Attract customers, increase the average order value and drives sales in your retail store with the help of a top data analytics service.
Online and offline retail have long identified the essence and impact of data science for their business. We support you in the development and operational implementation of your data science use cases and projects.
The retail industry is facing a digital revolution. What was apparently a topic of online retail until a few years ago is now making its way into brick-and-mortar retail. Data science, big data and machine learning are revolutionizing the way business-relevant decisions are made in retail.
Most retail companies are often in the comfortable position of already having massive amounts of data at their disposal: Sales, pricing and disposition data form the basis for many exciting use cases in the field of data science and machine learning.
Our team of data scientists supports you as a retail company with the challenges of digital trade. Together with you, we develop comprehensible systems for you to forecast, optimize and control demand, quantities and prices. Take advantage of our experience in the retail industry and arrange a consultation to clarify your options and needs.
Application of data science in retail
• Dynamic Pricing: Adjust prices to the characteristics and demand behavior of individual customers or groups.
• Sales Forecasting: We develop models to predict the amount sold for your products and product groups.
• Disposition optimization: Modeling of order behavior at store level to avoid out-of-stock situations.
• Customized prices: Determination of customer-specific price elasticities and demand functions using choice sets.
• Up- und Cross-Selling: Analysis of historical shopping carts to generate product recommendations and purchase advice.
• Price elasticities: Statistical modeling of price-sales functions on store for optimal pricing.
Data analysis in Retail
Many retailers already recognize the impact that big data has on customer service, supply chain optimization, and business strategies. Big data in retail offers stores the opportunity to learn and capitalize on market trends to increase profitability.
Retail data analytics involves collecting big data to improve pricing strategies, supply chain control, and the customer experience. Big data refers to market collections and internal company information that reveal global industry and customer trends. Scientists traditionally define big data by evaluating its volume, speed, and variety. In other words, the data collection must be large, fast to process, and diverse. These characteristics make it more valuable to the company.
Big data in the retail industry helps companies improve their customer service. Retailers can use valuable customer data to improve product development, the shopping journey, and the overall buyer experience. Retail data analytics continues to transform the way stores optimize operations and approach
Why retail businesses should consider data analytics
With big data, stores understand exactly when and how product prices need to change to attract customers and drive sales. By monitoring big data and adjusting prices, companies can determine which strategies benefit both the consumer and the store. This balance is essential, as a higher price could potentially increase profits but decrease customer retention, and vice versa.
Big decisions require big data that provides insight into the retail market and emerging trends. Stores that consistently monitor and aggregate information can improve their decision-making and overall strategy development, both in the short and long term.
Consumer preferences are always changing in response to the economic climate, technological advancements, and market trends. This means that retailers must be able to monitor and tailor their customer service accordingly.
When retailers collect customer data, they can also personalize advertisements, emails, and promotions to increase sales. By monitoring purchase histories, stores can generate product recommendations to encourage impulse purchases.
Establishments that integrate their management systems can forecast sales and customer demand through predictive analytics. This technique aggregates sales and historical data in real-time to define fluctuating trends in consumer demand. Retailers can use this data to mitigate emerging risks, capitalize on passing fads, and optimize inventory management.
Supply chain management
Supply chain management is challenging, especially for multi-store retailers. Every business in the retail industry faces a certain level of risk, from inventory control to logistics. However, big data allows owners to optimize their supply chain network to mitigate threats and avoid disruptions.
Some of our top data science tools at HData Systems