3 types of alternative data that can elevate your investment strategy

Alternative Data Isn’t Just for Hedge Funds Anymore

Alternative data is no longer reserved for hedge funds alone. As investors continue chasing alpha, firms are putting more effort into using nontraditional data sources—such as web scraping and sentiment analysis—to gain an edge.

These same tools now play an important role in digital marketing, helping brands better understand audiences, trends, and behavior.

If you follow the fintech space closely, you’re likely familiar with alternative data. Unlike traditional data sources like quarterly reports, alternative data provides real-time and granular insights into corporate performance and consumer behavior.

This data can include everything from satellite imagery and IoT data to credit and debit card transactions.

Alternative Data: From Hedge Funds to Mainstream Adoption

Alternative data originally emerged as a way for hedge funds to outperform competitors by collecting insights through creative, nontraditional methods. Today, it has become standard practice across the investment landscape.

In fact, more investment firms now use alternative data than those that don’t.

Many alternative data types have also been used in digital marketing for years. Social listening and sentiment analysis, for example, help brands monitor what consumers say on social media platforms and in product reviews.

These insights allow companies to improve offerings, refine messaging, and actively participate in conversations with their audiences.

Below, we’ll explore three widely used types of alternative data and how they apply to marketing and investment strategies.

Social Listening and Sentiment Analysis

Social listening—often paired with sentiment analysis—allows brands to monitor conversations happening across social media platforms and the broader web.

Using AI and language learning models, companies can analyze massive volumes of unstructured data to understand how customers feel about their products, services, or competitors.

What are people saying about your brand?
Are they raising concerns, sharing praise, or offering suggestions?

Brands can use these insights to:

• Address customer feedback directly
• Identify emerging trends in real time
• Turn audience sentiment into marketing campaigns

Who follows your brand on Instagram or Facebook? Are users talking about you on X or leaving reviews elsewhere online?

By responding to trends quickly—or analyzing product reviews to uncover common issues—brands can improve customer engagement and campaign effectiveness. When customers feel heard, trust grows, leading to better conversions and stronger lifecycle marketing outcomes.

Web Scraping for Market and Brand Insights

Web scraping uses AI and machine learning to collect information from large numbers of websites automatically.

This process helps marketers understand:

• What is being said about their brand across the web
• How competitors are positioned
• Broader market trends and sentiment

Web scraping transforms vast amounts of unstructured data into organized databases. These insights can then be used to uncover patterns, identify opportunities, and inform data-driven marketing and investment decisions.

Geolocation Data and Location-Based Targeting

Geolocation data is especially valuable for location-based advertising and brands with physical storefronts.

With accurate geolocation insights, marketers can:

• Deliver more relevant mobile ads
• Encourage online shoppers to visit nearby physical locations
• Optimize campaigns based on real-world movement patterns

A strong foundation of geolocation data can significantly improve both digital and in-store performance.

Even More Types of Alternative Data

Social data, web scraping, and geolocation insights are just the beginning.

Other alternative data sources include:

• Credit and debit card transactions
• Email receipts
• Mobile app usage data

For both investors and marketers, data-driven strategies can extend well beyond Google and Meta’s walled gardens—and beyond cookies.

The number of alternative data providers has grown rapidly in recent years. If you need a specific type of insight, there is likely a provider offering it.

As AI, machine learning, and large language models continue to advance, alternative data will only become more accurate, scalable, and actionable.

As cookies fade away, new opportunities are emerging. Now is the time to say goodbye to outdated methods—and say hello to alternative data.