Leverage Data Analytics to Spot Consumer Purchasing Patterns
Using technologies such as generative artificial intelligence, retailers can analyze historical data to identify patterns and correlations in consumer behavior. Drawing patterns from data such as browsing history and purchasing habits can better equip retailers to provide personalized shopping experiences for each customer, including tailored product recommendations. Examining seasonal patterns, geographic trends and other broader data sets can also prove vital to forecasting trends.
DISCOVER: Make data-driven decisions with analytics.
Use Predictive Modeling to Assess What Consumers Want
Related to pattern recognition, advanced analytics tools and machine learning algorithms can build predictive models to forecast potential trends by extrapolating future behaviors from historical data. In this way, predictive analytics can help forecast business outcomes based on anticipated consumer behaviors.
Predictive modeling can also help manage logistics. Walmart uses predictive analytics to forecast demand and optimize inventory levels accordingly, aiding in a range of logistical matters across its network of stores. And in addition to providing decision-makers with the detailed data they need to best prepare their stores to meet customer expectations, predictive analytics can also help mitigate fraud.