Arabic Sentiment Analytics for Banking Customer Experience
Sentiment analysis of banking customer experience is of the most important needs of any bank, given the complexity of today’s banking needs.
Sentiment analysis of banking customer experience is of the most important needs of any bank, given the complexity of today’s banking needs and competitive landscape. Having a machine learning model that can analyze sentiment in customer feedback is a valuable instrument to enrich a bank’s performance and increase revenues through customer retention, loyalty, and word of mouth brand awareness.
For banks that need Arabic sentiment analysis, this becomes even more important because they must ensure that the model they are using is the right one for them. In this article, we explain all about banking sentiment analysis and how the right CX analytics platform for the Arab-speaking world can help you grow.
What is Banking Customer Experience?
Customer service is the support you give to your customers before, during, and after a sale of your product or service. The kind of customer service differs from industry to industry. In banking, customer service can range from helping customers choose the right financial products, the closest ATM, making a direct bank deposit, helping with online transactions or mobile applications, as well as in-person transactions.
The experience that customers have in their interactions with a bank thus becomes their banking customer experience. For a bank, or any other business for that matter, analyzing customer experience for sentiment is extremely important because it can give a very granular picture of how a business is performing in terms of customer satisfaction as well as in comparison to their competitors.
There are numerous other sentiment analysis applications, all of which, ultimately, is meant to improve business performance through nurturing happy customers.
What is Banking Sentiment Analysis?
Analyzing customer experience data to see what customers like and don’t like about your bank is what banking sentiment analysis is all about. Analyzing customer sentiment needs to be done objectively and from a wide variety of sources as well as a large amount of data to give the most accurate results.
That’s why you need a machine learning model that can gather, process, and analyze customer feedback data at speed and scale to give high-precision customer insights. This helps avoid human error due to limitations and biases that can creep in while manually analyzing feedback data.
Machine learning insights can give you a clear indication of market trends, customer intent, and customer satisfaction. Banking customer experience analysis can help banks improve customer engagement and value by helping them use insights in several important ways, some of which are:
Using contextual data to better understand customer pain points
Improve customer engagement through better customer service initiatives
Monitor and be prepared for new market trends
Measure customer satisfaction over time and make necessary adjustments
Taking measures to increase financial literacy amongst customers
Develop relevant CX strategies for different kinds of customers such as individuals, small businesses, and major corporations.
Introduction To Arabic Banking Sentiment Analysis
Sentiment analysis of Arabic banking customer experience needs special attention because Arabic is a complex language, with numerous dialects, all with their own grammar and linguistic rules.
Accurate sentiment analysis of Arabic banking data requires that the customer experience analytics platform analyzes all the data natively in Arabic without using translations. To achieve this, it is necessary that the CX platform is built and trained on Arabic natural language processing (NLP) machine learning tasks.
What Are The Challenges in Arabic Banking Sentiment Analysis
The challenge that Arabic banking customer experience analysis for sentiment mining faces is that most CX platforms on the market offer sentiment analysis in various languages but unfortunately most often it is via machine translations. While machine translations can work for certain languages with common roots such as Italian, Spanish, Latin, and English, this same approach cannot work for languages with completely different linguistic rules. This is because translations do not cover the complexity of nuances in rich languages like Arabic.
They also lose out on colloquial expressions and use of the language because customer experience reviews are mostly written in everyday language and not necessarily with formal grammar and sentence structure.
Overcoming Challenges in Analyzing Sentiment In Banking CX Data
To overcome the challenges that arise in analyzing sentiment in Arabic banking customer experience data, the machine learning model for CX analytics needs to be trained on Arabic datasets with Arabic NLP. It needs to go through a series of steps to make it ready to analyze sentiment in Arabic feedback. These can be broken down as below.
Build a part-of-speech tagger: Arabic words are classified at a grammatical level so that the CX platform can identify conjunctions, nouns, adjectives, subordinate clauses, and prepositional phrases in the data.
Lemmatization. In this step, the rules of conjugating nouns and verbs based on gender, tense, etc. are applied, which help the model determine the roots of words to help with context and grammar. For example, “banking” and “banker” are based on the root word “bank”.
Building prior polarity. This step determines the positive, neutral, and negative context of a word. It is because of this step that eventually, the model’s sentiment analysis API will calculate the intensity of the sentiment polarity of words in a review, which will aggregate and determine whether the review is good or bad. For example, Excellent (+1), Good (+0.5), Average(0), and Poor(-0.5).
Grammatical constructs. Negations and amplifiers are determined and applied so that the banking customer experience analytics model can understand the overall review flawlessly in Arabic. This is where Arabic NLP becomes extremely important because it is essential that the model reads Arabic natively as rules of negations and amplifiers are different for different languages.
Sentiment scores. Once all the above steps are concluded, sentiment scores are fed to the algorithm. Now that the model is trained you can use it to gain an overall sentiment overview or dive deep with aspect-based banking sentiment analysis.
What Are Important CX Analytics Insights For Banking Customer Experience?
Considering how a single review can give such a clear indication of what aspect of a bank needs improvement, it can only be imagined how many action-oriented insights an analysis of a multitude of such banking customer experience reviews can give.
A CX analytics model built on Arabic NLP gives you brand experience insights based on the following:
Sentiment analysis of customer feedback data
Semantic analysis of data through topic identification, classification, and extraction
Aspect clustering for contextual analysis and removing redundancies
Topic, aspect, and emotion co-occurrence
Customer experience trends over time
Named entities, classifications, and their frequency
Semantic search insights in banking customer experience data
Multilingual analytics for different Arabic dialects or other languages
Arabic social media listening insights
Allow you to set alerts for social mentions
Segregation of text, audio, and video analytics
Multi-channel insights for chatbot data, surveys, podcasts, Twitter, emails, Google reviews, news, and more.