DXi: A Winning Approach to Optimizing Online Conversion

Today, digital marketers and UX professionals alike are focused on running tests that will increase the conversion rates for online sales. However, their current methods of either depending on their intuition or post-mortem analytics data to create better hypotheses is no more proving to be the best bet to achieve the same. Additionally, the elements involved in the new age, such as heavy and dynamic e-commerce websites as well as complex behavior of visitors make it challenging for marketers to run any analysis. The massive amount of data is just too difficult to segment and analyze and find out why a specific customer did or did not buy a product. Searching for a solution is also a cumbersome task for the stakeholders involved—not because of the dearth of solutions but due to the availability of numerous tools that promise to achieve differing goals. This is where DXi steps in with their unique proposition. The system of “Scores.”

The thinking was simple; if each of these aspects is translated into a score, it can help measure and improve a business’ growth. “Everyone is trying to solve the same issue, but what makes us unique is our ability to achieve the same goal, but with a mathematical simulation of the subjective problem,” smiles Dr. Srinivas Kilambi, founder of DXi.

DXi or Digital eXperience Index is the concept of quantifying a subjective behavior and arriving at a score of 0 to 100, rating the possibility of a visitor buying a product or a service through the website. DXi enables this ability through predictive analytics, powered by machine learning (ML) and the company’s proprietary advanced algorithms.

Through its solution, the company handles the data of websites and the visitor behaviors by directly taking it from different sources including Google Analytics, Adobe Analytics, or structured data shared by its client and stores them in a CSE file. DXi then removes the outliers on cleansing and normalizing the data. This ready-to-use cleansed data is then processed through a series of 25 ML algorithms, reducing the metrics to a definite number with the assessment criteria that matters.