The Rise of Omnichannel Retail: Revolutionizing the Point-of-Sale Experience with AI

This article is brought to you by Retail Technology Review: The Rise of Omnichannel Retail: Revolutionizing the Point-of-Sale Experience with AI.

By Ryan Edwards, freelance writer.

Omnichannel is one of the most effective retail strategies for engaging customers, driving sales, and building lasting relationships. A way to strengthen this game-changing approach is the revolutionary power of Artificial Intelligence (AI). With the help of AI, retailers are reshaping the point-of-sale (POS) experience by leveraging data that had previously gone unused by businesses.

In what follows, we’ll explore how AI has become the keystone of building point-of-sale systems to create omnichannel retail environments.

Defining the Role of AI in Omnichannel Customer Experiences

Implementing AI in POS completely changes the scale at which retailers can deal with data and personalize customer experiences. Since AI is much more efficient at handling the large datasets that POS systems accumulate, it can automate tasks like product recommendations, inventory optimization, and predictive analytics.

Although AI can significantly help retail operations, even in customer service interactions with chatbots, it still isn’t a full replacement for the empathy that humans can provide. At the same time, it’s a powerful tool that can augment the efforts of retail workers and business leaders to provide far more engaging and personalized services and products to their customers. 

How AI-Powered POS Systems Can Enhance Omnichannel Strategy

Many use cases show how POS systems can play a critical role in omnichannel strategies. Let’s discuss some of them.

Data Analytics Provides Omnichannel Consistency

The data that your POS systems collect can make building consistent experiences for your customers a much simpler task. By keeping track of customer data across your omnichannel landscape, you can provide a far more unified brand experience. The primary vehicles for this are customer profiles. Using that database, AI can reveal valuable insights like customer preferences, purchasing trends, and refining personalization strategies.

According to ExplodingTopics, 60% of customers said they would become repeat customers after a personalized shopping experience. By tailoring experiences so strongly to customers, satisfaction increases. This improves sales performance and strengthens your customer loyalty.

Demand Forecasting to Meet Customer Needs

AI’s data-analysis capabilities are perfect for demand forecasting. Machine learning models can aggregate historical data to find patterns to predict future demand. Data like sales, customer buying patterns, and other information can be translated into an automated pipeline. This pipeline can report demand forecasts automatically in a regular period, like weekly, monthly, or quarterly. 

Even if you don’t have a large database, three months of sales data can be enough to get started forecasting for a new product type. The minimum time to get the model developed will depend on forecasting accuracy standards and your business’s goals. 

The demand forecasting process begins with data collection and analysis. Engaging AI consultants is crucial here, as they will help you find out how to make sense of the data you have and build a solution that can bring real value to your business.

Smart Recommendations to Drive Personalization

Once POS systems have been augmented by AI data analysis, they can adapt to customer preferences and behaviors in real-time. Each customer’s shopping experience can be customized with relevant product recommendations, prices, and promotions. Suggestions can change over time in response to contextual factors like location, time, and purchase history. 

With enough data, smart POS systems can anticipate customer needs and preferences in advance. When customer needs are catered to, brand loyalty grows, and so does the revenue of your business. 

There are three broad approaches to providing smart recommendations:

1. Collaborative filtering: find shoppers visiting your site who have similar interests. If one shopper shows an interest in an item, other similar shoppers may like that item as well.
2. Content-based filtering: identify the qualities of items sold by your business. If a shopper is interested in one item, they may also be interested in other items with similar characteristics. 
3. Hybrid systems: This approach combines collaborative filtering and content-based filtering to leverage the strengths of both and mitigate their weaknesses.

Machine learning and other data analysis techniques can advance these approaches for greater accuracy. However, every use case is different and will depend on your objectives. 

Don’t Let Your Data Go to Waste

The data your POS systems generate may not be useful on its own, but when properly cleaned and analyzed it can be one of your business’s greatest strengths in an omnichannel market. Data analytics, demand forecasting, and smart recommendations are powerful resources that can help you provide better services and experiences for your customers and give you the edge you need to succeed. The process can seem daunting, but with the right experience and talent, making your POS data work for you will pay off in the long run.