Being aware of market demand and making data-driven decisions is paramount for any business strategy and management. Leveraging the information available within the organization, combined with market knowledge, help organisations work smarter and reduce costs while providing agility and capability. As market knowledge is built upon trends and competition research, using information available company-wide is the key to a better management of internal resources.

Today, the automotive manufacturers are managing an increasingly wide range of metrics that now cover every aspect of their industry, from manufacturing to sales and road performance. For players in the industry, having relevant insights regarding prices and stocks is a key aspect for increasing efficiency and, consequently, revenue.

How to achieve better data-driven decisions in automotive

To improve the efficiency of the decision-making process and achieve business growth, automotive businesses need to respond to business objectives such as:

• Gathering data insights: it is important to access and centralize data about partners, owned dealers, business insights, vehicles sales and so on.

• Enabling data enrichment: upgrading existing data with information coming from external platforms such as data about partners or clients.

• Implementing dynamic stocking and pricing: creating an AI/ML engine that recommend vehicle pricing and stocking based on data analysis and trends prevision.

• Improving time to market: adopting a high-speed release cycle to increase agility.

• Offering a consistent experience throughout the company's ecosystem be cross-platform-ready regardless of the touch points used (desktop, web, IOS and Android tablets).

• Improving efficiency: reduce to a minimum manual data entry.

Centralized stock and pricing platforms for data-driven decisions

Going digital is a must as the industry itself relies on newest technologies and innovation. Smart Connectivity, AI, Big Data & Analytics and Cloud-Based Services are just a few of the trends shaping the future of competitivity in automotive.

To better respond to the challenges and start making data-driven decisions, companies in the automotive industry encompass personalized solutions in their digital strategy. One example of such implementation is a centralized stock and pricing platform for manufacturers and dealerships.

One of Accesa's clients was an automotive player that needed such a platform for increased accuracy of the decision-making process. With an iterative approach, we managed to deliver great results for our client:

Discovery

We initiated a two-day discovery workshop with the company's stakeholders. As part of our initial efforts, the discovery process enabled us to quickly determine the needs, goals, and success criteria of the future platform.

Design

After the discovery period, we assigned an autonomous product team that, together with the stakeholders, attempted to find the most suitable solution to address the company's challenges. Our team covered all areas, from architecture, data, and business analysis to UX design and back-end implementation.

Development

The chosen architecture is based on standard Google Cloud solutions and took an API-first approach so that other applications could benefit from the platform, centralised data, and the AI pricing engine. To support the goal of fast delivery, we proposed a Kanban approach to ensure constant high-speed delivery and regular client interaction. We put in place a CI/CD pipeline for more efficient testing and to easily deploy the code to various environments like integration, staging, or production.

Delivery and Results

In less than three months, we delivered a publicly available, yet secure mobile and API-first application deployed in Google Cloud. With the help of this application, the dealers and the company's business users can see centralised and enriched data in terms of vehicle information, stock, and sales volumes.

Several automated processes were put in place. The one with the most business impact was the vehicle identification number scan function. Through a custom OCR technology, the app automatically scans the identification number, thus eliminating the risk of human error that resulted from the previously used manual input.

The platform offers now price recommendations and enables data-driven decision-making within the company, decreasing the time spent by employees doing research, as well as possible inconsistencies regarding prices and stocks.

After the launch of the app, the company could already see an approximately 10% increase in revenue for the areas where the platform was used.

Conclusion

Competitive companies always rely on data to make better decisions, especially in terms of profitability and productivity. Having started their journey to digital transformation, these organizations fully render their digital capabilities to obtain personalized solutions that gather, analyze, and leverage company data. Enabling data-driven decision-making will hinder inconsistencies in business and operational processes, minimize manual work and take companies one step further on the way to success.