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

Tracking customer sentiment is an essential business activity. Customer feedback lets businesses know which efforts are working and highlights customer difficulties. More significantly, understanding consumer desires enables predictive action. If every customer who enters a store asks for a certain product, the store owner knows that she should stock more of the product for the following week. But by tracking customer feedback, the store owner can dig deeper and understand why customers are demanding the product. If the store owner determines the all-important “why,” she will know whether the increased demand was due to global consumer trends, a marketing campaign, a celebrity endorsement, or any number of other reasons. In other words, customer feedback allows businesses to pursue insights they would otherwise not be aware of.

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 keyphrase extraction.

Our Process: Benefits of Azure Databricks

The client’s previous feedback architecture used Python scripts to process customer feedback. During processing, contractions were expanded, inflectional endings were removed, HTML tags were removed, punctuation marks were removed, characters were rewritten in lowercase, spelling mistakes were corrected, and junk words were removed from the feedback. Sentiment analysis was then performed on the cleaned data. The system was functional but slow and non-scalable compared to modern serverless solutions.

We knew that Azure Databricks would offer our client the exact kind of speed and flexibility that they were looking for. Azure Databricks allows users to run robust analytics algorithms and drive real-time business insights. It offers one-click, autoscaling deployment that ensures that enterprise users’ scaling and security requirements are suitably met. Azure Databricks also features optimized connectors, which we used to run Microsoft Cognitive Service APIs. This allowed our team to quickly implement entity recognition and keyphrase extraction. And because the Azure Databricks solution was managed from a single notebook, our teams could collaborate more effectively across office locations. Now, when our India team begins working on contraction processing, our team in Redmond can continue with lemmatization without missing a beat.

Immediately Put to the Test

We completed our client’s new Azure Databricks-based feedback analysis implementation just in time for the biggest shopping test of the year: Black Friday. As it turned out, feedback analysis was crucial in ensuring 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 customers reloaded the checkout page, customer transactions could not be completed. Customer support lines were inundated with calls from holiday shoppers, so the client was initially unaware of the problem. Fortunately, the customer feedback tool immediately compiled, analyzed, and made information about the checkout issues available. The voice of the customer team forwarded the feedback information 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. As evidenced by the Black Friday feedback, the tool’s speed and accuracy was a success right from the start. The technical improvements from the Azure Databricks architecture resulted in the ideal business outcome: the discovery of actionable business insights, faster. Ultimately, of course, improved insights mean a better customer experience, which is crucial to any business’s success.