Guiding Future Staffing Expectations
If a software company leader is able to determine that sales and implementations are on track to significantly increase 2 or 3 months down the road, they can prepare accordingly by hiring implementation people now so that they will be properly staffed in 2 or 3 months. Without that visibility, leader will be in reactive mode rather than proactive mode and will launch fire drills to find people to help with the implementation. Obviously a much healthier situation to have the visibility and the ability well in advance.
Identify pipeline shortcomings well in advance of discoveries of last-minute shortages
Through various forecasts and pipeline analysis exercises, leaders become aware of pipeline levels for various products, teams, regions, etc. well in advance. If a leader observes that the pipeline for Q2 is very light, he/she can proactively attempt to accelerate some Q3 deals into Q2. That can result in avoiding a big miss in the Q2 sales figure.
Allows for alignment with marketing. Can launch initiatives to drive pipeline when future quarter(s) pipeline is short
Let’s say pipeline for the remainder of the year is very light and the company needs it to be stronger. Upon observing the low pipeline levels, sales leaders can knock on the door of marketing folks and ask them to help. Specifically, marketers might be able to launch various marketing initiatives in an attempt to generate interest in various product while identifying some selling opportunities. The thinking is that the marketing activities will generate selling opportunities (which leads to growth in the sales pipeline).
Provides predictive capabilities to management along with insights to expected bookings
One benefit of an effective sales analytics campaign is that it can yield predictive capabilities. I’ve been part of working with management teams who have experienced 30% – 40% variance of actual sales bookings to forecasted sales bookings. Having such inaccurate forecasts can be damaging since financial forecasts are frequently based on sales forecasts. So if sales forecasts are way off, then financial forecasts will also be way off. The beauty of sales analytics is that predictive models and engines can be built. Historical behavior of data can be combined with current pipeline levels in order to produce pretty accurate sales predictive models.
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