Consumer spending behavior is directly correlated to household income that dictates disposable income. One can build a user profile of consumers with a set of attributes that could be contextualized towards specific market trends. Globally different regions have their own taxation. But, invariably to map an entire user behavior one would have to look at an entire calendar period - day, week, month, year. So, in UK this would pertain to the April-to-April tax year. By doing this one can obtain clearer set of patterns during bank holidays, weekends, weekdays, seasonal, social events, and other periods to glean on specific contextual behaviors. Once an anonymized user is mapped to Y1 the following Y2, Y3, Yn could be mapped to discover historical trends. Using machine learning approaches like clustering can provide for a means of visualization of complex networks to identify churn, segmentation, and intents for conversion. Additionally, semantic enrichment could provide further context for answering specific data science questions and end-to-end predictive storytelling. From looking at big data standpoint it would certainly help to process batch and in stream mode. However, one would have to take into account the difference between processing and event time of recorded behavior as well as to maximize in-memory computation. The below highlight key indicators that could be analyzed for consumer behavior.
- Economic conditions
- Group/Social Influence
- Historical Trends
- Location-Awareness
- Marketing Campaigns
- Personal Preferences
- Purchase Power
Additionally, the following could further add value in cyclical process to identify, discover, and understand:
- Consumer Habits
- Conversion Targets
- Product Choices
- Consumer Reviews and Ratings
- Consumer Sentiments
- Identifying and Predicting Churn, Segmentation, Price Optimization
- Profiling for insights, forecasting, personalized promotions/offers/discounts
- Consumer Experience
- Consumer Price Index
- Consumer Satisfaction Index
- Consumer Protection
- Market Trends
- Consumer Interests - unconscious consumption
- Consumer Intents - conscious search
