Competition in the market is fierce. Companies compete for higher sales and customer satisfaction. Sales is also under great pressure to operate successfully. Therefore flexible and fast sales planning models are necessary, which adapt to the changed conditions in the market.

Many companies use manual solutions such as Excel spreadsheets for their monthly or quarterly planning. However, the calculation of current sales data is error-prone and takes a long time. The company cannot coordinate and optimize sales planning for market opportunities. In short, the manual process is inefficient.

One step ahead: Information in real time
Many competitors on the market now rely on automated systems to obtain information and forecasts in real time. At the same time, the forecast can be flexibly adjusted by updating the system based on the available data.

This means that sales teams have to rely on dynamic solutions to survive in a competitive environment. To do this, the available data must be analyzed across multiple applications and databases to optimize performance.

Fast and flexible sales planning
Companies that rely on automated sales planning solutions can save time and money. Objectives and targets can be changed in real time. For this reason, sales teams are able to act adaptably – for example, when a certain product in a certain region sells out faster than planned. In this example, the figures can be flexibly adjusted upwards.

Thanks to the real-time information, more precise forecasts and sales evaluations can be derived. Based on this information, sales teams can align their activities.

Even more benefits
The management also gains an important insight into the required capacities and can see how these aspects influence the forecast. Sales teams can also be managed in times of fluctuation or changing market conditions. Sales staff have access to information systems that give them insight into their area. In this way, it is possible at any time to record where they stand with regard to the achievement of their goals.

Conclusion: Predictive analytics software for Big Data
In an increasingly complex world with increasing amounts of data and growing risks, strong pressure to minimise costs and optimise business processes, companies have to rely on predictive analytics methods. With the help of the software, it is possible to analyze and process disordered data in such a way that those responsible have the necessary data at their fingertips in real time. Better decisions for the future can be derived from this. If necessary, it is also possible to react adaptably to events that occur.

For a company that wants to survive in an increasingly tough competitive environment, it is essential to be able to analyze, recognize patterns and correlations, and process large amounts of data in order to obtain meaningful data and profit from it successfully.

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