Solutions and Services

Our primary objective is to leverage data within business process tools, rather than solely interpreting data derived from various software solutions platforms.

Operational analytics uses data and statistical analysis to enhance an organization’s operations and decision making. It involves gathering and examining data from diverse sources, such as customer interactions, business processes, and machine-generated data. The objective is to provide real-time insights for informed decision-making, optimizing processes, cutting costs, improving customer happiness & satisfaction, and boosting efficiency.

In OptimUz we developed the levels for each segment in the operational analytics as follows

Geo-Spatial Modelling: Spatial Modelling allows for in-depth analysis of the POS (Point of sale) Profit and loss statement by tying the revenue compared to the cost on the POS level using Data Science algorithms.

Customer Journey: Understanding the path and trajectory each respondent makes while visiting any business digital property from landing the page to checking out. The analysis includes the optimal route for purchase and revenue uplift using an optimal path.

Customer Segmentation: Different customers proclaim different behaviour while engaging with eCommerce or any digital asset. Segmenting customers on their behaviour has many opportunities for product selling, cross-selling and promotional activities.

Supply Chain: From the manufacturer to the customer’s doors, analysing the milestones and correlating different activities with the sales performance and cost minimization using the most advanced statistical algorithms.

Machine Learning Models: Understanding what website controls and features are affecting call-to-action activities by correlating different web events with a call to action on a session-by-session basis and segregating the models by visitors’ metadata to understand further the impact of socio-demographics on customers’ final purchase decisions.

Data Mining: Deep diving into the website hit level events that customers produce is imperative. Understanding sneak peek that customers declare during weekends, week-days, unlocking seasonal influences on product purchases using data pivots and data drill-through.

A/B Testing: Refers to a randomized experimentation process wherein two or more versions of web pages are shown to different segments of website visitors at the same time to determine which version leaves the maximum impact and drives business performance metrics.

Pattern Recognition: Some product variants follow certain sales trends or behaviour in a fiscal year and some others follow a declining sales trend, understanding and classifying product items into different yearly selling trends is essential to monitor stock activities and sales optimization using the most sophisticated cluster analysis tools.