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How AI Is Redefining the Consumer Goods Industry
Artіfісіаl intеllіgеnсе (AI) is currently taking the retail world by storm and will increasingly continue to do so. Thе mаrkеt ѕіzе оf AI ѕоftwаrе аnd ѕуѕtеmѕ is expected to reach $38 million by 2025, and the potential opportunities for interacting with customers in new and increasingly customized ways are causing retailers to invest in such technologies.
AI to Enhance the Overall Customer Experience:
AI is in fact an umbrella term for 3 distinct capabilities: machine learning, natural language processing and optimization. These technologies are being applied across the entire рrоduсt аnd service cycle – from product assembly tо роѕt-ѕаlе customer service іntеrасtіоnѕ. The main goal is to develop leaner, more optimized operations and to enhance the overall customer experience. By 2020, 85% of customer interaction in retail will be managed by AI, according to Gartner.
Consumer-goods companies have been at the forefront of the current rise in digital innovation in commercial areas such as marketing and sales. The overall goal, according to Bruce Macinnes, chairman of BrandAlley, is to move towards personalizing the entire customer journey from homepage to checkout. AI can be used to provide services such as digital shoppers to simplify the shopping process and provide personalized experiences that increase customer loyalty and retention. Companies are benefiting by achieving higher sales, reducing manufacturing costs and improving the retail brand.
Regardless of the hype, AI technologies are still in their infancy. If a retailer does not already value data and analytics for their current operations, investing in an AI solution may not appropriate. The technology itself is still growing and changing at a rapid rate, and concurrently, understanding how such technologies can be applied to retail is also evolving rapidly. With all this in mind, we highlight various aspects of the consumer goods industry that are currently being affected by AI technology.
AI & Manufacturing:
The predictive analytics capabilities enabled by AI can provide manufacturers with plant-floor and plant-wide collaborative visibility of all work in process and provide insight into possible bottlenecks and pain points. An operational supervisor can identify a floor-based problem that arises in workflow in real-time instead of spending time making time-consuming walk-throughs of entire manufacturing facilities.
Toray Plastics is one example of a company that is using GE’s Plant Applications product to allow management to collect granular-level data throughout production and reduce defective products and wasted productivity. By taking advantage of predictive analytics, some of the highest-impact developments have been in quality control, predictive maintenance, and supply-chain optimization.
AI & Logistics and Delivery:
In addition to manufacturing, AI will have a great impact on this logistics and delivery of consumer goods. Amazon recently received a patent for “anticipatory shipping,” which is a method to start delivering packages even before a consumer has placed an order. The system first predicts your future purchases based on previous purchases and interests, site searches and online lingering time. It then preemptively will ship items to the warehouse nearest the consumer before they even press the button to complete the purchase.
AI & Household Connectivity:
Among the various consumers goods that can be integrated with AI capabilities, household electronics and appliance manufacturers have perhaps been the most active in leveraging such technologies to simplify and enhance consumer interactions with their products.
- For example, more than 10 years have passed since the Roomba Vacuum Cleaning Robot came to market in 2002. Since then, kitchen appliances, such as microwaves, refrigerators and dishwashers, have become increasingly intelligent, interactive, and connected. Effective robotic applications in household products offer the promise of a connected and intelligent household.
- Electronic personal assistants such as Amazon Echo, Google Home, Microsoft Cortana, and Apple Siri are acting as the central hubs that can connect to various devices throughout one’s household and the inhabitants themselves.
AI & Online Advertising:
Machine learning can provide a more personalized, contextual, and anticipatory experience when making a purchase.
- Flash sales site BrandAlley worked with marketing automation company Emarsys for persona-based targeting in its email campaigns. It has led to a 16% conversion increase.
- AI firm Sentient Technologies provided 256 real-time website design variations for consumers for Swedish flower delivery chain Euroflorist. This resulted in a 17% increase in conversions.
- Guess integrated True Fit machine learning software into its web and mobile platforms. The True Fit algorithm matches consumer preferences with clothing or footwear to offer the best fit. It also leverages its machine learning capabilities to track returns and successful purchases, enabling it to increasingly tailor its recommendations to users.
AI & In-Store Advertising:
According to Deloitte, digital interactions currently influence more than one-third of retail in-store sales. PepsiCo and Frito-Lay are using image recognition to drive in-store promotion and consumer engagement via contests in which customers photograph their products to compete for prizes. As stated in the Deloitte report:
“As an example, one apparel manufacturer is developing a virtual personal shopping assistant that aims to act like an experienced in-store salesperson. The prototype app, Expert Personal Shopper, was developed by Fluid Inc. using IBM’s Watson platform. Speech recognition enables customers to interact via voice with the app, which uses NLP [natural language processing] to understand customer questions so that it can make appropriate recommendations based on its analysis of product information. Machine learning is used to improve the quality of the app’s recommendations over time.”
The ultimate goal of incorporating AI with consumer goods is to enable simpler and more direct shopping for both the producer and consumer. This will enable consumers to spend more time doing (and finding) what they really want. Companies can leverage these innovations to contending with increasingly value-conscious and tech-savvy consumers. As increasing numbers of consumers depend on using digital and mobile devices to shop, many consumer goods companies are investing in existing and emerging technologies to better understand, connect with, and engage with consumers.
It is important for manufacturers to understand how technologies such as voice recognition, natural language processing, and computer vision will enable leaner operations and more direct interfacing with their consumers.
Daniel is one of PreScouter's Project Architects. He specializes in the consumer goods sector. Daniel received his PhD in Chemical Engineering from North Carolina State University in Raleigh. His dissertation research focused developing stimuli-responsive polymer networks for micro-robotics applications. After his graduate studies, he completed post-doctoral work at the LPCNO Lab in INSA Toulouse, France where he focused on incorporating microfluidics with nanoparticle assembly techniques to develop multi-parametric sensors. In addition, Daniel has industrial experience in pharma manufacturing and polymer processing. Daniel joined PreScouter as a Project Architect after two years as a Global Scholar.
Latest posts by Daniel Morales (see all)
- The shift toward bioplastics: Insights from Lego and current opportunities - April 20, 2018
- Non-Invasive Biosensing Companies to Look Out for in 2018 - January 19, 2018
- How AI Is Redefining the Consumer Goods Industry - December 11, 2017