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October 24, 2018

Why Small Banks need Big Data

"With segmented groups, banks can offer hyper-local and customised features. Community banks can begin with personalised marketing campaigns that target each group’s pain points"

By CBR Staff Writer

In the world of banking, consumer demand drives success. To remain competitive, banks must offer products and services that consumers want and find useful. Large banks have the capability to do this because of the vast amount of resources at their disposal. Small banks, however, find themselves falling short in terms of new banking tools and therefore try to offer a more personalised experience.

Jennifer Roubaud, VP UK&I, Dataiku

Statistics show that 42% of small businesses fail because there’s no market demand for their services or products. Smaller or community banks often fall into this category as customers want more ATMs, online banking options, and a bevy of financial services. While small banks usually offer fewer fees and hyper-local options, the pull of big banks and their technological tools is strong.

Smaller banks can better compete if they harness the power of big data. While most large banks have robust data science teams, community banks can use data to provide local offers to customers that big banks cannot provide. Using data, small banks can improve customer segmentation, personalised marketing campaigns, mitigate risk, and improve internal business performance.

How Community Banks can Use Big Data

No matter the size, banks have a lot of data. Whether they use this data effectively or not is another matter. Here are ways that smaller banks can use big data to compete.

Customer Segmentation

using consumer data

Community banks can take data on spending habits and banking needs to segment groups of customers.

Segments can consist of demographics as well such as age and income.

With more accurate customer segmentation, small banks can address each group’s needs individually and more accurately. Customer segmentation is the foundation of a personalised marketing campaign, and since community banks already have this data, it makes sense to organise and analyse it.

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Personalised Marketing Campaigns

Small banks already offer more personalisation for customers, but with segmented groups, banks can offer hyper-local and customised features. Community banks can begin with personalised marketing campaigns that target each group’s pain points. For example, banks can target age and income groups that are possibly going to purchase a home and need a loan with rates they can afford. Smaller banks can find this group and offer them services to match their needs.

Community banks can further their local, personalised feel by offering customised financial forecasting. Most small banks do not have the manpower to go through each customer and accurately assess their financial situation. Data tools can do this automatically based on customer insight and behavior. Community banks can then offer this service without a significant internal investment.

Risk Mitigation

using consumer dataBig data mitigates a lot of risk for banks, both for the bank itself and for its customers.

Community banks can automate credit risk testing through predictive analytics tools, decreasing the time and increasing the accuracy of the process.

Community banks can, therefore, avoid customers that pose too much risk and meet the needs of those that are low risk. Data can also predict risk for individual financial portfolios, which will help customers make sound financial decisions.

Internal Business Performance

Community banks do not have the time or resources to launch the vast amount of financial services offered by large banks. Small banks must assess which financial services meet customer demands and which they can afford to provide. Using analysed data, community banks can understand which financial services their customers want and need. They can then launch these services knowing they will be a success, rather than playing a guessing game of what customers want. Using data, banks can measure the performance of new features, ensuring customers utilise the services.

Furthermore, data can examine the internal performance of employees to make sure internal goals are met. Data scientists can also analyse company risk factors to make data-driven decisions to improve the overall business.

Big Data in a Competitive Market

With the influx of banking startups and large bank’s online services, community banks must find a competitive edge to compete.

Data can provide just that. An IBM banking study found that only 30% of customers believe their banks provides personalisation. Small banks can capitalise on this finding by offering personalised services based on data. Banks do not have the time or resources to analyse each individual customer’s financial portfolio, which is why they must rely on data to handle this process. Small banks can take the analysis and offer customised recommendations and products to the delight of its customers.

There is still a place in the market for community banks, but the new technology offered by large banks is a pull for customers wanting a new way to bank. Therefore, smaller banks should rely on data to help them compete in an increasingly online banking world. Without data, community banks may find themselves losing customers. With data, however, small banks can offer a customised, local approach that many individuals still desire.


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