Activating Your Data with Google Cloud Platform’s Natural Language AI

As a digital treasure hunter, I know better than to leave this gold in the ground—and as a Google partner, I also know how to mine it. Through Google Cloud Platform’s (GCP) Natural Language Processing (NLP) AI, digital marketing partners can help brands conduct sentiment analysis, among other methods, to gather insights into customer behavioral patterns, expectations, complaints and moods, and therefore determine the level of brand loyalty. 

The quantitative data that you obtain through this research method allows you to build dashboards and visualize brand sentiment across regions. The aim here is to discover any areas for improvement, as these data points can be used to optimize a brand’s mobile and web applications or products and services—thus informing their next steps in the experimentation process and helping them get closer to meeting their audience’s needs. 

Over the last few months, I’ve focused on integrating sentiment analysis into our experimentation offering, and it’s quickly changing the game. In the spirit of sharing learnings and making sure no brand leaves their valuable data untouched, let’s talk about why this method is as good as gold. 

Leveraging textual data to determine brand sentiment.

Imagine you’re a top-tier global brand in the food and beverage industry. You’ve recently added new features to your app, and so you’re eager to find out if customers are enjoying this enhanced experience. Right now, there are over 500 thousand reviews on the Google Play Store. Scouring through them would most certainly go a long way, but who’s got that kind of time? It’s a classic case that we see all the time: brands tracking everything, but not doing anything with the info they keep track of. However, this trove of data from active customer interactions is only a treasure if it’s activated and applied effectively. 

This is where sentiment analysis comes in. Made possible by GCP’s suite of tools, this research technique analyzes digital text to determine the emotional tone of a message, such as a review. As part of experimentation, which is all about creating impactful changes to meet the needs of your customers, sentiment analysis allows you to translate qualitative textual data into quantitative numerical data. The aim is to surface key insights about brand loyalty—in the case of said brand, how customers feel about the app’s new features. And then? That’s right, much-needed data activation.  

Put your data to work to improve your business. 

Diving into the nitty-gritty of conducting sentiment analysis, you’ll see it’s very easy to adopt this method. With this AI solution, there’s no need for marketers to manually go through one review after another to get a sense of people’s opinions.

Here's the rundown. Once you have access to a Google Cloud account, you can organize your qualitative, transactional and behavioral data in Google Sheets and Google Cloud Storage. Then, use Apps Script (or another cloud client library) to create a custom menu and leverage GCP’s natural language API. Once you've enabled the natural language API and created an API key, you can start processing your data in a request to the NLP API and then automatically perform sentiment analysis. Ultimately, this opens the door for you to act on those insights through A/B testing campaigns, web and app optimization, brand marketing, and product marketing.

GCP’s Natural Language Processing API is so powerful because it combines sentiment analysis with named-entity recognition, which is a sub-task of information extraction that seeks to locate and classify named entities mentioned in unstructured text into predefined categories. For example, in the sentence “I get a cappuccino every day and I love that I can now earn points on the app and get a discount on my favorite product” we can already identify two types of entities: the product and the platform. So, the tool not only provides information about people’s sentiment, but it also connects this sentiment to the entities in the text.

Of course, this isn’t all new—it’s just become mainstream now that Universal Analytics has officially sunsetted, and we’re all moving on with GA4 (if you haven’t yet, this is your sign to do so).

Never let your customer data go to waste. 

Understanding user behavior, expectations and struggles should always be at the core of your efforts. Such critical information fuels all your experiments and supports you in fine-tuning your products and services. So, next time you’re thinking of leaving reviews unread and letting gold wither away, think again—because this easy, AI-powered solution and the partners that know how to apply it are here to help you extract meaning from your valuable first-party and zero-party data. And to add some fresh cherries to the pie, Google has new AI services that would allow you to automatically reply to those reviews and comments, using a Large Language Model (LLM)—but more on that next time.