According to Google trends, there has been a 50% increase in volume of financial search phrases on search engines over the last five years. This upward trend for search phrases such as “car loans,” “quick loans,” “mortgages,” and “home loans” show
s that customers are looking for financial products that cater to their specific needs and are starting their search online, rather than in-person branch visits.
As these customers venture out across the web to find options, they are being met by few banks and not enough fintechs with products tailored to their personal needs and true spending habits. This is usually because of generic product development and brand marketing, as financial institutions deploy legacy infrastructure
The traditional model of using static datasets like age, gender, salary, and residential area were valuable insights in the past. This has however turned out to be a rather blind approach to product development and marketing, preventing businesses from optimising their customer lifetime value. A high earner could have a reckless spending habit and thus would be ideally less desirable for a loan than low earner who is more prudent in spending and planning. Non-data driven marketing could also see a provider offering customers the right product but at the wrong time in terms of their spending patterns. Increasingly eroding gaps between product-customer market fit like these is the main goal of product personalization.
Data-driven personalization should be important to financial service providers, particularly to large financial institutions, which often have a dizzying array of products and services that customers will never know about unless specifically informed and/or purposefully nudged. A data analytics approach can help financial services providers gain precise insights to inform product development and targeting. For example, customer financial transaction categorization technology can be employed to monitor customer transaction activity to suggest products and services, like loans and insurance, when they’re most likely to be needed and also as a way to tailor insurance premiums. The technology can also provide insights on specific risk profiles of customers.
By analysing customer transaction data, financial services providers can segment customers into ‘spendographies’, a cluster of customers with similar spend patterns and by assumption, financial personae. This is a far more useful approach to customer segmentation, as it is a dynamic representation of buyer behaviour. Analysing these transaction data within the help of tools like geo-tagging, completely amplifies the value of the insights that can be generated from such transaction data. This provides opportunity for financial services providers to upsell & cross-sell value propositions within the context of the user behaviour; precise contextual marketing. With the global economy, particularly commodity dependent economies like Nigeria predicted to suffer economic contraction as a result of the COVID-19, financial service providers who will achieve organic revenue growth have to engage customers at the point of impact. This is the promise of contextual marketing.