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Inflation is causing an uncertainty with consumer spending this holiday season, and retail businesses are struggling to extract value and insights from their data to increase sales and enhance the shopper experience. With 83 percent of customers saying personalization would make them more loyal to brands, this is a crucial factor for indie retailers to utilize to ensure positive holiday sale outcomes. 

“Retail is all about the customer experience and customers are all about personalization. Retail was already a demand-driven industry, but now it is on steroids,” Deena Lawrence, Director of Retail and CPG Industry Solutions at TigerGraph, said. “Consumers have the power to shop anywhere, anytime, and on any device. In some sub-sectors, the products are often the same, so retail must figure out how to differentiate and compete.” 

Graph Analytics Can Help Increase Personalization

Product recommendations are a critical part of retail that often gets overlooked with ecommerce shopping since there are no sales associates able to make this interaction in person. Graph analytics is equipping brands with contextuality and relevant product and personalization recommendations to help enhance the customer experience. 


What is Graph Analytics?

Graph analytics refers specifically to the process of analyzing data in a graph format using data points as nodes and relationships as edges. According to Lawrence, a graph database is fundamentally different from a traditional relational database in how it stores data. Rather than rows, columns, and tables, graph databases are natively built as a network of relationships. 

For example, graph databases are widely used by social media platforms. The data points are people, and the relationships are ‘friends’ or ‘colleagues.’ Using this simple model, Facebook and LinkedIn build very accurate representations of social networks and analyze the data to promote further links, benefits, and personalization. 

“Omni-channel marketing, personalization, and loyalty are ideal use cases for TigerGraph because they are relationship based. 

  • How is the customer related to the product? 
  • Why are customers churning from memberships, subscriptions, and loyalty programs? 
  • What changes in the customer journey would lead to more effective conversion? 

While not all questions are graph questions, the benefits are immense, and it is important to point out that graph technology shows context in data relationships,” Lawrence said. 

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Give Shoppers What They Want: Personalization

Customers are unique, varied, and complex, which can be difficult for businesses to understand, especially at scale. With graph analytics, retailers can convert that weakness into a strength and deliver experiences that are relevant and meaningful. 

“Personalized recommendations boost sales and help retailers build a deeper relationship with their customers by giving them a sense of being understood through engagement, products, and services,” Lawrence said. “Consumer behavior changes fast, and retailers need to be able to understand and respond in real-time to the demand signals that consumers are driving, and it is hard.”

Retailers who can connect with shoppers on this level are connecting at a human and practical level, and that is what consumers want. They want to be seen, understood, and offered something that they need or want and to be served in a way that works for them. 

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Creating Personalized Product Recommendations Both Online & In-Store

While there is still a lot of focus on online and digital channels, Lawrence says retail is a multi-channel operation and the majority of sales are done in stores. One of the most essential exercises for retail is to make sure that the experiences are seamless, irrespective of channel. Graph analytics helps to connect customer data across these different channels. 

With a deeper understanding of shoppers, retailers can choose how to serve them, both in-store and through digital channels. Whether multiple and flexible fulfillment options, or perhaps a journey that started online but turned into an in store, personalized event, or experience based on knowing the customer, the possibilities are endless. 

“The reality is that retail is a demand-driven industry, and consumers are the drivers, so for the foreseeable future, retail must find innovative ways to personalize and differentiate while meeting business goals. Graph databases can perform deep analysis and deliver results in milliseconds,” Lawrence said.

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