Commit 071879cb authored by Carlo Angelo Cordero's avatar Carlo Angelo Cordero
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<title>Tulay sa Pag-unlad, Inc. | Home</title>
<meta name="description" content="This is the description">
<link rel= "stylesheet" type= "text/css" href= "{{ url_for('static', filename='css/styles.css') }}">
<header class="main-header">
<nav class="nav main-nav">
<li><a href="/">Home</a></li>
<li><a href="/web/about.html">About</a></li>
<li><a href="/web/data.html">Data</a></li>
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<h1 class="band-name band-name-large">Tulay sa Pag-unlad, Inc.</h1>
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<br> -->
<section class="content-section container">
<h2 class="section-header">Loan Collection Prioritization Strategy Proposal</h2>
<h3 class="section-sub-header">Scenario</h3>
<p>No existing process to determine which customers to collect from and what strategy to use.</p>
<p>No personal incentives for center officers (clients) to collect from past due members.</p>
<p>No disincentive for customers with late payments.</p>
<h3 class="section-sub-header">Goal</h3>
<p>Come up with a straightforward mechanism to determine which customers to target
through the organization’s collection efforts.</p>
<h3 class="section-sub-header">Methodology: RFM Analysis</h3>
<img class= "about-band-image2" src="{{ url_for('static', filename='css/rfm.png') }}" width="500">
<p>RFM (Recency, Frequency, Monetary) analysis is a customer segmentation technique that uses past
purchase (payment) behavior to divide customers into groups.</p>
<p>RFM helps divide customers into various categories or clusters to identify customers who are more
likely to respond to promotions and also for future personalization services.</p>
<h3 class="section-sub-header">Solution</h3>
<p>RFM classes are identified upon running the jupyter notebook using the TSPI's raw data</p>
<p>Created interactive datatables under Data page where the user can see a summary of their
PAR accounts and extract directly the results of the RFM analysis to a csv file </p>
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<h3 class="band-name">TSPI</h3>
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