Fine-Tuning Customer Segmentation In Financial Services With Big Data

Businesses categorize their customers based on specific attributes to ensure accurate record-keeping and personalization.

Companies that provide financial services are no different in this regard. Big data and IoT in financial services, with their myriad qualities, can add new and unique dimensions to the customer segmentation process for such companies.

Customer segmentation is a vital component for businesses in today’s age of consumerism. Effective segmentation allows organizations to serve their existing customers in a targeted way while also drawing in newer ones. The main benefits of customer segmentation are better communication with consumers, an increase in revenue and greater awareness for your brand in the market. Naturally, organizations that provide financial products and services also need to categorize their customers intelligently to have a competitive edge over their rivals.

As we know, big data plays a significant role in sales and marketing today. The abilities of the technology that are used in sales and marketing can be applied for the purpose of high-quality customer segmentation too. Apart from big data, the Internet of Things (IoT) also possesses data collection and transfer capabilities that can be put to good use for effective customer segmentation. The involvement of technologies such as big data and IoT in financial services can make customer segmentation much more deep and varied.

Using the Internet of Things In Customer Segmentation

Using information from various sources, IoT-powered devices enable banks and other finance-related organizations to use their research and marketing budgets while keeping their customers in mind. As a result, the integration of IoT in financial services allows businesses to effectively boost their profitability by using their monetary resources carefully, and, thereby, also increasing their sustainability for the long term.

Broadly, customer segmentation is divided into two main categories using IoT: Data-based and value-based.

The 'data' in data-based segmentation is the information related to customer behavior stored in an organization's databases. Financial companies that incorporate IoT-powered devices tend to use the technology to receive behavioral data of their customers from various sources (questionnaires, customer income records, purchase history). This information can be used to gauge the popularity of certain financial schemes and investment options provided by financial companies. Organizations can create more financial products similar to the popular ones and identify and tweak the problematic aspects of the unpopular ones to increase their demand amongst current and future customers.

The data collected by IoT-powered systems enables financial service providers to create customer segments based on the products that they have shown to have an interest in. Only modern technologies such as big data and IoT in financial services can provide real-time information useful for the creation of super-specific customer segments. Traditional segmentation methods, which rely on attributes such as geographical locations and gender, cannot hold a candle to the extensive and precise nature of IoT-driven customer segmentation.

Data-driven segmentation also allows financial institutions to maintain communication with their customers in a customized way. For example, customers who visit a bank's website will receive different communication from those who visit the bank personally to invest in financial services of their choice.

As we know, certain customers invest more money in financial products and services than others. Such customers are known as ‘high-value’ clients. Value-based segmentation allows financial companies to tailor their schemes according to the budget and requirements of their customers. The inclusion of IoT in financial services removes the risk of alienating customers who do not have the financial means to invest in exorbitantly priced financial products by using streamlined communication and pricing strategies.

Accordingly, an IoT network of information-receiving devices provides accurate customer data to financial organizations so that they can advertise specific products to specific clients based on their 'value.’ An example of this is the targeted communication of large investment schemes (involving millions of dollars) for high-net-worth individuals and businesses only. By using value-based segmentation, marketing budgets can be kept in check by financial companies. This, in turn, leads to an increase in revenues for them. In short, you only sell high-value products to those customers who can really afford them.

Traditional segmentation methods come with a big set of drawbacks. Most of these drawbacks generally revolve around a distinctive lack of data for analysis and forecasting. IoT resolves these problems by providing relevant data in real-time to complement other technologies such as AI to process. Accordingly, trends are detected and analyzed using the information generated by IoT-powered systems. As we know, IoT mainly functions as collectors of data from various sources. Eventually, this data morphs into extensive and multifaceted big data. Now, we will see what role big data plays to meet the most significant requirement of customer segmentation: enhanced personalization.

Leveraging Big Data In Personalization

Every organization, not just the financial ones, looks to personalize its services and communication for its customers. Understandably, each customer may have their own set of requirements and expectations from financial service providers. As stated earlier, traditional segmentation techniques cannot ensure enough depth in personalization for each individual customer of a financial company. However, big data, with its vast reservoirs of customer information, enables financial organizations to meet customer expectations, and then some, through enhanced personalization.

Businesses need to use needs-based ideas for segmentation to guarantee greater personalization for their customers. Today, customers cannot be boxed into static categories. To ensure precision in financial services, businesses must look to create segments of customers based on their specific product and service requirements.

As we know, humongous amounts of data pass through the internet on a second-to-second basis. As a result, customer-related data present in online databases keeps updating and evolving in real-time. In addition to traditional segments such as the ones based on demographics and psychographics, organizations can also attain details of their customers' online behavior, purchase history and interests with the help of big data. These details allow organizations to understand customers on a deeper level and device customer service and marketing strategies for them specifically. Hyper-personalization is an ideal scenario where organizations can create segments for each customer. These individual segments are then targeted with tailor-made financial products and services in real-time using the information stored in business databases. Big data allows the variety, volume and velocity of information to be consistently high.

Big data allows organizations to answer a whole list of consumer-related questions, such as whether they are risk-averse or risk-taking or whether they have the monetary budget to invest in expensive financial schemes or not, amongst others.

Accordingly, organizations can create personalized customer profiles based on the answers to such questions. For example, if a customer is in their 20s or early 30s, the chances are that they may have graduated recently and are on the lower end of the income spectrum. Accordingly, such customers will be price sensitive and risk-averse while making investments. Such customers, due to their young age, may be technologically savvy and tend to carry out their investments over the internet. Based on these inferences, a financial company can target such individuals through the web medium (social media posts or emails to market financial products and services). Additionally, financial avenues that require minimal monetary investments and offer greater immunity to market risks can be suggested for purchase to such individuals. As we can see, information such as a customer’s age, his or her purchasing habits is used by AI-powered systems to forecast what kind of product or services will be attractive to them.

While the involvement of big data and IoT in financial services allow financial organizations to enhance the quality of customer segmentation, such organizations must also make sure that they follow all the global compliance regulations related to privacy and data protection. Financial institutions, like all other companies, need to comply with regulations related to those elements. Generally, compliance policies cover financial products, the risk associated with each product or service, as well as the details related to information disclosure and usage between individuals and financial companies. There is a good chance that such policies may negatively affect the performance of your customer segmentation strategies. So, it is an organization's responsibility to design strategies to meet the highest number of segmentation goals while also not falling on the wrong side of the compliance policies and established legal terms.

The combination of big data and IoT in financial services is a winning one as the latter collects the data from a host of sources, and the former stores, updates, and provides the information to other applications, such as AI-powered systems, for further use. Quite simply, IoT, big data and AI are here to stay and continue to cause revelatory developments in the field of finance.