RE-GrievanceAssist: Enhancing Customer Experience through ML-Powered Complaint Management

In recent years, digital platform companies have faced increasing challenges in managing customer complaints, driven by widespread consumer adoption. This paper introduces an end-to-end pipeline, named RE-GrievanceAssist, designed specifically for real estate customer complaint management. The pipeline consists of three key components: i) response/no-response ML model using TF-IDF vectorization and XGBoost classifier ; ii) user type classifier using fasttext classifier; iii) issue/sub-issue classifier using TF-IDF vectorization and XGBoost classifier. Finally, it has been deployed as a batch job in Databricks, resulting in a remarkable 40% reduction in overall manual effort with monthly cost reduction of Rs 1,50,000 since August 2023.

RE-RFME: Real-Estate RFME Model for customer segmentation

Marketing is one of the high-cost activities for any online platform. With the increase in the number of customers, it is crucial to understand customers based on their dynamic behaviors to design effective marketing strategies. Customer segmentation is a widely used approach to group customers into different categories and design the marketing strategy targeting each group individually. Therefore, in this paper, we propose an end-to-end pipeline RE-RFME for segmenting customers into 4 groups: high value, promising, need attention, and need activation. Concretely, we propose a novel RFME (Recency, Frequency, Monetary and Engagement) model to track behavioral features of customers and segment them into different categories. Finally, we train the K-means clustering algorithm to cluster the user into one of the 4 categories. We show the effectiveness of the proposed approach on real-world Housing.com datasets for both website and mobile application users.

RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain

We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and clickstream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of <40 ms serving 1000 rpm.