Artificial Intelligence

Retail Sales: AI for Voice and Image Search

Artificial intelligence in retail is transforming the way people shop and buy items ranging from clothes to cars. Voice search and image search are now widespread. Amazon and many other retailers now incorporate these tools in their apps. Next generation AI is also taking shape. For example, augmented reality (AR) lets shoppers view a sofa or paint color superimposed in their house or office. Virtual reality (VR) allows consumers to sit inside a vehicle and even test drive it without leaving home. Audi, BMW and others have developed VR systems for shoppers. But the AI use cases don’t stop there.AI in retail extends to bots and virtual assistants that recommend products and provide information; algorithms that helps sales teams focus on high value customers and high probability transactions; and predictive analytics that factor in weather, the price of raw goods and components, or inventory levels to adjust pricing and promotions dynamically. Clothing retailer North Face, for instance, asks customers a series of questions related to a purchase at its website. Not only does this lead customers to the right product, it taps machine learning to gain insights that potentially lead to higher cart values and additional sales.
Customer Support: AI for Natural Language
AI in retail is emerging as a powerful force, but customer support is also harnessing the technology for competitive advantage. Bots and digital assistants are transforming the way support functions take place. These technologies increasingly rely on natural language processing to identify problems and engage in automated conversations. AI algorithms determine how to direct the conversation or route the call to the right human agent, who has the required information on hand. This helps shorten calls and it produces higher customer satisfaction rates. A Forrester study found that 73 percent of customers said that valuing their time is the most important thing a company can do to provide them with good online customer service.
Manufacturing: AI Powers Smart Robots
Robotics has already changed the face of manufacturing. However, robots are becoming far more intelligent and autonomous, thanks to AI. What is machine learning used for in factories? Many companies are building so-called “smart manufacturing” facilities that use AI to optimize labor, speed production and improve product quality. Companies are also turning to predictive analytics to understand when a piece of equipment is likely to require maintenance, repair or replacement.

Big Data

360° View of the Customer

Many enterprises use big data to build a dashboard application that provides a 360° view of the customer. These dashboards pull together data from a variety of internal and external sources, analyze it and present it to customer service, sales and/or marketing personnel in a way that helps them do their jobs.​For example, imagine the sort of dashboard an insurance company might create with information about its customers. Naturally, it would include demographic data, like customers’ names, addresses, household income and family members, as well as sales information about which types of policies the customers hold. It could also pull information from the company’s customer relationship management (CRM) solution about the customers’ past interactions with the firm and even provide links to transcripts of recent calls, email messages or chat sessions. It might also show which pages of the company website a particular customer had recently visited, providing valuable clues about the reason a customer might be calling. The dashboard could also pull in external information, such as the customer’s recent social media posts. Or if an auto insurance customer had agreed to have a tracking device from the company installed, it might even provide details about the customer’s current location and recent speed.All of that information would obviously help prepare company staff to interact with the customer, but the most sophisticated dashboards don’t stop there. If it used advanced analytics or machine learning tools, the dashboard take a guess about the reason for a customer call. It could suggest opportunities for cross-selling or upselling customers on products, or if it detects that a customer might be in danger of defecting to a competitor, it might suggest potential discounts that could lower the customer’s rate. Some tools can even analyze customers’ language to detect their current emotions and suggest appropriate responses to sales or customer service agents. This might sound far-fetched and futuristic, but many companies today already have systems like this one in place, and they are using them to improve customer satisfaction and increase revenues and margins.


Fraud Prevention

For credit card holders, fraud prevention is one of the most familiar use cases for big data. Even before advanced big data analytics became popular, credit card issuers were using rules-based systems to help them flag potentially fraudulent transactions. So, for example, if a credit card were used to rent a car in Hawaii, but the customer lived in Omaha, a customer service agent might call to confirm that the cardholder was on vacation and that someone hadn’t stolen the card. Thanks to big data analytics and machine learning, today’s fraud prevention systems are orders of magnitude better at detecting criminal activity and preventing false positives. In the example already mentioned, for instance, a sophisticated fraud prevention system might be able to see that the customer had recently purchased airline tickets, sunscreen and a new swimsuit before the rental car purchase. Based on historical patterns, a predictive analytics or machine learning system would be able to tell that the rental car was thus less likely to be a fraudulent purchase. But fraud prevention systems can get even more sophisticated than that. According to Experian, fraud tends to be concentrated in certain geographic regions—often near airports, which make it easy for criminals to move stolen goods. However, which zip codes are riskiest tends to change over time. Big data analytics can look at past records of fraudulent transaction and quickly identify changing trends. Credit card companies and retailers can then pay more attention to transactions in zip codes that are emerging as hotbeds for criminal activity. Credit card issuers are understandably hesitant about disclosing all the advanced analytic techniques that they use to detect and prevent fraud. However, many credit card firms and other consultants offer technology, advice and services to other firms to help them set up systems to stop criminal transactions.


Security Intelligence

On the theme of criminal activity, organizations are also using big data analytics to help them thwart hackers and cyberattackers. Operating an enterprise IT department generates an enormous amount of log data. In addition, cyber threat intelligence data is available from external sources, such as law enforcement or security providers. Many organizations or now using big data solutions to help them aggregate and analyze all of this internal and external information to help them prevent, detect and mitigate attacks. Big data security solutions vary in sophistication and they are sold under a wide variety of names. For example, vendors sell log analytics tools that can detect anomalies in network data, security information and event management (SIEM) tools that offer real-time analysis of security alerts generated by other security software, and user and entity behavior analytics (UEBA) solutions that use analytics and machine learning to detect unusual patterns in device or user activity. Other big data security solutions are labelled as security intelligence offerings or network intelligence offerings.


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