Throw Out Your Spreadsheets and Drive a More Predictable Forecast and Quarter

By Michael Lock

“Aviso delivers real insights we just couldn’t get using Excel.”  Anna Gilstrap, VP Global Sales Operations, Splunk

Recently, we’ve been blogging about the challenges that surface when using spreadsheets as your primary tool for sales forecasting. Not only do spreadsheets contain errors, but you won’t get the visibility you need to take control of quarterly performance or your business. And, there’s yet another problem that could jeopardize your quarter: spreadsheets, even coupled with your CRM, do not house the full spectrum of data needed to leverage a truly predictive solution.

Spreadsheets and CRM Fall Short. Spreadsheets don’t keep deal-level data, it’s impossible to double click and see which deals are making up your forecast. They also don’t keep deal-level historical information. Deal-level information is stored in the CRM, but even the CRM contains precious little historical information. Occasionally CRM administrators turn on certain audit fields on a limited number of standard fields, but the CRM system is not much better than spreadsheets in providing the data you need to apply meaningful advanced data analytics.

Despite an incredible amount of hype around using predictive data science to help drive forecast accuracy, if the two main data stores – spreadsheets and CRM – don’t store enough data, what can you do?

You need access to an independent data store that looks at millions of data points, keeps a history of past successes, a record of every change to your deal, and takes into account exit velocity out of your last quarter, seasonality, and other important signals. Additionally, you need to be able to have access to custom fields and objects. Think you can do predictive forecasting without custom fields or objects? Here are two reasons why you shouldn’t:

  1. Most CRM systems use a custom field to store competitive information. Do you really think you can use machine learning to do predictive forecasting without taking into account competitive history?
  2. Most price quote information is generated by separate CPQ systems and frequently stored in the CRM as a custom object. If you want to accurately predict deal size, you will need access to quote data.

At Aviso, we snapshot our customers’ entire CRM system every 15 minutes, looking at every field and keeping track of all changes. Armed with that level of information we can take on the difficult job of predictive forecasting.

“Aviso has given us the confidence to know where we’ll really finish for the quarter.”  Bill Dolby, Sr. Director, Sales Operations, RingCentral

 

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