Consumer transaction data is one of the most popular and widely deployed types of alternative data by investment managers. The data is also used extensively by corporates with exposure to the retail segment of the economy. Quants and discretionary managers typically deploy the data to predict quarterly revenue growth and earnings. As the data is available before quarterly earnings are released it is ideal to gauge if a company is going to beat or miss Wall Street estimates. As seen in the plot below transaction data has a high correlation with reported company revenue (Fig 1). However, consumer transaction data can also be used by long-term investors to gain insights into consumer purchasing behaviour, acceptance of new products and evaluation implications of promotions and discounts. Over the years Data Strategy has collated content and use case examples, workshops, expert contributions and legal considerations surrounding the use of consumer transaction data.
Figure 1: Credit Card Forecasts are Positively Related to Revenue Growth
What is Consumer Transaction Data?
There are four primary sources of consumer transaction data, credit & debit cards, email receipt data, point of sale data and more recently fintech app data like PayPal. As the data is almost real-time in nature it can give a very strong read on how a consumer-facing company is performing. The data can be aggregated up to a sector level or it can be granular at a merchant or company level and sometimes product-level data. Given the visibility, frequency and granularity offered by consumer transaction data, it can be costly to acquire. Some credit & debit card data vendors have sold their data for up to $1m annually. This puts the users of the data is somewhat of an exclusive club as not many funds can afford such price points. In response to this, some data vendors have started to sell their data at a ticker level or basket of tickers, rather than requiring clients to purchase the whole dataset. This has given many more funds access to the data to help answer research questions and to use it for a select number of investments in a portfolio.
Workshop and Expert Hub Content
Eagle Alpha clients can access a replay and summary of our September workshop on consumer transaction data. After an overview of the segment the head of data science at Eagle Alpha, Thomas Combes, gave an overview of an Eagle Eye data quality report of two credit & debit card datasets. Thomas discussed many aspects of his analysis of the data and highlighted the importance of panel variability that may arise from the transaction count by card types over time. Thomas provided a visual of this from his Jupyter notebook output (Figure 1). This highlights the importance of a constant panel and how a vendor may use certain filters in their presentation of the data.
On the workshop, we also hosted an Expert Hub contributor, David Schwartz who until recently was Head of Data Acquisition at Coatue. David shared his experience on using transaction data and the challenges presented using the data. David mentioned that unbundling, new technologies, better compliance from vendors and improved ticker mapping has increased the access to transaction data across the fund industry. This may have eroded alpha somewhat, but alpha is still present in the data. David also noted that for the larger funds in the industry using consumer transaction data can be “table stakes”.
Daniel McCarthy, another Expert Hub contributor, discussed how he uses consumer transaction data in a longer-term fundamental use case and equity valuation model. Daniel’s model, called Customer Based Corporate Valuation (CBCV), uses consumer transaction data to evaluate customer loyalty and churn to measure the value of consumers to a company over time. Daniel discussed his model in detail and mentioned issues that need to be monitored to get a clear view of consumers to interact with a company. When executed correctly credit card data “represents the next great frontier for consumer driven revenue forecasting”. Daniel also presented a brief rundown of the CBCV at Eagle Alpha’s virtual conference in May.
Alpha Centre has numerous case studies applying alternative data to consumer stocks and also has case studies related to consumer transaction data. Some recent examples from vendors focused on quarterly results are related to subscriber trends for Disney+ and two separate examples where email receipt data was used in the food delivery sector (McDonald’s and Delivery Hero). The case study for McDonald’s used email receipt data to show how the company gained a share in breakfast delivery by using delivery services like Uber Eats and Door Dash in the months after the outbreak of COVID-19 (Figure 2).
In June 2020 we published 15 Consumer Transaction Use Cases for Long Term Fundamental Investors. This case study examined ways that consumer transaction data can be used to monitor such things as cohort analysis, customer overlap, churn, market share and pricing strategies. An example of churn can be seen below where a US credit & debit card dataset shows churn among streaming providers. For Netflix, two-thirds of the company’s customers are still subscribed to the service twelve months after first signing up (Figure 4).
[Data Strategy clients can click here to access all available case studies]
Investors using consumer transaction data need to be acutely aware of the legal considerations surrounding personal identifying information (PII), material non-public information (MNPI) and data provenance. Legalities surrounding these issues have become even more pronounced with European GDPR rules and the CCPA rules in California. Given the importance of these topics, the Legal & Compliance portion of the Data Strategy offering has numerous workshops and legal articles for clients to delve into. These legal articles and workshops are produced in conjunction with our partner Lowenstein Sandler. For example, these articles look at GDPR and CCPA. and this article looks at the recent class-action suit brought against Yodlee. These workshops looked at PII and data provenance and this workshop addressed the call by US lawmakers to bring Envestnet/Yodlee to the FTC.
Consumer transaction data can be highly correlated with reported revenues of consumer-facing companies like retailers and restaurants. Investors use the data for shorter-term quarterly trading signals, but it can also be used for long term fundamental analysis. It can be costly to acquire and implement in trading strategies but given the utility and signal in the data, it is extensively used across the fund management industry. The data needs to adhere to rigorous legal and compliance regulations so performing due diligence on data vendors is critical before it is used in a trading environment.
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