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Full-Text Articles in Business Intelligence
Identifying At-Risk Clients For Xyz Packaging, Co., Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, Brendon Jones
Identifying At-Risk Clients For Xyz Packaging, Co., Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, Brendon Jones
SMU Data Science Review
We present a multi-algorithmic modeling approach for the identification of at-risk customers for XYZ Packaging Inc. We define at-risk customers as those having declining seasonally adjusted gross income forecasts which are a strong indicator of impending customer churn. Customer retention is an area of interest regardless of industry but is especially vital in commodity-based low margin industries. We employ traditional Autoregressive Integrated Moving Average (ARIMA) and Anomaly Detection algorithms for discriminating changes in customer revenue patterns. Ultimately, we identify a meaningful proportion of clients whose forward-looking quarterly demand can be predicted within an actionable degree of accuracy.
Optimize The Effectiveness Of Recruiting Campaigns, Ryan A. Talk, Lakshmi Bobbillapati, Marshall Coyle
Optimize The Effectiveness Of Recruiting Campaigns, Ryan A. Talk, Lakshmi Bobbillapati, Marshall Coyle
SMU Data Science Review
Abstract. Recruiting marketing plays an important role in the talent acquisition strategy today. To find the best candidates, companies make substantial investments through numerous recruiting agencies, job boards, and internal systems such as Indeed, LinkedIn, Monster, Talent Communities. In this paper we obtained a company’s LinkedIn Job Posting data to try to predict the number of visits they will receive for each job posting based on the time of the year it is posted. We compare AR(1), AR(2), AR(52), MA(1), and ARMA(1, 1) time series methods to a baseline of a persistence model. We found that out of these 5 …