Advanced Analytics

Analyzing behavioral data allows automotive companies to unlock the potential for cost reduction, open new revenue streams, or even create new business models.
In the insurance industry, it is a fundamental feature allowing for pay-per-use offering as well as granting safe drivers with discounts for the insurance policies.

For the automotive industry, however, it can be treated as the base for predictive maintenance – it allows to plan maintenance works based on the usage of car components, but also to help to analyze the range of the electric car battery based on the long term driving patterns.

For the rental car business, it allows to offer discounts for safe drivers but also allows to propose features and vehicles most suitable for them based on the patterns of their previous behavior.

In the modern automotive business, not only traditional dealerships or over-the-counter sales drive revenue for today’s mobility providers, vehicle manufacturers, and OEMs. Today, recommendation engines and mobile applications generate an increasing amount of sales records. Acknowledging the business value of recommendation systems, Netflix estimated that their recommendation engine is worth $1bln yearly.

Rental car companies use these solutions in upselling to encourage customers for additional insurance, a higher grade vehicle, and other additional features. Leveraging recommendation systems vehicles enterprises increase sales and improve customer experience.
Our data science and data engineering departments have a proven track record of creating machine learning algorithms and combining them with e-commerce systems allowing automotive companies to take benefit from recommendation engines based on previous purchases.

Artificial Intelligence has proven to be a good way to tackle problems, which seemed impossible before. For the general audience, most of the ML is a black box, which accepts data and responds with prediction or identification. Algorithms are complex and hard to understand for non-data scientists. With explainable AI, the problem resolution path can be exposed to customers and stakeholders, making the bottlenecks and reasons for wrong reasoning visible.

Grape Up Data Scientists can help you build an ML system allowing stakeholders, developers, and customers to comprehend the prediction process and, as a result, have more trust in the results.

Building multiple platforms for storing the data is costly, hard to maintain, and can result in data loss due to incorrect handling. This problem can be resolved by building a common data streaming platform able to handle real-time data for the whole enterprise. Built with scalability and redundancy in mind this kind of platform can effectively withstand and resist problems in having a system of distributed, different platforms and storage systems. The support cost is also lower.Grape Up experts are highly experienced in creating and maintaining efficient and scalable data streaming platforms based on Kafka that are currently being used among Grape Up customers representing automotive and financial institutions around the world.

Data is a lifeblood of an organization. Each department generates and gathers enormous amounts of data about customers, products, or the environment. This data can be used to enable new revenue streams by creating better offers and improving understanding of customer needs. Data analytics can also result in reducing the cost by improving the bottlenecks and identifying pain points.Building systems or ML algorithms leveraging the available data can be extremely complicated if the data is siloed and available only inside the department. Democratic access to the data across the enterprise helps to build better products faster and easier while having a single point for data storage makes the operations easier. Storing the heterogeneous data in data lakes and data meshes is easier for developers and data scientists.For one of our customers, Grape Up engineers have already implemented the modern data storage, starting from the ETL/EL(T) system, through the data lake and ML system built on top of it while maintaining enterprise-grade privacy and GDPR compliance.