Friday, June 24, 2022

Driving retail success with graph data platform

Whether it’s building a product or promotions recommendation engine, personalising customer experiences, or redesigning supply chain to meet ever-changing customer demands, retailers are facing challenges that require the ability to leverage connected data in real-time.

By Neo4j ANZ General Manager Peter Philipp.

Neo4j ANZ General Manager Peter Philipp.

Traditional relational database systems that keep information in columns and rows no longer suffice. Graph data platform stores data as linked nodes that encompasses relational information – a much more powerful and flexible solution for storing and analysing complex datasets, building a 360-degree view of the customer. They are ideal for retailers for creating a personalised, seamless customer experience, driving loyalty and revenue.

Delivering personalised product and promotion recommendations

Delivering real-time recommendations to online shoppers is a proven way to improve customer experience and increase sales. To be effective, recommendations must be personalised based on the consumer’s preferences, shopping history, interests and needs – in addition to what’s already in their current shopping cart.

Real-time recommendations require data products that connect masses of complex buyers and product data to gain insight into customer needs and product trends. Graph data platforms quickly query customers’ past purchases and instantly capture any new interests shown in their current online visit. Because relationships are treated as first-class entities in a graph database, retailers can connect customers’ browsing history with their purchasing history, including their offline product and brand interactions – enabling a real-time recommendation algorithm to utilise a customer’s past and present choices to offer personalised recommendations.

Furthermore, to counter dynamic pricing from giants like Amazon, retailers need the ability to change pricing and promotions at any level of a product hierarchy in real-time. Similarly, retailers must be able to implement competing promotions. Real-time promotions involve complex rules that are easily managed with a graph data platform. Walmart and eBay found graph data platform performs thousands of times faster than a traditional relational database.

Providing personalised experiences

Retailers can personalise online customer experiences by providing relevant content based on a customer’s desires, interests, and needs. This improves customer engagement and leads to increased revenue and customer loyalty. For example, relevant blog posts placed besides product descriptions can position retailers as trusted experts. Customers will increase their visits and purchases because they know they can get valuable information from a reliable source.

Path analytics also help improve outcomes. It analyses customer behaviour leading up to purchase and uses that data to guide customers along a more profitable path. This may entail adjusting content or changing where a link takes future customers.

Retailers have plenty of data to use to determine the best paths and content to serve customers. These data often reside in information silos, making it difficult to consolidate and identify opportunities to deliver customers the most relevant content.

Graph databases enable retailers to keep data where it is and adding a graph analysis overlay provides a view of the bigger picture of the customer relationship to quickly navigate back into the original systems anytime a customer interacts with the company.

Gaining supply chain visibility

Products are often composed of different parts that move through different vendors, and each of those elements may be composed of subparts that may still come from other vendors worldwide. Because of this complexity, retailers tend to know only their direct suppliers. This can be a problem when it comes to risk and compliance.

Retailers need transparency across the entire supply chain to detect fraud, contamination, high-risk sites, and unknown product sources. This requires managing and searching large volumes of data without delay or other performance issues. Transparency is also important for identifying weak points in the supply chain or other single points of failure.

A graph data platform enables retailers and manufacturers to manage and search large volumes of data with no performance issues and achieve the supply chain visibility they need.

Retailers face numerous challenges, from delivering real-time product recommendations to dynamic pricing and optimised delivery routing. Retailers must overcome these challenges quickly to remain viable and gain their competitive advantage. Retailers must also achieve greater agility to respond to changing consumer and technology trends ahead of their competitors.

About Peter Philipp

Peter brings more than 20 years of technology and leadership experience, working with business and technology leaders on data-driven projects, from government agencies in Europe and Asia-Pacific to several of the world’s top 10 financial institutions.

Prior to joining Neo4j, Peter held regional leadership positions with SAP, HP Autonomy and most recently as GM of Europe for Attivio, a Gartner Magic Quadrant leader for Insight Engines.

About Neo4j

Neo4j is the world’s leading graph data platform. They help organisations – including Comcast, ICIJ, NASA, UBS, and Volvo Cars – capture the rich context of the real world that exists in their data to solve challenges of any size and scale. Neo4j’s customers transform their industries by curbing financial fraud and cybercrime, optimising global networks, accelerating breakthrough research, and providing better recommendations. Neo4j delivers real-time transaction processing, advanced AI/ML, intuitive data visualisation, and more.

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