For Australian retailers to survive, gain lost ground and thrive beyond the pandemic, customer data is key to designing great journeys and experiences. If you get it wrong and fail to delight your customer, you might also risk enraging them. 

Knowing the customer starts by putting yourself in their shoes. What is it about them that you ought to know? The challenge is to have an up-to-the-minute 360-degree view of the customer. 

To know your data is to know your retail business. Knowing your customers is dependent on having great access to data about them. But collecting and analysing this data has often been a complex, costly and time-consuming process. 

That old-school approach relied on moving the data into a central data warehouse or data lake. That meant waiting for the data to be extracted, translated, loaded and integrated. It means you only know about the customer when the data was loaded – things might have changed since then.

It’s one thing to understand the customer from a current or historic relationship perspective. However, when considering a real-time permission-based interaction, what’s equally important is to understand the customer’s current context. What’s happening right now? What is the customer doing and where they are? How are they affected by the pandemic and how might this have altered their spending habits? 

If you’re a fashion retailer, you might need to rethink what items your customers may require, especially if they’re working from home. How is this changing their preferences? 

To produce a 360-degree view of a customer with no latency, you need to be looking at real-time data as well as historic data. This can only be done effectively by accessing data when you need it wherever it is. You can do this through an analytical data virtualisation layer. 

Context is found in textual, geo-spatial, image, video, transaction and other multi-structured data in disparate data stores.

A retailer may have a free Wi-Fi service with customer clickstream data in a web application database. Or video of customers shopping on a video server and store loyalty data in a database. In order to join the dots that form a view of the context, customer systems need to handle multi-structured data from different places in real-time.

Armed with knowledge about the customer’s interactions with you and their current context, you can start to examine their intent. Without sensing the intent in the customer’s mind, it could all go wrong.

Intent is the hardest thing to detect. It needs much deeper algorithmic capabilities. It is derived from all the data you have – historic, real-time and behavioural from every relevant data source across your data centre and the network.

Zetaris uses new machine learning techniques coupled with SQL based event detection and statistical modelling to detect intent in real-time. Importantly, we implement governance and privacy rules to ensure everything we do is within ethical and policy standards.

The myth from the past few decades of data science is that data needs to be centralised in order to analyse and act upon it. But an analytical virtual data lake turns this inside out and avoids costly duplication of data, human effort, and processing. It means retailers can unlock real insights fast and use this knowledge to pivot offerings and marketing approaches. 

Instead of bringing the data to the query, you can take the query to the data. This gives data scientists working in the retail industry the ability to query data without needing it to physically be in the same place. 

There are amazing opportunities the digitally transformed device-driven world presents to Australian retailers who have been hit hard during the pandemic. Knowing what your customers want, need and their preferences will enable you to anticipate what they will demand in the future so you can tailor offers that resonate and succeed with your target customers.

Vinay Samuel is founder and CEO at Zetaris