Here’s a statistic that keeps CFOs up at night: 79% of sales organizations miss their forecasts by more than 10%.[1] These missed numbers can turn into layoffs, lost funding, lower stock prices… You can see the effects of on the business pages every quarter.
There are steps you can take to make your forecasts more accurate.
The insidesales.com blog post, 5 Strategies for More Accurate Sales Forecasting, and others like it, offer many ways to drive more accurate forecasts. But they all depend on one thing:
“Ensure sales reps maintain accurate CRM data.”
This is critical. So critical, in fact, that some sales organizations link KPIs and compensation to data quality. With complete, accurate and timely data, you can:
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Streamline the sales forecasting process. There’s less ferreting of information. Less guesswork. Even less need to forecast: Once you know that every forecast is on the mark, you don’t have to forecast as often.
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Use the CRM for both pipeline management and forecasting. Eliminate the complexities, errors and inaccuracies that compound when you export data to spreadsheets. Sales and finance are on the same page because they’re using the same data.
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Take the guesswork out of sales forecasts. With quality data, you can score deals by comparing them to the deals you’ve won in the past. With an objective data-based view of a deal’s likelihood to close, you have a solid benchmark that finance and sales leadership can use for comparison.
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Getting complete, accurate and timely data into Salesforce
For many reps, data entry is a necessary evil. For others, it’s just an evil. The typical rep spends over an hour each day punching activities and outcomes into a keyboard. And that’s time they’d rather be selling.
Look into Rollio. The rep simply texts or tells Rollio about each action and outcome. Then Rollio uses Artificial Intelligence to turn this little briefing into data and populate Salesforce fields for the rep. It’s like hiring an assistant for each rep.
You get better data. Better forecasts. And more time to sell.
[1] https://www.siriusdecisions.com/Blog/2016/February/TheDefinitiveWaytoMeasureandGradeSalesForecastAccuracy
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