Resulting in Better Decision-Making and Planning Process

Briefing About Our Client

Among our most important customers was a dairy manufacturing company situated in Switzerland. They held a dominant position in their home country's domestic market. Additionally, they have an excellent exporting reputation, as they sell their products to more than 15 other countries.

Challenges Faced by Our Client

Our customer was frustrated with the inability to maintain a constant balance between sales success and planning efforts. As a result, they began to fall short of their sales quota. Our customer desired precise sales forecasts as well as targets that were within reach. Based on product category, brand, or shop, this should be carried out in the appropriate manner. Our customer wanted to know why there was a disparity between their sales forecast and actual sales results. They needed to find areas where there was room for improvement.

Solution by Allianze InfoSoft

Our team of artificial intelligence professionals began the process of cleaning up the historical data. In-depth examinations of the plans and actual sales performance reports (which were broken down by product category, brand, store, and region) were conducted by our professionals. In order to achieve accurate sales forecasting, our team eliminated the influence of promotional activities. We created an algorithm that facilitates the selection of the most appropriate statistical model. Models included linear regression, autoregressive integrated moving average model, zero forecasting, and median forecasting, with each model having a different weighting. One of the four models was automatically selected based on the length of time the model has been on the market.

While doing an extensive investigation, our team discovered a few criteria that could help customers boost their sales effectiveness. The benchmark product, store, and location were all identified by our team. After comparing the results to the applicable standards, they calculated the sales prospects for additional products, retailers, or locations. In the forecasts, they took this possibility into account.

What Was the Final Result?

Our customer was given a precise sales prediction based on statistical models and algorithms applied to historical sales data. It also included a higher growth rate. Customer sales improvement has a potential of roughly 20%, according to Allianze InfoSoft. This was determined using a benchmark product, store, and region as a starting point. These enhancements were incorporated into the sales projection provided to our client.