Performance Analytics

Company Profile

Fortune 500

Industry

Telecommunications

Function

Business Intelligence and Analytics Team

Region

Global

Executive Summary

A Fortune 500 Tech Company faced the problem of having continuous streams of operations data and no easy way to ask business-critical questions without needing time-consuming data work. Any data driven decision-making needed weeks or months of work to generate the full BI reports. To address this issue, the business implemented Natural Language Querying (NLQ), powered by
Chat with Aible within 23 days.

Challenge

The Business Intelligence (BI) team found it difficult to have their constantly expanding operations data ‘analysis-ready’ for the critical timeframes of the business, leading to:

Delayed Decisions: It often took months for BI teams to finish generating reports and visualizations this caused a significant delay in business units ability to leverage insights to support business decisions

Unscalable Data Processing: With no quick and reliable way to analyze the data for reporting and new data sources growing by the day, the process grew unsustainable with more nimble methods to generate business-critical information being needed

Solution

Within 3 weeks, the Global Leader in Tech implemented Natural Language Querying, powered by Chat using Aible, to improve and speed up the way their company interacts with operations data:

Direct Access to Critical Business Info: With Chat available, business teams are enabled to directly communicate with operations data via simple questions, e.g. “Which product team had the best average performance over the last 3 quarters in Louisiana?” and auto-generate reports and dashboards as needed.

Modernizing Business Intelligence: NLQ capabilities from Aible allow the business’ data teams to move beyond traditional BI workflows to bring data interactivity and accessibility to the next level while still maintaining what’s familiar to the business users

Results

The Global Tech Company has greatly improved its speed to leveraging data for critical business decisions while placing significantly less strain on its data teams. Now business users directly ask questions to their operations data and don’t have to wait longer than a few seconds to have their answer. This level of autonomy to investigate and probe their data allows business leaders to make better-informed decision making, faster.

https://www.aible.com/explore-customer-churn-with-chat

Case Study

Fortune 500 company, Customer Churn

Analyzed 2B+ rows to identify areas of customer churn and enabled a chat application that reviewed over 2.5M variable combinations to find and summarize meaningful patterns

Read Use Case