Real-Time Omnichannel Insights and Dynamic Pricing on Hadoop

A leading national retailer engaged Taller to build the Hadoop data platform and business-intelligence layer behind its omnichannel analytics and real-time pricing.

Dynamic pricing at this scale was not a single algorithm. It was an operational capability that only worked when the data, the analytics, and the pricing-decision tools moved together at the speed the retail business ran. This client had the data, but what it lacked was the connective layer that would let pricing decisions respond to real-time consumer and sales signals across all its channels.

Taller’s engineers designed the on-premises Hortonworks Hadoop cluster (a system for storing and processing very large volumes of data), built the Tableau visualizations on top of it, and developed custom business-intelligence components that powered both omnichannel analytics and the real-time pricing surface. Dynamic pricing at this scale was bound by three constraints, and the platform had to hold all three at once. The first was data freshness: pricing against stale data eroded margin quickly, so the streaming layer had to keep the price-moving variables current to the second. The second was the need for rules and models to coexist, because pure machine-learning pricing was rarely deployable in retail while category buyers kept strategic control, so the system had to host both algorithmic recommendations and human-overridable rules. The third was an audit trail, since every price change had to be traceable back to the inputs that drove it. Hadoop, Java, Scala, Python, and Tableau together formed the foundation that let the client hold all three in one operational picture.