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Dreams sells over 13,000 mattresses, beds and headboards per week nationwide and attracts 24.7 million visitors to its website and needed to eliminate its data siloes in order to optimise in-store and online customer experiences. It will use Exasol for analytics applications that involve large volumes of granular data, such as ecommerce customer journey analytics to segment consumers and build a better understanding of their behaviour.
Petra Kasperova, Insights and Analytics Director at Dreams, said, “We chose the Exasol analytics database because its functionality, versatility and scalability allow us to do much more with our data, integrating multiple sources and running more complex queries much faster. This will help us make more timely data-driven decisions across the business.”
Dreams’ existing business intelligence (BI) layer includes market-leading tools Alteryx and Tableau, but they needed a secure, low maintenance and scalable database that could integrate easily with its ERP system. Utilising Exasol’s Virtual Schema technology has enabled ERP data to be made available to Exasol via SQL without the need to create complex ETL/ELT programs, enabling Dreams’ IT team to spend their time more productively.
Mathias Golombek, CTO, Exasol comments, “The dramatic shift towards online shopping means it’s vital for every business to be able to analyse the large amounts of data that are collected through its website in order to optimise the e-commerce experience. Dreams also manufactures over 460,000 units of specialised products a year across 100 different models, and data will ensure every item is the best it can be for customers. With a centralised, integrated view, they will now be able to solve complex challenges faster, accelerate time to value and accelerate decision making.”
Now Exasol is in place, Dreams will take full advantage of the database’s capabilities. Exciting potential applications include Sleepmatch – Dreams’ proprietary mattress fit technology, which carries out thousands of live calculations to recommend the ideal mattress for each customer. Analysis of the large volumes of data collected from Sleepmatch would help to further optimise future mattress recommendations to help its customers get a better night’s sleep.
