Customer Sentiment Analysis has come to the centre stage after a continuous increase in access to the opinions and feedbacks of customers on the services and products they use. Fintech companies and other corporations can analyse and understand the pros and cons of their products and services quickly and effectively after taking into account Customer Sentiment Analysis.
The benefits of using Customer Sentiment Analysis in decision making
The use of Artificial Intelligence, Machine Learning, Data Mining and Big Data Analytics tools has made real-time Customer Sentiment Analysis, possible.
Fintech companies can use the feedback and information received during Customer Sentiment Analysis as an invaluable resource for increasing revenue, optimising services and creating the most desirable services for different customer groups.
Using Customer Sentiment Analysis for planning marketing strategies has now become a guaranteed way to derive maximum returns on investment.
Customer Relationship Management functionality can also be improved by taking into account the sentiments and opinions of different customer groups. As a result, fintech companies can become more proactive in their business decisions and operations.
The public relations practices of fintech companies, financial institutions and other businesses can also be made more effective by factoring in Customer Sentiment Analysis.
Why has Customer Sentiment Analysis become more significant?
The advent and popularity of social media platforms has been one of the biggest contributors to the development, relevance, accuracy, and better utilisation of Customer Sentiment Analysis.
Customers generate a lot of content on these platforms. Their posts on social media platforms, reviews of products & services, and input on blogs & forums can give fintech companies and other businesses valuable insights.
Methods of Customer Sentiment Analysis
Depending on their requirements, fintech companies can make use of different methods of Customer Sentiment Analysis. The most prominent ways include:
- Manual Processing - Using people to carry out Customer Sentiment Analysis can be slow, but it can serve some critical functions in improving quality of insights. Detecting sarcasm in social media posts and product reviews along with the analysis of ambiguous textual content isn’t possible without manual processing by human analysts. Manual processing can be quite expensive because of the cost of human analysts.
- Keyword Processing - The use of well-defined and AI enabled algorithms for processing can lead to real time Customer Sentiment Analysis. However, it has some downsides. Analysis of words with multiple meanings and understanding the meaning of sentences with double negatives becomes difficult, leading to inaccurate interpretations.
- Natural Language Processing (NLP) - The use of data mining techniques and text analytics for processing can eliminate the shortcomings of Manual and Keyword processing. NLP takes into account the emotions being expressed in sentences and performs the tasks of Customer Sentiment Analysis in minimal time at substantially lower costs.
Challenges in optimising and improving the quality of Customer Sentiment Analysis
To arrive at accurate conclusions with the help of different methods of Customer Sentiment Analysis, fintech companies need to tackle some serious obstacles. Some of them have been listed below:
- Language barriers - When the customer sentiments of English-speaking countries and regions are to be analysed, the process is relatively simple.
However, fintech companies and other businesses offering their products and services in other countries need analysts and algorithms which can process the sentiments of users expressed in multiple languages. Hence, using appropriate linguistic resources and incorporating them into existing solutions is a significant challenge.
- Escalating costs - Using people for processing can drive up the costs of Customer Sentiment Analysis. At times, the rewards received by insights of Customer Sentiment Analysis aren’t as valuable as the resources used in achieving these pieces of information.
- Fake Opinions - Users expressing bogus and partial opinions with the direct intent of providing benefits to specific businesses or products can lead to inaccurate information gathering. Dealing with fake opinions posted on social media platforms and blogs is among the most significant challenges for fintech companies and other businesses.
- Limitations of Manual Processing - Manual processing has numerous benefits, but it is time consuming and is one of the most prominent contributors to the overall expense of Customer Sentiment Analysis.
- Poor strategies related to Customer Sentiment Analysis - Optimising Customer Sentiment Analysis to garner maximum benefits isn’t easy. The increase in sentiment data present on social media happens at an extremely quick pace. So, Customer Sentiment Analysis methodologies should be able to handle voluminous datasets.
How can Big Data analytics enhance standards of customer sentiment analysis?
Big Data Analytics, Machine Learning & Artificial Intelligence tools can enhance the Customer Sentiment Analysis practices of all organisations, including fintech companies.
To truly optimise Customer Sentiment Analysis practices, fintech companies need to use these tools not only to improve the quality of NLP algorithms but also their linguistic resources.
Linguistic resources have certain flaws which can only be removed by incorporating Machine Learning tools in Customer Sentiment Analysis solutions.
Businesses need to create an extensive list of keywords which must be fed into algorithms allowing them to determine whether opinions are positive or negative. At the same time, the algorithms must be able to assess the degree or extent of positivity and negativity in different keywords. For instance, ‘terrible speed’ must be given a greater weight when compared to ‘relatively slow’.
The algorithms of the NLP tool used by fintech companies for Customer Sentiment Analysis can become able to understand human sentiments when they have ML applications embedded in them. The algorithms must be fed with voluminous sample data containing relevant keywords with specific values.
Solutions configured with ML applications also have the ability to continuously improve their accuracy when they are used for larger datasets. To ensure consistent improvement, the keywords originally incorporated in the system should be accurate and relevant.
There is no doubt that Big Data analytics and Machine Learning tools have a vital role in improving the standards and efficiency of Customer Sentiment Analysis.
How Spyrosoft is approaching Customer Sentiment Analysis using the latest methods
At Spyrosoft, we pride ourselves on creating high-quality tools for Customer Sentiment Analysis. Our expertise in Big Data analytics and Machine Learning methodology enables us to create optimised solutions for different organisations.
We understand the significance of Customer Sentiment Analysis for fintech companies and the need to create empowered Natural Language Processing solutions.
As a result, we aim to create solutions that can perform the dual role of creating scalable Customer Sentiment Analysis applications embedded with Machine Learning solutions, whilst also bringing down the overall costs.
It is vital for fintech companies to use Manual Processing methods of Customer Sentiment Analysis alongside NLP techniques to derive accurate insights. So, we also focus on developing tools to facilitate Manual Processing at a relatively quick pace.
Spyrosoft places significant emphasis on the ability of our NLP solutions to handle voluminous datasets collected from various social media platforms and other sources.
As a result, you can rest assured that the Customer Sentiment Analysis applications designed by us will be able to remain effective even after the amount of data present in the systems increases substantially.
Our Head of AI, Tomasz Smolarczyk tells more about the possible uses of Customer Sentiment Analysis:
Sentiment analysis of social media channels can give companies a better understanding of their brand perception and can generate insights for marketing activities. Moreover, real-time monitoring can be used to highlight and alert businesses about possible harmful messages, comments or communications. This allows for quick response times and damage limitation. Such monitoring could also be used to keep up to date with recent trends and for providing recommendations for time and context specific communication with customers.