From Insights to Impact: Driving Business Growth through Data Science and ML Projects
In the world of data science and machine learning, it’s easy to get caught up in the technical intricacies and the temptation of cutting-edge algorithms. However, to truly make an impact, data teams must ensure our efforts align with the specific needs and goals of the business. In this article, I’d like to explore the importance of bridging the gap between technical solutions and business requirements. Talking specifically about a Natural Language Processing (NLP) model we built for analyzing customer reviews.
Understanding the Business Perspective
As engineers and data leaders, it’s natural to get immersed in the technical aspects and strive for optimization. However, it’s essential to keep in mind that the ultimate purpose of data teams is to provide intelligence that directly contributes to business growth. Focusing solely on technical efficiencies or novel approaches may lead to wasted efforts if they don’t align with the specific needs and objectives of the organization.
From Words to Wisdom
Our Customer Engagement and Advocacy team wanted to understand the pain points of our customers. They wanted our help to make sense of customer feedback on social media and review sites such as Trustpilot, Apple App Store, and Google Play Store. Our first instinct was to create a data engineering pipeline to collect all the reviews and wow our business partners with a Word cloud depicting the most cited words and phrases.
Of course, we would use all the machine learning techniques — clean the data, use stop words and even bring in a sentiment lexicon to throw in some sentiment scores. That should do the trick, right? They’ll start parading us for how smart we are, yes? Well, it turns out not :)
Even though Word clouds and sentiment analyses are cool, they seldom uncover any meaningful insights the business can act on. So we went back to the drawing board and thought about what could bring value to our business. We trained a Natural Language Processing (NLP) model in the context of our product and business. Trained it on some of the themes and situations pertaining to us.
Through this iterative process, the enhanced NLP model became a powerful tool for extracting actionable intelligence. It enabled the classification of customer reviews at three levels:
Accolades and Grievances:
By classifying reviews into accolades and grievances, we gained deeper insights into customer sentiment. This categorization enabled us to identify what our customers appreciated most and the pain points they experienced.
Departmental Classification:
We took it a step further by classifying them according to the specific departments customers were referring to. This capability provided us with granular insights into how different departments were perceived by our customers. This let us know the broader areas our customers needed us to focus.
Top Issues and Themes:
The power of the enhanced NLP model truly shone when we successfully identified the top issues within each department. This critical intelligence enabled us to prioritize our efforts and allocate resources effectively.
Bridging Data Science and Business Success
The success of data science projects lies in our ability to translate data into actionable insights that drive business growth. By bridging the gap between technical wonders and business requirements, data teams can unlock the true potential of our efforts.
