"customer classification" is a correct and usable phrase in written English, and it can be used in a variety of contexts.
For example, if a company wanted to better understand their customer base, they might categorize them into different classes based on income, age, or other factors. This process would be called "customer classification."
Although individually meaningless, these records fit together to form a huge mathematical structure called a directed multigraph, which represents the interconnectedness of the 300m phone numbers known to AT&T's computers.Careful analysis of this information could help with infrastructure planning, customer classification and marketing.
This paper parallelizes overall customer classification process.
The ISODATA algorithm, which includes temperature dependency and outlier filtering, is proposed in [32] for customer classification.
It also relies heavily on establishing a strategy for the unlocking of that customer data, creating classification schema and rich taxonomies for activating that data, and enabling permission based sharing of that data so it can be put to use without losing control of what remains a proprietary asset.
Furthermore, sample customers are selected by considering the energy consumption (kWh) range of customers, their activity classification, and their locations in distribution feeders.
Via an examination of the literature and based on the concept of customer value hierarchy a classification of the different models and techniques of co-creation is developed.
Yang, S. Zhong, R.N. Wright, Privacy-preserving classification of customer data without loss of accuracy, in: SDM' 2005 SIAM International Conference on Data Mining, 2005] which was the first scheme that used the homomorphic encryption primitive for PPDM in the fully distributed setting.
Intensive research revealed that the classification of the deliberate customer and the careless customer with machine learning.
Nissan also permits dealers to move approved customers from a higher credit classification to a lower one, which has the effect of raising the customer's interest rate.
This study is investigated the product properties affecting the customer emotions by using KE, classification and association rule which are data mining methods.
