Consumers have been recognized as epicenter of decision-making processes across various domains including business, design, and marketing. The purchasing decisions of consumers are shaped by a multitude of external influences (social and cultural factors), as well as internal factors (psychological and personal aspects) (Social marketing. Open Learning. https://www.open.edu/openlearn/money-business/business-strategy-studies/social-marketing/content-section-3.2#Fig.003_001 . Last accessed 21 May 2024). These factors collectively shape consumer’s cognitive development and purchasing behavior, steering the direction of business ventures. Consequently, organizations increasingly craft solutions based on consumer behavior or to shape desired outcomes. Considerable research has been conducted on consumer behavior and nudging, providing the design industry with numerous valuable models. However, the fast-paced global shifts in technology, lifestyle, mindset, and perceptions highlight the need for identifying behavioral patterns and prediction models. Existing behavior models, such as Fogg Behavior Model and Hook Model, exclude external factors like technological advancements and cultural factors which also play a major role in behavioral output (Choong in Applying Behavioral Science to Predict and Influence (2020); Fogg in a Behavior Model for Persuasive Design). Additionally, mainstream predictive models rely heavily on machine learning and data mining, making it hard for designers to leverage their advantage. Thus, there is a need for a simple guiding model tailored using the designer’s language, ‘visualization’. This study aims to conduct a mixed-method approach by performing a literature review on consumer behavior evolution, studying existing models and influencing factors. Followed by primary research on Indian consumers with purchasing power, the objectives include: (1) examine history of consumer behavior for pattern identification; (2) assess existing models and investigate the correlation between external and internal factors with consumer behavior; (3) develop a predictive behavior model for designers through primary research. The research findings contribute to appreciation of human factors in design and advocate for further exploration within the realm of behavior forecasting.