strategy.
Here are five actionable tips to ensure you get the best results.
1. Create precise prompts for accurate AI results.
The quality of AI output directly depends on how well you structure your query. Clearly define your audience, goals, and context so AI can generate more targeted and actionable insights.
2. Watch for outliers and know when to validate.
Set yours AI agents to flag outliers and notify you when results deviate from expectations. This will help you determine when to invest in resources such as manual analysis or additional surveys to validate results.
3. Integrate AI into your existing tools and internal data.
Improve contextual accuracy by Integrating your AI marketing tools with internal data – such as 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 assess and update your AI toolkit.
AI models are constantly evolving. Therefore, it is important to ensure that you always use the most current version. Check and update yours regularly AI tools 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, This will ensure you are well-positioned and agile enough to incorporate future AI improvements as they become available.
By using AI in brand tracking, your team can respond faster to market changes and customer behavior while future-proofing your AI marketing strategy. To learn more about AI for brand tracking, check out the full guide Consequence from Marketing Against the Grain below:
This blog series is a partnership with Marketing Against the Grain, the video podcast. It delves deeper into the ideas of marketing leaders Kipp Bodnar (CMO of HubSpot) and Kieran Flanagan (SVP, Marketing at HubSpot) as they craft growth strategies and learn from outstanding founders and peers.