A recent press release reports, “TigerGraph, the only scalable graph database for the enterprise, today announced that Kickdynamic has chosen TigerGraph’s technology to help its more than 200 global customers in fashion, retail, travel, and other sectors achieve maximum email personalization and relevancy. Studies have shown that businesses able to develop content that’s highly relevant and personalized to each individual recipient will see increases in open and conversion rates. In addition, a Gartner survey showed that 38 percent of customers will stop doing business with a company because of poor marketing personalization efforts. Kickdynamic is tapping into the power of TigerGraph’s native parallel graph database on AWS Cloud for its open time platform that hyper-personalizes email with dynamic, real-time content based on multiple factors, such as demographics, user preferences, product features, location and browsing, search and purchase history.”
The release continues, “As the digital era has given consumers unprecedented choices, all just a click away, simple recommendations such as ‘customers who bought this item also bought’ or ‘these products are often bought together’ are no longer enough. To foster brand loyalty, businesses must gain a more sophisticated understanding of the unique, varied, and complex characteristics of every customer and deliver, on the fly, offers and recommendations that truly speak to them. A standard collaborative filtering algorithm, used by popular sites such as Amazon.com, creates a recommendation based on the buying behavior of other users delving only one or two levels or hops into the data. The first hop is from the customer to the product they are reviewing or have purchased. The second hop is from the product to other customers who have bought that product. The third hop is from these customers to other products they have bought over time that aren’t yet purchased by the customer.”
A standard collaborative filtering algorithm, used by popular sites such as Amazon.com, creates a recommendation based on the buying behavior of other users delving only one or two levels or hops into the data. The first hop is from the customer to the product they are reviewing or have purchased. The second hop is from the product to other customers who have bought that product. The third hop is from these customers to other products they have bought over time that aren’t yet purchased by the customer. Simple next best offer recommendations — such as “Customers who viewed/bought this product, also bought (these other products)” or “Frequently bought together” are losing the competitive advantage and revenue to companies that leverage advanced technologies like Kickdynamic with more personalized and meaningful communications.
By using TigerGraph’s native parallel graph database on AWS Cloud, Kickdynamic is able to go 10 or more hops deeper and provide real-time deep link analytics that opens up vastly more information about a customer’s likes, needs, desires, and intent. This supports their advanced personalization capabilities that can display products that customers have browsed on site and live in email, automatically at open time.
Ted Baker, a luxury clothing retail company, works with Kickdynamic to offer live, automated, and personalized email to their customers. Kickdynamic enables the company to reduce its internal manual email build processes, increasing customer engagement and enhancing the performance of its email marketing by delivering relevant content in real time. Product feeds are used to fully automate the products displayed, including live pricing and CRM data to add personalization layers based on factors such as preference and browse data.
By leveraging the Kickdynamic partnership with TigerGraph, it will become possible to instantly understand the recipients’ profiles and deliver relevant recommendations showing products that are more likely to capture their interest, exploiting past onsite browsing behavior, similarities across products and users, and cross-user behavioral patterns to discover and extract similarities between users, products, or both. All in real time, on-demand using the latest data, the brand will continue to gain insights into their customers’ buying and browse behaviors over time to deliver highly relevant and engaging content.
“Kickdynamic is on the leading edge of marketing personalization and working with them is a vivid illustration of how our industry-leading graph database can improve customer engagement,” said Dr. Yu Xu, CEO of TigerGraph. “Working with London-based Kickdynamic is all the more gratifying because TigerGraph recently opened a new European headquarters in London to help support our rapid global growth.”