By: Eric Siegel, Founder, Predictive Analytics World
In anticipation of his upcoming conference presentation, Macy’s Advanced Analytics in 
Customer Centric Strategies at Predictive Analytics World San Francisco, May 14-18, 2017, we asked Daqing Zhao, Director, Advanced Analytics at Macy’s, a few questions about his work in predictive analytics.
Q: In your work with predictive analytics, what behavior or outcome do your models predict?
A: Macy’s has 150 years of history and is an iconic brand in retail, providing superior omni channel shopping experience for our customers through stores and online channels. Macy’s makes extensive efforts to protect customer privacy and identity information. In order to serve our customers better, we need to understand our customer preferences in order to recommend the right product, and send the relevant information, to give our customers the frictionless shopping experience. In particular, we predict what categories, and brand a customer will likely to make a purchase, how many they would spend, as well as how they interact with our marketing channels. We also predict customer retention, customer life time value and other customer metrics to help our marketing activities.
Q: How does predictive analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?
A: Using Predictive Analytics, we are able to best organize our data to predict customer preferences and behaviors, in order to optimize our marketing activities, such as emails, direct mails and product recommendation.
Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?
A: We do not disclose quantitative numbers. Predictive analytics, however, is very useful to drive results. For example, if we predict a customer having high propensities to make a purchase in some categories, and very low propensities to convert on some other categories, we often see differences of an order of magnitude in conversion rates or average spend in these categories.
Q: What surprising discovery or insight have you unearthed in your data?
A: One such surprise is that for some segments of customers, having low scores in all categories may be because we do not have sufficient data about these customers. It is not necessary that they have no interest in any our categories. With higher uncertainties in the lower scores, it may not be optimal to make decisions based on the small differences in the scores.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: Our customers are diverse and our products and business goals are complex. We don’t rely on one methodology, one solution, or one platform for our data driven, omni channel, personalized marketing efforts. We take a portfolio approach in our predictive methodologies, data sources and hypotheses, perspectives and strategies.
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Don't miss Daqing’s conference presentation, Macy’s Advanced Analytics in Customer Centric Strategies, on Wednesday, May 17, 2017 from 10:25 to 10:45 am at Predictive Analytics World San Francisco. Click here to register to attend.
By: Eric Siegel, Founder, Predictive Analytics World