DeepAI AI Chat
Log In Sign Up

Surveys without Questions: A Reinforcement Learning Approach

06/11/2020
by   Atanu R Sinha, et al.
Google
adobe
11

The 'old world' instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers' online interactions; failing to capture a customer's interactions over time since the rating is a one-time snapshot; and inability to tie back customers' ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer's online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.

READ FULL TEXT
07/18/2020

Feature-level Rating System using Customer Reviews and Review Votes

This work studies how we can obtain feature-level ratings of the mobile ...
02/13/2023

UNDR: User-Needs-Driven Ranking of Products in E-Commerce

Online retailers often offer a vast choice of products to their customer...
05/24/2018

Meta-Gradient Reinforcement Learning

The goal of reinforcement learning algorithms is to estimate and/or opti...
02/19/2018

When Sheep Shop: Measuring Herding Effects in Product Ratings with Natural Experiments

As online shopping becomes ever more prevalent, customers rely increasin...
03/23/2021

Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning

Customer support via chat requires agents to resolve customer queries wi...
12/05/2022

Benchmarking Offline Reinforcement Learning Algorithms for E-Commerce Order Fraud Evaluation

Amazon and other e-commerce sites must employ mechanisms to protect thei...
11/19/2021

DeepQR: Neural-based Quality Ratings for Learnersourced Multiple-Choice Questions

Automated question quality rating (AQQR) aims to evaluate question quali...