Friday, February 15, 2019

Case Study: Improve Feedback Analysis with Azure Databricks



Key Challenges

   Transition feedback analysis architecture from VMs to Azure Databricks.
   Improve analytics execution speed and scalability.
   Add entity recognition and key phrase extraction services.

Fast and Accurate Feedback Analysis is Crucial

Our client, the voice of the customer team for a large software company, wanted to improve their text analytics system. The client’s system relied on VMs to compile online customer feedback and perform sentiment analysis. To improve execution speed and increase scalability, the client wanted to move the system to a serverless architecture. The client also wanted to incorporate two new features: entity recognition and key phrase extraction.

Benefits of Azure Databricks

The client’s previous feedback architecture used Python scripts to process customer feedback. Customer feedback was processed using the following steps:

   Contractions were expanded.
   Inflectional endings were removed.
   HTML tags were removed.
   Punctuation marks were removed.
   Characters were rewritten in lowercase.
   Spelling mistakes were corrected.
   Irrelevant words were removed from the feedback.

Sentiment analysis was then performed on the cleaned data. The system was functional but time-consuming. The system also was not scalable.

Azure Databricks offered our client the speed and flexibility they were looking for. Azure Databricks allows users to run robust analytics algorithms and drive real-time business insights. Azure Databricks also offers one-click, autoscaling deployment that ensures enterprise users’ scaling and security requirements are suitably met. Azure Databricks also features optimized connectors, which we used to run Microsoft Cognitive Service APIs. These APIs allowed our team to quickly implement entity recognition and key phrase extraction. Because the Azure Databricks solution was managed from a single notebook, our teams could collaborate easily across office locations.

Receiving Customer Feedback

We completed our client’s new Azure Databricks-based feedback analysis implementation for Black Friday. Feedback analysis is crucial to ensure a smooth purchasing process for customers.

Shortly after Black Friday sales started, the client’s online checkout tool began having technical difficulties. Due to the large number of online transactions, the checkout tool failed. No matter how many times buyers reloaded the checkout page, the transactions were not completed. Customer support lines were occupied with calls from holiday shoppers, so the client was initially unaware of the problem. Fortunately, the customer feedback tool immediately compiled and analyzed the checkout issues. The voice of the customer team forwarded the feedback to the technical team, and the problem was addressed quickly.

Improved Feedback Analysis Leads to a Better Customer Experience

Our Azure Databricks feedback analysis tool improved speed and brought a new level of scalability to our client’s business operations. The tool’s speed and accuracy was an immediate success. The technical improvements from the Azure Databricks architecture resulted in the ideal business outcome: the discovery of actionable business insights, faster.