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How To Track Automated Sales Forecasting With Predictive Analytics

Predictive analytics are increasingly becoming a priority in business. In fact, more than half of global FP&A teams are looking to implement this by 2020. This type of technology ranks higher in terms of meeting business need than many others, including automation, AI, chatbots and blockchain. Predictive analytics has a fundamental role to play in predictive planning and forecasting for any organisation and could be truly transformative in terms of progress and results.

Using predictive analytics for sales forecasting

An accurate sales forecast is essential, not just due to the impact it can have on planning, but also because it generates such a wide range of ancillary decisions. For example, the sales forecast will be a crucial component in the overall budget, which may then have a knock on impact on everything from marketing spend to recruitment. However, the process of sales forecasting is traditionally laborious – can be filled with errors – and often involves the use of multiple spreadsheets. Using predictive analysis for sales forecasting can make this process much simpler, automating rolling forecasts and providing a wealth of data and insights for more accurate decision-making at every level.

Where is the trust?

One major stumbling block for the use of predictive analytics in sales forecasting is the trust issue that can arise with respect to automating processes such as this. As many organisations have traditionally relied on human input for forecasting, there may be a lack of trust when it comes to what the output of a machine-generated process might be. However, particularly if existing sales forecasting is slow, inaccurate or just not really delivering much in the way of benefits, there are many advantages to be gained from considering a move to automation. It’s also a process that can be monitored and checked too – comparison of predicted and actual sales over time enables evaluation of the accuracy of automated sales forecasting, particularly in comparison to the results of doing this manually.

The why and the how

The idea behind using predictive analytics in this way is to improve accuracy and reduce the potential for error, as well as the resources that are required to create and deliver forecasts. A fully automated process can be established with predictive analytics that is able to forecast on a continuous basis using real time data for incredible accuracy. Although this may take some investment in order to ensure that the right data is in the right place at the right time, the benefits of establishing this kind of system are obvious. Two key advantages stand out:

  • With automated forecasting in place it will be easier for an organisation to identify new opportunities that can be leveraged – as well as to spot early warning signs with respect to potential issues.
  • Better use of resources. Automation removes the low value, time consuming elements of the forecasting process from the finance team, enabling them to add more value elsewhere, including improving business agility and growth.

Predictive analytics has a lot to add to the sales forecasting process and could provide significant opportunities for transformation and growth.

By |2019-08-29T09:30:38+00:00August 22nd, 2019|News|