Anticipate how much you will sell in the future
Many different factors influence the level of sales. Ranging from prices, weather, moves of the competition, promotion, advertising budgets to the general economic situation and labour market. The effects of some factors are postponed. In this case, predicting what level of sales can be expected in future periods becomes a big challenge. However, predictive modelling comes in handy.
Advanced statistical models take into account dozens of different factors. They combine data from internal sources with external data, such as the weather, forecasts of the Polish Central Statistical Office (GUS), competition and identify relationships between these factors and the level of sales.
This allows for forecasting sales of the entire network or by individual outlets, product categories and even specific SKUs in different time horizons (e.g. 30 days, 6 weeks, a quarter). The forecast may focus on the sales value, the number of products sold or the number of customers. Predictive models built by the experts of Data Science Logic benefit from the latest advances in machine learning that make it possible to achieve high forecasting accuracy with deviation of up to a few percent.
Each subsequent week the models take into account new sales data and are calibrated to make forecasting more and more effective. The results of forecasts are presented in an intuitive graphical form so that managers can easily make decisions based on them.
The knowledge of future sales trends and the factors influencing them allows us to plan the level of stock , the capacity and staffing of sales outlets/customer service centres well in advance.
Benefits:
- Better planning of production, orders, stock levels, staffing of the outlets
- Better understanding of the direction and strength of sales drivers
- Identification of long-term sales trends and short-term deviations
- Identification of the threat to the execution of sales plans in advance and the possibility of taking timely countermeasures
Clients: Leading marketer from retail industry
