Building trust with big data
If you grew up in a small town, or are old enough to remember the “good old days,” you have likely experienced the care and personal touch which local merchants and bankers applied to their work. They knew us and our families personally, greeted us with a smile and a handshake, and understood our needs as individuals. Remember how it felt? How we trusted them? While modern life has made this one-on-one commerce paradigm difficult, there are businesses that are successfully creating this feeling of trust for consumers on a large scale.
Banks have historically struggled with public perception and reputation. According to the Harris Interactive Reputation Quotient Study in 2013, the banking industry in the US, while improving from 2012, was ranked third from the bottom in public reputation compared to other industries, just above government and tobacco. This is contrasted with industries like technology and retail, and companies such as Apple and Amazon, who were rated the highest in the survey.
The key differentiator for Amazon is trust. It received nearly 100 percent positive ratings in the Harris study on all questions related to trust and consumers feel the retailer anticipates their needs and fulfills their brand promise of quality and value.
To stand out among a plethora of financial competitors, and compete with new non-financial entrants, banks must transform by emulating retailers. Leading retailers engender trust because customers feel that they are recognized as individuals. These companies win over customers by anticipating their behavior and preferences, engaging on their terms.
Data leads the transformation
Deep analysis of the customer-rich data that banks already possess, and taking prompt action on insight derived from that analysis, is the key to transforming banking into an industry consumers can really trust. This is the path to a better understanding of individual customer needs and preferences, real-time response to issues and opportunities and delivering a differentiated customer experience. This transformational analysis is made possible only recently by new capabilities and advances in big data and analytics:
- Sharpen the view of each customer by leveraging more data, and more types of data from more sources, than ever before
- Use more accurate and powerful predictive analytics to determine, with greater certainty and speed, the next best, most individualized action to take with the customer
- Take action on the new insights at the time of customer decision, at the point of interaction and customer decision
What big data and analytics technology capabilities are needed?
- Master customer data management to manage and operationalize master customer data across the enterprise to gather and manage customer profiles
- Hadoop system to quickly land, store and analyze large volumes of multi-structured customer data to facilitate customer analysis
- Purpose built data warehouse appliance to speed the analysis of customer data and run complex customer analytics in minutes not days to dig deeper into customer needs and preferences
- Stream computing to provide real-time analysis on data to deliver faster customer engagement
- Content analytics to analyze content for customer insight
- Predictive analytics to predict next best action to anticipate customer needs or actions
- Campaign management and systems of engagement as a means to deliver the appropriate action
- Decision management to detect customer events or behavior and deliver personalized treatments to the right channels
Banks are showing consistent advancement in their data and analytics capabilities to better understand and serve customers. This technology is one way to help banks improve their reputation in the eyes of consumers.
For more on consumers and trust, take a look at this retail study and survey of over 28,000 consumers from IBM Institute for Business Value, from which bankers can gain valuable insight: Winning over the empowered consumer: Why trust matters.