Cracking the behavior code

2. User context and information needs: Different users approached the content with distinct mindsets, e.g., a user who is looking to stay on top of market trends is looking to 'monitor' content as compared to others who might be seeking new ideas/approaches to looking at a problem are reviewing content with 'exploring' or 'searching' mindset. Content should align with users' specific information needs based on their daily lives, roles, responsibilities, and goals to increase engagement. Mapping out users' larger lifecycles and validating them with secondary research provided insights into their behavior and motivations to understand the short-lived journey on the platform. 3. Segmentation of User actions: The engagement rate (ER) proved inadequate as it grouped different actions user performed on the site under a single metric. To gain a more accurate understanding of engagement, user actions on the platform needed to be segmented based on what they indicated about user behavior and hence user engagement. While it is difficult to capture the richness of user context in metrics, an attempt can be made to account for this by defining the metrics in terms of the user behavior.

VALUE ADDITION

By integrating data analysis with an in-depth understanding of marketer behavior and context, we created a strategy framework and recommendations that revealed limitations of the data model and how existing data was made more actionable. Importantly, this framework creates a foundation on which other marketer behaviorcan be analysed. Through meticulous Be.Data analysis, we delved into the intricacies of user patterns and trends, moving beyond mere event tracking. Armed with the right data and a model enriched by user insights, we gained a comprehensive understanding of what constitutes an engaged user, what content holds relevance for them on the platform, and the nature of their relationship with the platform itself. These trends and patterns can have significant real- world implications by continually identifying and iteratively testing.

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