Using Data For Hyper-Personalisation In Credit Card Offers: A Case Study
In this digital era, hyper-personalisation is the baseline for addressing customer demands with more individualised experience in terms of products, services and content. Today’s customers want to engage with brands which can: Instantly identify them Have immediate access to information about every interaction, on every channel (mobile, website, apps etc.) Based on the data, understand their issue and know how to solve it. Need For Hyper-Personalisation According to Google, ‘best’ search phrases have increased by 80% in the past 2 years on mobile devices. People are researching online heavily to make more informed decisions According to individual preferences, consumers are more likely to purchase from someone whose offers are personalized User engagement with content has gone down and information overload is making consumers tune out. Credit card market in India As per RBI figures, India has primarily been a debit card market. Unified Payments Interface (UPI) and mobile wallets have grabbed the attention of policymakers, but the increase in the number of credit cards is not only an indication of growing digital payments but also the expansion of retail borrowers in the ecosystem. Trends in Indian Credit Card Market Upgraded technology for Enhanced Security: Banks are increasingly using advance technology(EMV) to make use of credit cards more secure Outsourcing and Joint Venture(JV): Many Indian banks are entering into JV or outsourcing their overall credit card business to develop a separate and dedicated business unit Credit card gaining prominence in Rural India: Realising the importance of rural population, banks have started to focus and encourage customers to use credit cards Benefits of Hyper-Personalisation Deliver better results through higher conversions Positive lift in online purchases Improved brand affinity and retention Challenges Organisational constraints /silos make it difficult to hold anyone accountable to personalise goals Understanding buyer behaviour Assembling a real-time view of the customer with full context Integrating third party data Objective To make relevant hyper-personalisation card offers to bank customers using distance basis geocode and maximise Cross/Upselling opportunities. Data Hyper-personalisation requires a well-integrated framework of multiple technology tools and processes to produce the desired real-time targeting results. Data Elements Methodology In the real world, we find locations based on some description. This might be house number, street name, city, state, or country. Geocoding is the process of transforming a description of a location – such as a pair of coordinates, an address to a location on the earth’s surface. Contextual data analysis can provide much-needed insight into customer behaviour patterns, helping the bank to understand their customers better and improve their experience. Possible Solution Perform data collection of customer touchpoints from various source systems and store in a big data system Use Geo-location services to extract the geocodes(Latitude & Longitude ) of customer addresses Then find the nearest restaurants/cuisines from customer addresses using proximity distance analysis method Segment the customers into groups of individuals that are similar in specific ways such as Age, average spend, demographics, spending habits etc. using un-supervised Machine learning models Analyse contextual behaviour with factors such as the location from which the card is accessed, the time of the day when the event occurred or the industry in which the customer works etc. Test and evaluate the performance of the model Conclusion With the rising number of communication channels and self-service interactions, customers are increasingly expecting personalisation at every point of their journey. The businesses able to offer this level of service and intelligently manage decisions will be the ones that will be the next generation of customer service leaders.
In this digital era, hyper-personalisation is the baseline for addressing customer demands with more individualised experience in terms of products, services and content. Today’s customers want to engage with brands which can: Instantly identify them Have immediate access to information about every interaction, on every channel (mobile, website, apps etc.)  Based on the data, understand […]
With the rising number of communication channels and self-service interactions, customers are expecting personalisation at every point of their journey.
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["big data", "Go", "machine learning", "programming_languages:R", "AI", "programming_languages:Go", "Git", "RAG", "GAN", "R"]
["AI", "machine learning", "RAG", "R", "Go", "Git", "big data", "GAN", "programming_languages:R", "programming_languages:Go"]
Biswajeet is a part of the AIM Writers Programme. He is a Senior Manager at a noted bank. A Data Science evangelist, he has been a Big Data and Analytics certification from C-DAC. Biswajeet loves to innovate ideas about ML, AI and Big data.