Data-Driven Merchandising in eCommerce: How to Uplift Customer Experience and Drive Sales Using AI

What is E-commerce Merchandising?

You surely would've noticed how physical stores are designed, merchandised, and positioned their best products or items to attract the shoppers' attention; E-commerce merchandising does that – but virtually. 

In other words, AI in the retail industry is a strategy for displaying and selling customers the best products or services.

Such an approach often involved promotion, improving search results, and focusing on overall needs and customer satisfaction. 

Ultimately, it is all about offering online users a spectacular and smooth shopping experience. This would help customers find what they want and can't resist hitting the 'Buy Now' button.

 

Why Ecommerce Merchandising Matters?

Ecommerce merchandising is important because it helps you control how your product is perceived while driving brand awareness. 

It also helps to make shopping more convenient and enjoyable and offers personalized suggestions; it's like you're helping customers navigate through a physical store, showing them exactly what they might like. 

This way, shoppers locate exactly what they are searching for, feel confident about their choices, and are encouraged to purchase more. 

In short, strategic e-commerce merchandising is a key part of a comprehensive sales strategy. It helps turn casual visitors into happy buyers, boosting sales and customer satisfaction.

 

The Role of AI in eCommerce Merchandising

Big data and analytics are becoming essential development strategies, and more significantly, artificial intelligence (AI) plays a critical role in transforming eCommerce. 

A key application of AI is redesigning the concept of 'web stores', helping improve recommendations for appropriate products, and changing how products are sold online.

Artificial intelligence systems handle large data sets, filtering, processing, and applying different equations to provide useful findings. 

This is true because artificial intelligence (AI) is being utilized in eCommerce merchandising to assist companies in developing products that will enhance their consumers' shopping experiences by gathering information about their preferences, buying habits, and degree of interaction.

In online commerce, artificial intelligence merchandising enables the creation of specific customer experiences, product assortments, and decisions. 

Artificial intelligence (AI) has applications in several retailing fields, including recommenders, dynamic repricing, stock control, picture search, and conversational commerce.

For example, it can use bots to promote products, dynamically set prices, determine how much stock is needed, and provide round-the-clock customer support.  

AI is a useful tool for supporting merchandising within any modern eCommerce strategy. It enhances the online shopping experience while also boosting sales and the effectiveness and efficiency of the eCommerce company.

 

Key AI-Driven Merchandising in Ecommerce Techniques

Following that, we have shared some key AI-driven eCommerce merchandising techniques. Let's learn:

1. Personalized Product Recommendations

“According to a McKinsey report, companies can generate as much as 40% more revenue from personalization.”

AI manages customer relationships by predicting which items will interest each customer. This is the history of product searches, purchases, and surfing on the internet.  

With personalized product recommendations, people find it easy to get what they want or need when goods and services are tailored to their individual needs. It helps with time and effort savings in addition to the other advantages.

In conclusion, the application of AI in customer communication has raised conversion rates and accomplished the aims of improving customer happiness and enjoyment.

 

2. Dynamic Pricing

Dynamic pricing helps you stay ahead of the competition. Using AI in merchandise, you can monitor and track competitors in real time and adjust your product prices accordingly.

This ensures prices remain competitive, helping maximize sales and profits. This strategy allows you to cut costs, make decisions more rapidly, and stay profitable. 

For example, during peak shopping seasons, AI might offer special discounts to attract more customers or raise prices when demand is high.

 

3. Inventory Management

Artificial intelligence (AI) assists merchants in effectively managing their inventory by identifying slow-moving commodities and forecasting which products are likely to sell out.

This reduces the frustration of out-of-stock merchandise and reduces extra inventory, ensuring customers always find what they're looking for.

 

4. Assortment Planning

Choosing the right combination of products to satisfy consumer preferences and increase sales is known as assortment planning. 

AI assists merchants in selecting which products to highlight by analyzing customer preferences, trends, and purchase history. 

For instance, an online retailer might use AI to determine that its customers are more interested in eco-friendly products, which would provide more sustainable options.

Artificial Intelligence (AI) finds the optimal set of products to appeal to various client demographics, so the most profitable and popular items are constantly available. It aids merchants in keeping a well-rounded assortment that satisfies consumer needs and boosts revenue. 

Implementing AI for Merchandising: A Step-by-Step Guide

Let’s now understand step-by-step how you can implement AI in merchandising in detail:

1. Data Collection

The first step is to collect data from various sources. These include website analytics, browsing behavior, search queries, customer purchase history, and product details. 

This extensive data sets the stage for useful AI-driven insights by understanding consumer preferences and purchasing patterns in-depth.

After that, this raw data is cleaned up and arranged to ensure accurate information, which enables AI models to evaluate it efficiently and produce reliable, useful insights. 

Example	Details
An eCommerce fashion store	An eCommerce fashion store might collect data on customers' favorite clothing categories, sizes, colors, and purchase frequencies to tailor product recommendations. This refined data enables the AI system to create personalized product recommendations, enhancing the shopping experience and boosting sales.

2. Choosing the Right AI Tools and Platforms

The next stage is to choose the relevant AI platforms and technologies. Implementing effective merchandising strategies is crucial.

Examine AI options that meet your business's needs. This includes ready-to-use solutions from Google Cloud AI, IBM Watson, and Amazon Web Services (AWS) AI for demand forecasting, dynamic pricing, and product suggestions.

You'll also need to decide between creating a custom AI solution, which offers more adaptability but requires more resources, and using pre-built solutions, which are more straightforward but might not allow for as much modification.

Example	Details
An online bookstore	An online bookstore might opt for a pre-built tool like AWS Personalize to efficiently set up personalized product recommendations, enhancing the customer experience without the complexity of developing a custom solution.

3. Training and Deploying AI Models

Training AI models teach the system to recognize patterns in your data and produce accurate predictions.

First, you must feed the AI model with refined and organized data to learn from the patterns, trends, and client preferences revealed in the data. Due to the volume and intricacy of the data, this process may be lengthy.

To put the model into practice after training, you incorporate it into your eCommerce platform or website. The AI model can start working in real-time as users interact with your site; it can then analyze user activity and provide insights or recommendations. 

Example	Details
A beauty product retailer	A beauty product retailer might train an AI model to recognize which skincare products are popular among different age groups. The system would then be able to recommend targeted products to customers as they browse the website.

4. Continuous Monitoring and Optimization

For AI models to remain useful and adjust to shifting user preferences, they require continuous observation and modification. 

Important metrics like click-through rates, conversion rates, and revenues from AI-driven recommendations should often be monitored to determine how effectively the model works. This will help identify potential improvement areas.

Ensure the AI model is kept up-to-date and accurate by utilizing the information gleaned from this monitoring to make the required modifications.

Update the model whenever new products are released or customer preferences change to ensure it continues to provide informative and useful recommendations.  

Example	Details
A retailer	A retailer might notice that customers need to engage with certain product recommendations. By analyzing the data, they can adjust the AI model to improve the relevance of recommendations, leading to higher sales and customer satisfaction.

Real-World Examples of AI-Driven Merchandising in eCommerce

Here, we've thoroughly discussed the application of data-driven merchandising in eCommerce with real-life eCommerce examples:

1. Amazon

Amazon utilizes an advanced recommendation system that analyzes user data, such as browsing history, purchase history, and product reviews, to produce tailored product recommendations. 

This tailored approach boosts revenue, customer satisfaction, and repeat business by allowing customers to select products that match their interests.

 

2. Netflix

Netflix uses artificial intelligence (AI) algorithms to monitor user interactions and viewing habits to provide tailored suggestions for films and television shows. 

By creating personalized watch recommendations based on genre preferences and past viewing behavior, Netflix increases user satisfaction and retention while efficiently marketing its extensive material library.

 

3. Walmart

Walmart uses artificial intelligence (AI) to streamline its inventory control and online purchasing. It uses machine learning (ML) to forecast product demand, ensuring that best-selling items are consistently available. 

In addition to analyzing consumer behavior to improve product placement, Apptunix makes personalized online recommendations to enhance the shopping experience and increase sales.

The Future of Data-Driven Fundamental Merchandising with Artificial Intelligence

The landscape of data-driven merchandising in eCommerce is evolving rapidly, driven by advancements in AI and technology. Some of the key emerging trends include:

1. Enhanced Personalization

As artificial intelligence advances, it will be feasible to give offers and recommendations tailored to a user's conduct and context in addition to their overall preferences. We call this hyper-personalization. AI can, for example, analyze real-time data, such as the weather, and make suitable recommendations (e.g., marketing raincoats when it's raining).

2. Voice and Visual Search

As voice-activated devices and visual search capabilities grow, consumers will use voice commands and images more frequently to locate products. Companies must modify their products to accommodate these new search strategies, requiring AI to analyze images and understand natural language correctly.

3. Predictive Analytics

Another important benefit of using AI is the capacity to adjust merchandising in response to customer activity, forecast customer behavior, and design merchandising plans using AI identification or applications. Using previous sales data, for example, retailers may predict which products will be in high demand during particular seasons and place early orders.

4. Sustainability Analytics

With customers growing more concerned about the environment, data-driven merchandising in eCommerce integrated with AI can assist shops in determining how sustainable their items are. Companies can more successfully promote environmentally responsible solutions by examining supply chains, packaging, and product life cycles.

Best Practices for eCommerce AI Data-Driven Merchandising Success

To thrive in the future of data-driven merchandising in eCommerce with AI, businesses should adopt the following best practices:

1. Invest in Quality Data

Collect relevant, comprehensive, and up-to-date data. Quality data improves AI models, making them more effective in producing better insight.

2. Embrace Continuous Learning

Learn how modern advances in AI are applied and adopted throughout eCommerce platforms. Promote education throughout the firm to keep up with the new developments in technology and innovation.

3. Focus on Customer Experience

Shift the focus to customer orientation, especially in merchandising. For retail, employ AI to tailor shopping for individual clients. However,  beware of creating unnecessary layers between customers and your company.

4. Leverage Cross-Functional Teams

Encourage cooperation between data scientists, marketers, and merchandising teams to develop detailed AI solutions. Moreover, AI will facilitate collaborative work to align with your business objectives & goals. 

5. Monitor and Adapt

AI merchandising strategies should be evaluated periodically to increase their effectiveness. As markets evolve, be open to changing and adjusting a model in response to analytics and customer feedback to remain viable enterprises.

Partner With Apptunix to Incorporate Data-Driven Merchandising in eCommerce

Choosing Apptunix, a leading mobile app development company, as your partner ensures you leverage cutting-edge data-driven merchandising in eCommerce. This will help to enhance your eCommerce business strategy. 

With extensive experience delivering innovative solutions, Apptunix helps businesses implement intelligent merchandising techniques. These include personalized recommendations, dynamic pricing, and advanced search functionalities, which drive more sales and improve customer satisfaction.

Our team of experts tailors data-driven strategies that fit your brand's unique needs and requirements. By partnering with Apptunix, you'll gain access to the latest AI-driven technologies. This will transform your online store into an intelligent, customer-centric platform that maximizes revenue and elevates the shopping experience.