Analysis Important Factors of Sales Scorecard Using AI Data Wardatul Jannah (a), Bambang Syairudin (b)
a) Department Of Industrial And Systems Engineering,
Sepuluh Nopember Institute of Technology
Kampus ITS Sukolilo, Surabaya 60111
Wrdatljnh2607[at]gmail.com
b) Department Of Industrial And Systems Engineering,
Sepuluh Nopember Institute of Technology
Kampus ITS Sukolilo, Surabaya 60111
bambangsyairudin[at]gmail.com
Abstract
This study examines how the influence of the four perspectives of the balanced scorecard affects sales performance. Where companies need to know the important factors of more comprehensive sales performance. AI helps to find important current factors more quickly and flexibly which can be known at any given time period. This system is called Artificial Intelligence-based Sales Scorecard (AI-based Sales Scorecard). This research was conducted by obtaining important factors which are also important indicators of sales management from Artificial Intelligence (AI) information. Then grouping these indicators into four Balanced Scorecard perspectives. To test the relationship with each perspective, the PLS-SEM method is used. According to AI, there are several important factors in measuring current sales management, namely revenue, number of products sold, average sales value, sales growth rate, customer data analysis, percentage of returning customers, competition in the market, and employee competition. Then a grouping is carried out where Revenue, Number of Products Sold, Average value of sales, are grouped into a financial perspective. Analysis of customer data and the percentage of returning customers are grouped into a customer perspective. Competition in the market is included in the internal and business perspectives. Employee competition is included in Learning and Growth. Data was collected by survey using a Likert scale of 1-5 to 250 respondents in a manufacturing company and 250 respondents in service companies. The result is valid with the output of this study showed that 8 factors met the AVE criteria above 0.5 and Factor Loading above 0.7.
