One of the most common uses of data is to better target advertising. Retail companies need to reach their potential customers in a very competitive environment, so identifying them is critical.

But the amount of information out there is growing every day, and companies need to process and analyze increasing amounts of data to find what they need.

Traditional databases cannot scale to meet these increased data volumes, nor can they process them fast enough. As a result, companies must turn to complex and expensive data architectures.

LeanXcale was born as a solution to this problem. It provides high linear scalability and key-value fast ingestion to process any amount of data, real-time analysis capabilities and some unique features that improves calculation, such as Online Aggregations.

Problem: To increase the conversion rate, in the last years e-commerce evolved to be smarter, adapting their products, prices, even the full user experience to a specific customer. This strategy requires managing a lot of information in real-time. Commonly they use complex platforms combining relational and NoSQL databases. These platforms are costly to develop, maintain and scale, and usually, they don't work properly in the workload peaks. In other words, when their usage means the biggest revenue.

LeanXcale benefit: A single database that combines the fast intaking capacity of a key-value with the query capacity of a SQL database and able to scale-out.

LeanXcale value: reduce the total cost of ownership and the time-to-value of the platform. Optimize the conversion rate in the peaks, in other words, the return of the service.

Problem: Coupons are a very popular strategy to increase sales. Anyhow, an increasing number of customers optimize visits and coupons to maximize the revenue without incrementing the average ticket. Retail wants to provide offers on the spot after observing the first customer movement in the store. Anyhow the underlying technology is not ready for this massive real-time processing without a high investment.

LeanXcale benefit: Enables applying ML learning algorithm to cross a high volume of information intaken in real-time. LeanXcale can scale out to manage any future workload

LeanXcale value: Increment the average ticket since the very first customer visit.

Problem: Geomarketing uses geolocation to perform marketing activities. Since mobile phones and IoT sensors appeared, the amount of geospatial data has grown dramatically. Traditional relational databases with geographical capabilities have a problem to intake and scale up to the current needs.

LeanXcale benefit: LeanXcale can process large amounts of geo-temporal data in real-time.

LeanXcale value: As a result of gaining access to real-time data on a large scale, new opportunities for cross-relations that are only visible for a short time are leveraged.