2017 is the year when Artificial Intelligence is one of the hottest topic in finance. Machine Learning is redefining processes in financial institutions and challenges some of the decade-old business models. Wealth management companies are using deep learning solutions for long-term value investments and advisors are being replaced by chatbots, successfully covering up to 95% of all queries.HDFC has recently introduced an interactive humanoid which helps customers with cash deposits, foreign exchange, loans and other banking services. Another example is of OCBC Bank, which has become the first bank in Southeast Asia to pilot a robo-advisory service.
In this era of digital banking, it is even more important to integrate digital and physical contexts to improve relevance to each account holder. Interactions on online banking platform from smartphones as well as in a physical branch contribute to a rich behaviour profile of a customer, with the potential for more relevant offerings.
These days it’s all about customer experience, and almost all the banks are feeling the pressure because they are not delivering the level of service that consumers are demanding, especially in regards to technology. With growing competition, and increasing popularity of FinTech companies, banks need to employ new tools to reach its customers in a significant manner.
The best time to act is at that very moment of need.If the offering is provided after some time has lapsed, the customer will be less interested. Worse, the customer could become negatively biased. Timing of an offering for a product or service is crucial to achieve the desired conversions. The contextual relevance of a particular engagement – be it a swipe at a Point-of-Sale, at a partner establishment, or an online purchase, or a contact-center product enquiry – is usually short-lived, with the relevance decaying as more time elapses. Banks can leverage to reach their customers in a timely manner with Cadence.
Banks have a large customer base, and face the challenge of effectively targeting the entire customer base in a manner that is relevant, personalized, contextual and timely. Banks have opted for traditional methods in the past like sampling, this has not resulted to be relevant to the entire customer base. In order to get the maximum business, the bank should cater to all its customers ensuring a good customer experience. Each touchpoint in the customer’s journey is very crucial and impacts his/her experience with the bank.
The Data Team has engineered Cadence for this purpose, an internet-scale contextual advertising and service engine by using insights from customer journey analytics. Cadence delivers real-time, relevant and contextual interactions for 100% users and guarantees excellent customer experiences and retention. It has the capability of profiling customers and understanding their behaviors, interests, preferences, financial aspects instinctively. Cadence is built by exploiting advanced data science expertise with the best-of-breed big data technologies of Kafka, Spark and HBase. This engine can be deployed and integrated with the bank’s existing technology ecosystem within a few weeks.
The Data Team has assisted a leading multi-national bank drive business to partner merchants by tapping into shopping behaviour of their customers. The team analyzed customer behavior and profiles to successfully deliver contextual offerings. For example, a customer currently shopping in a partner’s neighborhood with a probable need for that partner’s product/service was given an offering from that establishment in real-time. As the customer was already in that area, and had the need for the offering this communication led to a successful conversion as it was relevant, personalized and timely.
For every sale, the partner made through leads from the bank, the bank received a commission. These offerings also aided in customer acquisition and retention. Largely, the bank significantly drove more business by targeting the entire base with more relevance, powered by Cadence.