Keeping Up With Big Data: How to Stay Relevant in a Busy Marketplace

October 16, 2020
Big Data in financial services

Keeping up with BIG Data

How do you feel when you hear the words Big Data? Ominous? Intimidated? Overwhelmed? Whether you have positive or negative feelings towards the term, Big Data is playing a major role in virtually every industry across the globe, and arguably none more than in financial services.

Why is that? Today’s increasingly global market thrives on a host of data. Think: transaction data, customer data, market data, social data, and the list goes on. The way financial institutions contact clients, deliver products and services, and create business opportunities has changed drastically in the past decade. Every single transaction and decision influences data, how it’s captured, why it’s captured, and what to do with it next.

Even a financial transaction as simple as making a deposit generates everything from email follow-ups and solicitations for additional banking and lending products, to the ability to follow your bank on Facebook. In this series, we’re taking the big and scary out of Big Data. I’ll break down the various trends and applications of Big Data in financial services and how it impacts its ability to do business and stay relevant in a data-driven marketplace.

The four Vs of data

Big Data has moved to the forefront of what’s required for financial institutions to remain competitively intelligent, driven by its Four V’s: Volume, Velocity, Variety, and Value.

With the enormous volume of data available, distinct and more advanced processing technology is required. Take the number of credit card transactions within a day in the United States.

Velocity, the speed at which the data is being generated, requires distributed processing techniques in order to keep pace. Social media posts from Twitter is a good example of high velocity data.

Variety is what makes Big Data big. Data is generated from many sources and typically comes in three types: structured, semi-structured, and unstructured. Given the variety of data available, advanced processing capabilities and special algorithms are necessary to make sense of it and prepare the data for analysis.

Value refers to the quality of the data being generated and analyzed. High value data contributes to analysis and provides meaningful insights. Low value data, known as noise, does not provide much meaning and useful information.

Data that is high in volume, with high velocity, and high in variety must be processed with advanced tools and algorithms to be of value, especially in financial services.

Actually using Big Data

Financial institutions’ relationships with its customers vary greatly. For some, it’s no more than basic checking, saving, and deposit services. For others, the relationship can be much deeper, like providing financial guidance, mortgage services, small and large business loans, or other commercial services. In order to optimize the relationship with its clients, companies can segment their customer base into like groups. This provides a more efficient way of communicating and providing optimal service. Segmentation reveals specific intelligence that would otherwise be obscured by the sheer volume of data available. Additionally, segmentation helps financial institutions better understand their customers’ lifecycle and predict customer behavior in order to be better positioned to respond to customers’ needs.

To begin segmentation, a target market is divided into smaller, more defined categories using data collected at all touch points. This helps to identify what is needed in a market segment and provides a better idea of how those needs can best be met with a product or service. The goal is to differentiate your brand from competitors, build customer affinity, and identify niche markets that might otherwise go undetected.

Data, along with market analysis, is the driving force behind the ability to reach your customer base in ways not previously attempted. By analyzing your existing customers and creating opportunities to attract potential customers, financial services organizations can better target customers using their preferred channels and devices to increase brand engagement and digital conversions.

Financial services organizations can leverage customer segmentation in several ways:

  • Customer insight – How do companies deliver the right message, at the right time, for the right product or service? Segmentation is a way to immediately begin communicating with new customers, even with limited transactional information. Detailed customer insights provide the ability to identify the most valuable customers and attract more like them.
  • Proposition Development – Insights from segmentation efforts can help identify and predict future products and services for the customer base. For example, understanding and distinguishing the demographics of your customer base assists in identifying a customer’s needs before they are lost to a competitor who offers that service.
  • Understand digital behaviors – Combining digital customer insights with other data helps organizations align digital marketing for the target audience. Companies can gain better understanding of what their audience is searching for and avoid preconceptions by cross-referencing those insights with segmentation. Additionally, knowing your target market’s online journey will help you to know where to make your presence known in order to reach them.

We’ve talked about how Big Data can help financial institutions understand who their customers are, where they are, what they need, and when and how they should market to them. Next, we’ll dive into another important area where Big Data can help: risk management. Stay tuned. In the meantime, if you’re interested in learning how you can begin implementing these strategies today, send me a message below.

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Reggie Moore

Data Technical Director

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