According to authors, the development of Artificial Intelligence (AI) creates several opportunities for retailers to improve their customer services , insights and business processes. AI makes it possible to generate insights on a scale like never before, it does this by combining customer data  across all platforms( from social media to CRM) like past purchases, search habits, click behavior, age, gender, season of the year and various other variables and self learning algorithms. By crawling the web and aggregating various forms of customer data, AI gives retailers the opportunity to engage with customers on a more personal level. Through chat applications such as Facebook and Messenger and by using chat bots on websites, the potential customers can communicate with the retailers using speech or text, that will assess and answer customer queries, thereby assisting in the selection process and helping in the execution of simple tasks. Read more at https://aitrends.com/retail/robots-ai-retail-8-things-must-know/

Retail stores has thousands of choices for fashion and the salesperson may influence you in your decision but with the help of AI and Machine learning your experience is going to more simpler and engaging.AI monitors the browsing patterns of any customer and give product recommendation according to that.AI powered chatbots can work more smarter and intuitively than humans. Machine learning in AI is improving product search capabilities on e-commerce sites.With the tremendous potential of AI, it can revolutionize the retail and e-commerce sector.Read more at: https://www.entrepreneur.com/article/296657

India’s retail market is predicted to double from $600 billion in 2015 to $1 trillion by 2020 as per IBEF. The growth in e-commerce influences development in the retail sector. Faster internet speed, reliable telecom operators, increased adoption of online services plays a key role in gaining that tremendous growth. In upcoming years, we will witness advanced technologies and innovations to revolutionize the customer’s shopping experience and collect more online sales.AI based chatbots will be the future of customer service. As consumers are gaining digital prominence mobile wallet will be crucial for cashless transaction. Read more at:https://www.entrepreneur.com/article/293462

Research has estimated a loss of about 6 - 7.5 million jobs in the US within the next 10 years due to automation. The worst hit will be the retail sector where the sales division is most likely to get automated. Women working mostly as cashiers are the most vulnerable. 'Stock shelves' and 'inventory control' will be made easy by robots. With increasing wages in the retail sector and stiff competition from e-stores like Amazon, automation is beneficial from the producer's point of view but the idea is devastating for employability in the retail sector. Read more at: http://money.cnn.com/2017/05/19/technology/future/retail-job-robots/index.html

All new on-line communication that both consumers and retailers access on a regular basis is creating even more data for retailers to store. It is time to extract valuable information from all the existing data in order to meet customer demands, increase sales and improve business performance.

In the simple terms, predictive analytic is a technique that is used for forecasting. It uses past data like how many products were sold and at what rate? Predictive Analysis is a big help in the retail industry. It doesn't mean that the whole process has to work on automation. Human decision making (sometimes gut feeling or intuition) and software like Predictive Analytic would give even better results. Some constraints in adapting this type of analytics is that decision power is transferred to a machine. This is always a difficulty because downsizing or resistance can also be a result. Analytics work on big data; which is difficult, expensive and can fluctuate.

To read more:http://www.in.techradar.com/news/world-of-tech/What-is-predictive-analytics-ndash-and-should-we-fear-it/articleshow/51945739.cms

Big data analytics are helping organizations connect their data and use it to identify new opportunities. Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. Here are some sectors where big data are making an impressive impact-: 

# 1. Healthcare 

Big Data Analytics in the healthcare sector helps patients and the healthcare ecosystem to be aware of the appropriate treatments and measures to be taken.

#2. Retail

In the retail sector, analytic solution offers insights to understand and respond to changing customer experience.

#3. Banking

The financial sector has to deal with vast volumes of data. This sector poses higher vulnerabilities to fraud and financial crimes.

#4. Manufacturing

In manufacturing, operations managers can use advanced analytics to dig historical data to study and improve production.

To know more, please read the article by Arya MM (Writer at infotechlead) -: http://www.infotechlead.com/big-data-2/biz-booms-investment-big-data-analytics-38700

People are always going to go shopping. A lot of our effort is just: 'How do we make the retail experience a great one?'

-Philip Green

The basic ingredient for success in a retail business is a magnificent customer experience. And, in today’s world, where technology has become an important part of our existence, then why not should we use its power in retail? That’s the next milestone that needs to be achieved i.e. to make retail more technology oriented and thus, customer friendly.

The following article at business-reporter.co.uk, lists out 5 technologies that will change the customer experience in retail. They are:

1. Projection
2. Touch Screens
3. Augmented Reality
4. Nighttime Displays
5. Robots

To know more about them in detail and how they have been actually brought into use by various retail stores, please visit the following link:

http://business-reporter.co.uk/2015/04/28/5-technologies-set-to-change-customer-experience-in-retail/

Real estate investment trusts (REITs) are a major consideration for any fixed income portfolio and offer a wide diversification and higher returns. There are many types of REITs available but we will have a look here at some of them. Approximately 24% of REIT investments are in shopping malls and freestanding retail. This represents the single biggest investment by type in America and is known as Retail REITs. REITs that own and operate multi-family rental apartment buildings as well as manufactured housing or the residential REITs are mostly urban centered. Healthcare REITs invest in the real estate of hospitals, medical centers, nursing facilities and retirement homes. The success of this real estate is directly tied to the healthcare system. Office REITs invest in office buildings and they receive rental income from tenants who have usually signed long-term leases. Approximately 10% of REIT investments are in mortgages. Here investing in mortgages instead of equity does not imply that they are risk free. The bottom line is REITs, like every other investment in 2008, suffered greatly. But despite this, they continue to be an excellent addition to any diversified portfolio. Read more at: : http://www.investopedia.com/articles/mortgages-real-estate/10/real-estate-investment-trust-reit.asp

Traditionally business have relied on graphs and charts to analyse crucial information. But these basic visualizations have a propensity to miss two of the most important aspects of a retailer’s data — where things are located and what is happening around them. Imagine being able to better understand where customers live, what they buy, what they do and why they do. Location analytics is a game changer. It helps organisations see where data is, not just what it is. Location analytics brings together dynamic, interactive mapping; sophisticated spatial analytics; and rich, complementary data to enhance the overall picture of business operations. Best of all, it is available from within already-established analytics software, so there is no need to say goodbye to familiar business tools or workflows.

The combined solution joins key business intelligence (BI) data with spatial location, resulting in improved store performance driven by better marketing decisions. It covers all stores operated by the group to guide expansion and development strategy, optimize direct marketing actions such as distribution of weekly circulars, monitor store performance, and gain a better understanding of the sales territory. Moreover, it helps in viewing and analysing data, including traditional retail information such as trade and mailing areas, competition analysis, customer locations, and advertising hoardings. Geographic data used includes Bing Maps, Nokia data, and aerial and satellite images. A BI map service’s bi-directional link provides a unique and dynamic integration solution between the mapping and BI systems.

The geo-marketing application is used for many strategic activities such as guiding expansion and development strategy of the company and optimizing direct-marketing actions including distributing weekly circulars store performance can be monitored and a better understanding of territories can be provided. All this information feeds one database and can be shared across the enterprise. Location analytics is enabling a refined and deeper understanding of how to improve marketing and other store-level operations. It enriches data for a more intimate understanding of customer relationships, behavior, and need. See more at: http://blogs.hbr.org/2014/03/how-location-analytics-will-transform-retail/

Most retail companies today operate in the master franchisee model where master franchisee manages the operations of the different stores of the brand. In such cases, it is important to understand the difference between what the company sells to its franchise stores and what the stores in-turn sell to the consumer. This metric is where the insights are hidden. Hence it is very important that companies track &understand this metric to optimally manage stocks and avoid costly stock-out or over-stock situations. Important decisions like the number of new stores that should be opened, best location & size of each store in different cities are not a straightforward choice! Data analytics usage in this space has proved to solve this dilemma. It helps companies to decide the optimal size of each new store they open, while ensuring that these decisions takes into account key parameters like real estate rental costs, catchment area, average incomes in the geographical area, presence of competition etc. Therefore, data analytics is simplifying complex business problems.

Arvind Nagpal wrote about this aspect in his recent article. To read more, please visit the following link:

http://www.informationweek.in/informationweek/perspective/288161/analytics-helping-retail-outlets-critical-decision