Creating Predictive Models for Consumer Behavior Using Historical GIS Data
Consumer behavior trends in 2024
Consumers are getting younger. By 2030, 75% of consumers in emerging markets will be between 15 and 34. The opposite is true in advanced economies, where dropping birth rates and longer life expectancies have led to a rapidly expanding consumer base of people over 65.
The tendency to switch brands more often than before has become pronounced. In the days of the pandemic, around 50% of consumers switched products or brands. Supply chain disruptions made it hard for them to find what they needed.
More than 35% of consumers have tried different brands in advanced markets, and around 40% switched retailers in search of discounts and better prices.
The more things change, the more they stay the same. This change in consumer behavior proved quite persistent. They remain open to exploring alternatives, and all demographic groups are demonstrating waning brand loyalty.
Creating Predictive Models for Consumer Behavior Using Historical GIS Data
How GIS helps analyze consumer behavior
Geographic information systems help stakeholders analyze consumer behavior by mapping demographic changes, sales, and foot traffic over time. This helps them predict future trends, such as seasonal foot traffic or changing shopping preferences.
A comprehensive purchase pattern analysis is very useful for retailers. They can grasp consumers’ product associations and why they are making them. Identifying consumer preferences can support cross-selling, bundling, strategic product placement, and other initiatives that impact future buying behavior.
GIS is used to collect, save, and pinpoint detailed lines, points, raster and other images, and boundaries of geographical areas. It provides a layer to visualize data using GIS software in combination with geospatial data.
The Maptive GIS mapping software transforms rows of geospatial data into maps, alleviating the analysis of massive data volumes.
Using predictive analytics to forecast business outcomes
The market size of predictive analytics is expected to increase from $18 billion to $95.30 billion between 2024 and 2032, or 23.1% a year. Users of predictive analytics can forecast business outcomes based on historical consumer behavior.
Predictive analytics uses statistical significance to assess customers’ historical data and plan future actions.
Predictive modeling uses not only statistics, but also mathematical models and machine learning to reveal common behavior among customer groups.
Predictive modeling can help retailers manage their logistics as well. For example, Walmart uses it to predict demand and optimize inventory levels, which helps with a number of logistics issues across its network.
Key consumer behavior factors
Demographics is the most critical factor in consumer behavior. Grouping customers based on demographics is the most effective way of predicting behavior. To be fair, demographics is a broad concept that is made up of age, location, gender, income, etc.
Data from the Bureau of Labor Statistics reveals that Generation X is the biggest spender. People born between 1965 and 1980 spent the most money in 2022, amounting to $91,382 a year on average. They spent the most on transport, housing, and food. Gen X remains the generation with the highest expenses so far in 2024.
Type of purchase
Types of purchases include promotion, typical (a habit), variety-driven, impulse buys, etc. Knowing the type can provide insights into buying patterns and how customers might respond to various marketing campaigns. According to a commonly cited statistic, 40-80% of all purchases are made on impulse.
Average amount spent
Knowing your customers’ spending habits (do they spend a lot, a little, etc.) can help you understand their buying power and market relevant products or services.
Frequency and recency
This involves the average time interval between purchases and how often people engage with your brand. This can help your business target buyers with repeat or one-time purchases.
Final thoughts
Thank you for reading this article on creating predictive models. Insight into consumer behavior is nothing short of fascinating, and being able to predict it can be immensely lucrative.
