How AI-powered Conversational Commerce Will Transform Shopping

Ready to step into the future of shopping? Say hello to AI-powered conversational commerce—a game-changer that’s making waves in the retail world. If you’re a retailer, you can’t afford to sit this one out. Why? Because this tech marvel is all about delivering hyper-personalized shopping experiences through chatbots, voice assistants, and more. Companies like Amazon and Sephora are already riding the wave, using AI to offer spot-on product recommendations and instant customer support.

For instance, Amazon Go has brought forth the “Just Walk Out” shopping experience where customers don’t have to wait in line or scan any items at checkout. The experience is quick, friction-free, and super-convenient. This is just one example of AI shopping in action and there’s a lot to unpack.

But it’s not just big names that can benefit. Whether you’re in fashion, beauty, or tech gadgets, conversational commerce can be your secret weapon for boosting sales and building customer loyalty. Imagine a chatbot that knows your customer’s style down to the last detail, or a voice assistant that can find the perfect pair of sneakers while applying all available discounts. That’s the level of service today’s shoppers are craving, and with AI, you can deliver it around the clock.

Of course, this isn’t a plug-and-play affair. Shoppers will expect these interactions to be smooth, accurate, and secure. That means you’ll need to invest in top-notch AI algorithms and robust data protection measures. But make no mistake, the investment is worth it.

AI Powered Shopping in 2023: A Look at the Current Landscape

Shopping today is as immersive as watching a movie on IMAX. With the increasing reliance on emerging technologies, the lines between offline and online spaces have begun to blur.

Recently, Google announced how it would integrate generative AI into shopping by allowing customers to try on clothes ‘virtually’.

This is the kind of shopping experience customers demand today. When shopping, they want ultra-convenience at their fingertips. They demand an omnichannel shopping experience that allows them to shop till they drop, without ever leaving the platform they are on.

These intrinsic changes in consumer behavior are driving the widespread adoption of AI shopping:

• Skyrocketing Demand: Such is the demand for AI in retail that the global artificial intelligence in retail market size is projected to grow from $7.14 billion in 2023 to $55.53 billion by 2030.
• AI-powered Growth: As per Accenture, AI absorption can nearly double the economy’s growth rate for the following countries:
• Faster Path to Purchase: Around 47% of online shoppers prioritize speed over a selection of items. Plus, two-thirds of shoppers will head over to a competitor’s website if they can’t find what they are looking for after just one search.

The Writing is on the Wall: Customers are increasingly looking for one-click, personalized shopping experiences, irrespective of the channel or device they are shopping on. Ecommerce brands that do not adopt AI in a contextual capacity will find themselves behind the pack.

The Challenges E-Retailers Can’t Ignore

Let’s cut to the chase: the retail landscape is evolving, and not all is rosy. Traditional methods are falling short in several key areas, creating pain points that can’t be ignored.

1. Impersonal Shopping Experiences

Customers are no longer satisfied with one-size-fits-all solutions. They crave personalized interactions, and failing to provide this can result in lost sales and dwindling loyalty.

2. Inefficient Customer Support

Long wait times and generic responses are a surefire way to frustrate customers. In today’s fast-paced world, people want instant, accurate answers to their queries.

3. Inventory Mishaps

Out-of-stock items or, conversely, overstocking can severely impact your bottom line. Traditional inventory systems often lack real-time accuracy, leading to inefficiencies.

4. Security Concerns

With the rise of online shopping, data breaches and fraud are becoming increasingly common. Customers need to know their information is secure, or they’ll take their business elsewhere.

These challenges are more than just bumps in the road; they’re roadblocks on your path to retail success. But don’t despair—AI-powered conversational commerce offers a way to navigate around them. Stay tuned as we delve into how this technology can turn these pain points into points of delight for your customers.

Benefits of AI for Shopping: From the Consumer and Brand Perspectives

According to Capgemini, 76% of customers expect companies to understand their needs and expectations. This makes AI a critical tool as it can offer a host of customer-friendly benefits such as:

6 Examples of Conversational Shopping

1. Personalized Shopping

AI is empowering brands to get personal (pun intended). Much like Netflix or Amazon, eCommerce brands are now leveraging AI to analyze customer data (think: user behavior, order history, etc.) and offer personalized product recommendations.

Nike’s ‘Nike by You’ feature takes personalization to the next level by allowing customers to take charge and customize shoes of their choice.

2. Instant Responses with 24×7 Chatbots

Ecommerce websites that deal with high volumes of customers must integrate an AI-powered chatbot as Watson demonstrates below.

This way, customers can get their queries addressed on the spot. They can ask for information about a product and get answers without waiting, enhancing the customer experience.

On the business side, having an automation tool such as an AI-powered chatbot can reduce the support ticket volume and help the agents spend their valuable time on more creative, empathy-driven tasks.

3. Innovative Visual Search

Pairing artificial intelligence with visual search is a stroke of genius. Take the example of ASOS’ “Style Match” feature, which allows customers to use a visual search tool and find products on the app with one quick tap.

As per estimates, the visual search retail shopping experience is gaining momentum in the US.

4. Targeted Sales and Marketing

Unearthing useful customer data with an AI analytics tool can inform your marketing and sales campaigns. Take for example Amazon’s use case. As per estimates, Amazon US Prime Day sales are expected to reach $8.03 billion in 2023 ( up by 10% as compared with 2022).

Amazon is embracing a data-first, customer-first approach to boost sales in 2023. Instead of focusing on tech deals (as it did in 2022), the brand is shifting its messaging to highlight the changes in consumer spending patterns such as:

• • Offering in-store discounts to capitalize on brick-and-mortar retail
  • Partnering with Priceline to offer travel-related deals
  • Rewarding customers for shopping for groceries from Amazon

5. Content Marketing at Scale

If your Ecommerce brand is constantly adding to its inventory of products, you need to update the product descriptions around the clock. After all, your customers should have relevant and updated information at all times. This is where an AI tool can lend a helping hand.

These tools can create social media posts, write accurate product descriptions, and help your brand present a cohesive front to the end consumer.

6. Accurate User Persona

If Ecommerce brands want to target customers at a more granular level, they need to start by building a comprehensive and highly-segmented user profile.

Here’s a step-by-step lowdown on how AI can assist in creating segmented user profiles:

Step 1: Data Collection

Start by collecting relevant data from various sources such as customer interactions, purchase history, website analytics, social media, and demographic information. This data will serve as the foundation for creating user profiles.

Step 2: Data Integration

Integrate the collected data into a centralized database or data management platform. This step ensures that all the information is accessible and can be analyzed efficiently.

Step 3: Data Cleaning and Preprocessing

Clean and preprocess the data to remove any inconsistencies, duplicates, or errors. This step typically involves tasks like data normalization, handling missing values, and ensuring data quality.

Step 4: Feature Extraction

Identify and extract relevant features from the data that can contribute to user segmentation. These features can include customer demographics, browsing behavior, purchase patterns, preferences, and any other relevant data points that are relevant to your brand.

Step 5: AI Algorithm Selection

Select appropriate AI algorithms based on the nature of the data and the desired outcomes. Commonly used algorithms for user segmentation include clustering algorithms like k-means, hierarchical clustering, or density-based clustering.

Step 6: User Segmentation

Apply the chosen AI algorithm to segment the user data based on the extracted features. The algorithm will group users into distinct segments based on their similarities or patterns in the data.

Step 7: Profile Creation

Create individual user profiles for each segment by analyzing the characteristics and behaviors of the users within each segment. These profiles should capture relevant information such as demographics, preferences, purchase history, and any other significant attributes.

Step 8: Validation and Refinement

Validate the created user profiles to ensure their accuracy and effectiveness. Continuously refine the profiles by incorporating new data and feedback from user interactions and campaigns.

Step 9: Personalization and Targeting

Utilize the segmented user profiles to personalize marketing campaigns, product recommendations, and user experiences. You can also tailor messaging, offers, and promotions to each segment’s preferences and behaviors to maximize engagement and conversion rates.

Step 10: Continuous Learning and Optimization

Leverage AI and machine learning techniques to continuously learn from user interactions and optimize the segmentation models and user profiles over time. Plus, make sure to regularly update and adapt the profiles based on new data and changing user behaviors.

9 Best Practices for AI in Shopping

1. Implement personalized product recommendations

Use AI algorithms to analyze customer data and behavior and deliver personalized product recommendations as well as customized emails. One brand that exemplifies the use of AI for driving personalized emails is Grab.

Customers get a sneak peek into their Grab orders and understand the impact their orders have made on small businesses – a customer-centric feel-good strategy at its best.

When collating customer data, ask the following questions:

• How can you collect and analyze customer data effectively?
• How can you ensure the accuracy and relevancy of your product recommendations?

2. Optimize Search Functionality with AI

A powerful search engine directly translates to profitable sales. This is why, you can think about investing in robust AI-powered search algorithms to enhance search functionality. This includes the coveted autocomplete, auto-suggestions, and semantic search functionalities.

For example, eBay’s search engine employs AI to understand user intent and provide relevant search results.

When chalking out the must-have search functionality features, ask the following questions:

• How can you leverage AI to improve search accuracy and speed?
• How can you optimize your search results to match customer preferences?

3. Enable Chatbots for Instant Customer Support

Chatbots are the future of accelerated and high-quality customer support, and the future is here. Whether you want the chatbots to handle routine customer inquiries or assist with order tracking, a chatbot can multitask and provide instant responses.

For instance, H&M uses a chatbot on its website and social media platforms to answer customer queries and offer styling advice.

When integrating a chatbot, ask the following questions:

• What are the common customer inquiries the brand routinely receives?
• How can you train the chatbot to handle complex queries effectively?

4. Optimize Pricing with Dynamic Pricing Strategies

You can also utilize AI algorithms to analyze market demand, competitor pricing, and customer data and implement dynamic pricing strategies.

Uber leverages the power of dynamic pricing to adjust prices based on factors like demand, time of day, and distance:

When using dynamic pricing, ask the following questions:

• What factors should you consider when implementing dynamic pricing?
• How can you strike a balance between optimizing revenue and maintaining customer loyalty?

5. Implement Visual Search for Product Discovery

Use AI-powered visual search technology to allow customers to find products by uploading images or using the camera on their devices. Take a look at the following example by Nourison Home. It allows customers to easily discover similar styles in three easy steps.

When implementing visual search, ask the following questions:

• How will you integrate visual search into your shopping platform?
• How will you ensure accurate and relevant search results using visual data?

6. Leverage the Power of AI for Inventory Management

AI algorithms can also double up as your trusted inventory manager. They can analyze real-time sales data, market trends, and supply chain information and ensure that your stocks are always up to date.

For instance, Walmart uses AI to optimize inventory levels, reduce stockouts and improve overall efficiency.

The brand built an AI solution that successfully identifies the next-best item for customers when instances of out-of-stock occur.

When using AI for optimizing your inventory, ask the following questions:

• How will you leverage AI to forecast demand accurately?
• How can you optimize your inventory turnover and reduce carrying costs?

7. Enhance the Customer Journey with Virtual Assistants

AI-powered virtual assistants are all the rage and for good reason. One, they provide 24/7 customer support. Two, they are adept at offering personalized recommendations. Three, they can assist with basic tasks such as order tracking.

Take for example Sephora’s virtual assistant, which offers personalized beauty recommendations and helps customers find products within minutes.

When setting up a virtual assistant, ask the following questions:

• How can you provide a seamless experience between virtual assistants and human support?
• How will you ensure accurate and helpful recommendations?

8. Implement AI for Fraud Detection and Prevention

AI algorithms are not only trained to analyze your customer’s needs. They can also be trained to analyze transaction data and identify patterns as well as anomalies for fraud detection and prevention. Established giants such as PayPal employ AI to detect and prevent fraudulent activities in real-time and prevent security mishaps.

When using AI for fraud detection, ask the following questions:

• What data points should you consider for fraud detection?
• How will you minimize false positives while effectively detecting fraud?

9. Enable Voice Commerce with AI

Voice recognition and natural language processing can lay the foundation for a voice-controlled shopping experience. One of the most successful instances of voice shopping is Amazon’s Alexa, which allows users to order products through voice commands.

When using AI for voice commerce, ask the following questions:

• How will you optimize your shopping platform for voice-based interactions?
• How will you provide a seamless voice commerce experience?

Is Conversational Shopping for You?

The role of AI in retail is no longer a theoretical concept. Ecommerce brands are constantly looking to optimize costs without compromising on the customer’s shopping experience. This is where AI is making its mark.

Given the current AI momentum, AI-powered shopping will be table stakes in the next five years. To make the most of this tectonic shift, Ecommerce businesses must adopt AI early on to reap the benefits in the long run.