  Topological Data Analysis (TDA) is a recent approach to analyze data sets
from the perspective of their topological structure. Its use for time series
data has been limited to the field of financial time series primarily and as a
method for feature generation in machine learning applications. In this work,
TDA is presented as a technique to gain additional understanding of the
customers' loyalty for business-to-business customer relationship management.
Increasing loyalty and strengthening relationships with key accounts remain an
active topic of discussion both for researchers and managers. Using two public
and two proprietary data sets of commercial data, this research shows that the
technique enables analysts to better understand their customer base and
identify prospective opportunities. In addition, the approach can be used as a
clustering method to increase the accuracy of a predictive model for loyalty
scoring. This work thus seeks to introduce TDA as a viable tool for data
analysis to the quantitate marketing practitioner.
