Retailers in the ASEAN region have undergone immense change in the past few years. Nearly overnight, everything is digital, online, multichannel, and omnichannel, as well as the rise of super apps. With ecommerce being a global phenomenon, national and regional retailers have to compete with major global players for market share.
In this fiercely competitive world, it is all about data and delivering an exceptional customer experience, from product recommendations engines and personalisation to redesigning supply chains to meet the ever-evolving customer demands.
The problem is that traditional relational database systems, storing information in columns and rows, and sheets, are no longer up to the task. Instead, retailers are looking to a new technology: graph data science.
Graph data platforms work in a very different way. Instead of a two-dimensional spreadsheet-style database, they store data in knowledge graphs of linked nodes that also include relational information. It is a much more powerful and flexible solution for storing and analysing complex datasets, to build a 360-degree view of the customer.
Here are five ways they can help:
1. Delivering personalised product and promotion recommendations
Retail giants like Amazon have led the charge with delivering personalised recommendations to customers in real-time. It is a proven way to enhance customer experience and increase sales. Retailers can flash up relevant suggestions and encourage shoppers to add last-minute extras to an online shopping cart – just like the “impulse buy” section by a bricks-and-mortar supermarket checkout.
There are benefits on both sides: relevant and potentially discounted products for the consumer, and a way for vendors to promote higher margin items or overstocks. But to be effective, recommendations must be highly individualised by instantly correlating product, customer, inventory, supplier, logistics, and even social sentiment data, as well as instantly capturing any new interests shown in the customer’s current visit.
Graph data platforms quickly query customers’ past purchases and capture their previous and current interests shown in their current online visit. Retailers can connect customers’ browsing history with their purchase history – enabling recommendation algorithms to generate personalised recommendations.
2. Dynamic pricing
Another tactic used by global retailers is dynamic pricing. Instead of fixed product prices, the prices are variable, reflecting market demand and different customer groups, as well as time-based promotions and internal campaigns. Airlines, for example, routinely adjust prices based on the number of seats sold and the amount of time before the flight departs.
To dynamically change pricing in real-time, and implement competing promotions with other retailers, a lot of real-time data analysis is needed. Real-time promotions involve complex rules that are easily managed with a graph data platform. Major retailers such as Walmart and eBay are able to generate insights thousands of times faster with graph data platforms than by using a traditional relational database.
3. Providing personalised experiences
Research shows that personalised experiences are extremely important in retail, improving customer engagement and driving increased revenue and customer loyalty. Accenture data shows that 91 percent of consumers are more likely to shop with brands that provide them with relevant offers and recommendations. In an Epsilon survey, 80 percent of respondents indicated that they were more likely to do business with a company offering personalised experiences.
Retailers can personalise online customer experiences by providing relevant content based on a customer’s desires, interests, and needs. Graph data platforms are powerful enough to do this in real-time.
4. Path analytics
Path analytics also enhances outcomes. It analyses customer behaviour along their buying journey and uses that data to guide customers along a more profitable path. This might involve changing where a link takes future customers or adjusting content.
Retailers have large amounts of data to use to determine the best paths and content to serve customers. But this data is often siloed in different repositories, making it very hard to analyse and identify opportunities to optimise the path to purchase. But with graph data science, data can remain where it is, with a graph analysis overlay to get a big-picture view of the customer relationship.
5. Gaining supply chain visibility
Many products involve multiple parts and multiple vendors, coming from all over the world. Due to this complexity, retailers are often only aware of their direct suppliers. This can be problematic 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. Transparency is vital for identifying weak points in the supply chain. Graph data platforms can pinpoint performance issues and enable supply chain optimisation.
Ultimately, graph data platforms empower retailers to achieve their targets and ultimately drive competitive edge and overall success for their businesses.
By Nik Vora, Vice President, Neo4j Asia-Pacific
This article was first published by Retail in Asia