AI In retail

AI In Retail Artificial intelligence in the retail sector is being applied in new ways, from the whole product and service cycle to assembly-to-post customer service interactions, but the key questions for retail players.

What AI applications play a role in the automation or growth of the retail process? How retail are companies today using this technology to stay ahead of their competitors, and what innovations are posed as potential retail gamechangers over the next decade?

In this article, we will cover the various examples of AI being integrated into the retail industry, divided into the following sub-categories: In this article, we cover a variety of examples in which AI is being integrated with the retail industry, broken down into the following sub-categories:

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Sales and CRM Applications Customer Recommendations Manufacturing Logistics and Delivery Payments and Payment Services

Innovation is a double-edged sword, and like any innovation, the results are a mixed bag. Many AI applications have yielded increased ROI — this case study of AI in the retail marketing department is an example — while others have failed and failed to meet expectations, such innovations shed light on the obstacles that must be overcome before becoming industry drivers.

Below are 10 brief use cases in five retail domains or stages. How AI technologies are being used today and are being created and piloted as potential retail industry standards in e-commerce and brick-and-mortar operations.

Readers can find additional insights into the retail space recently covered in our report on business intelligence.

Sales and CRM Applications For more insights on this topic, Emerge writes on current CRM applications such as Salesforce, Oracle, AI using SAP, and more.

The Pepper Robot In 2010, Japan’s Softbank telecommunication operations, together with French robotic manufacturer Aldebaran, could develop a humanoid robot called Pepper that could communicate with customers and “sense human emotions.” Pepper is

already popular in Japan, where it is used as a customer service greeter and representative for 140 Softbank mobile stores. According to Softbank’s Robotics America, Pepper Pilot in California’s B88T stores in both Palo Alto and Santa Monica increased 70% of foot traffic in Palo Alto and 50% of Neo-Pen sales in Santa Monica.

In addition, AI has spent time at the Hip Apparel Store Away, where it has experienced a 98% increase in retailer customer interactions, a 20% increase in foot traffic and a 300% increase in revenue. Nestle announced in January 2016 that it plans to buy Pepper robots to keep its 1,000 cafes in Japan.

Not only is the retail robot in use, but store-to-store robots like Pepper will also at least initially increase store interest and sales. It remains to be determined whether this will be a novel effect to wear once retail robots become “the norm”.

Conversica Conversica’s “Sales Assistant” software is designed to automate and improve sales operations processes by identifying and communicating Internet leads. The sales lead and Management Company claims that the average engagement rate is 35% as a result of standard-sounding messages.

In a case study, Star 2 Star Communications launched its Conversica-powered sales rep “Rachel” in 2016 and saw an email response rate of 30% in hours.

Customizable sales assistant software can also be used to sell or reinvest existing leads. New England-based Boch also used automotive conversion software, which averaged 60-plus sales a month at a Toyota dealership.

IBM Watson Cognitive Computing It is no secret that iCam’s Watson offers order management and customer engagement capabilities to eCommerce retailers. In 2016, 1–800-floors.com launched The Gifts You Need (GWYN), which the company calls the AI gift gatekeeper.

Gift recommendations are generated by information provided by users about the gift recipient, by comparing the specifics provided to the gifts purchased for the software recipients. The GWYN experience (which we included in our full article on chatbot use cases) attempts to reflect the role of the storekeeper in the store through personal and detailed communication with customers. 1–800Flowers Chris McCann spoke to Digital, saying that within two months, 70% of online orders were completed by GWYN. North Face has adopted IBM Watson’s cognitive computing technology to help consumers decide which jacket is best for them based on variables such as location and gender preference. For example, hiking in Iceland in October and Toronto in January can give different results.

The published 2015 pilot results, based on data collected from 55,000 customers, resulted in a 60% click-through rate and 75% total sales conversions. We cannot say whether these results represent more or less the general results of the North Face and whether these results are consistent or driven by the initial odds in the user interface.

The above is an example of North Face’s conversational interface, which prompts customers to ask a series of questions about their purchase. It is safe to say that similar systems, like the ones above, are built with rules that are simple, and not machine learning.

The advantage of using machine learning in the Q-and-A interface is that North Face can run tens of thousands of users through this communication engine.

Given the specific amount of customer interactions, the system can be expected to collect important insights and models on the “work” (high buy-to-buy, or high-value to buy), and what not effectively allowing the company. Get higher and higher conversions over time.

Manufacturing Readers interested in our machine learning in product reports may find additional insights into this topic.

Brilliant manufacturing General Electric (GE) Brilliant Manufacturing Software has been inspired by GE’s relationships with client manufacturers over the past two decades, designed to make the entire manufacturing process more efficient, from design to distribution and service, and thus saving large costs over time. The software includes analytics and operational intelligence tools suitable for a range of manufacturers.

For example, WIP Manager software provides industrial and discrete manufacturers with plant-floor and plant-wide collaboration visibility for all tasks in the process. An operational supervisor sitting behind a computer can now identify the ground-based problem that arises in the workflow in real-time, without having to take the time to do the time-consuming walks of the entire production facility.

Torrey Plastics is an example of a company that uses GE’s Plant Applications product, which enables management to collect granular-level data throughout the product and reduce defective products and waste productivity.

Gakushu Learning Software Embedded in manufacturing robots, Fanuk’s Gakushu Learning Software (“Gakushu” in Japanese means “learning”) speeds up “smart” operations on a specific task, originally designed for spot-welding and assembly lines. In 2016, Fanuk partnered with Nvidia to accelerate deep learning in robots through Nvidia’s GPUs. Gakushu endowed robots learn the manufacturing process by using a sensor to collect and store data.

Robots’ ability to adjust to real-time environmental conditions and mobility (according to Fanuck) leads to 15 percent cycle-time improvements in spot

welding. Once the robot’s learning process is complete (after about 18 cycles), the sensor is removed and trained robots can complete a task automatically.

The practice of robots is coupled with vibration control, which learns the movement of the robot and provides increased mobility stability. Tesla employs about 600 Fanuc robots at its factory in Fremont, and according to Bloomberg Business Week, in July 2017, in an effort to accelerate manufacturing efforts for the next slated delivery of its Model 3, more robots are in place.

Logistics and Delivery

In addition to Domino’s claims that its prototype delivery robot can keep food and beverages at the appropriate temperature, the DRU’s sensors help navigate the best route for delivery. The DRU integrates robotics technology previously used for military combat training. In March 2016, DRU pilots were made in Australia, New Zealand, Belgium, France, the Netherlands, Japan, and Germany.

The Dominos does not provide dates for when the DRU will be launched on a commercial scale, but the distribution of robotic food and other goods has proven to be a growing reality over the next decade.

Amazon drones In July 2016, Amazon announced its partnership with the UK government to make small parcel delivery via drones a reality. The company is working with aviation agencies around the world to work out how to implement its technology within the stipulated regulations. Amazon’s “Prime Air” is described as a future delivery system for safely shipping and delivering 5-pound packages in less than 30 minutes.

The first 13-minute unmanned flight by Amazon took place on December 2016, as can be seen in the video below. Currently, Amazon’s priority is to determine the optimal security and reliability of operations and systems. Amazon says it will work with regulators in “different countries”, though there are no updates

on the dates forecast for commercial use. Similar to Domino’s DRU concept, it is possible to deliver goods and food by air over the next decade.

Payment Services Amazon Go Amazon’s brick-and-mortar locations, called Amazon Go, use check-free technology that uses the Amazon Go app to allow customers to shop and leave, but then the entire shopping experience is automated. Sensors track what customers pick up and put in their basket, and customers’ Amazon accounts are automatically charged after exiting the store.

The proposed launch was not without its obstacles, and in late March 2017, sources close to the retail giant announced that Amazon was delaying opening its convenience stores while working on “technology glitches” in the automated shopping and buying process.

PayPal Since 2013, PayPal has leveraged fraud detection algorithms to protect a customer’s digital transactions. Over the past few years, thousands of purchase patterns or “features” have been learned through the security identification system, which now (in the example provided by MIT Tech Review) makes sense among friends who buy concert tickets simultaneously and make a burglary with a list of stolen accounts.

The aforementioned study by LexisNexis found that PayPal’s deep learning approach to transaction security reduced the fraud rate to 0.32% of revenue, which is 1% lower than the average rate seen by most of my e-commerce merchants.

Payment Fraud Fraud and Payment Security is a huge area of AI investment, and there are plenty of fraud/security companies worth watching. Sift Science is one of many

organizations that apply machine learning to detect consumer and payment fraud both of which are related to retail applications. This will become even more so as U.S. commerce continues to be a percentage of retail sales (and that growth is steady, according to the US Census Bureau).

AI in retail — closing comments In our interviews with retail-focused AI services vendors, we were told that “bigbox” retailers (Best Buy, Target, Walmart, etc.) were slow to adopt the latest technology. Given the large companies with the budgets and the volume of data needed to make the most of today’s best AI technologies, we are absolutely certain that the “AI revolution” in the retail space is unlikely. It may take another three to five years for most large retailers to have significant, business-critical AI applications in manufacturing, supply chain logistics or customer service.

Applications that are more prone to widespread retail adoption have a direct, rigorous return on investment. “Improving Customer Engagement” with case studies and examples is a softer benefit than “reducing lost packages by 6–10%”.

Our retail executive guests who use AI expect the relatively quiet committees of these large companies to be high. Critical, Safe, and Bottom-Line Focus (For more insights from machine learning industry executives, visit our AI Podcast Interview Channel).

As with many areas of AI innovation led by big industry players, leading industry players can effectively shape the future through proven retail AI use cases. It’s safe to say that every at-scale retailer in the world is looking to Amazon for “nextstep” cues, and we can expect that relatively small retailers will look at Amazon, Walmart, Best Buy and others. For their own ideas on strategy.

P.VenkatVajradhar

Marketing Team,SEO Executive USM SYSTEMS 8-2-293/82/A/270E, Road No – 10, Jubilee Hills, Hyderabad-500034