6 ways AI is changing the retail shopping experience

Even though it hasn’t been long since Artificial Intelligence (AI) made its foray into the retail segment, the value it has delivered in such a short period has come as a blessing for businesses all over the world.

By consolidating different CRM systems with in-store customer data, AI parsers can study shopping patterns and preferences to deliver loyalty-based, value-added services at the point of sale. Machine learning (ML) has also helped retailers improve supply-chain management practices to increase output and reduce bottlenecks. 

However, in the current scenario, the true potential of AI as a more customer-facing tool has begun to take precedence over its quintessential functionality. In the midst of a global pandemic, retailers needed to discover new methods of interacting with customers and marketing products to their target demographic. 

1. Making retail employees more productive

One of the biggest crises for employees across industries, verticals and functions is the inability to be productive while on the job. A portion of the workforce is constantly engaged in repetitive or routine tasks, hampering their ability to focus on tasks that contribute directly towards personal job satisfaction. 

With the power of conversational AI, internal teams within an organization can now offload routine tasks onto these intelligent virtual assistants, freeing up time to address more critical demands. In the human resources management (HRM) space, intelligent chatbots can now handle employee onboarding, form-filling, training, and query resolutions without the need for human intervention. 

The IT Service Management pipeline has also enjoyed a dramatic facelift with the help of AI-powered virtual assistants. Support teams, who are often plagued by high volumes of ticket backlogs, can enlist ITSM Virtual Assistants to help address requests in real-time and resolve L1 queries

2. Enhancing in-store experiences

With the COVID-19 virus in swells and lulls, brick-and-mortar stores were required to reduce their on-ground workforce and limit customer interactions. With the advent of conversational AI-powered chatbots, physical retail stores are now able to deliver the same level of service that customers were accustomed to before.

Interactive chatbots can assist customers in addressing queries in real-time, automating check-out counters through cashless payment modes, and facilitate stock replenishment through Real-Time Monitoring.     

3. Guided discovery

Customers that frequent or prefer physical stores are usually individuals looking to explore their options. Before the pandemic, retail personnel aided in the discovery process of a customer’s journey by recommending products based on quantity, price-point, and preferred brands.

AI and NLP-powered (Natural Language Processing) virtual assistants give customers the ability to narrow down purchase possibilities through targeted/curated content, price optimization, and visual searches (e.g. “Looking for a long, black hoodie with short sleeves”). Despite a dip in footfall, many retail stores have managed higher conversion rates during this time due to e-commerce solutions. 

4. Personalization and customer engagement

According to research by Semrush, 40 per cent of sales and marketing strategies favour AI and Machine Learning over any other technology.

The whole idea behind Intelligent Retail Shopping revolves around the concept of personalizing the customer’s purchase journey by customizing in-store product displays, prices, discounts, and loyalty recognition based on historical data. Retailers are drumming up sales via online and offline channels with the help of AI and NLP algorithms, predicting customer interests based on focused datasets such as demographics, geographical locations, and social media interactions. 

5. Demand forecasting

The ability to pivot is what truly makes a business successful. As market trends and sentiments sway every quarter, organizations are constantly engaged in the game of cat-and-mouse with the shifting market. By mining insights from diverse channels, such as consumer, marketplace, and competitor data, retailers can now create forecast models to help predict industry trends and effect changes across their Marketing and Merchandise Procurement strategies. 

6. Customer service improvements

With the application of omni-channel marketing, retailers have managed to ensure that interactions with customers do not stop at the door.

Retail store owners have improved the quality of customer engagement initiatives by building interactive communication channels. For example, chatbots can ‘talk’ to customers in human-like and empathetic ways, ensuring a smooth experience. They can also answer FAQs, recommend products and address grievances in real-time. 

The feather in the cap from all of this is the ability of a chatbot to harness crucial data from customer interactions and use it to build robust customer profiles. This form of self-learning allows the AI Bot to perfect itself when interacting with customers, ensuring that businesses do not miss the mark when engaging with people. 

Experts point out that the average spend for AI products in the retail sector will increase drastically over the next few years. According to a study by Juniper Research, the total growth in AI and Machine Learning retail spending will stand at 230 per cent between 2019 – 2023.

The future of retail shopping arrived some time ago, yet the journey towards optimum utilization carries on.

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Jim Love, Chief Content Officer, IT World Canada
Pranay Jain
Pranay Jain
Pranay Jain is the CEO and Co-founder of Enterprise Bot. He founded the company alongside his wife, Ravina Mutha, and CTO Sandeep Jayasankar. While studying business in the United State, he became fascinated by information technology, particularly its problem solving nature, and decided he wanted to pursue business in tech.

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