In 2018, FinTech has exploded. According to CB Insights in the first quarter of 2018, fintech funding hit $5.4 billion – a 68% increase compared to the same period in 2017. Consumers now feel more encouraged to trust their money and data to 3rd party companies over traditional financial institutions. As the funding is ripe and the consumer demand is active, more and more companies are venturing into this domain. Today, there are enough companies in this sector that it is becoming highly competitive. Naturally, to gain a good market share of consumer business, financial services enterprises need to be providing innovative services and products to consumers and businesses. So, how does a FinTech company discover what types of products to offer to both individual and collective consumers? It has to engage in customer service data analysis:
- Mine data from lots of different sources and get that data organised into information that is meaningful.
- Utilise this data to understand current consumer behaviours and predict future consumer behaviour. This is known as predictive analytics.
- Use data to predict what a specific type of consumer might most likely purchase.
- Analyse data from both internal sources (current visitors and customers) as well as from external sources.
This data analysis, coupled with the latest technologies in data science, allows businesses to understand their customers far more intimately and to deliver products and services that will precisely meet their wants and needs. Here’s a straightforward example: suppose internal data shows that there is an “uptick” in visitors looking at small business insurance.
External data suggests that there’s a rising interest in entrepreneurship and starting a business thanks to friendly government policies. When financial services utilise their data analytics and process this information, it is evident that the company can improve its customer service by offering more and more varied small business insurance options. So, if you have been asking exactly what is customer service analytics, you now have at least a partial answer.
How Big Data Analytics Can Improve Customer Service in a Fintech Solution
Merely accumulating data in financial services is not an option. You will not survive by just collecting it. Companies need to use the be able to pragmatically use the insights gleaned to provide better products, better engagement, and future growth. Youtap has been in the business of providing insights for data analysis and successfully offered analytics as a solution to several companies across different domains.
In this post, we’d like to outline the options you may be missing within your product. Let’s break these into areas further into the exact processes, which could be significantly improved with big data analytics services:
Customer Acquisition – external and internal data could be combined to create more comprehensive customer profiles. Segmenting your audience will allow you to create more personalised offers and entice the leads with razor-targeted propositions. An algorithm could be used to predict, for instance, which kind of additional services the user would like to purchase based on their on-site behaviour or whether you should target a particular age group with a specific product.
Customer Retention – imagine that you could leverage the customer’s social data and have it “crunched” into actionable insights. For instance, you can build advanced prediction matrix, which would show you with high-accuracy want kind of products the user is interested in the most.
Customer Experience & Service – by continuously studying a customer's habits and financial decisions, you can train the algorithms to generate real-time suggestions ideas based on the current knowledge about the client. You can offer smarter options to personalise promotions portfolios and in general, leverage various insights to provide them with genuinely tailored portfolio allocations without assigning a dedicated agent. In a nutshell, that’s how robo-advisors already operate. These are just a few examples of how data analysis can improve customer service for a fintech company. However, there’s more to that.
Building New Gen CX Through Predictive Customer Analysis
Did you know that in 2016 the US Consumer Financial Protection Bureau has received the record high volume of consumer complaints? Over 1 million US citizens have voiced their complaints on debt collection, mortgage terms, and credit reporting. In fact, financial companies of all shapes and sizes, including start-ups, get a fair share of negative reviews online. Catering to each customer personally might be hard. But you can leverage insights you already have and tweak your products further and leverage customer experience onto a new quality level. Here’s a quick predictive analytics use case to illustrate how to improve your customer service with Big Data.
You can develop an algorithm that would source both structured and unstructured data (social media posts, comments, and reviews posted online) about your product, group the complaints into specific “clusters” and assign priorities for their resolution. Additionally, you can leverage customer service data analysis and obtain insights from your customer support emails, survey responses, call centre transcripts and other sources to identify the most pressing customer experience issues. In fact, your predictive matrix can take at least four different factors into account
- Customer behaviour – payment history, transactions, usage history.
- Personal data provided – demographics, self-declared information, additional attributes.
- Overall sentiment – indicated preferences/needs expressed on 3rd party channels; opinions shared online or privately with your company.
- Customer interactions – email support history, chat transcripts, call centre notes and transcripts and more.
So Yes, Big Data in Fintech is the “New Oil”
You’ve likely heard this phrase before, but for the financial service industry, the value of customer data analysis cannot be overstated. The sector is becoming highly competitive, and only harnessing big data analytics solutions will keep financial services enterprises “in the game.”