Unit II (1) study for mba and psgm student.pptx

Unit II (1) study for mba and psgm student

1. Unit II Consumer Decision-Making

2. Consumer Decision-Making Frameworks: EPS, LPS , RPS

3. When consumers buy a new or   unfamiliar product it usually involves the need to obtain substantial information and a long time to choose. They must form the concept of a new product category and determine the criteria to be used in choosing the product or brand. Extensive Problem Solving (EPS)

4. Limited Problem Solving (LPS) Sometimes consumers are familiar   with both product category and various brands in that category, but they have not fully established brand preferences. They search for additional information which helps them to discriminate among various brands.

5. Routine Problem Solving (RPS) When consumers have already purchased a product or brand, they require little or no information to choose the product. Consumers involve in habitual and automatic purchases.

6. Advances in Consumer Research: Predictive analytics and Big Data insights.

7. Introduction to Consumer Research Consumer research is the process of understanding consumer behavior, preferences, and decision-making processes. It allows businesses to gain insights into how consumers make purchasing decisions, how they interact with products, and what influences their buying behavior.

8. Predictive Analytics in Consumer Research Predictive Analytics refers to the use of statistical algorithms, machine learning, and data mining techniques to analyze historical data and predict future consumer behavior. It helps businesses forecast trends, understand consumer needs, and optimize marketing strategies.

9. Key Concepts of Predictive Analytics Historical Data : Using past consumer behavior data to identify patterns. Statistical Models & Machine Learning : Using techniques like regression analysis to make predictions. Forecasting : Predicting future consumer behavior, sales, or demand based on trends in the data. Customer Segmentation : Classifying consumers into different groups based on their behavior, preferences, or demographics.

10. Applications of Predictive Analytics Customer Retention : Predicting which customers are likely to churn and creating targeted campaigns to retain them. Personalization : Offering personalized recommendations and targeted content based on predictive models (e.g., product recommendations on e-commerce websites). Pricing Strategy : Forecasting demand and setting dynamic pricing strategies. Supply Chain Optimization : Predicting product demand and optimizing inventory management.

11. Example Amazon uses predictive analytics to recommend products based on your browsing history and past purchases. Their algorithm anticipates what products you might be interested in, leading to higher conversion rates.

12. Big Data in Consumer Research Big Data refers to the massive volume of data generated from various sources, such as social media, online transactions, sensors, and customer interactions. This data can be analyzed to uncover insights into consumer behavior and improve decision-making processes.

13. Characteristics of Big Data (The 5 Vs) Volume: The sheer amount of data generated from multiple sources. Variety: The different types of data (structured, semi-structured, and unstructured) such as text, images, and videos. Velocity: The speed at which data is generated and needs to be processed. Veracity: The reliability and quality of data. Value: The usefulness of the data for decision-making and insights

14. Applications of Big Data in Consumer Research Customer Behavior Analysis: Analyzing data from multiple touchpoints (e.g., social media, websites, mobile apps) to understand how consumers interact with brands and products. Real-Time Personalization: Using data in real-time to offer personalized experiences to consumers (e.g., personalized ads, tailored discounts). Market Trend Identification: Identifying emerging trends by analyzing massive amounts of consumer data from different sources.

15. Example Netflix uses Big Data to analyze viewer habits, predict what shows a user is likely to watch next, and personalize their recommendations.

16. Models of Decision-Making Kotler’s Black Box Model Nicosia Model Fischbein Model Howard- Sheth Model

17. Kotler’s Black Box Model The black box model shows the interaction of stimuli, consumer characteristics, decision process and consumer responses. It can be distinguished between interpersonal stimuli (between people) or intrapersonal stimuli (within people). The black box model is related to the black box theory of behaviourism , where the focus is not set on the processes inside a consumer, but the relation between the stimuli and the response of the consumer. The marketing stimuli are planned and processed by the companies, whereas the environmental stimulus are given by social factors, based on the economical, political and cultural circumstances of a society.

18. The buyer's black box contains the buyer characteristics and the decision process, which determines the buyer's response. The black box model considers the buyer's response as a result of a conscious, rational decision process, in which it is assumed that the buyer has recognized the problem. However, in reality many decisions are not made in awareness of a determined problem by the consumer.

19. The Black Box Model is named so because it represents the consumer's internal decision-making process as a mysterious, unobservable "black box" where marketers can't directly see what's happening inside the consumer's mind.

20. Nicosia Model The model proposed by Francesco Nicosia in the 1970s, was one of the first models of consumer behavior to explain the complex decision process that consumers engage in during purchase of new products.

21. Key Components of the Nicosia Model Field 1: Firm’s Attributes and Consumer’s Psychological Attributes The firm sends messages (advertisements, promotions) to consumers. Consumers process these messages based on their attitudes and previous experiences. Field 2: Consumer’s Search and Evaluation Process Consumers seek more information and evaluate alternatives. They may develop a positive attitude toward the product or service. Field 3: Consumer’s Motivation and Decision If the attitude is favorable, the consumer’s motivation leads to the purchase decision. Field 4: Feedback Loop Post-purchase experience provides feedback to both the consumer and the firm, influencing future behavior and marketing strategies.

22. Significance of the Nicosia Model: Highlights the importance of communication between firms and consumers. Emphasizes psychological factors and the dynamic nature of consumer behavior. Provides a structured approach to understanding how marketing efforts impact consumer decisions. Limitations: Does not account for direct influences like cultural or social factors. Assumes a linear decision-making process, which may not always be realistic.

23. Howard- Sheth Model of Consumer Behavior The Howard- Sheth Model, developed by John Howard and Jagdish Sheth in 1969, is a comprehensive framework that explains consumer decision-making processes, particularly in complex buying situations.

24. Key Components Inputs: External stimuli such as marketing efforts (price, product, promotion, place) and social influences (family, peers). Perceptual and Learning Constructs: Internal processes including perception, motivation, and learning that influence decision-making. Perceptual Constructs: Selective attention, stimulus ambiguity, and perceptual bias. Learning Constructs: Motive strength, brand comprehension, and attitude formation. Outputs: Observable consumer behaviors such as brand choice, purchase intention, and satisfaction. Exogenous Variables: External factors like social class, culture, and financial status that indirectly affect consumer behavior.

25. Stages of Decision-Making Problem Recognition Information Search Evaluation of Alternatives Purchase Decision Post-Purchase Behavior Significance in Marketing Helps marketers understand the complex interplay of psychological and social factors in buying decisions. Useful for segmenting markets and designing targeted marketing strategies. Limitations Complex structure, making it difficult to apply in simpler buying decisions. Focused more on high-involvement purchases, limiting its use in low-involvement contexts.

26. Example A consumer buying a car may be influenced by advertisements (input), perceive brand reliability (perceptual), learn from past experiences (learning), and finally make a purchase (output) while being influenced by social status (exogenous).

27. The Fishbein Model of Consumer Behavior The Fishbein Model, also known as the Multi-Attribute Attitude Model, is a psychological framework that explains how consumers form attitudes towards products or services based on their beliefs and evaluations of multiple attributes.

28. Key Components Attitude (A): A consumer’s overall evaluation of a product. Beliefs (Bi): The perceived likelihood that a product possesses certain attributes. Evaluation of Attributes ( Ei ): The importance assigned to each attribute by the consumer.

29. Application in Marketing Helps marketers identify key product attributes valued by consumers. Assists in designing marketing strategies that emphasize favorable attributes. Used for predicting consumer choices and preferences. Limitations Assumes consumers make rational decisions. Overlooks emotional and social influences.

30. Consumer Buying Process Need Recognition: Identifying a need or problem. Information Search: Gathering data from internal (memory) and external (friends, advertisements) sources. Evaluation of Alternatives: Comparing products based on attributes like price, quality, and brand. Purchase Decision: Selecting and buying the most suitable product. Post-Purchase Behavior: Assessing satisfaction, which influences future buying decisions.

31. Impact of Psychographics, Lifestyle (AIO’s), and Variables on Decision-Making Psychographics: Refers to the psychological attributes and behavioral traits of consumers, including personality, values, attitudes, interests, and lifestyles. Psychographics help marketers segment the market and design tailored marketing strategies by understanding consumers' inner motivations.

32. Lifestyle (AIO’s – Activities, Interests, and Opinions) Activities: Daily routines, hobbies, shopping habits, social engagements, and entertainment. Interests: Preferences in areas like fashion, technology, food, sports, and travel. Opinions: Individual viewpoints on politics, culture, environmental issues, and social trends. AIOs are crucial for marketers to create products and campaigns that align with consumers’ lifestyles, enhancing brand connection and loyalty.

33. Key Variables Influencing Decision-Making Personal Factors: Age, gender, income, education, occupation, and lifestyle shape preferences and buying patterns. Social Factors: Influence from family, peers, social class, and cultural background. Psychological Factors: Motivation (Maslow’s hierarchy), perception (selective attention, distortion, and retention), learning (classical and operant conditioning), beliefs, and attitudes. Situational Factors: Time constraints, purchase context, and environmental settings.

34. Technology-Driven Decisions Augmented Reality (AR) and Virtual Reality (VR) AR: Enhances the real-world environment with digital overlays, allowing consumers to visualize products in their surroundings before purchase. For example, virtual try-ons for clothing, furniture placement previews, and interactive product demos. VR: Creates immersive digital environments where consumers can experience products or services. VR is used in virtual showrooms, travel experiences, and interactive storytelling to influence purchase decisions.

35. Voice Commerce Definition: Refers to using voice commands through smart devices (e.g., Amazon Alexa, Google Assistant) for shopping purposes. Impact: Provides hands-free convenience, personalized recommendations, and faster transactions, leading to increased impulse buying and enhanced customer experiences.

36. Gamification Incorporation of Game Elements: Integrates rewards, challenges, and interactive features into shopping experiences, such as loyalty programs, points collection, and digital scavenger hunts. Benefits: Enhances consumer engagement, fosters brand loyalty, and motivates purchasing through fun and rewarding interactions.