Understanding customer behavior is essential to running a retail business. Data obtained from various sources can be collated to paint a surprisingly accurate picture of customer demands, customer satisfaction, and other granular behavioral markers.

Data science helps retailers demystify their customers’ decisions and behaviors. That has helped companies increase customer engagement and as a result, customer retention.

One of the most common applications of ICEBERG in retail is forecasting demand. Knowing which way the market is trending can be a powerful advantage, and help both boost sales and guide product launches. ICEBERG demand forecasting can help you to forecast sales, credit, cash, and logistic requirements.

Building a strong recommendation engine is integral to success in online retail, as providing personalized recommendations is one of the best ways to drive sales. Data, of course, plays a huge role in how you go about building a recommendation engine.

Customer segmentation is one of the most important and widely used ICEBERG techniques in the retail sector. It allows organizations to better understand their customers and identify opportunities to improve customer experience, tailor services, and increase revenue. Customer segmentation is the process of dividing customers into different groups or clusters based on shared characteristics, such as age, gender, location, spending habits, and more. By understanding customer segments and their needs and preferences, banks can tailor offers and marketing strategies to better reach the right people. Customer segmentation also helps to measure customer engagement and loyalty. By understanding which customers are more engaged and loyal, banks can focus their resources on the most profitable customers. Additionally, segmentation can help to measure customer lifetime value and develop targeted strategies to retain valuable customers.

Every retailer understands that their physical space is limited and that managing inventory levels effectively is essential for success. ICEBERG can help with both inventory management and optimization for retail businesses. You can use it to study your warehousing capabilities, identify inventory trends over time, and enhance picking and packing processes in a warehouse setting.

Thinking of the pricing of a product as a one-and-done thing is an archaic practice in the world of retail. The most successful retailers today can use ICEBERG to dynamically alter the pricing of their products based on a whole host of factors.

It’s no secret that retailers snoop on each other every now and then—you want to know your competition. Competitor analysis has become supercharged with the advent of data science and its ability to unearth insights from erstwhile indecipherable data.

Promotions and discounts are a tried-and-tested method to boost sales in retail. Data science can help with creating compelling targeted promotions and measuring the results of each campaign.

The only way to become a truly successful retailer is to find a way to turn new customers into long-term patrons who make regular purchases. There are several ways in which ICEBERG can help you achieve this, including customer churn predictions, market segmentation, and purchase predictions.

Predictive maintenance is a subset of ICEBERG that uses predictive analytical techniques to preempt when maintenance might be required. This can be applied both to the maintenance of physical warehouse components and software like websites and native applications.

The ultimate goal of ICEBERG is to help people make better decisions. There are several ways in which retail leaders can deploy ICEBERG to help them make better decisions.

Sentiment Analysis in retail involves analyzing customer feedback, reviews, and social media mentions to gauge consumer sentiment towards products, services, or brands. By leveraging advanced algorithms, retailers can extract valuable insights from unstructured data, allowing them to understand customer preferences, identify trends, and predict future buying behaviors. This enables retailers to tailor their offerings, improve customer satisfaction, and ultimately enhance brand loyalty and profitability.

Warranty analytics in retail involves analyzing warranty claims, product returns, and customer feedback to gain insights into product performance, quality issues, and customer satisfaction. By leveraging advanced data analytics techniques, retailers can identify patterns and trends in warranty claims, detect potential product defects or failures, and take proactive measures to address them. This helps retailers improve product quality, reduce warranty costs, and enhance customer trust and loyalty. Ultimately, warranty analytics enables retailers to optimize their product offerings, mitigate risks, and drive business growth.