<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Bukalapak | Figarri Keisha</title><link>https://kfigarri.github.io/tags/bukalapak/</link><atom:link href="https://kfigarri.github.io/tags/bukalapak/index.xml" rel="self" type="application/rss+xml"/><description>Bukalapak</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 25 Mar 2022 00:00:00 +0000</lastBuildDate><image><url>https://kfigarri.github.io/media/icon_hu1335818982437251170.png</url><title>Bukalapak</title><link>https://kfigarri.github.io/tags/bukalapak/</link></image><item><title>📈 Causal Inference - Can We Trust What Happens After?</title><link>https://kfigarri.github.io/post/difference-in-differences/</link><pubDate>Fri, 25 Mar 2022 00:00:00 +0000</pubDate><guid>https://kfigarri.github.io/post/difference-in-differences/</guid><description>&lt;h2 id="overview">Overview&lt;/h2>
&lt;p>How do you know if your new business strategy actually worked or if the trend was going to happen anyway? In this article, I introduce the Difference-in-Differences (DiD) method, a powerful statistical technique for estimating causal impact from observational data, using a real case study from my work at Bukalapak.&lt;/p>
&lt;p>We walk through the full pipeline: from identifying the problem with naive before-after comparisons, validating assumptions like parallel trends, to implementing DiD with Python regression models and even conducting a placebo test. I also showcase a custom DiD Python library that makes the process seamless and reproducible.&lt;/p>
&lt;p>&lt;a href="https://medium.com/bukalapak-data/difference-in-differences-8c925e691fff" target="_blank" rel="noopener">Read the full article on Medium&lt;/a> and discover how we turn business questions into causal answers.&lt;/p>
&lt;h3 id="did-you-find-this-page-helpful-consider-sharing-it-">Did you find this page helpful? Consider sharing it 🙌&lt;/h3></description></item><item><title>🛍️ Predicting Users’ Intention When Searching on Online Marketplace Platforms</title><link>https://kfigarri.github.io/post/search-user-intention/</link><pubDate>Tue, 25 Feb 2020 00:00:00 +0000</pubDate><guid>https://kfigarri.github.io/post/search-user-intention/</guid><description>&lt;h2 id="overview">Overview&lt;/h2>
&lt;p>Ever wondered why some people search with laser focus, while others just explore? In this post, I walk through how we tackled this challenge at Bukalapak by modeling users&amp;rsquo; search intentions, specifically whether they are exploring or locating. Using historical behavioral data, we built an unsupervised learning model to predict these intent types based on queries, enabling personalized search strategies and a better user experience. Curious how a simple keyword like “iPhone 5” can lead to very different behaviors? Dive into the full story to find out.&lt;/p>
&lt;p>👉 &lt;a href="https://medium.com/bukalapak-data/predicting-users-intention-when-searching-on-online-marketplace-platforms-a40a1792195a" target="_blank" rel="noopener">Read more on Medium&lt;/a>&lt;/p>
&lt;h3 id="did-you-find-this-page-helpful-consider-sharing-it-">Did you find this page helpful? Consider sharing it 🙌&lt;/h3></description></item></channel></rss>