Philip Duplisey - BardessMore info
Accurate forecasts of sales and demand in the retail industry can make a huge difference in allowing a company to adapt to changing times. Furthermore, sales planners frequently rely on forecast-driven tools to adjust levers such as product pricing and the timing of promotions. There is frequently also a need to apply these techniques dynamically across different levels of product hierarchy, geography, or other dimensions. However, many companies are still using relatively rudimentary forecasting techniques, which can affect the accuracy of these forecasts. Many enterprise-facing tools are also designed with inefficient workflows, reducing the ability to do effective analysis for end-users such as FP&A and sales planning teams.
Machine learning techniques provide the most up-to-date approach for accurate forecasting, but are time-consuming to implement from scratch. This is where TIM’s highly automated, lightning-fast, ML-driven capabilities can make the difference. Automation means that analysts can use the tool without needing experience in ML theory or in programming. Lightning-fast means that end-users will be easily able to apply forecasting to any data that requires it, and to quickly iterate on scenario planning. It also means that TIM’s forecasting abilities can be applied at scale, on any data that needs to be forecasted. It can also be used in interactive BI applications. For example, planning teams could use the same BI tools that they use every day, select a particular slice of data, adjust some inputs such as pricing and promotions, and use a TIM BI tool integration to get a forecast result – all without leaving the BI tool.
Typical inputs for a sales forecasting application might include historical sales (usually split across product hierarchy, geography etc.), regional store information, local demographics, level of competition, and indicators of consumer demand, industry performance, and economic performance. Sales planning applications might also include pricing and promotion start/end dates as adjustable inputs, allowing planners to see their effect on the sales forecast.