The Role of Sentiment Analysis in Digital Transformation

The role of sentiment analysis in Digital transformation

In a world that revolves around data, can we talk about digital transformation without looking at the impact of data in any digital transformation initiative? If you do have the requisite data, how do you ensure the workability and effectiveness? 

Data is gathered from different sources, unstructured, semi-structured, and structured. What puts head above shoulders is to ensure that your data is structured, and this is where sentiment analysis comes in. 

Walkme for example, refers to Digital Transformation as the reimagining of a business by implementing new technologies and optimizing legacy systems to improve operations. But looking at digital transformation, especially with data, you need to consider how it’s enabled and enhances processing across your business. 

Your focus must be on how to deliver value through great understanding, alignment, and actioning of online and offline data. Digital transformation must not be consigned to only offline brands, it should work towards bringing fragmented data points and platforms together across an organization’s wider ecosystem. 

Whether you run an offline or online business, the need to digitally transform your business can arise. As the world revolves around data, any brand must think about relevance. 

As a marketer, whether you campaign on mobile devices, social media, or you operate from brick and mortar, the most important thing is how you connect with your consumers. What are their pain points? 

Do you fully understand their sentiments about your brand, product, or service? Consumers leave a trail of their feelings along with their purchasing history; data is scattered everywhere; how do you access and utilize these great volumes of data? 

What every brand strives to do is to understand how to align all that growing data in real-time, across all sources and platforms, and to leverage it to inform their customers and transform the business. Once you have the correct strategy, platform integration, and can access the requisite data, you have initiated digital transformation as regards data. 

You just don’t think of adopting a new digital tool alone for your digital transformation, the idea should be to fuse sophisticated technology that gathers and orchestrates data, and then makes use of that data intelligently to inform your organization. The data that’s gathered will then be combined with technology to enhance superior, data-driven customer brand experiences.

Digital transformation works with the data you have available, which can be in large volumes that are structured or unstructured. Since you aim at making your brand to have valuable insights that can help in digital transformation, your digitization priorities, strategies, automation, as well as improving workflows should be your focal point.

You have reports and process improvements as metrics to understand how far you are going with your digital transformation, but you still need to incorporate big data as well as IoT data from different sources, devices, phones, and machine sensors. This will enable you to extend your digital transformation project across all operations. 

How do you structure the data you collect from different sources to ensure your digital transformation project is successful?

What is sentiment analysis?

Sentiment analysis is also known as “opinion mining,” is the analysis of a text and interpretation of the sentiments behind it with the aid of automation. By deploying machine learning and text analytics, you make use of algorithms to classify statements as positive, negative, or neutral.

Companies and brands use sentiment analysis as a strategy for social media monitoring to manage large amounts of data and gain consumer insights; it’s a process that you can use to learn more about your customers’ sentiment and how your competitors are faring.

How can you use sentiment analysis for digital transformation?

Since the basic essence of digital transformation is to improve customer experience and ultimately enhance ROI, you need to understand how your customers feel about your products, hence you can deploy sentiment analysis to analyze social media posts, tweets, and online product reviews, with the aim of tracking customers’ opinions and reactions that can help in your digital transformation project. 

It’s a veritable tool for market research, brand and product reputation monitoring, customer experience analysis, and predictions.

Sentiment analysis using product review data

Sentiment analysis using product review data is very vital for any organization that wants to embark on digital transformation. It gives you the insight you need to comprehend your customers’ sentiments towards your product or service. 

You can easily ask your customers to review your product with the intention of understanding where changes are necessary and what digital tools you may need for the transformation. However, it may not be an easy task to just collect and interpret product review data, since you must first analyze the data, and the volume can be quite huge.

A good case study of how you can use sentiment analysis for digital transformation with the aim of improving customer experience is the sentiment analysis using product review data Revuze did on Lysol VS Clorox. Though the report showed that customers were more satisfied with Clorox, the two brands can use the report for improving customer experience.

Sentiment analysis of customer product reviews using machine learning

The fact that you need the infusion of technology and data for your digital transformation project does not mean that any data you collect from customer product review works, the data you source from review sites and social channels are basically unstructured. Unstructured data is not easy to analyze, and you need to deploy Natural Language Processing and machine learning to successfully carry out this task. 

Machine learning tools can decipher context, sarcasm, or misapplied words. By using techniques and complex algorithms such as Linear Regression, Naive Bayes, and Support Vector Machines (SVM), you can detect user sentiments and tag them into positive, negative, or neutral. This enables you to understand customers’ sentiments in real-time.

Conclusion

Where you don’t have the requisite technology, tools, and expertise, it does not mean that you can’t embark on digital transformation. The idea is for you to remain relevant in the market, and that can only be realized when your customers are satisfied and happy with your products and services.

Digital transformation does not stop with your customers alone, where you don’t have the necessary expertise, you can outsource to reputable companies such as Revuze that can carry out quality sentiment analysis for your brand. Where data is involved from different sources, sentiment analysis is the only tool to make it workable for digital transformation.

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