4 ways AI connects retailers and customers
Editor's Note: This article originally appeared on Softweb Solutions' Insights blog.
Retailers will have to come up with schemes that can gain attention of their customers to keep them interested in their brands and to retain them for a longer period. To convert a customer into a loyal one isn’t a piece of cake. It requires retailers to engage customers and understand their buying behavior.
The profit of retail stores is mapped by overall sales that they get in a given duration. Often, retailers might gain or lose customers to their competitors in that period of time which results into customer churn.
Retailers struggle to absorb higher costs. Hence, they should start strategizing, planning and implementing technologies that help them with better cost control, optimization of operations, and higher customer satisfaction.
Recommendation system
Technologies like machine learning and AI help retailers to process massive amounts of customer data. And with this data in hand, retailers can thoroughly understand their customers’ buying behavior. They can record, store, and analyze consumer data from a variety of data channels like social media, their own eCommerce website, their mCommerce platform, or their internal CRM systems which can result in actionable recommendations for the customers.
A recommendation engine will help retailers with the following benefits:
- Anticipate demand and drive sales
- Recognize when customers might need products that they have purchased before
- Provide more context-aware choices to customers
- Create cross-selling and up-selling opportunities
- Get acknowledged with items that are not very well-known or popular but have niche appeal
- Get updates on market shift and plan marketing strategies accordingly
Price optimization
The goal of any retailer is to set the right price for their products. However, it is a challenging task to identify the correct price. There are numerous pricing strategies that retailers adopt based on their objectives. A certain brand can seek maximum profitability on each unit or overall market share, while another might want to introduce a new product or gain access to a new market. Retailers, therefore, tend to accept prices suggested by the manufacturers. While this method is widely used, artificial intelligence allows them to develop strategies that help them to better optimize prices.
If retailers want to market their new product(s), clustering algorithms can help them to associate with similar products that are already available in the market and provide them with information on those products for them to obtain a probable price segment. AI also offers another compelling method to predict prices and demands for items that were never sold.
AI helps not only in deciding the right prices but also addresses questions like:
- In what way is the sale of product ‘A’ impacted when prices of product ‘B’ are drastically cut?
- When efforts are made to sell more of product ‘A’, are the related products impacted?
- Are inactive clients in the last year sensitive to a promotion campaign?
By analyzing a large amount of past and current data, AI helps retailers to anticipate trends early enough. This allows them to make appropriate decisions to adjust prices. Moreover, in case of a competitive pricing strategy, AI models allow store owners to gather valuable information about prices of competitors for similar products. They can also get insights on what customers say about products and competitors and find out trending products among their customers.
Personalization
Source: https://www.adobe.com
Artificial intelligence solutions for retailers enable them to analyze customer profiling, conversion rates, content usage trends, and buying activity. This helps them to get insights into customer behavior and allows them to ascertain their customers’ preferences.
With AI solutions, retailers can automatically identify high-value customers who generate majority of revenue. They can further use this information to deliver high-performance loyalty experiences. AI solutions for retailers help them to efficiently and effectively engage lower-value customers and categorize potential consumers who can gradually become high-valued. With these insights, they can push offers that are tailored as per customers’ needs at the right time.
Retailers can better understand the percentage of consumers who like or dislike offers proposed to them and can also determine which customers are happy with their products or offerings. These insights can be further analyzed to help them to create campaigns and help them in targeting customers effectively.
AI-powered chatbots for seamless communication
There are many limitations of human assistance like 24/7 non-availability. Fortunately, there are no such limitations with chatbots. They can cater to any number of customer requests or queries and serve them whenever required.
AI-powered chatbots for retail can help customers find the solutions, comparison reports etc. for various items and enable them to make an informed decision. It can also advise customers with recommendations based on their budget and purchase history. By leveraging chatbots integrated with recommendations based on their budget and purchase history. By leveraging chatbots integrated with AI, retailers can anticipate customers’ preferences and offer a more personalized retail experience which empowers them to keep their customers engaged with the brand and enhance customer service.
Chatbots provide the following benefits to retailers:
- Help users to navigate to stores, restaurants, and other services
- Answer customer queries on deals
- Notify users of any events happening nearby the store
- Provide custom recommendations
Digital transformation of the retail ecosystem
AI solutions empower retailers by offering data-driven personalization, customer service, and many more functionalities. Retailers can achieve greater business automation, customer engagement and make informed business decisions by utilizing customers’ data efficiently and applying AI techniques on that data.