Predictive Analytics for Fintech and Banking
Social media provides institutions with a greater opportunity to connect with their customers, it can also provide extremely valuable insights into consumer behavior. With information from social media analysis and predictive analytics, bank marketers can think about their roles and use these tools to reshape the industry. Check out this article to learn how the banking industry is using predictive analytics.
Steven Ramirez is the conference moderator for Predictive Analytics World for Financial, Oct 29-Nov 2, 2017 in New York—where he will also provide the opening keynote, “Chatbots, Robo-Advisors, and AI, Oh My! Predictive Analytics and Machine Learning Case Studies for Financial Services and Fintech.”
As social media adoption among consumers continues to accelerate, we’ve seen most financial institutions expand their use of social platforms such as Facebook and Twitter. While social media provides institutions with a greater opportunity to connect with their customers, it can also provide extremely valuable insights into consumer behavior. Armed with this information, banks have an opportunity to significantly impact the success of their marketing efforts to both new and existing customers.
Advanced analytics in banking:
Social media is one component of the Big Data equation. The key to successfully utilizing social data lies in transforming it into truly actionable insights by layering in analytics and combining it with other sources of data.
In order to win and retain customers, financial marketers will have to acquire additional competencies. For example, data science and predictive analytics can help banks synthesize all of these inputs to better target the right customer with the right offer at the right time. Advanced segmentation strategies that help to identify niches based on consumer behavior can significantly boost marketing effectiveness.
Traditionally, segmentation has been used to divide customers into groups based on their demographics, attitudes, or buying behaviors. This helps in trying to target specific groups with the message that will best resonate with them. Utilizing predictive analytics, previously hidden patterns in the data help banks to generate more in-depth and behavior-based customer segments. The resulting segmentation is more precise and nuanced, and is ultimately based on the likelihood that a consumer will accept a given offer. The result is a win-win situation as customers are offered more relevant products and services, leading to a more profitable relationship with the bank.
Beyond segmentation, there are several other ways that predictive analytics can positively impact your marketing success. These advanced analytics techniques can help you to boost cross-sell, taking advantage of the data to determine which products to offer to which customers. Analytics can also make upsell campaigns more effective, by looking at the patterns in how relationships evolve over time. Predictive analytics can also be applied to your Voice of the Customer program, to identify customer pain points and develop strategies to reduce attrition.
Social media, Big Data, and Predictive Analytics are some of the forces reshaping the way that bank marketers think about their roles. If you’re not harnessing these capabilities yet, you may be missing opportunities to more clearly differentiate your bank in the marketplace.
About the Author:
Steven J. Ramirez is the chief executive officer of Berkeley, Calif.-based Beyond the Arc, Inc., a firm recognized as a leader in helping companies transform their customer experiences by leveraging advanced analytics.
In addition to developing and executing the vision for Beyond the Arc, Ramirez leads teams of data and strategy consultants committed to client success. They analyze customer and social media data, combined with text analysis, to drive customer growth, improve customer retention, understand service breaks and build stronger customer loyalty.