Gartner Research

Cool Vendors in AI for Retail

Published: 29 May 2020

Summary

Retail digital transformation requires continuous adaptation. Retailers succeed by leveraging AI technologies to understand customers, creating new customer experiences while generating cost savings through operational efficiencies. CIOs can use these Cool Vendors to assist in this transformation.

Overview

Key Findings
  • Post-COVID-19 recovery activities will include driving revenue generation and reducing costs through better information management and automation.

  • Useful understanding of customer sentiment must be based on specific and relevant analysis of products and experiences to enable action.

  • Artificial intelligence (AI) must function as a nervous system, serving as a foundation for retail adaptation strategies; providing intelligence, automation and augmentation of the human workforce.

  • In the near future, there will be great pressure to predict exactly what a customer wants or needs, know when they will need it and facilitate its arrival in the customer’s hands.

Recommendations

Retail CIOs seeking to drive customer insights through digital transformation and innovation should:

  • Leverage AI applications to facilitate better information management, analysis and automation for efficiency.

  • Improve understanding of customer behavior across the retailer’s touchpoints by using AI and machine learning (ML) to truly understand and anticipate your customers’ behaviors to deliver on their expectations. Adjust your roadmap and investment plans accordingly.

  • Explore how AI can act as a central nervous system, constantly reading signals, reacting to events and proactively anticipating what will happen.

  • Leverage Gartner’s strategic AI framework to engage with senior business leaders to discuss AI and align with adaptations required by high-level business strategy.

Strategic Planning Assumptions

By 2025, the top 10 retailers globally will leverage AI to facilitate prescriptive product recommendations, transactions and forward deployment of inventory for immediate delivery to consumers.

By 2025, the top 10 global retailers by revenue will leverage contextualized real-time pricing, through mobile, to manage and adjust in-store prices for customers.

Analysis

What You Need to Know

Enabling robust customer understanding and commensurate experiences that facilitate unified retail commerce is a guiding principle for AI implementations in retail. AI will play an important role in proactively identifying and monitoring customer expectations and supporting the ongoing development and maintenance of appropriate experiences. In all cases, retailers will benefit from the automation of labor-intensive, repetitive and even more complex tasks.

During the recovery from COVID-19, the next 12 to 18 months will be about survival, with heavy emphasis on cost containment and risk mitigation. Retailers must stem the tide on market share loss by redirecting resources toward activities that differentiate the customer experience and increase the speed of innovation. To support unified retail commerce, retailers must build a robust understanding of customer expectations, and a way to monitor for the constant change in the market. By starting with automation of highly repetitive, formulaic and data-entry-intensive but necessary tasks to improve efficiency, associates are freed to pursue new avenues of customer engagement. The foundational step of conducting deep learning about customer behavior must be used to support each additional step in the cycle. Figure 1 shows the progression of customer understanding enabled by AI.

Figure 1: Retailers Can Experience a Virtuous Cycle by Leveraging AI

The future of retail is found in agile flexibility, based on the understanding of customer expectations, innovation and continuously delivering more value for customers. Retailers will succeed in the new decade by embracing AI as a central nervous system (see ).

Like the human nervous system, it constantly reads signals, reacting to events and proactively anticipating what will happen. Resources will be automatically reconfigured and redeployed to maximize opportunities, while higher-level conscious decision making is informed by various signals to drive adaptation strategies.

The Cool Vendors featured in this research drive use cases further by building customer understanding, learning the language of the customer and preparing for the use of AI as the central nervous system for the retailer.

Hearful

Chapel Hill, North Carolina (www.hearfulhub.com)

Why Cool:Hearful offers a solution designed to make sense of all the noise found in customers’ product reviews, allowing executives and decision makers to lead with a customer-centric approach. It leverages large and diverse data sources to understand how customers feel about, use and interact with the products they buy. The resulting insights enable organizations to make merchandising, marketing and design decisions with greater precision to improve business results.

To accomplish this, it analyzes and structures product feedback data (primarily from reviews) into relevant industry themes with associated sentiment. Hearful uses rule-based, hybrid natural language processing (NLP) and ML models. It has developed industry-specific modeling techniques to derive accuracy that is estimated at 10% to 15% higher than traditional NLP, enabling more granular insights from unstructured text. The result is product-level insights and the ability to dive much deeper than traditional sentiment analysis. It benchmarks against a competitive set, offering a more holistic view of risks and opportunities in the marketplace. Hearful goes to market with three offerings:

    Challenges: To date, it has only operated in the North American market. Heavy dependency on reviews requires careful consideration for verification, accuracy and curation in review content. Although having well-developed models focused on apparel and footwear, its primary go-to-market strategy has been focused on branded manufacturers. It is mainly an apparel-focused organization, but has been seeing expansion into other markets.

    Who Should Care: Retail CIOs, chief data officers (CDOs), chief marketing officers (CMOs) and heads of merchandising, marketing, e-commerce and digital strategies

    Peak

    Manchester, U.K. (www.peak.ai)

    Why Cool:Peak’s full-stack enterprise AI System combines the required infrastructure, data processing, AI workflow and applications in a single SaaS product. Using it, organizations can deploy AI solutions leveraging datasets from across the enterprise, while integrating those solutions into all other business systems to automate action and optimization.

    Peak’s AI System is built on Amazon Web Services (AWS). This system can handle vast amounts of data, from multiple sources, rapidly. Peak’s AI System can leverage a business’s data from across the entire value chain, in order to drive tangible outcomes and ROI across different business functions. Peak’s AI-powered solutions include:

    • Customer AI

    • Demand AI

    • Supply AI

    Customer AI is designed to help businesses get more from their marketing. It helps companies to leverage more value via prescriptive outputs from data in order to drive rapid ROI and tangible outcomes focused on both customer acquisition and retention. For example, this could be targeting and acquiring more ideal customers to drive sales, or increasing the lifetime value of customers through the delivery of more targeted, personalized communications.

    Demand AI is a solution to help forecast demand and maximize profit. It takes data from across the retail business to inform the large volume of SKUs that need to be managed, before creating a predictive demand view. This provides merchandisers and planners with a global view of predicted demand requirements, rebuy timelines, markdowns and suggested stock movements.

    Supply AI utilizes data from across the supply chain to help optimize processes, reduce costs and improve efficiencies. It adds a layer of intelligence to warehouse and logistics operations, enabling lowest-cost fulfillment.

    Peak’s go-to-market strategy hinges on ease of AI adoption. The business charges no setup or consultancy fees and offers its service on a 12-month subscription basis. This provides Peak customers with access to the Peak AI System, and a data science team with a keen eye for delivering real business with a short time to value. Peak also offers a free proof of concept (POC) that supports early-stage discovery.

    Challenges: Its focus is currently on the Tier 2 and specialty retail market. So far, Peak only has clients in the U.K. and Europe, and is looking to expand into the U.S. Its relationship with AWS offers potential avenues for growth but may pose a problem with retailers that see AWS as feeding revenue of a competitor. It also faces competition from larger, more widely implemented solutions such as Salesforce. Data science tools, including its AI Studio, are in pilot but not generally available.

    Who Should Care: Retail CIOs, CDOs, CMOs and heads of merchandising, marketing, e-commerce and digital strategies

    SO1

    Berlin, Germany (www.so1.ai)

    Why Cool: SO1’s Vertica Analytics Platform utilizes AI and ML to provide a detailed product map where every product in a retailer’s assortment is clustered based on how likely it is to co-occur in customers’ baskets across one or multiple shopping trips. It uses this information to offer AI-based analysis on the shopping habits and patterns of individual customers which could help businesses to develop better customer relationships by providing personalized promotions. It refers to this as a “segment of one.”

    Key benefits include AI- and ML-based capability to quickly analyze large volumes of data and provide solutions that can be personalized for individual customers. The automated nature of the tool has been designed to minimize retailer’s complexity. Retail solutions include:

    • Smart Recommendations

    • Optimized Discounts

    • Programmatic Promotions (more for branded manufacturing)

    SO1 conducts research with several leading institutions including MIT, Humboldt University of Berlin and Chicago Booth. It leverages advanced ML approaches such as deep neural networks and gradient boosting machines (GBMs) to aggregate over embeddings, models and (expert) features enabling SO1 to make precise predictions about consumers’ future purchases and the impact of marketing.

    Challenges: Limited experience, with all existing work in the consumables categories, and geographic reach having its only clients in Germany and the U.S. It also faces competition from solution providers and custom solutions already in the market.

    Who Should Care: Retail CIOs, CDOs, CMOs and heads of merchandising, marketing, e-commerce and digital strategies

    Where Are They Now?
    Syte

    Tel Aviv, Israel (www.syte.ai)

    Profiled in

    Why Cool Then: Syte enables a retailer to add a visual search camera button to its mobile website or application in just 24 hours. It subsequently uses AI to turn images of interest from real life, the internet or social media screenshots into new and more robust shopping opportunities. 

    Where They Are Now: Syte has continued to develop its patented technology and its offering, leveraging the image recognition engine to enhance both the shopper’s experience and the retailers’ capabilities. 

    It now offers three main solutions:

    • A visual discovery-based marketplace directly connecting consumers and retailers

    Who Should Care: Retail CIOs, CDOs, CMOs and heads of product development, merchandising, marketing, e-commerce and digital strategies

    Gartner Recommended Reading

    Analysts:

    Robert Hetu

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