Future of financial institutions and customer behavior
With better mobile sites, e-commerce platforms and accessibility, merchants and customers have more options to provide goods and services from the digital space.
Forbes presented a simple overview in their article measuring the different ways to gauge the effectiveness of marketing in metrics and parameters. This is great news for companies, to be able to work within organised structure.
From ecommerce and online transactions, there is enough information for traditional machine learning to leverage on, which comes in the form of structured data – information with a high degree of organization, invoices and purchase orders for example.
However, with social media bringing brands and customers closer, we are seeing a paradigm shift where social media becomes an influence to company and customer engagement, we are given a whole set of differing metrics which proves a problem for traditional machine learning. Much of these stem from unstructured data – unorganised information that doesn’t sit well in a database, text messages and videos for example.
“At the end of the day, business users will still need a data scientist on their team to make the most of the tools.” mentions machine learning author, Louis Dorard. Business Insider has shown how with the right deep learning and predictive analytics tools, companies can start making sense of data from social media interactions (which are mostly in the category of unstructured data). AIDA helps businesses make sense of both structured and unstructured data, with a system comprehensive and robust enough to be your analytics and data science solution.
With the accessibility to numerous social media and communication platforms, we will only see greater increase in unstructured data that could provide insight to consumer habits and patterns. Brand loyalty and association playing a bigger role in the retention of customers, results in companies needing to know much more beyond just purchasing habits. AIDA’s modular predictive analytics solutions will help you understand and predict outcomes that will give insights to what engages or disengages your customers. Giving you options to provide very specific, focused recommendations and changes to cover all your niches for effective AI driven analytics for your consumers.
Beyond customer behavior, statistics show that the global business intelligence and analytics software market is expected to increase from $17.9B in 2014 to $26.78B in 2019, attaining a CAGR of 8.4%. Banking, financial services, insurance, retail, IT, and telecommunications will account for the largest percentage of the analytics and BI market.
Financial Times has written about the impact deep learning and predictive analytics will affect financial services. With tighter and changing regulations for compliances being exacted with the growing complexity of financial services and the customer base, being able to compile data, structured and unstructured will give greater transparency between financial services and their customers. With certain databases that blacklist potential customers, a robust, modular predictive analytics system can offer a deeper, more insightful opinion and insight. Such capabilities would enable financial services to tally up a borrower’s credit rating beyond just the structured data presented by bank statements. But a character reference deciphered from available unstructured data.