Mike and Kevin applied to Y Combinator with the idea of using credit card data for better investment decisions. They expanded their idea to include working with companies as well. Their goal is to create an analytics platform for investors and companies to analyze credit card data and find answers to their own questions. The idea for the company came from a friend who needed help loading two terabytes of data into Excel. This realization led to the understanding that there is a huge opportunity to provide investors with an information edge by analyzing billions of transactions and understanding consumer behavior. The primary use case of Second Measure for VCs is to provide visibility into the performance of companies they are considering investing in, as well as their competitors. Second Measure analyzes billions of transactions to understand consumer behavior and provides metrics such as customer spending, unit economics, and market comparisons. The company's sales process has achieved high virality, with all 150 clients coming through inbound channels. Recently, they raised Series A funding with Bessemer and Goldman Sachs leading the investment.
What idea did Mike apply to YC with?
- Mike and Kevin applied to Y Combinator with the idea of using credit card data for better investment decisions.
- They expanded their idea to include working with companies as well.
- Their goal is to create an analytics platform for investors and companies to analyze credit card data and find answers to their own questions.
Where did the idea come from?
The idea for the company came from a friend who needed help loading two terabytes of data into Excel. This realization led to the understanding that there is a huge opportunity to provide investors with an information edge by analyzing billions of transactions and understanding consumer behavior.
- Friend at a hedge fund needed help loading data into Excel
- Realization of opportunity to provide investors with information edge
- Analyzing billions of transactions to understand consumer behavior
- Need for reliable metrics to gauge sales and performance
- Relying on metrics like website visits as a leading indicator of sales
- Making informed investment decisions through data analysis
From project to company
The transition from a project to a company involves optimizing games through data analytics, tracking player behavior, and using metrics to determine game difficulty. Challenges include integrating analytics into game design and building high-scale infrastructure and data pipelines. The speakers emphasize the importance of empowering users to answer their own questions through tools and recognize the potential for applying this approach in the investment industry. The video also discusses analyzing messy and unstructured credit card transaction data to understand consumer behavior.
What info did investors want to know that Second Measure could provide?
- Investors wanted to know the impact of events like Chipotle's food poisoning incident on their revenue.
- Previously, this information was obtained through expensive and time-consuming surveys.
- Second Measure provides a solution by offering direct observations of millions of US consumers' purchases.
- Investors can quickly and easily access this information themselves.
Their first customers
- The company's first customer was a venture capitalist who also invested in their company
- Being part of Y Combinator helped them get meetings with interested VCs
- They had to pitch their product and business model during these meetings
- Many VCs ended up becoming their customers
- Their product is useful for predicting consumer trends and understanding companies that target US consumers and sell directly to them
- It is not suitable for analyzing B2B enterprise companies
- The company uses the market as a sizing tool
- Investors primarily use it for diligence
The primary use case of Second Measure for VCs
The primary use case of Second Measure for VCs is to provide visibility into the performance of companies they are considering investing in, as well as their competitors.
Key points:
- Second Measure analyzes billions of transactions to understand consumer behavior.
- It provides metrics such as customer spending, unit economics, and market comparisons.
- VCs can use this data to evaluate the growth potential and profitability of companies they are interested in.
- Second Measure also helps identify metrics that excite investors, such as cohorts and lifetime value.
What questions are they trying to answer?
- The video discusses the analysis of billions of transactions to understand consumer behavior.
- The main questions they are trying to answer include company performance and consumer behavior.
- Examples of questions they address include market share, customer retention, lifetime sales, and customer shopping habits.
- Their core product focuses on empowering users to answer their own questions rather than providing direct research services.
Data examples from their blog
- The video discusses analyzing billions of transactions to understand consumer behavior.
- The speakers mention their blog and how they work with the press to provide information for articles.
- They regularly publish updates on the comparison between Uber and Lyft based on questions from the press and their own research.
- They have a dedicated team of data scientists and writers who monitor news and interesting companies to provide insights.
Post: Fashion retailers have nothing to fear (yet) from the rise of Stitch Fix
Stitch Fix does not cannibalize department store sales, but rather inspires customers to spend more on clothes overall. Stitch Fix's best customers actually spend more on clothes before becoming a Stitch Fix customer. This suggests that Stitch Fix piques customers' interest in fashion and exposes them to a variety of clothing options. The rise and fall of brands can be actively tracked, such as the increase in Peloton memberships ahead of SoulCycle.
- Stitch Fix does not harm department store sales, but instead encourages customers to spend more on clothing overall.
- Stitch Fix's top customers spend more on clothes even before using the service, indicating that Stitch Fix sparks interest in fashion and exposes customers to a wider range of clothing options.
- The success and decline of brands can be monitored, as seen in the rise of Peloton memberships before SoulCycle's decline.
Post: Holiday sales rocket Peloton memberships ahead of SoulCycle active riders
- Peloton has more active members than SoulCycle based on spending behavior
- There is a transition of people from SoulCycle to Peloton
- SoulCycle did not dispute the metrics
Post: Prime members deliver for Amazon every day
- Amazon's revenue is heavily dependent on Amazon Prime subscribers
- Even former subscribers continue to spend more on Amazon than before
- The exact reason for this is unknown, but one possibility is that subscribers feel obligated to make use of their membership by ordering and buying more products.
Second Measure's product development process
Second Measure's product development process involves multiple streams for feeding their backlog, including internal vision, user research, and custom research for customers. They also use their own app to identify gaps in functionality. This process is highly appealing to data scientists due to the interesting and diverse problems it involves.
- Multiple streams for feeding backlog: internal vision, user research, and custom research for customers
- Use of their own app to identify gaps in functionality
- Highly appealing to data scientists due to interesting and diverse problems
Finding good data scientists who work from first principles
Finding good data scientists who work from first principles is important. Key points include:
- Hiring data scientists with strong quantitative backgrounds who can understand problems from first principles.
- Need for a strong statistical foundation and the ability to think critically and challenge assumptions.
- Mathematical skills are essential and cannot be taught.
- Data scientists should approach complex problems, structure and decompose them, and tackle them piece by piece.
- Screening for this skill during the hiring process is important.
- Technical evaluation includes working with messy data sets and presenting findings.
- Common mistakes include not being able to handle open-ended problems.
- Successful applicants approach tasks creatively and independently.
- Data scientists should not make assumptions about the data and should be diligent in identifying and addressing potential problems or distortions.
- Understanding the foundation of the data before drawing conclusions is crucial.
- Challenges of working with real-world data, such as inconsistencies in classification.
Why is credit card data so messy?
Credit card data is often messy due to multiple text strings associated with a single merchant and limited space on credit card statements. This leads to different variants for a single merchant, making accurate mapping difficult. Franchises also have unique point-of-sale systems, further complicating the data. The messy nature of credit card data is attributed to human error and various transaction processing methods. Users often do not correct the data, exacerbating classification problems. The lack of a unique identifier for companies adds to the complexity. Overall, the messy nature of credit card data poses challenges for accurately classifying and analyzing consumer behavior.
Cleaning data
Cleaning data is crucial for understanding consumer behavior. In this video, the speakers discuss the process of ingesting raw transactional data and using machine-based approaches to extract useful information. Key points include:
- Entity resolution: Identifying merchants and determining purchase locations.
- Debiasing the data: Ensuring it represents the greater population.
- Data pipeline: A process for cleaning and preparing the data.
- Analytics platform: Performs specialized analyses on the clean data.
- Corporate use: Data is used for competitive analysis.
Using their product for competitive analysis
The platform helps investors understand company performance and also helps companies analyze their competitors' performance. Y Combinator's portfolio companies often inquire about obtaining the product after seeing its effectiveness. The platform is not solely focused on consumer companies, but also software companies.
Their sales process
The company's sales process has achieved high virality, with all 150 clients coming through inbound channels. Their product offers a unique perspective, contributing to its spread. Recently, they raised Series A funding with Bessemer and Goldman Sachs leading the investment.
- All 150 clients came through inbound channels
- Product offers a unique perspective
- Recently raised Series A funding with Bessemer and Goldman Sachs leading the investment
Raising money from Goldman Sachs and Citi
Raising money from Goldman Sachs and Citi:
- Goldman Sachs and Citi have invested in alternative data companies, including those dealing with credit card data.
- These companies provide information beyond traditional fundamentals and stock prices, such as credit card data, satellite imagery, web traffic data, and geolocation data.
- Goldman Sachs and Citi have extensive networks that help connect these companies with investors and clients.
- Citi sees the investment as a solution to their struggle with messy transactional data.
- The video discusses the challenges faced by Mint in accurately identifying transactions.
Focusing on a specific problem
Focusing on a specific problem is crucial for developing a successful product or service. Constraints can make the problem more manageable and lead to unexpected applications. However, when a company becomes a standard in the industry, the challenge is to continue developing the product and keeping it interesting for users.
- Importance of focusing on a specific problem
- Constraints can lead to unexpected applications
- Challenge of continuing development and keeping the product interesting
Keeping the product compelling when it's table stakes
Keeping a product compelling when it becomes a basic requirement in the market involves focusing on asking better questions rather than selling insights or signals to hedge funds. This approach can provide an edge even if everyone has access to the same data. Using the product for competitive analysis is a major benefit on the corporate side.