Artificial intelligence (AI) has reinvented the entire retail industry with its inclusion across retail operations. But what is the hype all about? It’s predicted that over the course of three years, AI has helped in accelerating retail sales by $40 billion, with its implementation in both the back-end and front-end operations.

A miss at this, in our opinion, can clearly set aback a retail company to several notches in terms of user experience, data analytics, product regeneration, strategy, and ultimately profit!

Are AI implementations new to the retail industry?

NO! As a matter of fact, the adoption of AI across retail companies has already witnessed huge growth in recent years. Inclusions like the application of AI in CRM software help in automating marketing tasks while predictive analytics identifies a potential customer for a particular goods/service. Simultaneously, the cloud makes it possible to store and process AI workloads that call for large amounts of data from numerous sources. These stored data, mostly unstructured, are categorized and structured using machine learning and artificial intelligence to create value-driven actionable insights.  These further help in strategic decision making, product designing, product displays, analyzing consumer behavior, etc.  

Some of the other use cases of using AI in retail are:

• Customer relationship management, data forecasting, inventory management
• Real-time, 3D computer vision of brick-and-mortar shops
• Insightful data-backed decisions
• Assists in intelligent display ads, smart shelves, real-time inventory control, smart self-checkouts
• Personalized conversations via chatbots
• Insightful-powered customer interactions for upselling and cross-selling

 Ignorance of artificial intelligence = Doomsday!

Let’s understand this through our detailed analysis of what exactly AI offers to retailers in particular.

Personalized customer care for enhanced service delivery: Presently, retailers are focusing on a new AI deep-learning technology known as ‘computer vision. This technology helps them in ‘actually seeing’ and interpreting visual data. Additionally, digital signage embedded with computer vision measures customer engagement. This facilitates in serving customers on a real-time basis with preferred and personalized advertisements, opinions, and recommendations. For example, Sephora’s color IQ scans a customer’s face to identify and recommend the appropriate (personalized) shades of make-up for them. It not only helped them increase their sales but also boosted customer confidence and satisfaction level with the right product purchase decision.

Demand forecasting and merchandising for optimum customer service: AI helps in analyzing consumer trends, behavior, and their changing purchasing decision factors. Along with this, predictive analytic tools support retail owners in forecasting the exact stocks for optimum utilization with a smart decision on preferred pricing. All of these strategies help retailers in maximizing their sale opportunities at stores (both online and offline) Not only this, but artificial intelligence helps retailers with ‘product placements’ in the brick and mortar stores, based on historical consumer trends and traits of purchasing decisions. Walmart deploys robots (for visual analytics and transactional data) to scan shelves to report missing items, and recognize fast-moving goods, restock needs, and price-tag change requirements, among others.

Adaptive Homage to harness immediate opportunities: With smartphones being the new purchase portal, customers are being recognized by mobile and digital portals.  At this point, AI can help in personalizing the e-retail experience by considering factors like their current situation, previous purchases, buying habits, etc. It can also adjust to this dynamic situation and produce hyper-relevant displays for each encounter. For example, Lowes developed LoweBot to help and guide its customers across their stores, resolving simple queries and helping them locate their required products!

Chatbots and NLP for uninterrupted, 365*24*7 query resolve: Interactive chatbots and chat programs use AI and NLP to interact with customers at least for the first few minutes of general queries. This process helps in offering 24x7x365 customer service at a comparatively lesser cost. Also, if these NLP solutions offer guided conversation, the customer feels empowered and builds a stronger relationship with the brand.

Visual curation for augmented search capability: Through the use of image-based search and analysis, algometric engines can translate real-world browsing behaviors of customers into real-time digital opportunities for retailers. When we offer the opportunity and digital feature of searching for the desired item online, immediately, the purchase intent of the consumer is strengthened. Neimen does the same! Neimen Marcus uses AI-backed tools which help customers snap, find and shop the exact product (you just saw and snapped) across the Niemen inventory.

Real-time emotion interpretation: AI interfaces may identify shoppers’ in-the-moment emotions, reactions, or mentality and give suitable products, advice, or support by recognizing and analyzing facial, biometric, and aural cues, guaranteeing that a retail engagement doesn’t miss its target. For example, with the paucity of time, Taco Bell introduced its TacoBot to help customers make an order through a voice email or a text! This was made possible through the use of AI along with Slack.

Takeaways:

It is evident that AI is here to serve and is inevitable. With so much goodness to offer, it is just a matter of our choice to ask for the right AI tool suitable for our business requirements and visions.

Ignoring the possibilities of AI for retail means you are letting go of competitive advantage that this exciting technology offers.