Creating The Most Sophisticated Recommendations Using Native Graphs
We all appreciate Amazon’s ‘Customers who bought this like you, also bought this’ and
Netflix’s’ ‘Customers who watched this, also watched this’ recommendations. They are invaluable tools; and they have shown us the value of winning customer business by providing the most personalised product and service recommendations possible.
As a result, even the smallest retailer or services company knows that to survive in our increasingly digital world they need to offer equally personalised product and service recommendations to delight and engage the increasingly demanding global customer.
The problem: ‘Customers who bought that also looked at this’ style recommendations may soon be too basic, and stop being of interest to the consumer. Digital consumers now expect personal recommendations based on their individual preferences, history, interests and social context to be taken as read. That raises the bar considerably – maybe too high for many CIOs to reach.
To survive in this more challenging context and meet and surpass your customers’ expectations, retailers and service providers need to offer something much more advanced.
That has to be more along the lines of, ‘You bought this item or service today as well as this last week and the week before, and in last March; you’ve also looked at all these items and services today as well as last week and the week before, and a year ago – so how about this?’ level of suggestion.
The brand needs to provide savvy consumers with intelligent, highly context-sensitive prompts that are able to understand and remember what a shopper is truly seeking to find. However, the reality is that such hyper-personal recommendations can only be generated with the assistance of technology as a way to embed far more intelligence and data-fed capability into your recommendation engine.
AI (Artificial Intelligence) and data-driven, real-time smart software is what is required to do this. And the key enabling backend engine, which will allow these next generation recommendations is graph database technology.
eBay’s AI-based ShopBot is a prime example of why. The online retail giant used graph database to create the service for US customers – a smart, personal shopping ‘bot’ that converses with users via text, voice or photo search capabilities. The application also understands not just shopper text, pictures and speech but also spelling and grammar intention while parsing conversations for meaning and context.
In practice that means for the customer who requests to purchase a pair of gloves costing less than £25, eBay knows what details to ask about next, such as type, style, brand, budget or size. As it accumulates this information by traversing through the graph database, the application is continuously checking inventory for the best match.
