THE FUTURE OF BIG DATA

Internet and technology have been changing the way in which business operate, the way in which companies acquire new customers and the way how they interact with their clients. Technologies that businesses utilize allow them to collect, store and analyze large amounts of data (such as company’s transactional data, customer behavior data and other) and use that data to leverage their business performance. With the increase in popularity of Internet usage customers also have been changing the way they interact with their favorite brands and the way they shop in general. People have been shifting more towards online shopping because it is more comfortable and less time consuming. They can also compare products they are planning to buy by checking a variety of online stores and finding the best price for it or they can choose the best product by reading online customers reviews. Our online behavior or transactions we perform as customers’ leaves data residue that can be captured and tracked by companies and used to their advantage.

Handheld technologies have been improving and enabling more and more people to perform a variety of online transactions at any place and any time they desire to do so. As online transactions continue to grow, the amount of Big Data will also continue to grow. Businesses will continue to develop more sophisticated software that will allow them to better collect and analyze data and to make better SMART business decisions. Machine learning will be the next and biggest change in Big Data Analytics. We already see how large companies such as Amazon, Netflix, Facebook, and LinkedIn have been successfully using machine-learning techniques to improve customers’ user experience (by suggesting the products you might be interested in buying or movies that you might like to watch based on your viewing history and others) and therefore increased profitability and customer loyalty. More robots will be created to substitute human work functions (such as delivery and packaging services, virtual personal assistants, smart advisers and others) and we will also see more autonomous vehicles on our highways. According to International Institute for Analytics, businesses using data will see $430 billion in productivity benefits over their competition not using data by 2020.¹

Sources:

1. Forbes, March 15, 2016, Bernard Marr (https://www.forbes.com/sites/bernardmarr/2016/03/15/17-predictions-about-the-future-of-big-data-everyone-should-read/#747df3801a32)

General Motors and Big Data

Many companies are using big data to leverage their business performance and drive revenue. General Motors (a fortune 500 corporation that manufactures, markets and distributes vehicles, and their parts, as well as sells financial services) is a prime example of a large company that utilizes big data in a variety of ways.  Some examples in which GM utilized big data include gathering and analyzing rich data sets to generate additional sales, determine strategic price alignment strategies, improve car safety and design, create vehicles with sophisticated navigation systems and safety radars systems, and much more.

General Motors is using geographic information systems and data analytics to better allocate their marketing budget and improve their dealer’s performance. Location services help the company to determine the best place for new dealerships and find which locations they should let go. Having information about their customers’ location and other demographics including income, age, sex and car type preference allows the company to better segment their targeted audience for future marketing campaigns. General Motor’s advanced analytics manager said: “We can chase those households that buy new cars, rather than spending money on households that hold on to older models or only buy used vehicles. The result is lower spending and higher sales. We can bring all the data in to it to make better decisions, understand customers and provide better service.”¹

Sources:

1. https://blog.dell.com/en-us/how-gm-uses-big-data-to-generate-sales/

Predictive Analytics

Predictive Analytics is used by companies to predict future events. The process typically includes analyzing historic data they collect and creating machine learning techniques combine with a variety of statistical algorithms.  

Companies can use predictive analytics to:

·      Help improve the accuracy of forecasted revenue. For example, Microsoft IT is using predictive analytics – built on Azure Machine Learning and open source technologies to help the sales team to better predict sales. According to Microsoft, “they incorporated a predictive analytics tool in opportunity management in Microsoft Dynamic CRM Online. This model uses machine-learning algorithms and opportunity-scoring data for near real time win/loss predictions of a sale. It helps sellers prioritize by showing whether an opportunity is hot, warm, or cold, and advises them about actions to take.“¹

·      Optimize marketing campaigns. By collecting customers’ behavior data for key learnings (such as what products they tend to purchase, what marketing campaigns they tend to respond, which are loyal customers and how did they get to that point) predictive analytics can determine which of these characteristics combine and correlate to turn a prospect into a customer. Predictive analytics helps companies to determine which marketing campaigns they should employ next as well as to help them attract, retain and grow their most valuable customers.

·      Improve their product recommendation techniques – determine what product their customers are more likely to buy next based on his/her purchase history and then recommend that product. For example, if a customer bought a ski jacket you might recommend ski goggles.  As another example, if you notice that a customer bought facial moisturizer and you can offer similar moisturizer of a different brand that is highly rated you might suggest it to your customer. These strategies provide a personalized customer experience and should result in increased sales. Amazon is a great example of a company that successfully utilizes product recommendation techniques. “The company reported a 29% sales increase to $12.83 billion during its second fiscal quarter, up from $9.9 billion during the same time last year. A lot of that growth arguably has to do with the way Amazon has integrated recommendations into nearly every part of the purchasing process from product discovery to checkout”. ²

·      Determine price strategy. Predictive analytics helps companies to determine the right price for a specific product or service by analyzing historic data about sales, customers and other product specific metrics. There are different factors that help each system to determine the right price for a product.  Some examples include demand for a product and how much it is affected by competitive prices, seasonality (some products have stronger sales in the summer, therefore there is more price elasticity in the summer season), store capacity, weather patterns and much more.

·      Better manage the supply chain. By collecting information about customer demand, predictive analytics can help businesses to better manage their supply chain process and optimize the use of available warehouse space while minimizing the likeliness of out of stock items. “Large retailers are realizing the benefits of predictive analytics for supply chain management, as evidenced by Walmart’s recent acquisition of Inkiru, a predictive analytics startup with models for supply chain optimization.”³

·      Detect Fraud. Cybersecurity is a big issue that a lot of business are suffering from and trying to prevent. By utilizing a verity of predictive analytics tools you will be able to collect and store historical data that will also include past fraudulent transactions. When combined with rich transactional data sets and combined with 3^rd party data, analytical tools can help detect and prevent fraudulent transactions by running verification algorithms, in real time and prior to the completion of each customers purchase.

Sources:

1. https://www.microsoft.com/itshowcase/Article/Content/770/Predictive-analytics-improves-the-accuracy-of-forecasted-sales-revenue

2.  http://fortune.com/2012/07/30/amazons-recommendation-secret/

3. http://www.practicalecommerce.com/6-Benefits-of-Predictive-Analytics-for-Online-Retailers