The rapid evolution of technology within the fintech sector is transforming what we know about our customers on a daily basis. Now, as the ongoing AI revolution has created an explosion in the volume of data at our disposal, more businesses are discovering new ways to find appeal with their customers.
Whether you’re an e-commerce innovator or a bootstrapping startup, the importance of using the data generated by customers has never been more important to steal a march on rivals and improve the quality of service provided.
The cornerstone of the artificial intelligence boom has been the proliferation of big data, and its impact across a vast array of industries can’t be understated.
Customers aren’t merely interested in an improved CX when engaging with businesses, they expect an unparalleled level of personalization. According to McKinsey data, some 71% of consumers expected companies to deliver personalized interactions, while 76% got frustrated when these interactions didn’t materialize.
This means that big data has become big business. But how can your company use AI to make customer insights stretch further? Let’s take a deeper look at the impact of data, and how it’s helping innovative organizations to win over an increasingly demanding target market:
Driving Data Insights
McKinsey data has suggested that 42% of companies in the financial sector spend between 5% and 20% of their digital budget on analytical AI, which can help automate processes that would take human analysts extensive amounts of time to create.
The technology can also optimize business processes, address talent shortages, and significantly reduce instances of human error, helping to save time and money across the board.
Big data insights can be particularly prevalent at the point of sale (POS), where structured and unstructured reporting has the ability to tell businesses more about their customer than ever before.
Intelligent reporting has broken down POS data insights into over fifty different pages categorized into transaction data, sales reports, customers, stock, banking, accounting, and auditing. These can combine to generate more valuable insights than ever before based on customer transactions.
Systems can document each transaction a business makes, revolutionizing refund and complaint resolutions. Transaction data can also help businesses to understand the method and amount paid at the checkout, as well as the items sold, which tills and staff members made the sale, and at what time and date the purchase was made.
This holistic overview of customer purchases means that any dispute can be resolved at a rapid pace with decision-makers able to take the appropriate action as and when required.
The Path to Personalization
We’re also seeing generative AI tools lean on customer data to deliver an unprecedented level of personalization at scale.
Using GenAI tools, more fintech firms have been capable of creating hyper-personalized banking experiences, helping to create fresh user insights that are well-aligned to their personal spending habits and financial goals.
It’s these personalization tools that can enhance a great number of businesses across many industries. From using behavioral data to create bespoke marketing campaigns, to personalized offers that accurately reflect their purchase intent, generative AI will play a leading role in the CX revolution.
Already, the arrival of large language models (LLMs) like ChatGPT is seamlessly enhancing the quality of customer service available to users online. We can expect this level of personalization to improve the in-store experience at scale in the future, with store assistants using handheld generative AI tools to rapidly source contextually appropriate responses to customer queries.
Faster Decision-Making
As much as 57% of global finance leaders are utilizing AI insights to support key decisions, in a move that overhauls age-old traditions in financial leadership.
The impact of this could transform how businesses conduct their risk assessments. When it comes to credit risk, businesses generally relied on risk modeling tools to anticipate how likely customers were to repay loans.
Risk management is an area that AI has the ability to transform. With the help of algorithmic insights, artificial intelligence tools can identify patterns and trends that constitute risk. This means more precision in identifying customers who are more likely to default on loans and a more bespoke selection process in determining which customers are best positioned to make full repayments.
This paves the way for AI to overtake traditional statistical models for credit score calculations, helping to create a case-by-case basis that fosters better financial inclusion.
Big Data’s ‘New Normal’
While 71% of consumers have become accustomed to personalization throughout their experience with brands, the artificial intelligence boom is on course to aid businesses in driving bespoke solutions in what will soon become the ‘new normal’ for CX models.
With the ability to drive unprecedented insights at the point of sale, brands can not only offer personalized experiences to drive customer loyalty but also convert high-quality reporting into a more focused marketing strategy that takes lead generation to new levels.
The artificial intelligence boom may be well underway, but we’re only beginning to uncover its potential in driving customer insights. By riding the technological wave, it’s possible to use personalization to scale up your value proposition to customers and expand your reach to new frontiers.
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