Sales Forecast automation with predictive analytics

Sales forecast automation can be efficiently done with a method of predictive analytics. Before we start with the role and techniques used in predictive analytics. A brief definition of Predictive analytics is: 

It is a kind of analytics that uses predictive (foresighted) patterns and algorithms in historic data.  

They collect data from the CRM software and generate sales forecasts, predict the most relevant prospects, and organize a marketing campaign for B2B as well as B2C sales. 

Forecast automation with predictive analytics comparatively reduces human error because it mainly depends upon Artificial Intelligence and machine learning to predict the consequences, revenue, and sales done at the end of the quarter. In short, with the help of AI, predictive analytics determine the insight produced by the business.  

Read about other techniques of sales automation:

Now, let us discuss the role of predictive analytics in sales forecast automation: 

1. Firstly, predictive analytics highly relies on the data that the CRM retrieves. Upon which they perform the sales forecasting which will be very precise if the data is accurate. Therefore we suggest you to keep clean, well-arranged, and error-free data.  

If the historical data is not valid, predictive analytics won’t be correct either. But, it becomes difficult to manage the data when it is stored in a huge amount.  

2. The analytics abilities increase with business growth and many organizations. It includes various platforms and CRM integrations, or any platform that needs to store data in their operations. These kinds of platform changes can alter the pipeline also. It can have a positive or negative impact on the business. Depends on how flawlessly you can synchronize your multiple data. As it wholly depends on the data, your sales reps need to be very careful and precise while uploading the database. 

3. It generates real value for the business. As discussed earlier, sales forecasting is a vital process because any crucial business decisions are dependent on it. The advanced technique help relax the burden of planning manually in spreadsheets. Automating sales forecasts and helping the sales rep have a transparent idea about the performance and decision to be taken.  

 Predictive analytics Techniques that enhance forecast accuracy 

We recommend you to use these techniques not because many companies adapted these techniques to improve their forecasting results, but it has real advantages of employing these techniques to your business. Unless you start using these techniques you will not realize the benefits provided by them.  

When  software makes use of predictive analytics they keep on improving the planned models by thoroughly revising the historical data and the feedback received from the data source. They compare the past performance with the present circumstance so that it can analyze the future outcomes of the organization.  

While using  predictive analytics, two events that take place: 

Coverage

Based on historical events they completely scrutinize the data sources that they accumulate to form an accurate analysis and predictive function.  

Identify the changes 

The inconsistency in data or the iteration of the data patterns can be discovered or detected only by the machine learning and AI technique that is used in  predictive analytics. They provide alerts to the users making use of  insight and data.  

The role of predictive analytics is to provide the organization with an opportunity to make decisions and predict outcomes that are based on a prospect’s interest and behavior and not just on mere guesswork and biases.  

There is a major issue that comes up with the technique of predictive analytics. Many organizations do not have the trust and confidence to execute the automation process of sales forecasting. The report is generated by the machine and based on AI. The reliability of the report is one big challenge for sales forecasting.  

There is a way to build up  trust in these techniques of forecasting. 

• Quality-  You have to keep the quality factor in mind so that they can analyze which prediction suits best for the forecast reporting. 

• Comparison-  The actual sales and the predicted sales has to be compared at some point in time, which has upper and lower limits which will help to determine the prediction of the resultant of the business.  

The way these predictions are given is confident and accurate which makes the users trust the technique of predictive analytics. Besides producing high-quality forecast reports, it completes the task much faster compared to the manual process and frees up valuable time for the sales and finance team. The managers get transparency and clearly, they can spot out the errors which  becomes very difficult while doing it manually.   

Predictive Lead scoring software from HubSpot :

https://www.hubspot.com/products/lead-scoring