5 Ways to Leverage Big Data for AI

In today's digital age, e-commerce organizations are accumulating massive volumes of data on a daily basis. This includes everything from customer behavior and sales data, to inventory information. This stream of information is known as ‘big data’, and it has become a critical asset for organizations across all industries. However, managing, analyzing, and utilizing this info can be an incredibly daunting task.

That's where artificial intelligence (AI) comes in. 

AI algorithms can be trained to process and analyze big data, which can then be used to uncover hidden patterns and insights normally impossible for humans to identify. And this technology is in high demand. In fact, according to a study by Precedence Research, the global retail AI market is projected to balloon in the coming years. In fact, it's estimated it will reach nearly $46 billion by 2032, from just $8.41 billion in 2022. 

However, 54% of retail executives say that their companies lack the expertise needed to implement AI solutions. In this blog, we explore five ways retailers can take the first steps to leveraging Big Data for AI.

1. Fraud Detection

Big data can be utilized to identify patterns of fraudulent behavior. This can enable e-commerce retailers to better protect themselves and their customers. When implemented correctly, AI systems can analyze unstructured data to identify suspicious activity. Examples of suspicious activity could include: unusual order patterns or payments, strange transaction amounts or oddities in the frequency of transactions. 

AI technologies can identify unusual transactions by using techniques such as anomaly detection, network investigation, and even real-time surveillance. For example, when the same credit card has been used to make purchases by multiple people, the AI should point out that this is suspicious behavior.

2. Customer Service

Artificial Intelligence can be used to automate customer service and support. This in turn reduces the workload on your customer service team while also improving response times. In fact, 21.5% of retail companies currently use AI for customer relationship management. When implemented correctly, AI algorithms can: provide personalized support to shoppers with questions, improve customer satisfaction, and increase brand loyalty. 

Some of the benefits of implementing AI algorithms include:

• Providing personalized support to shoppers with questions
• Improving consumer satisfaction with their shopping experience
• Increasing customer loyalty to your brand

Chatbots and Voice Assistants

AI-powered chatbots and voice assistants can be used to provide personalized customer support and assistance. Both technologies use natural language processing to understand customer inquiries. Voice assistants also now utilize speech pattern recognition learning models. These softwares can provide personalized responses based on customer behavior and preferences, tailored to the individual.

By analyzing customer interactions, AI algorithms can identify common questions,  uncover issues, and provide automated responses to shoppers with specific questions. Despite this, AI in customer service is one of the areas that has the most room for growth. Currently, 43% of shoppers believe that there’s room for improvement when it comes to chatbots.

360-Degree Voice of the Customer Programs

A Voice of the Customer (VoC) program is a structured data approach used by retailers to collect and analyze customer feedback. The purpose of these programs? To gain insights into customer needs, preferences, and experiences.

VoC programs can have value at every phase of a shopper’s journey. The best VoC programs can even lead to more efficient supply chains, better product quality, more intuitive experience navigation, and faster customer support. To make these outcomes possible, your VoC needs to collect relevant data, then synthesize that data into a framework of insights. This framework then notifies your team of customer experience issues, while also identifying opportunities to make it right. 

Related: How to Manage and Meet Customer Expectations

3. Predictive Analytics 

Big data can be used for AI development, that is, to train AI to make predictions about future behavior. For example, AI algorithms can be used to predict customer demand for certain products, allowing e-commerce companies to optimize their inventory and pricing strategies.

Predictive analytics have the potential to revolutionize retail businesses. But only a small portion of midsize companies are actively preparing to utilize it. While the applications are virtually limitless, some of the most straightforward ways to implement predictive analytics include:

Sales and Inventory Management

By analyzing sales and inventory data, AI algorithms can make predictions about demand for your products. This can enable you to optimize inventory levels and pricing strategies to maximize your sales while minimizing dead stock. 

Forecasting

AI can be used to analyze historical data to make predictions about future trends and patterns. This can help retailers plan for seasonal fluctuations, identify new opportunities, and stay ahead of the competition. In fact, RetailBrew states that using AI-powered forecasts in supply-chain management can reduce errors by up to 50%. This can lead to a reduction in lost sales and product unavailability of up to 65%.

Marketing Optimization

AI algorithms can analyze customer data to make predictions about which marketing campaigns will be most effective for each customer. By integrating AI with your CDP or DXP, you can efficiently orchestrate your omni-channel experience. The benefits? Optimizing your marketing spend, identifying which channels to invest your budget in, and ultimately increasing your ROI!

4. Personalization

Personalization software (like Granify!) uses AI algorithms to analyze customer behavior and preferences. This enables organizations to deliver 1:1 experiences at scale for their shoppers. Personalization also happens to be one of the primary ways that retailers are looking at implementing AI. As well, as third-party cookies are set to become obsolete, a privacy-oriented approach to customizing experiences will be critical. There are a variety of ways that personalization can be incorporated into each shopper’s journey:

Dynamic Pricing

AI can be used to optimize pricing strategies based on customer behavior and demand. AI algorithms can analyze customer data in real-time to identify the price points that are most likely to resonate with each individual customer. This allows prices to be adjusted to maximize sales, as well as maximizing the value provided to the shopper.

Personalized Messaging

AI is a great tool to personalize messaging and communication with customers. For example, Machine Learning technology (like the Granify Brain) can analyze a shoppers digital body language. The AI then identifies the optimal time to send tailored messages, when they'll have the highest likelihood of resonating with the customer.

AI also can personalize the content of these messages based on individual customer behavior. In fact, over a third of retail marketers are already leveraging AI to customize their communications and promotions.

Product Recommendations

Real-time AI can be used to analyze big data to identify product and shopper trends, as well as improve decision-making. This knowledge can then be used by e-commerce companies to make data-driven recommendations for their shoppers.

Product recommendations can be based on a variety of factors, such as:

• Past product views 
• Past product purchases
• Browsing history
• Search queries

These recommendations can help customers find products they may not have found otherwise, in turn leading to increased sales. In fact, a recent study by CDP.com showed that 81% of people were in favor of companies using AI to personalize recommendations.

Related: The Role of AI in E-Commerce Personalization

5. Process Optimization

Finally, big data can be used to optimize processes within an organization. AI models can identify areas where production or supply chain management processes can be improved. This can result in significant cost savings and increased efficiency for organization’s of any size.

For example, a manufacturing company can use AI algorithms to analyze production data to help identify areas where they can optimize their production workflows. As reported by McKinsey, 66% of businesses have experimented with using automation for at least one of their processes. 

Predictive Maintenance

By analyzing data from sensors and other sources, AI algorithms can predict when equipment is likely to fail. Organizations can leverage this information to schedule their maintenance proactively, avoiding costly downtime and repairs.

Data Entry

Data entry, while essential for many business processes, is a time consuming and error-prone task. Despite it’s monotony, it’s often critically important that data entry work is executed correctly.

90% of employees feel burdened with tasks that could be easily automated. Artificial Intelligence is the perfect tool to alleviate that strain. A well implemented AI program can easily analyze large amounts of data and extract only the most relevant information.

For example, an AI algorithm can be trained to recognize and extract information from invoices. This would completely eliminate the need for the invoicing team to do any manual data entry.

Related: How To Make Instant, Data-Driven Business Decisions

Moving Forward Intelligently

To recap, e-commerce companies can leverage big data for AI to:

1. Improve their shopper experience
2. Optimize their operations
3. Increase sales

Leveraging the Power of AI with Granify

Granify’s machine learning technology utilizes real-time data to boost conversions as well as engage and retain shoppers by presenting the right message at the right time. Request a consultation with our experts to uncover how your organization can leverage the full power of AI.