Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk
Memenuhi Strategi Pengelolaan

Pemetaan Pelanggan dengan LRFM dan Two Stage Clustering untuk
Memenuhi Strategi Pengelolaan

LRFM, Clustering, Davies Bouldin Index, Customer Mapping, Data Mining

Maibus is a company of transportation services located in Bali. Transaction data that is owned has not been managed properly.
This results in data accumulation and only as a turnover calculation, so LRFM and clustering methods are needed to assist
the calculation and processing data in fulfilling customer management strategies. The research was conducted by collecting
and understanding data, preprocessing, applying LRFM (Length, Recency, Frequency, Monetary), normalizing LRFM,
evaluating the number of clusters with Davies Bouldin Index (DBI), clustering with K-Means, and analyzing cluster results.
The data used is transaction data from January 2017 to December 2018 with a total of 14.292 data. The clustering method
with the K-means algorithm helps in mapping customers based on transaction data. DBI was used to determine the optimal
number of clusters and LRFM used to test the determination of variables in determining customer behavior and loyalty. The
results of testing 7.193 invoice using 5 clusters with DBI value is 0.135. The result of customers in cluster 0,2,4 are new
customer groups with the proposed strategy is enforced strategy, while the customer in cluster 1 and 3 are lost customers with
the proposed strategy is let-go strategy that refers to the customer value and customer loyalty matrix

Ni Putu Viona Viandari1
, I Made Agus Dwi Suarjaya2
, I Nyoman Piarsa3

Universitas Udayana

27-02-2022

Fajar bagus W

PDF

Indonesia

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