How a European leader in construction material trading increased profit margin by 30% in 12 months and revenue by 10% with predictive models

Margo Ovsiienko

About the Client

The Client is a European leader in construction material trading. They aim to offer their customers competitive prices, fast order completion and delivery, as well as tailor-made additional services.

Challenge

High market unpredictability

The Client operates on an unpredictable market where prices of construction material, currency rates, and demand for the offered products constantly change and need to be monitored and benchmarked. These factors also impact the delivery time of the offered products. Due to this, the purchase process of the materials, as well as ensuring impeccable customer experience and timely delivery of goods, becomes a tough task.

These circumstances limited our Client’s capacity to secure a reasonable profit margin while offering customers the goods at a competitive price delivered on time.

Problems with making data-based decisions

In addition, multiple changing market factors influencing the prices of crude resources made the process of price evaluation difficult for the company’s employees.

Issues with communication flow, analytics, sales dynamics (the number of new customers, up-sells, and cross-sells) have resulted in company analysts’ wrong decisions about new product prices. These decisions were often entirely based on employees’ intuition.

Lack of information

The lack of information about multiple market factors made offer customization difficult and resulted in a decrease in sales volume, a low profit margin, and sometimes unprofitability of certain projects. 

Not knowing where to start 

Before the Client approached us, they already felt that AI could probably help solve the complex problem with offer customization, but they weren’t sure where to start.

They were lacking the know-how and technical skills needed to adopt AI and needed help with the selection of a predictive model to start with. 

The team had a general idea of what they wanted to achieve, but they weren’t able to specifically state how they would imagine the solution to their problem, which is very common – and natural – at such early stages of preparation for AI adoption.

Solution

AI Sprint workshops

To help the Client, we implemented our unique AI Sprint approach that is a rapid prototyping of an AI model. It aims at verifying if and how AI is applicable to solve a given business problem. It includes a 2-day workshop and prototype development and takes less than 1 month.

We started with the AI Sprint workshop (check more here), during which we aimed to find the most time-efficient solution that would assist employees in making decisions based on historical data (purchase history, financial data, delivery time) in correlation to changing market data. This was important to ensure the Client offers customers a sufficient amount of the product in a given time and can increase profit margin together with revenue. 

During the AI Sprint, we learned that the prices of products the Client offers rely on multiple factors, including listings on the stock exchange. 

We mapped out the whole sales process in the Client’s company and noted what the prices depend on. Having identified what data was needed for the models, we made a list of the data the Client collected as well as the data we had to obtain – such as information about competitors or prices of resources. This third-party data would be made available with the use of robotic process automation, automatically sourcing up-to-date information.

Proof-of-concept

After the workshop, the Client’s team and our data scientists collaborated over the tasks to create a proof-of-concept – exporting data in CSV format, building the flow and the predictive model as well as an RPA robot. Our team also prepared recommendations for the Client for further use and development of the model.

The outcome of the workshop was an offer/order price estimation model. This model predicts the total amount the customer will most likely pay for their order basing on the ordered resources, the volume of the order, and customer data. 

The developed predictive model analyzes available historical data and up-to-date records to provide accurate estimates of the order value. External data is sourced by an RPA robot and added to the predicted estimate to provide insight for the sales staff. 

The PoC was created and reached a certain accuracy within two weeks, but was not good enough to satisfy end requirements, so we created a plan for continuing the work and extended it into a 3-month project to reach the expected accuracy. 

Result

The development of the PoC took two weeks and as a result, the Client received a model providing estimates of the order value and delivering the estimates to the sales team. The process of getting an estimate includes exporting the order as a CSV file, sending the file to a specified email address – the email then triggers a robot that retrieves the missing data from the database, starts the predictive model, and gets the estimate. The estimate is then sent as an email to the sales team.

The solution will support the Client’s sales team in their everyday work, making it easier to provide calculations for the customers. The end customers will get more accurate estimates, and they won’t have to plan for a big margin of error between the due amount estimated on the day of placing the order and the actual one that they have to pay upon delivery.

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