5. Data analysis: Test the behavioral hypotheses through data analysis to identify patterns and trends in user behavior. This helps to recognize what aspects of user behavior are the metrics measuring, design interventions/ solutions and inform business strategy. E.g., If the data analysis confirms that users are seeking support on the website, then the organisation can provide intuitive support on the buy page to increase ‘add to cart’ and hence sales. The story behind the data can arguably be more important than the data itself. Be.Data Analytics reveals stories on user behavior, meaningfully contextualized across touch- points.
1. Redefining Customer Segmentation
APPROACH
A multinational bank intended to increase the adoption of its Autopay feature amongst credit card holders, at risk of delinquency. Current adoption rate was very low at 0.0002%. To identify behavioral patterns and build hypotheses, analysis was conducted on:
• Transaction behaviors • Credit scores • Shifts in available balance • Autopay conversion
INSIGHTS
Through our analysis, we made the following key discoveries: 1. Credit scores are not the most accurate indicator of user needs, contrary to the typical approach to customer segmentation. While credit scores provide a long-term composite view, payments are a moment of decision- making situated in a customer's immediate context. By examining behavioral intent tied to data markers, we better understood the contextual factors that drive customers to enroll in Autopay. 2. We uncovered two key behavioral insights: a. Customers wanted control and flexibility in payments based on the Autopay enrolment-to- Autopay edit ratio. Multiple attempts by users to modify the features of Autopay indicated their desire for customization, suggesting a need for adaptable payment options. b. Financially stressed customers are more inclined to enroll in Autopay, primarily due to the bank's offer of waivers during payment resolution calls. This finding highlights a sense of reciprocity, where customers perceive the enrollment as a reciprocal gesture following the bank's support during their financial challenges.
Be.Data Analytics in Action at Fractal
Be.Data Analytics can be applied across industries (e-commerce, social media, financial services, technology, healthcare, and more) to:
• Drive acquisition/adoption • Improve customer engagement • Prevent churn • Inform product development • Personalize user experience • Inform A/B testing
Analyzing user experience through Be.Data Analytics provides the opportunity to gain real- time feedback from users to design informed solutions. From Fractal’s past work with Fortune 500 companies, we share three case studies focused on driving engagement and purchase, where we have leveraged Be.Data Analytics:
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