Data Deep Dive: Web Mining Shows Inflections in Apparel Sector Results
Pricing data reveals strategy and performance inflection points for apparel companies, providing an early warning system for investors for upside and downside risk versus consensus estimates.
Welcome to the Data Score newsletter, your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of alternative data insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective that allows me to collaborate with end-users to generate meaningful insights.
Web-mined pricing and inventory data
Monitoring price trends is an incredibly important analysis for investment professionals and corporations. The price of a good or service is the only way any business monetizes its competitive value. Prices are not set randomly. Companies agonize over the correct price of their product or service to be able to maximize market share, maximize margins, and effectively manage inventory. Observed price actions provide insights into a brand's equity and corporate strategy.
This article goes into more details as a dataset deep dive explaining the use cases, methodology, and limitations associated with the analytics. The goal is to help insight seekers better understand the potential of various types of datasets and to help data providers think through how they can align their products with the outcomes needed by their clients.
Recent interview on the topic
I was recently interviewed by Ascential’s Alternative Data team, where we discussed the value of web-mined pricing data, including some examples. Check out the videos here to get:
Why collect prices with a web mining approach?
By collecting prices systematically with web mining1 techniques and applying the fundamentally driven analytics used by each industry to the data, one can reverse engineer the company’s pricing strategy and assess their ability to execute against that strategy.
The corporate behaviors around pricing are hiding in plain view, as seen in the price changes and product availability across the web.
By leveraging technology to collect and process the data, the insights can be revealed as early indicators for investment decisions.
This allows for improved forecasting of company operations, including the ability to catch inflection points in company performance.
To assess price strategy and execution, pricing data is gathered from multiple public sources across the web. With the application of appropriate analytical tools, millions of data points collected each week are cleansed, enhanced, and analyzed to provide comprehensive insights into the pricing strategies of corporations.
When analyzing web-mined price data, it's important to use the frameworks used by the industries themselves in order to see the data the way business decision-makers see and react to it. For example
Fashion industry pricing analytics should be built around merchandise planning frameworks,
Pricing in the consumer staples industry should be analyzed around category replenishment as a framework.
Pricing that is set based on demand relative to fixed supply, such as airlines or hotels, means that web-mined pricing data analytics should follow yield management as the framework.
Some products and services are priced together, which requires a framework to make bundled offerings comparable in price.
Financial products like mortgages and savings products require pricing data to be analyzed differently than other sectors, for example, by considering the offered rates relative to the underlying interest rate costs associated with the business.
In this deep dive into web-mined pricing data for fashion sector use cases, we are going to
Start with the common questions addressed with the data
Describe the underlying data sources
Explain the thought process behind cleansing the data
Discuss how the data should be enriched, including relevant metrics
How to use the metrics to find insights
Explore the limitations of the data to ensure appropriate use of the insights
Common questions addressed with the data
Fashion Pricing: A Deep Dive
This deep dive into pricing will focus on the fashion pricing use case. In future entries in the Data Score Newsletter, we will go into detail on how to generate valuable insights from pricing data in other sectors.