Top 10 Alternative Data Use Cases For Investment in 2024

Alternative data is transforming investing. But what exactly is it? And how are investors using it to gain an edge in 2024?

In this comprehensive guide, we’ll first explain what alternative data is. Then we’ll dive into the top 10 most valuable ways investors are using it this year – from forecasting profits to avoiding risk. With insights from data experts, real-world examples, and a look at what‘s ahead, you’ll see why alternative data is becoming essential for investment in the 2020s.

Defining Alternative Data

Alternative data refers to information coming from non-traditional sources outside of conventional financial reporting data. This includes data like:

• Social media activity – Twitter, Reddit, review sites
• Web traffic and search patterns – Google, Amazon
• Satellite and geospatial images – shipping traffic, factories, crops
• Credit card transactions and mobile payments
• Supply chain data – shipping volumes, inventory
• Internet of Things (IoT) sensor data – equipment, facilities
• Mobile app usage volumes and trends
• Surveys and consumer sentiment metrics

By tapping into these novel data reservoirs, investors gain unique, valuable insights into companies, industries, economies and markets.

According to recent Greenwich Associates research, 94% of institutional investors now use alternative data in their investment processes. Adoption is skyrocketing based on the value it offers:

72% say alternative data helps them forecast earnings more accurately.

63% say it provides earlier economic signals than conventional indicators.

61% say it enables better evaluation of ESG risks.

51% say it helps them better assess management quality and leadership issues.

As Arun Chaudhry, Chief Revenue Officer at Quandl notes:

“Alternative data opens up new ways to value assets, identify opportunities and risks, and ultimately generate alpha. Adoption is accelerating as investors recognize the edge it provides.”

Now let’s explore the top 10 ways investors are using this new world of data to their advantage in 2024:

1. Forecasting Profits and Revenues

One of the most common uses of alternative data is forecasting future revenues and earnings. By analyzing signals from sources like:

• Web traffic at ecommerce sites
• Search volume for specific products
• Social media brand mentions and sentiment
• Foot traffic at brick and mortar stores
• Supply chain shipping volumes

Investors can detect consumer demand shifts earlier and more accurately predict quarterly revenues.

For example, analysis of 800 million online conversations and activity helped JPMorgan anticipate shortfalls in iPhone demand in 2016. Their analysts lowered Apple revenue forecasts before the official earnings announcements.

According to Mark Minevich of Going Global Ventures:

“Billions of data points from search, social, and more provide a real-time pulse on consumer behavior. Analyzed properly, it becomes an early warning system for fluctuations in demand.”

Institutional investors are increasingly combining alternative data with financials in machine learning models to improve the accuracy of their earnings forecasts. According to Greenwich Associates, 72% of institutional investors say alternative data improves earnings forecast accuracy.

2. Monitoring Supply Chains

By tapping into data on shipping volumes, cargo flows, port activity and more, investors get an inside view into company supply chains. This enables real-time monitoring for disruptions that impact costs and revenue.

Sources like:

• Satellite tracking of shipping containers
• Vessel location data
• Freight booking activity
• Food/fuel pricing
• Weather data

Offer visibility into logistics snarls, route delays, inventory buildups and more. According to Barry Haigh of Green Peak Partners:

“We continuously track over 50 supply chain data sets. This provides an early warning system across our entire portfolio for disruptions that may impact earnings.”

In 2021, analysis of shipping container data helped some hedge funds spot upcoming supply chain turmoil. This allowed them to avoid losses and reposition their portfolios effectively.

The market for supply chain analytics is projected to reach $9 billion by 2028. As supply uncertainty grows, investors view supply chain data as an increasingly critical tool.

3. Analyzing Retail Performance

Retail investors are leveraging data on:

• In-store and mall traffic patterns
• Local ad spending
• Web traffic and search trends
• Credit card transaction volumes

To gauge real-time shopper demand and retail KPIs like sales per square foot.

For example, analysis of WiFi shopper traffic data from SafeGraph and Placer.ai accurately predicted a surge in holiday sales at retailers like Target and Walmart in 2021. Hedge funds using this retail traffic data repositioned their portfolios to profit from strong holiday spending.

Retail traffic data accurately predicted holiday sales spikes in 2021. (Source: Placer.ai)

As Barbara Soltis, Director at Boston Consulting Group notes:

“Retail investors used to rely on quarterly earnings data to gauge performance. Alternative data on shopper traffic and other KPIs provides intraquarter visibility to spot trends faster.”

4. Sentiment Analysis for Stocks

Analyzing investor sentiment and activity on social platforms like Twitter and Reddit has become a popular data source for equity traders.

By applying natural language processing to huge volumes of stock-related posts, algorithmic traders aim to detect shifts in sentiment on specific stocks. This data powers quantitative short-term trading strategies.

According to a McKinsey analysis, a strategy based on Twitter sentiment delivered 5-10% higher returns compared to a baseline model over 7 years. However, many experts warn that social data requires nuanced analysis.

According to Steve Mills of Trade the Chain:

"There are absolutely predictive signals in social sentiment once you understand how to extract them. But naive analysis can easily backfire. You need robust analytical methods."

With advances in NLP and machine learning, expect more sophisticated use of social data in equity trading. But simple sentiment metrics alone provide little value.

5. Gauge Success of New Products

Investors commonly use alternative data to determine whether new products like apps, games, and consumer devices are gaining traction. Metrics like:

• Search volumes
• Website and app usage
• User reviews/ratings
• Social media engagement

Offer leading indicators on market reception of new offerings. For example, analysis of download trends by Apptopia found that Snapchat‘s 2018 redesign alienated users – a signal detected before the plummet in Snap‘s stock price.

Analysis of download data provided early warning on negative impact of Snapchat‘s 2018 redesign. (Source: Apptopia)

According to Michael Bailey of Propellant Media:

"From early download momentum to reviews, alternative data on new products provides an accurate, real-time view of product-market fit. This signals underlying growth ahead of financials.”

6. Credit Risk Analysis

Lenders and credit investors are increasingly tapping into alternative data to evaluate default risk. Data like:

• Utility bill payment history
• Payroll information
• Online behavioral patterns
• Mobile wallet purchases

Offers additional insights on creditworthiness beyond traditional credit scores. This is especially valuable in emerging markets where credit data is limited.

According to the Omidyar Network, analysis of mobile payment histories allows more accurate credit risk scores for thin-file consumers in Africa. This promotes financial inclusion.

The alternative credit data market is projected to reach $10.6 billion by 2027. As Melissa Koide, CEO of FinRegLab notes:

“Alternative data paints a more complete picture. This allows more accurate credit models and expands access for the underserved.”

7. Real Estate Analysis

Real estate investors now routinely incorporate alternative data on:

• Mall/store traffic patterns
• Occupant sentiment on review sites
• Development pipeline activity
• Rental listing analysis
• Points of interest

This provides market intel that can help identify promising areas and assets. For example, monitoring mall traffic and restaurant reviews provides insight into the overall health of a particular region.

Cherre is one provider offering a platform to ingest hundreds of public and proprietary data streams for real estate analytics. According to Cherre CEO L.D. Salmanson:

“Alternative data helps identify high-potential investments, assess risk, determine fair value and monitor assets in real-time.”

Total investment in PropTech analytics reached $9 billion in 2021 as demand for market intelligence grows.

8. Predictive Maintenance

Analysis of sensor data from industrial equipment and facilities enables early detection of anomalies and potential failures. Investors use this to gauge operational risks and maintenance costs more accurately.

Predictive maintenance models built on data like:

• Machine temperature and vibration
• Flow rate and pressure
• Power consumption
• Acoustic signatures

Can predict mechanical failures weeks in advance with over 90% accuracy. For example, models using vibration data can provide 14-day advance notice of wind turbine failures, minimizing revenue losses.

Sensor data powers predictive maintenance models that forecast equipment failures weeks in advance. (Source: MATLAB)

According to Bill Kleyman of MNT Research:

“IoT and predictive maintenance modeling provides incredible visibility into operational risks. This is invaluable for evaluating industrial investments."

9. Insurance Risk Management

Insurers are increasingly looking to tap into alternative data to enhance underwriting and pricing. Data like:

• Auto telematics and driving data
• Smart home data
• Wearables providing health insights
• Climate and environmental datasets

Will allow insurers to price policies more accurately based on real-time risk indicators. This enables personalized premiums based on behavior – like basing auto premiums on actual driving habits.

Home insurers are also exploring using IoT and smart home data to proactively identify risks – like a leak detected by a water sensor. According to Deloitte, insurers will heavily invest in alternative data to power more predictive underwriting models.

10. Avoiding Investment Scandals

Analysis of alternative data also provides transparency that can help investors avoid companies engaged in fraudulent or unethical practices.

Sources like:

• Satellite imagery of facilities
• Global trade/shipment records
• Regional energy consumption
• Social media and local reports

Can uncover illegal or banned activity like unpermitted toxic dumping by miners, use of illicit shipping networks, or employing illegal factories.

For example, satellite images exposed massive accounting fraud at Chinese forestry company Sino-Forest that cost investors $4+ billion. Investors now routinely incorporate satellite imagery to validate company-reported information.

According to Jay Nadler of Kintai Capital:

“Alternative data provides extra transparency. This allows better assessment of management integrity and risk factors that financials alone miss.”

Key Sources of Alternative Data

Now that we’ve covered the top use cases, let’s discuss the major categories of alternative data available today:

Web & Mobile Data

• Search trends – Google Trends
• Website usage data – SimilarWeb, SEMrush
• Social media activity – Facebook, Twitter, Reddit
• Online reviews – Trustpilot, App Store
• Dark web activity

Location Intelligence

• Satellite/aerial imagery – Planet, Maxar, Airbus
• Geospatial mapping data
• Traffic analytics – visual, cellular, WiFi – SafeGraph, Placer.ai
• Local economic activity – credit transactions, wages

Supply Chain & Logistics Data

• Global shipping data – container volumes, vessels
• Freight bookings, customs docs, manifests
• Warehouse/inventory levels
• Food/fuel pricing

Financial Transactions

• Debit/credit card transaction data
• Mobile payments data – Safaricom, Alipay
• Loyalty card activity
• Ecommerce transaction activity

IoT & Sensor Data

• Connected home – Nest, Ring – usage patterns
• Auto telemetry – mileage, speed, diagnostics
• Industrial IoT – machines, wind farms
• Environmental – pollution, weather

Business Operations Data

• Retail – foot traffic, sales
• Supply chain – logistics activity
• IT operations – server activity, outages
• Marketing analytics – ad spend, campaigns

Public Data

• Government databases – demographics, records
• Academic datasets – experiments, climate
• Company filings – patents, trademarks, financials
• News archives

Hundreds of firms now specialize in aggregating specific alternative data sets for investors. Top global providers include SpaceKnow, 1010Data, Eagle Alpha, Thinknum, M Science, and INFOGATION.

Each offers dashboards and analytics tools to derive insights tailored to investor use cases – from scanning supply chains to tracking retail KPIs. Investors can also access select alternative data directly from major platforms like AWS Data Exchange and Snowflake Data Marketplace.

Key Conferences and Events

To stay on top of the latest developments and innovations in alternative data, investors attend popular industry events like:

• Battle of the Quants (BOTQ) – Popular quant investing conference featuring alternative data experts from hedge funds and research firms.
• Big Data & AI World – Showcases big data analytics advances with content on financial applications.
• Alternative Data Forum – Leading event centered on alternative data specifically for investing.
• Data Driven NYC –longstanding data science event with investor-focused presentations.
• Harvard Business Review‘s Alternative Data Conference – Research presentations on use cases and techniques for financial data science.

These events enable investors to connect with data providers, researchers, and fellow practitioners leveraging alternative data. They provide exposure to new data sources, methods, and use cases.

Challenges and Limitations

While adoption is accelerating, effectively leveraging alternative data comes with challenges:

Data Quality – Some sources lack rigorous controls, requiring extensive vetting and validation.

Analysis Expertise – Advanced analytics and data science skills are mandatory to surface valuable signals accurately.

Regulations – Usage of some data may require compliance with privacy laws like GDPR and CCPA.

Data Biases – Alternative data can reflect societal biases. Critical thinking is imperative.

Ethics – Careful consideration of data privacy and responsible usage is essential.

Data Context – Understanding the nuances and limitations of alternative data sources to avoid misinterpretation or false signals.

Investment managers need experts in disciplines like data science, analytics engineering, econometrics, and machine learning to ingest and analyze alternative data effectively. Models and findings require rigorous auditing to avoid issues that have tripped up some funds.

With thoughtful implementation, alternative data can provide invaluable insights. But integrating it successfully into the investment process remains challenging. Top funds invest heavily in talent, technology and principles to use alternative data judiciously and ethically.

The Future of Alternative Data

Adoption of alternative data in finance is still early but accelerating quickly. According to projections by Opimas LLC:

• Investment in alternative data by asset managers could quadruple from $1.7 billion in 2019 to over $7 billion by 2022.
• 90% of institutional investment organizations are expected to be using alternative data by 2024, up from just over 50% in 2019.

Key drivers of soaring adoption include:

• Increased availability of alternative data sources
• Improved analytics tools and methods
• Rising compute power enabling complex modeling
• Growth of unstructured data like satellite imagery and social media
• Pressure to find new alpha and gain investment advantages

As alternative data matures from a niche innovation to a core component of the investing toolkit, it will bring increased efficiency, transparency, and democratization to global markets.

But to tap into the full potential, investment managers must build rigorous capabilities while vigilantly navigating pitfalls like bias and ensuring ethical use. With the accelerating proliferation of data, these best practices will only grow more critical in the years ahead.