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The world today revolves around data; this simply means that without data, the world will stagnate. Statista reports that by 2025, global data creation is projected to grow to more than 180 zettabytes, this simply means more data out there for the grabs.
Businesses have come to the full realization that they must make the customer the centerpiece of any decision or policy they want to put in place. The customer has truly become the king, and a bad review or a bad customer experience is all it takes to sink a business.
This is more so now that the world has become a global village, and the marketing landscape has become highly competitive. If you have not become customer-centric, you are doing your organization great harm.
Esteban Kolsky in a lecture revealed that 72% of customers will share a positive customer experience with 6 or more people, while 13% will share their bad customer experience with 15 or even more.
The question here is, what does it take to make your business customer-centric? Another question is, what do you need to do to ensure that you have a quality customer experience?
The simple answer to these two questions is, getting into the brains and hearts of your customers. And to do this, you need to know what your customers want, when they want it, how they want it, and why they want it.
All these borders on data; you need data about your customers to satisfy them.
Sourcing for data
There are different sources through which you can get the data you need to ensure you give your customers satisfaction, however, the following two sources will serve your needs.
1 Review sites
You can source your data from sites like Capterra, G2Crowd, and Trustpilot collecting public reviews about different products. You can also leverage data reviews that eCommerce sites such as Amazon and eBay gather from customers that visit their sites about their experience with different products.
The only problem is that the data you get from these sites are not structured and you have to structure the data for comprehension before embarking on analysis.
2 Social media
Apart from product review sites, the birth of social media has created very good opportunities for businesses to acquire consumers’ data. Customers use platforms such as Facebook, YouTube, WhatsApp, Twitter, Linkedin, Instagram, Pinterest, and Reddit to freely express their views and pain points about products and brands.
Consumers also use social platforms like forums and Q&A sites to review products and engage in discussions on different topics and ideas. While it may not be easy to gather data from these sites about a particular product, social media remains a veritable source of data with a dedicated effort.
However, you need to know that whatever data you collect from social platforms has to be refined. The reason is that since posts are free, anybody can use social media platforms to post spammy content.
There is also no exact means to verify the account. Bearing this in mind, it may be difficult to understand the actual sentiments of a consumer. You may not clearly understand if an opinion is positive, negative, or neutral.
Sentiment analysis
To overcome the problems of understanding the actual opinion of consumers, you need to integrate sentiment analysis. This is necessary because of the volume of data in question, which may prove very cumbersome and almost impossible if you have to depend on human labor.
Sentiment analysis, also known as opinion mining, is analyzing and interpreting a text by leveraging automation to understand the actual sentiments in the text. Since the data you collect from social media and product review sites are basically in the unstructured format, you need to integrate machine learning and text analytics, hence ensuring that algorithms will classify statements as positive, negative, and neutral.
Businesses make use of sentiment analysis to manage the large volume of data they collect from social media monitoring. The process enables them to gain useful insights about the consumers’ pain points and apply that to improve customer experience.
It’s only when you know the true feelings of your customers about your product or brand that you will know how to serve them better and what areas of your operation need improvement.
Apart from using machine learning, Natural Language Processing is another technological advancement that you leverage to ensure that the unstructured data you collect from review sites and social media platforms can be analyzed using sentiment analysis.
With machine learning tools, you’ll be able to differentiate between context, sarcasm, and misapplied words. Some of the techniques and algorithms you can deploy to detect user sentiments include Linear Regression, K-means, principal component analysis (PCA), Naive Bayes, and Support Vector Machines (SVM).
The algorithms and techniques will enable you to clarify consumers’ sentiments into positive, negative, or neutral tags. Unlike where you have to use human labor, you can easily have the necessary insights into your consumers’ pain points and act promptly to improve customer experience.
This is absolutely necessary since according to a survey by PwC, more than 33% of customers said they will churn a brand they love after just one bad customer experience, while 92% would completely abandon a company after two or three negative experiences.
Where you don’t have the requisite skill within your organization, you outsource to review insights platforms that can help in automating your product review analysis to ensure you understand the voice of the customer from data you have gathered from social media platforms and eCommerce review sites.
Conclusion
It’s the era of big data; the data you need is out there, growing in leaps and bounds daily. Sentiment analysis ensures that you have the right insights that describe consumer needs.
Once you have the insights, you can use them to:
- Discover those things your customers like and dislike about your brand or product.
- Compare how your product is performing in the market with regards to similar products from your competitors.
- Have access to real-time product insights.
