Supporting an established financial services company with their endeavour to build an AI-powered hyper-personalisation system to modernise their offerings.
Key Services
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Hyper-personalisation
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Behavioural Data
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Business Insights
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Market Trends
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Data Strategy
Key Markets
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Wealth Management
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Financial Services
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FinTech
A well-established financial services company providing a range of financial products and advisory services to customers for over 25 years recognised an opportunity to increase revenue by upselling additional services to its existing customer base.
Accordingly, the company decided to implement an AI-powered hyper personalisation strategy, using its wealth of collected data on its customers, including information on their financial history, credit history, purchasing habits, and demographics.
However, their large datasets had not been effectively utilised to inform upselling efforts. Instead, the company relied on generic marketing campaigns and in-house sales software that were not tailored to individual business customers’ specific needs and interests.
Combining BehaviorLink and Datastack frameworks, Nebuli applied customised machine learning algorithms to analyse the company’s datasets accumulated over 25 years and identify patterns and trends that could be used to tailor marketing campaigns and service recommendations to individual customers. This was combined with external market intelligence data scraped with support from IBM Watson.
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The first step in the process was to clean and organise the company’s service datasets, which had been collected from various sources and stored in a dedicated cloud infrastructure. This involved rebuilding their data schema, standardising their data models and ensuring the datasets were properly formatted for analysis.
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Once the data was cleansed and organised, Team Nebuli applied augmented intelligence models to identify patterns and trends in the data. The designed algorithms were trained on a subset of the data and then used to form predictions about the needs and interests of individual customers.
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The results generated from the analysis were applied to create personalised marketing campaigns and service recommendations for each customer. These campaigns were delivered through a variety of channels, including email, direct mail, and in-app notifications.
The client saw a significant revenue increase thanks to its hyper-personalisation strategy. The personalised marketing campaigns and service recommendations resonated with customers, who were more likely to purchase additional services and establish stronger customer loyalty.
This case study illustrates the power of data-driven marketing and the importance of tailoring customer interactions to individual customers’ specific needs and interests beyond the traditional demographics analysis.