Data Monetization

What are Big Data Use Cases?

Knowing the potential use cases for your company’s data will not only help narrow down the target audience but assist greatly with marketing/sales efforts. We touched upon the differences between discretionary and quant funds in the previous post on How to Target Your Audience.

High-Level Cheat Sheet:

  • Discretionary funds – looking for data on specific publicly traded companies; best to start with several case studies on companies dominating the space/sector your company is focused on; focus correlation to the KPIs (revenue, gross profit, etc.); choose a company where the stock price is driven by a key metric, has large market cap and high volatility. Typically do not need to backtest.
  • Quant funds – these shops want a long time series covering 100-1000’s companies, indices, markets, etc.; need to backtest historical data and fully understand the data correlation with share prices to identify value before moving forward.

Examples of top applications for alternative data vendors:

  • Advertising – tracks corporate advertising spend across platforms and by campaign. The data is focused on consumer interests based on their internet browsing habits – can be used to track certain categories like mortgages, automobiles, luxury goods, etc.
  • App Usage and Web Traffic – wildly popular data in the hedge fund space as this data can be used to estimate company revenues. App usage, app reviews, purchase tracking can all indicate how successful a product is among consumers. This includes mobile banking, streaming media, food delivery apps. It is also possible for investors to track services embedded into the apps like payment providers and advertising services.
  • Business to Business (B2B)/Supply Chain – provides a read into the supply chain (typically private companies) which then adds color to a public company analysis and investment model. This data identifies sales, marketing, business development or contracts for a range of industries like industrial materials, oil contracts/drilling concessions, or B2B trade indices.
  • Consumer Transactions/Payment Processing – data tracks merchant level transaction data (e.g. retailer or service provider), product level purchase data (e.g. food, electronics), and macro high level data (e.g. trends). These data sets are valuable because they are used to estimate quarterly revenue growth before the corporate earnings. Also valuable to gain insight into consumer purchasing behavior like rate of adoption, trends, how well promotions and discounts fare, and insight into consumer demographics. Payment processing data from PayPal and Square is also picked up in this category.
  • Environmental, Social and Governance (ESG) – this area has recently taken flight as consumers are preoccupied with “investing with a conscience” and ” investing in companies that care about the same issues” as they do. Valuable ESG data is obviously company-specific and can be tracked via social media data, open and public data, consumer surveys, satellite imagery. Data that monitors consumer reviews, hiring trends, business complaints, compensation, etc. will be valuable in this category.
  • Geo-location – also popular with funds, this data provides read into visitation trends/foot traffic, identify impact of promotions, and understand the influence of weather events. Top industry applications are retailers, restaurants, hotels and travel. This data is usually gathered from a third party like a mobile application, satellites, sensors and Bluetooth beacons.
  • Internet of Things (IoT) – this data provides a better understanding into consumer and business activity via tracking digital footprint of IoT activity. Again, this correlates with product adoption and overall market growth.
  • Natural Language Processing (NLP) – using NLP, data vendors pick up on topic and sentiment trends among experts or key industry leaders in any industry or field of expertise from niche blogs and forums.
  • Open Source Data – this is publicly available data and includes government data, trade organizations, market data, industry data, weather data, free APIs, etc. Vendors often collect this data and then repackage it for hedge fund use.
  • Ratings Data – this data comes from online and app consumer reviews (positive and negative). Brand and company reputation is tracked here with consumer and B2B opinions. This data is collected via webscraping if open source or through a third party vendor. It is then aggregated and analyzed for trends.
  • Satellite Imagery – satellite data and intelligence provides insight into economic activity, construction, oil and gas, shipping/tankers, retail parking lot monitoring, trucks/vehicles, stockpiles of raw materials on factory sites, etc. of publicly traded companies.
  • Social Media – includes insights from social media posts that help analyze consumer trends, adoption of product launches, how popular a new product or brand is, how satisfied a customer is, what the promotions look like, corporate/customer engagement, etc. All of this reads into sales momentum and revenue.
  • Webscraped/Web-crawled – typically any type of data may be webscraped off any web page (considering there is nothing barring the activity from a Terms of Use/Terms of Service and robots.txt perspective). Data picked up could be e-commerce activity (pricing, listing descriptions, product info, reviews, commentary/posts, press releases, IR websites, government filings). Hedge funds with internal data scientist teams may scrape internally but at large volume, external data vendors are the preferred source for this data.


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