The e-commerce industry in China may look back at the transaction data over this period as a treasure trove of customer demographic studies, as well as an assessment of the potential upside in e-commerce for serving their customers’ every need! For many e-commerce customers in China, this is likely to accelerate their shift of purchasing habits from offline to online channels.
How do we quantify purchasing habits? How could this help an e-commerce company?
Last time, we discussed the vigilance required in understanding data characteristics, using e-commerce as a data domain and mitigating return-shipping insurance fraud as a scenario.
In this blog, we discuss another set of considerations in using data to train models to discover patterns, specifically innovating in feature engineering. We will also introduce the concept of clarifying modeling strategy, which will be the focus of the next blog.
Figure 6.1 Using Data to Train Models to Discover Patterns - Technology in TED-ERA fan
Innovations in feature engineering
Let’s continue with the scenario of mitigating return-shipping insurance fraud and explore how feature engineering could help in catching the fraudsters.
Wikipedia describes feature engineering as “the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms.”
In short, feature engineering allows us to summarize a vast amount of data so we can use models to meaningfully discover the patterns within. Simpler models such as linear regression rely heavily on feature engineering for summarization. More complex models such as deep neural networks may even automate the feature engineering process as part of the model training itself and use raw signals as inputs.
In many use cases, however, because of the small sample sizes, labeling difficulties, and interpretability and operability considerations, we often opt to use simpler models and engineered features to discover patterns in our scenarios.
Feature engineering can take on many levels of depth. In e-commerce, for a customer, feature engineering may include:
- Simple statistics: average price for good brought, frequency of purchase
- Complex statistics: weekday/weekend activities, impulse buy or considered purchase
- Taxonomy-based analysis: categories of purchases, diversity of needs
- Clustering analysis: the portfolio of categories, customer intent or life-stage
- Graph analysis: patterns of interaction with merchants, risks for fraud
Merchants, payment processors, shoppers can have similar stacks of feature extraction depths to summarize the diversity of information in the rich e-commerce data set.
How Do We Prioritize?
With such a diversity of feature extraction techniques, there is always limited time and resources to explore a rich data set. So how do we prioritize?
This is when domain expertise becomes crucial. In our return-shipping insurance fraud mitigation scenario, an experienced fraud investigator can often use clues from a few anomalies in customer or merchant behavior to piece together how a fraud scheme may be committed. Domain insights are valuable and necessary as starting points to focus feature extraction efforts but are not sufficient.
In one scenario, fraud investigators considered merchants with long transaction histories in physical goods more trustworthy than those merchants that sell mainly virtual goods (such as phone cards and gift cards). Historical data validated that fraudsters often look for shortcuts when building transaction history for their merchant accounts so they can be ranked higher in product search results, and virtual goods tend to be an easy way to build transaction history without the trouble of logistics operations. Understanding this domain-specific distinction can prioritize feature development and accelerate time to an accurate anti-fraud model.
In another scenario, fraud investigators discovered trends in password recovery security questions where confirmed fraudsters often use “cats” or “dogs” as answers for their favorite animals. This could be interpreted as the lack of “thoughtfulness” of fraudsters in setting up their merchant accounts. However, when assessing the trend as a fraud signal, we found that it happens that “cats” and “dogs” are most people’s favorite animals, regardless of any fraudulent intent. Using it as a signal in fraud investigations would bias fraud investigators’ assessment in an unjustified way.
These examples demonstrate that domain expertise and intuition, while crucial, must go through rigorous validation to be successfully entered into a model.
Models are effective when they match the patterns that exist in data. Domain expertise is also important for forming hypotheses about the nature of the patterns we anticipate.
We just discussed the role of innovating in feature engineering in using data to train models to discover patterns. In the next blog, we will focus on clarifying the modeling strategy where we will dive into details of different strategies and discuss how those apply to the return-shipping insurance scenario.
If you have views, questions, or examples you would like to share, feel free to comment below or contact us directly.
About the authors:
Dr. Jike Chong is a director of data science at LinkedIn, leading a team to develop products to advance people’s careers in the context of the hiring marketplace. He previously built and grew several high-performing data science teams at Acorns, Yirendai (NYSE:YRD), and Simply Hired. Dr Chong also advises several startups and teaches at Tsinghua University.
Yue Cathy Chang is an executive leader in business development, sales and marketing for data products and services. Cathy has held strategic roles serving customers using data science- and engineering-based solutions at bluechip software enterprise companies and startups. Well-versed in tech and business, she has frequently spoken at data and AI events.
The writing and opinions expressed are solely our own and are not shared, supported, or endorsed in any manner by our employers.
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