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Predictive Analytics for Accurate Sales Forecasting in CRM

As we all well aware how important is Sales Forecasting to the sales leadership team of any business. No business can perfectly predict the forecast figures, as it depends on various measurable factors. But definitely the accuracy of the sales forecasting can be improved to the maximum using predictive analytics.

Expected Closure Time

One of the main factors that contributes on getting close enough forecast figures is predicting the expected closure date of progressing opportunities. Doing a hard guess based on sales rep instinct won’t help on high accuracy. A prediction model can be built learning from the historical deal data. And using this model, we could analyze how long it was taken by deals with similar characteristics of current deal to get closed and could arrive at more accurate closure time of each progressing deal.

Weighted Forecasting

Weighted forecasting or the stage forecasting method is the advanced forecast method that is widely applied for improving the accuracy on the forecast prediction. This is the method, where using historical data, each stage will be given a closure probability. And then applying this method each deal is assigned a closure probability based on the current pipeline stage it is in.

Multi-factor Probability

Even though stage bases probability adds value on sales forecasting, it alone is not sufficient for the accuracy. It will be better if a prediction model can be built similar to one that we did for predicting the expected closure time for assigning a unique probability for each deal based on historical data. Each deal has to be assigned a probability score based on the historical conversion rate of similar deals. Both profile (Company Size, Industry, and Geo-location) and behavioral (Volume of email/call engagements, frequency of conversations) factors can be consider for the probability analysis.

Combining both the weighted and the multi-factor probability techniques can result in arriving at the most close value on sales forecast calculation.