We are living in the information era where data becomes available every minute from various angles and in every form. However, for most companies, an opportunity to produce more accurate information or predict patterns is in such strategic use.
This is where alternative data comes into play. Unlike traditional data (such as sales history and demographics), alternative data involves unconventional sources that provide much more complete insight into consumption patterns, customer behavior, and market changes.
Are you curious? Then follow along to understand how alternative data is transforming the market and how our organization uses this information to enrich analyses and deliver value to consumers and our partners!
What is alternative data?
Alternative data is information gathered from unconventional sources that go beyond traditional financial records or internal company data. Instead, it encompasses external data that offers differentiated and often deeper insights into the market, customer, and product.
From another perspective, we can conclude that alternative data is collected through sources that are not conventionally used . Despite this, the exact definition of the concept depends on the traditional data sources that a given company already uses, as well as those of its competitors.
This information comes from places like social media, sensors, Internet of Things (IoT) devices, geolocation, product review sites, search trends, and even weather data. Here are some more examples:
- Receipts received by email;
- Online browsing activity;
- Price trackers;
- Point of sale transactions;
- Air and sea trackers;
- Shipping container receipts;
- Publications on social networks.
Automated alternative data extraction techniques
To deal with the volume of structured, semi-structured and unstructured data (big data) — coming from mobile devices, satellites, sensors and websites —, market-leading companies and other major players use advanced information extraction and processing techniques.
These techniques enable large-scale and accurate data monitoring and analysis. The consequences are factors such as: clear perception of competitive market structures, competitive advantage , better targeted strategies and increased profits. But what are these techniques? We can define the most important ones:
- Web scraping (web harvesting or web data extraction) : is the technique of collecting data from websites and online platforms in an automated manner. It allows information to be captured from public sources, such as news sites, social networks and forums, offering a vast and up-to-date database for analysis;
- Natural Language Processing (NLP) : This AI technique is used to understand and interpret human language. In the case of alternative data, NLP is applied to analyze comments on social networks, product reviews and other texts that reveal consumer opinions;
- Machine Learning and Deep Learning : These technologies allow companies to identify complex patterns in data. In the financial sector, machine learning helps predict credit behavior and accurately assess risk;
- Text pattern matching : uses the UNIX grep command and matches it with programming languages like Perl or Python;
- Vertical aggregation : platforms created with enormous computing power that target specific verticals;
- HTML Parsing : Done using Java scripts and targets linear or nested HTML pages;
- XPath : query language that can be used in XML documents;
- DOM Parsing : Defines the style, structure, and content contained in XML files.
What are the effects of alternative data on investments?
When we talk about alternative investment data, for example, we notice that angel investors , portfolio managers and Venture Capital (VC) firms are making good use of it.
They are used to understand market trends, improve products or services, receive notifications when a company in the portfolio is mentioned or identify changes in consumer demand. Learn more below:
- Product data and reviews: Product comments and reviews provide insights into consumer satisfaction, which can be used to predict acceptance of new releases and adjust marketing strategies;
- Corporate news trends: monitoring news allows companies and investors to detect relevant changes before they impact financial results , as well as when a company in their portfolio is mentioned positively or negatively in the market;
- Changes in consumer demand: Consumer research data provides insight into emerging trends, helping companies adapt their offerings. After all, the level of volatility in consumer demand is high today. So staying in tune with changes is a major competitive advantage;
- Social media sentiment: Social media provides data that reveals what consumers think about brands and products, allowing companies to adjust their positioning;
- Industry trends: understand how your competitors are acting, whether in terms of marketing, financial management, inventory and even after-sales;
- Search engine data: Search engines (such as Google, Bing, and Yahoo) are where many people begin their buying journey. By mapping search trends, companies can identify interests and devise strategies to encourage users to move through the sales funnel;
- Regulatory compliance data: regulatory compliance — which takes into account ESG practices and the protection of sensitive data, for example — is quite significant when an investor shows interest in a company. It is common to have more investment opportunities when there is compliance with regulations , from economic aspects to social and environmental values.
How is alternative data integrated among industry professionals?
Alternative data has been gaining ground in the financial sector and is already part of the analysis and forecasting practices of many institutions. According to studies made available by Bright Data:
So, it’s worth finding out how this data is integrated into financial strategies: this involves several steps, from collecting and processing the information to analyzing and interpreting it in advanced machine learning systems . Let’s explore some of them:
- Credit analysis and risk management : in the context of credit, alternative data is used for detailed and personalized risk analysis.
- Fraud prevention : Alternative data allows you to detect suspicious behavior in real time. Using machine learning algorithms, you can monitor transactions and identify patterns that may indicate fraud attempts, such as unusual account activity or sudden changes in purchasing behavior;
- Personalization of products and services : By integrating alternative data into their systems, companies can support the development of personalized products that fit each customer’s profile. In the financial sector, this personalization is essential to provide a unique experience, as it allows banks and financial institutions to offer solutions that directly meet the needs of each individual;
- Forecasting market trends : Alternative data, by collecting information from various sources and in real time, allows analysts and investors to track trends that may impact their decisions, such as business news, search engine news and social media news.
All of these applications that we have discussed throughout the content show that alternative data represents a true revolution in the business world. They go far beyond conventional financial information, offering a broader and more strategic insight into consumer behavior, market trends, and potential risks.
