Market Data Analytics


Market Data Analysis

Market is a complicated system. Retailers, customers, and products are the main players in the market system. Within the system, many other factors can be mechanisms for getting the system running. Products, price, location, promotion, advertisement, and competition are dynamic components so retailers and service providers can work around to generate the best offers to fit their customer needs in the marketplace. 

Market data analysis is to analyze products sales and other related factors such as prices, promotion, advertisement, competition, time period, demographics, and location. Because general market data analysis is to give overviews of what is going on about the businesses but not to intend to find out WHY and HOW for the causes and solutions, such information is very intuitive and useful to track and evaluate market performance of products and services at various detail levels. The analytical results are usually presented in tables and charts. Online market analytical reporting tools provide retailers, CPG manufacturers, and service providers with easy to use tools to view the market performances for their products and services. 

Predictive Market Analytics

Predictive market analytics is an important aspect in retail, consumer services, and financial risk management. Market prediction analytics is to apply statistical models such as linear and logistic regression models to predict future sales, customer shopping habits, promotion response, customer churn, event occurrence probabilities, and fraud based on historical data. With predicted data in hand, retailers and service providers can make more strategic market plans to react to possible events, and to minimize risks and maximize profits. Market predictive analytics has been widely applied in retailing and banking in recent two decades. It has helped retailers, service providers, and credit card companies retain customers, increase sales and profits, and reduce risks.

In predictive market analytics, predictive modeling is critical, which is a highly professional job involving a great deal of understanding of businesses, proficiency in data analytics programing, and knowledge of advanced statistics. Good thing is that some commercial software such as SAS,  R, and Python are available to make predictive modeling tasks achievable in normal courses. By combining statistical software like SAS and Python with powerful SQL, data analysis can reach sophisticated and intelligent levels in revealing market insights and predicting future events. Many current hot areas in data sciences, such as machine learning and artificial intelligence, can be achieved by analytical computer programs through predictive analytics and data mining.

In modern retail marketing, predictive models have been used to predict sales under certain circumstances, customer spending and orders with certain offers or certain market conditions, probabilities of customers churning, and so on. Accurate predictions with corresponding market plans and implementations will make a big difference for retailers' successful businesses. 

Consumer Insights Analysis

Today's market is a consumer centric market. Understanding of consumer shopping behaviors and preferences is critical to retain customers and gain more customers. Consumer insights analysis is to gather consumer shopping data and other data such as survey and online review data to analyze for the answers of what consumers want and not want, where, when, why, and how they shop, what price ranges and promotion strategies can result in more profitable sales, and how to engage your customers with your products and services. Knowing consumer insights can help retailers and service providers to provide better products and services with reasonable prices and create large loyal customer bases. 

Customer Preference Analysis

Knowing your customers’ preferences is a smart way to gain customers by providing the right products and services. For example, families with young children may like to see products and services related to children, such as kids’ clothing, food, and toys. They may tend to have home-cooked meals as meal solutions. Young adults with stable jobs may be more interested eating in restaurants and buying stylish clothing and personal products. 

Customer preference analysis can be achieved through analyzing shopping basket data or online search and order data. Other data like demographic and geographic data will help to find causes and outcomes of customer shopping habits. Since each product has its own characteristics, deep analyses of shopping data and online search data will reveal customers' preferences and insights.