Retail analytics is the process of using big data to optimize pricing, supply chain movement, and improve customer loyalty services. Big data means a large volume of data that is used to reveal patterns, trends, and associations, especially relating to consumer behavior and interactions. Previously, it has been defined by three key factors: volume, velocity, and variety. For the retail industry, big data means a greater understanding of consumer shopping habits and how to attract new clients. Big data analytics in retail enables companies to create customer recommendations based on their purchase history, resulting in personalized shopping experiences and improved customer service. These super-sized data sets also help with forecasting trends and making strategic decisions based on Big Data Analytics Market analysis.
Amazon uses customer data to recommend items based on someone’s past searches and purchases. They generated 30 percent of sales through their recommendations engine which analyzes more than 150 million accounts. This has led to big profits for the ecommerce giant.
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For retailers, big data can create opportunities to provide better customer experiences. Costco uses their transaction data collection to keep customers healthy. When a California fruit packing company warned Costco about the possibility of listeria contamination in fruits such as peaches and plums, Costco was able to email specific customers who had purchased the items affected by the contamination instead of a blanket email to their lists.
In addition to big data, some algorithms analyze social media and web browsing trends to predict the next big thing in the retail market. Perhaps, one of the most critical and important data points for forecasting demand is the weather. Brands such as Walgreens and Pantene worked with the Weather Channel to account for weather patterns in order to customize product recommendations for consumers.
Walgreens and Pantene anticipated increases in humidity--a time when women would be seeking anti-frizz products--and served up ads and in-store promotions to drive sales. The purchase of Pantene products at Walgreens increased by 10 percent over two months and Walgreens saw a 4 percent sales lift across the hair care category during that same period. Retail forecasting and retail projections are used to properly allocate their resources the most effectively throughout different parts of the year.
Retail chains and retail businesses can use analytics to understand the differences in demand for their product across various geographic locations. Using consumer spending analytics, retailers can use this data to better service customers in specific regions and also stock products more efficiency.
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The adoption of big data by several retail channels has increased competitiveness in the market to a great extent. Retailers are now looking up to Big Data Analytics to have that extra competitive edge over others. Big Data Analytics will help retailers in anticipating a customer’s demand and therefore would empower them in taking effective and customer-centric decisions and thus personalizing their marketing based on consumer data. The sources of these shoppers’ data include websites, mobile applications, social media platforms, sensors, amongst others.
This will help retailers in achieving greater heights in the Big Data Analytics Market and thus will increase the competition as well.
They are rapidly adopting it so as to get better ways to reach the customers, understand what the customer needs, providing them with the best possible solution, ensuring customer satisfaction, etc.
The detailed research study provides qualitative and quantitative analysis of the global big data analytics market. The big data analytics market has been analyzed from demand as well as supply side. The demand side analysis covers market revenue across regions and further across all the major countries. The supply side analysis covers the major market players and their regional and global presence and strategies. The geographical analysis done emphasizes on each of the major countries across North America, Europe, Asia Pacific, Middle East & Africa and Latin America
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Global Big Data Analytics Market
- By Type
- Descriptive Analytics
- Diagnostics Analytics
- Predictive Analytics
- Perspective Analytics
- Outcome Analytics
- By Deployment Mode
- On-Premise
- Cloud Based
- By Verticals
- Banking Financial Services and Insurance
- Education
- Energy and Utilities
- Food and Beverages
- Legal, Gaming
- Government
- Healthcare and Pharmaceutical
- Information Technology and Telecommunications
- Manufacturing
- Media and Entertainment
- Retail
- Transportation and Logistics
- Others
- By Enterprise Size
- Small and Medium Sized Enterprises
- Large Enterprise
- By Region
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- France
- The UK
- Spain
- Germany
- Italy
- Nordic Countries
- Denmark
- Finland
- Iceland
- Sweden
- Norway
- Benelux Union
- Belgium
- The Netherlands
- Luxembourg
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- New Zealand
- Australia
- South Korea
- Southeast Asia
- Indonesia
- Thailand
- Malaysia
- Singapore
- Rest of Southeast Asia
- Rest of Asia Pacific
- Middle East & Africa
- Saudi Arabia
- UAE
- Egypt
- Kuwait
- South Africa
- Rest of Middle East & Africa
- Latin America
- Brazil
- Argentina
- Rest of Latin America
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