The Digital Transformation in Sales Forecasting.
Digital selling, data from digital selling, and remote interactions between buyers, sellers, and sales managers have created conditions to catalyze the shift towards modern web-based, collaborative digital forecasting that is both predictive and prescriptive and built in a data-first paradigm. Pre-digital sales, sellers would input a few signals per week in the CRM. That has now exploded to 1000s of buyer signals per week, per rep gleaned from sellers and prospects’ first-hand interactions in emails, calls, calendars, and the CRM.
This digital shift has rendered current non-digital forecasting methods, in spreadsheets or CRMs, out-of-date, inefficient, and ineffective. Gone are the days of manual, time-intensive, out-of-date, static, and inaccurate forecasts. Gone are the days of perpetual misalignment between sellers and managers, biased, partial seller-reported data, bad data quality, and unactionable data.
The Future of Forecasting is Here.
The future of forecasting is finally here. The shift to digital sales has led to the rise of digital forecasting, predictive analytics, and now prescriptive AI. Most forecasting solution providers fall short because they stop innovating and stop at predictive analytics, informing you of what is most likely to happen, but the real value is prescriptive analytics.
Prescriptive analytics recommends the actions you need to take to affect those outcomes, and in this use case, close deals. Fully automate the sales assembly process, enabling continuous forecasting with predictive time-series, engagement, activity, and conversation data, plus prescriptive analytics and recommendations.
At BoostUp, we believe the shift is here and focuses on these core eight tenets.
Before: Non-digital forecasting methods lead to constant and frequent misalignments between sales reps and managers. Putting together forecasts is time-consuming, and it is challenging to get everyone on the same page.
After: All data is in one place, and it is always up-to-date. There is a quick alignment between sales reps and managers because all information is visible to all. The forecast assembly process is automated.
Before: Non-traditional methods of forecasting typically has a low compliance and forecast submissions are late. The data is out-of-sync and out-of-date.
After: You have high compliance and adoption. Sales can easily submit their forecast numbers. Web-based alerts and inline updates allow for quicker adoption for on-time and accurate forecasting.
Before: Non-digital forecasting methods lead to often incomplete, inaccurate, and low-quality forecasts. Submissions are late and lack intelligence and actionable insights.
After: All data is up-to-date and forecast submissions are accurate, on-time, and in real-time.
Before: Non-traditional methods of forecasting typically presents shallow numbers, meaning they are high-level, contain little value, and don’t show deal level analysis.
After: You have deep granularity at all levels. Easily enforce process controls, include/exclude deals in the forecasts, enforce submissions cut-off dates, and allow management override of forecast projections.
Before: Non-digital forecasting methods have no out-of-the-box trending detail. If you want any history, you would need to enable a hard to maintain a BI plug-in.
After: Forecast changes, histories, and notes are tracked automatically at the deal level. Business and re-pacing and deal intelligence allow you to answers question like: Where were we last quarter, what changed since the previous forecast meeting, and what led to the change.
Before: Non-traditional methods of forecasting include only static, one-way communication between sales.
After: You have highly-collaborative, two-way communication, sales can quickly see forecast histories, deal notes, and best next steps assigned to each deal.
Before: Non-digital forecasting methods present numbers, but they have no context, which doesn’t help you hit your numbers.
After: Get context inside every deal with engagement and activity intelligence and recommendations that help you forecast with confidence and with much greater accuracy.
Before: Non-digital forecasting methods present information present in the CRM – but it doesn’t derive any insight or actionable intelligence from that data.
After: Prescriptive analytics informs you what actions and recommendations you should take to close the deal.