Customer Churn
Automatically identify causes of customer churn and proactively identify at-risk customers to improve customer retention.
Credit Delinquency
Identify delinquent accounts and detect accounts that are likely to become delinquent
Flight Analysis
Determine the best days to fly, which days to avoid, which airline has the fewest delays, and more.
Case Studies
Optimizing Ad Spend
Challenge
Digital media ads are a must for any business these days. However, digital marketing can be a wild west of results. With social media channels and advertising platforms constantly changing their pricing structure and content algorithms, it can be difficult to know if you’re getting the most out of your dollar.
Approach
With DataChat's powerful tools and simple interface, one of our customers set out to create a dashboard based on data pulled from their major digital marketing channels to figure out their true costs and return on investment.
Result
A marketer with access to data on revenue, number of customers, and marketing costs was able to create a robust Insights Board that allows them to track their average order value (AOV) and cost per acquisition (CPA) and use these metrics to track the return on investment of their marketing spend.
Data Viability Analysis
Challenge
The data from oil and gas wells is large, messy, inconsistent, and unwieldy. This includes duplicative entries, incorrect data input, orphaned wells without identifying information, no standard names, inconsistent entries, and thousands of datasets without a common link. Renee, a consultant at Culminate Strategy, needed a way to determine whether the data was viable before she got too deep into her wrangling. Renee’s current tools to review, validate, and wrangle the data are time and labor intensive and often result in unusable data after weeks of work. Additionally, Renee was unable to use her analytics systems to access and explore the data because loading the data resulted in timeouts and crashes.
Approach
Renee used DataChat to load datasets directly from her Databricks database. For her large (~6 billion row) datasets, DataChat can deliver a high-level summary within 30 seconds. Using the Sample skill, she then quickly explored a representative and manageable subset of that data without having to wait for the entire dataset to be processed.
For her datasets containing millions of rows, she can load and explore the entire dataset within minutes, again using DataChat to review the mean, median, and range of each column to ensure the data was within expected parameters, while visualizing the distribution of data via helpful histograms. With initial exploration complete, she used DataChat to remove data entries without meaningful information and then leveraged DataChat's automatic visualization features to examine her most important KPIs and let DataChat choose the best visualizations for her
Results
Using DataChat, Renee was able to quickly access, review, clean, and validate her data, saving her days (and sometimes weeks) of work to establish a usable, accurate dataset she could use for her analysis of oil well performance.
Renee was able to assess that the data wasn’t consistent, accurate, or salvageable within a couple of hours. Previously, it could have taken her days, if not weeks, to assess a dataset with millions of rows.
Logistics Optimization
DataChat’s supportive analytics platform and Guided English Language allow you to leverage self-served data analytics and machine learning to uncover sophisticated patterns in your data.
Challenge
A Fortune 500 company had dashboards demonstrating exponential equipment costs, but they couldn’t hone in on the meaning of the data with existing tools.
Approach
With DataChat’s self-service platform, a group within the company was empowered to address their questions on their own while working collaboratively amongst themselves and across departments more quickly and easily than they could with traditional tools, such as Excel, SQL, and Python.
DataChat also allowed the group to independently construct robust data science pipelines and workflows to analyze their device data. They were also able to leverage DataChat’s explainable AI and machine learning tools to quickly create models that revealed that the time between purchasing a device and the first repairs was the primary factor of device life, which would help inform the terms of new leases.
Results
DataChat replaced their old, complex data science tools and empowered the customer to achieve pre-defined ROI metrics, which allowed them to:
- Predict bulk inventory purchases more accurately
- Optimize existing equipment repair pipeline
- Meet environmental and social governance goals