The Secret to Accurately Forecasting Your Sales Revenue

by Kory Wagner

Data cubes, which record and store data points in a way that facilitates easy retrieval, reporting and analysis, are critical to financial analytics.

┬ęDome Studio/iStock/Getty Images Plus

When CFOs create their plan for the coming year, they spend a good bit of time asking their regional general managers or VPs of sales for their revenue projections for the coming year. Their answers come directly from the CRM system, which are updated daily by every sales representative in the company. Most systems allow representatives to record highly specific and robust data: opportunities, projected revenue value, stage in the sales funnel, closed deals, contract terms, products sold, customer market, and so on. Some CRM systems, such as Salesforce, allow organizations to customize every aspect of the records, enabling the company to obtain a real-time and direct line of site into its sales data.

Unfortunately, CFO’s don’t always benefit from that direct line of site. Without a direct integration, accessing the data to update a plan is a highly manual and time-consuming process to collect that robust data from the sales teams. 

The solution – and probably the top asks for most financial teams – is to integrate this robust data into the business planning platform in order to obtain a highly accurate view of short-term revenue and sales, as well as to layer this granular data onto the general ledger forecast in order to get a good idea of actual net sales.

A CFO’s first reaction is likely to be: sounds great but who and how is anyone to accomplish that level of integration? It would take a herculean level of data manipulation to feed CRM data (along with your marketing automation system data) into a planning and budgeting platform. But technology has a way of streamlining tough challenges, especially when it comes to data. Enter the multidimensional database, aka “the cube.”

The idea behind a cube is to record and store all of the data points in a way that facilitates easy retrieval, reporting and analysis. With this approach, all of the data points collected as part of the CRM record – prospect name, number of employees, revenue potential, product of interest -- are known as dimensions. The database arranges the dimensions in such a way that allows users to search by them. Cubes also allow you to define hierarchical relationships in the dimensions, i.e. districts that roll up to divisions, or cities that role up to states. The cube automatically calculates the values for each roll up, which is the functionality that allows a CFO to search for closed deals, or revenue from a particular product line.

If we apply this approach to financial planning, we see that cubes can link data pertaining to the same entity or item, such as a customer or prospect, regardless of data origination system. Let’s say your marketing department launches a first quarter lead generation program designed to build the sales funnel for the year. As part of that initiative, Acme Enterprises sees a digital ad, clicks on it, and requests a demonstration via your website. Your marketing team will enter that prospect into its marketing automation system, which will create a unique customer record for Acme Enterprises.

Next, that lead will be assigned to a sales representative, who will enter it into the company CRM system, and a new record will be created for Acme Enterprises. As Acme moves through the sales cycle, the sales representative will update numerous dimensions of the opportunity – status, revenue potential, products of interest, and so on. A third database, your GL, will record all of costs associated interacting with the client in order to close the deal.

A cube stores and connects all of those dimensions generated by each system – Marketo, Salesforce, general ledger – so that they can be searched on and retrieved by specific users, such as the CFO seeking to get a real-time update on sales revenue and opportunities. In other words, cubes allow employees to enter data and dimensions about a customer in their native programs, and have that data automatically feed into the planning program. Thus, expenses incurred in closing that customer is automatically tied to the appropriate G-L account.

Going further, the planning platform then automatically processes all of these sales and prospect’s dimensions according to the company’s unique business structure, as well as updates all outputs -- your P&L, Balance Sheet and cash flow statement.

The result is a never-before possible level of accuracy in forecasting sales revenue. But that’s just a start, by linking every dimension associated with a lead or customer, CFOs will have a much more accurate sense of the cost of doing business.

 Kory Wagner serves is Vice President of Demand Generation and Operations at Centage.