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Graph Advantage: User Personalization

Personalization through Digitizing Retail Knowledge

Relevant Recommendation Offerings

User Personalization in Real-Time with ONgDB on GraphGridUser personalization is intended to tailor each individual’s experience to them and really provide a more human element to the interaction. Providing this aspect of feeling known and understood rather than just being a generic set of eyes can go a long way towards more fulfilling and continued engagement with your users.

Digital retail is an major space where personalization surfaces because it’s challenging to find the right balance and approach for the interaction. By concentrating on the online retail journey of your customer and making it a personalized experience, you and your customers both benefit from the more personal and meaningful interaction.

Many strategies for personalization involve long-running offline batch processes that take a considerable amount of time to complete in order to consider changes to what the system understands about me as an individual user. This delay in response is a major barrier to a personalized engagement with your user. The amount of data to be considered in total is quite astounding for the large established retail chains. However even with all this data there is still the possibility to provide a real-time user personalization experience for your customers. The information that is relevant for enhancing the online experience of each individual customer is quite small, although complex from a data connectedness perspective.

Customers today have become quite willing to share personal details in exchange for improved shopping offers and experience. By using these details in a meaningful way you’ll be demonstrating an understanding of your customers and showing how you can utilize such knowledge to enhance their experience, they’ll be more likely to continue to lean into the experience by providing even more insights into their preferences.

A lot of online retail websites offer a plethora of navigation trees, but people have been trained to make use of search interfaces. Furthermore, search bars provide the possibility of a customer to show, in detail, what they want, which is a lot more effective compared to a bunch of static product categories. By opting for a schema-free open native graph database (ONgDB) via search and semantic support, it’s now possible to bring your product/services data in your industry knowledge and begin offering a more personalized search experience as part of the interaction.

Now, you have a plenty of information and context of your customers, without having to really know who they are. If they go for your sponsored ads or conducted a search on certain terms, they’re already showing you real-time insights into their interests. The best part of it all is that the same knowledge you’ve digitized can be made to convert such insight into “smart” recommendations.

In turn, this creates a flexible system that’s a lot better than a rules-based strategy and far more agile compared to a big data approach. Through this, you’ll be able to identify your customers of high value and make a correlation of such contextual data against these essential people. ONgDB has proven to be the perfect technology for making such dynamic and personalized recommendations

It’s important for any business to offer a great customer experience with relevant and timely content personalized for each individual. This can be accomplished through the use of ONgDB to create relevant, real-time recommendations and personalization to shoppers during their online retail experience. Much of this is thanks to the native traversal performance and immediately available nature of new relationship connections within the database as the user’s interactions stream into ONgDB.