Revisioning Retail through the Power of Data and Artificial Intelligence

The retail industry is being disrupted by the progression and expansion of data and artificial intelligence (AI). Retailers now have access to powerful AI-based tools that enable them to analyze vast amounts of customer data, empowering them to make informed decisions that cater to their consumer base. The COVID-19 pandemic has compelled retailers to adapt to contactless experiences, prompting the emergence of innovative solutions like buy-online-pick-up in-store (BOPIS) and curbside pickup. These measures prioritize customer safety while ensuring exceptional service. Forging ahead in the modern business landscape, especially in the age of digital transformation, retailers must harness data and AI to cater to dynamic customer needs.

Emerging technologies to keep a tab on

Autonomous operations

● Integrated technologies like IoT, computer vision, and deep learning enable seamless and digitally managed experiences.

● Autonomous retail reduces operational costs and ensures retailers remain relevant by delivering functional experiences and meeting the demand, all while transforming their cost structure.

Use cases: Staff-free stores, warehouse automation, drone/robot deliveries, cashless transactions, reduction of human errors, and frictionless consumer experience.

Adoption considerations: The cost of hardware setup remains high, posing a significant infrastructure challenge for autonomous operations that are primarily focused on hardware rather than software.

• Fluctuating consumer behavior and shopping patterns complicate the analysis of demand, leading to frequent changes in shopping aisles.

• Businesses must consider their target market and ascertain the desired automation level, as certain consumers may prefer human interaction.

Data Orchestration

• Improve supply chain stability with data orchestration to minimize disruption. Adopt circular economy principles to manage excess inventory and explore secondary markets.<.p>

• Enhance prediction and forecasting, gain customer insights, and make informed decisions influencing product development.

Use cases: End-to-end visibility, predictive & prescriptive analytics, demand fulfillment, customer loyalty, customer alignment across verticals, agile supply chain, customer entity resolution.

Adoption considerations

• As the data will be saved in multiple data stores, this implies that data would come in disparate formats.

• As regulations get tougher, brands will have to be prepared for a cookie-less world without third-party data.

• The absence of first/third-party data significantly affects consumer experience quality for brands.

Generative AI

• Retailers can apply generative AI to create new and unique visual merchandising displays in their stores as well as online.

• It can be used to generate new designs for products based on a set of parameters or criteria. This can help retailers create new and innovative products that are tailored to specific customer needs and preferences.

Use cases:Product design, visual merchandising, content creation for social media, personalization at mass in marketing, privacy preservation, rapid testing & experimentation.

Adoption considerations

• The computational complexity of generative AI necessitates powerful hardware and infrastructure.

• Regulating offensive language, hate speech, and misinformation poses a considerable challenge.

• AI language models can impact human decision-making, prompting inquiries about human autonomy and agency.

Metaverse

• The metaverse offers great potential for retailers to enhance the engagement and usefulness of both offline and online shopping experiences.

• By combining the best of both worlds, retailers can create a multi-sensory environment that captivates and delights customers.

Use cases:3D sensorial experiences for marketing, digital events for a global audience, metaverse wallets for improved transactions, community building, rapid prototyping, and sandbox.

Adoption considerations

• Metaverse faces cybersecurity risks and persistent data privacy challenges.

• Establishing infrastructure and acquiring skilled professionals is challenging due to the unique nature of the technology.

• The preference for virtual identity rather than real-life presentation can raise concerns as consumers get emotionally attached.

Conversational AI

• Engage consumers effectively with a single, versatile conversational AI tool that can address marketing, sales, and customer service use cases across multiple channels

• Reimagine intent recognition and automation to answer complex questions for consumers and ensure higher conversion rates.

Use cases:Holograms, two-way digital dialogue in store / assistant, order status, contact-free stores, rewards and loyalty programs, returns and exchanges, empathy builders / first-party data collection.

Adoption considerations

• Meeting the demands of a growing user base requires a robust infrastructure that can handle large volumes of requests.

• Consider the return on investment (ROI) based on each use case (for example, the level of personalization) before committing to a conversational AI solution.

• Consumers expect conversational AI systems to be natural and intuitive, and any issues with the user experience can negatively impact adoption and engagement.

Emotional AI

• With rising data privacy concerns, it is better to follow distinct behavioral cohorts throughout the consumer journey instead of tracking each consumer individually.

• Emotional AI can help retailers optimize store layouts and merchandising by tracking customer traffic patterns and analyzing purchase behavior.

Use cases: Behavioral analytics, hyper-personalization, spur engagement and make predictions, increase retention, CLTV, behavioral segmentation (over target segments)

Adoption considerations

• The lack of complete compliance and regulations presents challenges for businesses in establishing infrastructure and avoiding violations.

• Businesses must adhere to ethical and legal standards, including privacy and security regulations. Additionally, addressing bias is crucial to prevent unintended consequences.

• Users might feel reluctant to share their emotions with a machine, especially if they have concerns regarding privacy and security.

Image and Video Analytics 2.0

• Video analytics enables retailers to utilize predictive analytics to enhance brand loyalty among consumers, by accurately determining the required staffing levels on specific days, identifying product insights, and optimizing sales generation from shelves.

• Moreover, it effectively addresses theft and loss prevention, as well as expedites incident reporting by promptly identifying and flagging any abnormal behavior in real time.

Use cases: Retail heat maps, footfall analysis and interactions, in-store advertising, stocking and planograms, crowd analysis, fraud detection, and shoplifting.

Adoption considerations

• The size of image and video data can pose a significant challenge when it comes to processing, often requiring the utilization of cloud technologies.

• In domains like healthcare, where data sharing is restricted due to privacy regulations, collecting enough high-quality data becomes even more challenging.

• Consumers have the right to understand how their data is being utilized and should be informed about any potential biases or inaccuracies present in the system.

Responsible AI

• Responsible AI can effectively address bias through data features such as customer segments, demographic information, and order hours.

• Robust data governance and real-time monitoring techniques are crucial in identifying and preventing fraud within the supply chain.

• Removing data silos and utilizing technology can enable clean, consistent, and usable data.

Use cases: Transparency through a single source of truth, robust data strategy and governance, visibility and performance management, supply chain planning control tower, last mile tracking, logistics route planning.

Adoption considerations

• The importance of responsible AI is not widely recognized in various industries, which poses a significant challenge for its adoption.

• Many businesses believe that implementing responsible AI could lead to slower processes and therefore choose to maintain the status quo.

Future-forward utilities of emerging technologies for retailers:

• Building resilient Retail organizations by powering every human decision in the enterprise

• Leveraging customer analytics spanning across acquisition, engagement, retention, and growth

• Improving the Inventory ROI and profitability by enabling intelligent merchandising decisions across Plan to Sell cycle

• Enabling an insight and demand-driven supply chain by leveraging advanced analytics and intelligent automation

• Helping boost sales, and productivity and reduce costs across every facet of store operations operating in today’s ‘Data Economy’ using a Modern Data Estate.