Unlocking the Power of Customer Feedback Analysis in Retail with Databricks AI Functions

In today’s dynamic retail environment, staying connected to customer sentiments is more crucial than ever. With shoppers sharing their experiences across countless platforms, retailers are inundated with feedback that holds the key to enhancing products, services, and overall customer satisfaction. But sorting through this tidal wave of unstructured data can feel like searching for a needle in a haystack.

That’s where Databricks AI functions come in. This cutting-edge solution equips retailers with the tools to transform raw customer feedback into actionable insights. By harnessing the power of advanced language models and SQL-based functions, Databricks streamlines the process of analyzing reviews, categorizing comments, and uncovering trends that drive smarter business decisions.

What is Customer Sentiment Analysis?

Customer sentiment analysis is revolutionizing the way businesses understand their customers. At its heart, this powerful technique employs advanced natural language processing (NLP) and machine learning algorithms to interpret and categorize text-based feedback into positive, negative, or neutral sentiments.

Unlike traditional keyword-based methods, sentiment analysis dives deeper into the intricacies of human language. It captures context, detects sarcasm, and identifies subtle emotional cues, offering a more accurate and nuanced understanding of customer opinions. For businesses, this means moving beyond surface-level insights to truly grasp the emotions driving customer interactions—insights that can inform better decision-making and enhance the overall customer experience.

How does it work?

• Data Collection: Gathering text data from various sources such as blog comments, social media posts, customer reviews, and support tickets.
• Text Processing: Cleaning and preparing the data for analysis, including removing irrelevant information and standardizing text format.
• Sentiment Classification: Using AI algorithms to classify the processed text into sentiment categories.
• Analysis and Visualization: Presenting the results in an easily digestible format, often through dashboards or reports.

What does it help with?

• Product Development: By understanding what customers like or dislike about the product, retailers can make informed decisions about product development, such as flavor profiles, packaging, and pricing.
• Marketing Strategies: Customer sentiment analysis helps identify the most effective marketing channels and messaging to reach the correct target audience and drive sales.
• Customer Satisfaction: By addressing customer concerns and preferences, retailers can improve customer satisfaction and loyalty, which is critical for building a strong brand reputation and driving repeat business.
• Competitive Advantage: In a crowded market, customer sentiment analysis gives retailers a competitive edge by helping them understand what sets their product apart from the competition and how to differentiate.

Streamlining Sentiment Analysis with Databricks

Databricks provides a unified platform for seamless data ingestion, cleansing, storage, and analysis, making it ideal for tasks like sentiment analysis of social media feeds or customer reviews. While there are multiple approaches to implementing sentiment analysis on Databricks, this article focuses on leveraging Databricks SQL AI Functions to streamline the process and quickly extract actionable insights.

The Power of AI Functions in Retail

By incorporating AI functions into data pipelines, retailers can:

• Avoid complex setups and the need for specialized skills
• Eliminate the need for multiple tools
• Accelerate product development cycles

This streamlined approach allows retail teams to focus on what matters most: understanding and responding to customer needs.

Preparing and collecting feedback data (bronze):

As a Data Analyst persona, simulate a feedback collection process using Databricks AI functions to generate synthetic data. We're using the ai_query function to query Meta Llama 3.1 405B Instruct and generate data for social media (Facebook, X) and mobile communication (phone calls and text messages). This synthetic data will be stored in a bronze layer and used to inform analytics and insights. The benefits of this approach include high-quality and consistent data, scalability, and cost-effectiveness. Next steps include processing and transforming the data, developing analytics and insights, and refining the solution based on stakeholder feedback.

We leverage the power of Databricks to analyze customer feedback from various social media platforms, such as Twitter and Facebook, as well as phone call transcripts. By utilizing techniques like text analysis and natural language processing, we extract valuable insights from the data, including sentiment analysis of tweets and Facebook posts. We analyze the sentiment of customer feedback on a particular product or service, identifying trends and patterns that inform business decisions. In a real-world scenario, we ingest data from different sources, such as social media APIs, customer feedback forms, and call center recordings, into the bronze layer of Databricks, where we process and transform it into a format suitable for analysis. By applying techniques like text analysis and machine learning, we uncover hidden insights and provide actionable recommendations to stakeholders, enabling them to make data-driven decisions and improve customer satisfaction.

Applying Databricks AI functions Data Standardization (silver):

Once we have the initial feedback data through various channels (Facebook, Twitter, texts, phone call transcripts) we need to perform data cleansing using more AI functions.

To clean and standardize customer feedback, we apply several AI functions:

• ai_translate: Converts non-English text to English.
• ai_fix_grammar: Corrects grammar and typos for better NLP accuracy.
• ai_analyze_sentiment: Classifies text into Positive, Negative, Neutral, or Mixed.
• ai_classify: Further categorizes feedback by themes, e.g., "Product Quality" vs. "Pricing Issues."

We recognize that once we've collected the initial feedback data from various channels, including Facebook, Twitter, texts, and phone call transcripts, our next step is to perform data cleansing using advanced AI functions. To ensure that our data is standardized and ready for analysis, we employ the ai_translate function to convert all non-English text into English, and the ai_fix_grammar function to correct grammatical errors in the source data. This step is crucial in ensuring that our analysis is accurate and unbiased. Next, we utilize the ai_analyze_sentiment function to determine the sentiment of the feedback texts, categorizing them as positive, negative, neutral, or mixed. Additionally, we apply the ai_classify function to further classify the feedback into specific categories, enabling us to identify trends and patterns in the data. By leveraging these AI-powered functions, we're able to refine our data and gain a deeper understanding of customer feedback, which ultimately informs our recommendations and drives business decisions. Applying these AI functions, we can ensure that our data is consistent, accurate, and in a suitable format for analysis.

Example Input:
"This coffee is too expensive, but tastes good!!"

After Processing:

• ai_fix_grammar → "This coffee is too expensive, but tastes good!"
• ai_analyze_sentiment → "Mixed"
• ai_classify → "Pricing, Taste"

This prepares us to gain insights into customer sentiment and preferences, identify areas for improvement, and develop targeted strategies to address customer concerns. Overall, this approach enables us to transform unstructured feedback data into actionable insights, driving business growth and customer satisfaction in the retail store selling the coffee product.

Consumption-ready state (gold):

We've now reached the stage where we have clean and standardized data in our silver tables, and our next task is to make it usable for analytics. This involves combining the data from different sources, applying business rules, and transforming it into a format that's suitable for analysis. We recognize that business rules are a crucial part of data preparation, as they help ensure that the data is accurate, consistent, and relevant to the analysis. To achieve this, we apply a range of business rules, such as renaming columns to make them more descriptive and easier to understand, removing irrelevant data that are not necessary for the analysis, handling missing values or outliers in the data, and applying data validation rules to ensure that the data meets certain criteria. For instance, in our customer feedback analysis, we might apply a business rule to remove any feedback records that are missing a customer ID or a feedback date. This ensures that our analysis is based on complete and accurate data, and helps us to avoid any potential biases or errors. By applying these business rules, we're able to refine our data and make it more suitable for analysis, which ultimately enables us to gain deeper insights and make more informed recommendations.

We're excited to apply topic modeling to our customer feedback data to uncover underlying patterns and trends that can inform business decisions. We'll use Latent Dirichlet Allocation (LDA), a popular algorithm for topic modeling, to analyze our combined text data and identify the underlying themes and topics that are present in the data. To do this, we'll create a user-defined function (UDF) that takes the combined text data as input and outputs a set of topics or themes that are present in the data. This UDF will use the LDA algorithm to identify the topics and return them in a format that's suitable for analysis.

Once we've applied topic modeling to our data, we'll create two gold tables that contain the insights we've gained from our customer feedback analysis. These tables will be used to inform business decisions and drive action. We're confident that our analysis will provide valuable insights that will help drive business decisions and improve customer satisfaction, ultimately leading to increased revenue and growth.

But we don't stop there. We'll also apply some Databricks AI/BI Lakeview magic to our gold tables to make them even more useful and insightful. This involves creating visualizations that showcase the results of our analysis or using machine learning algorithms to identify additional patterns or trends in the data. By doing so, we'll be able to provide even more actionable insights to our stakeholders and help drive business decisions that will have a real impact on the company. Whether it's identifying areas for improvement, optimizing customer engagement, or informing product development, our analysis will provide the insights needed to drive business success.

Conclusion

We've gained insights from our customer feedback analysis. Our analysis reveals that customers were particularly fond of the flavors offered by the coffee product, with many respondents praising the rich and smooth taste. By leveraging Databricks AI functions, retailers can efficiently process and analyze customer feedback data from multiple sources, gaining valuable insights into customer sentiment and preferences. We've seen firsthand how these insights can be used to inform product development, marketing strategies, and customer support initiatives, ultimately driving business growth and customer satisfaction. Our sentiment analysis revealed two major insights: (1) Customers love the coffee’s flavor, and (2) Price perception is a barrier to sales. Based on this, the retailer can experiment with promotional discounts or bundling strategies to improve perceived value and drive repeat purchases.

Want to implement AI-powered sentiment analysis in your business? Try Databricks AI Functions today and unlock actionable insights from customer feedback.