Category Archives: Sales Analytics

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Difference #4: Scoring the Sales Forecast to Assess Quality

Monday, July 12th, 2010

Sales Forecast to Assess QualityOnce companies have created their sales forecasts, they often wonder what type of tiger they have by the tail. Which leads us to:

Difference #4: How systems help companies score the sales forecast to assess quality and reliability

The key question every business leader wants answered is:

How good is my sales forecast and how much can I rely on it to predict what I am actually going to sell?

Best-in-class companies have a consistent, systematic way to score and measure ongoing forecast accuracy, bias and completeness. They look at the forecast score by individual forecaster, region, product, and channel.

Scoring the forecast at a granular level enables them to:

  • understand where the likely risk is in the forecast
  • anticipate how good the forecast is going to be
  • give their company confidence in where to act
  • hold individuals accountable to the commits they are making to the company.

Back to our ball game — except now we’re looking at how to keep score.

A Sales Forecasting System has a built-in way to score the forecast and to assess its historical accuracy by slice (customer, product, region, time). A Sales Forecasting System also provides a way for Sales Managers to use the aggregated scores to manage forecast risk. This enables the Sales Managers to develop higher confidence in the forecast and offer better guidance on forecast outcomes to their peers in other functional areas, like operations and finance. Best of all, a Sales Forecasting System provides the tools necessary to hold individuals accountable and reward/penalize good/bad forecasting. By having an objective way to understand the quality of the forecast, companies can hold all parties responsible for improving it. As the old saying goes, “What can’t be measured, can’t be managed.”

A CRM System does not have a native way to score elements of the forecast. If one is desired, it needs to be custom-built, usually in conjunction with a Sales Analytics System. And, per our previous posts, the CRM system can only work with new business opportunity data which is a subset of the complete sales forecast.

A Sales Analytics System can be configured to measure the accuracy of the forecast elements that live within the CRM system, but this usually requires a custom-built set of analytics, in conjunction with customizations to the CRM system.

Once again, combining CRM and Sales Analytics is not enough to fully equip your sales team with the right equipment to win the game. One of the most intriguing books on winning the game with analytics is Moneyball. Companies can play Moneyball with a great sales forecast. In my next blog, we’ll discuss how to use the forecast to drive business processes.

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Difference #2: Collaboration in Building the Forecast

Wednesday, May 19th, 2010

Our second difference between CRM, sales forecasting and sales analytics revolves around how they handle the forecasting business process. Sales forecasting is one of those business processes that requires deep levels of collaboration to drive a good outcome for a company.

Difference #2: How systems support cross department collaboration in building the forecast

Although the sales forecast begins with a bottoms-up commit from sales people, many other departments are involved in constructing and vetting the sales forecast. The input from sales people is the start of a collaborative process that should result in an enterprise demand forecast. This integrated demand forecast reflects inputs and judgments from many people including executives, sales operations, product management, marketing, demand planning and finance.

A Sales Forecasting System understands the part all of these stakeholders play in the sales forecasting process and is configurable enough to support the definition of a business process that allows these individuals to contribute and collaborate in a way that is predetermined by the company:

  • Executives can forecast tops-down
  • sales people can forecast bottoms-up
  • product managers can forecast by product
  • sales-people can forecast by customer
  • product managers can forecast new products
  • finance can forecast cost.

An enterprise sales forecasting system allows the complete process. The entire enterprise demand forecast can be captured along any dimension by all stakeholders. This results in a complete and timely forecast that is actionable by any part of the company.

A CRM System is focused mainly on the sales people and capturing how they think they are doing with regard to new opportunities. Most CRM systems do not anticipate collaboration around the forecast by the other stakeholders. To achieve this, they need to be customized or the process needs to happen outside of the CRM system. In order to generate a complete sales forecast, the new business opportunity forecast contained in CRM is often augmented by spreadsheets that track the run-rate, or recurring business sales forecast. For many companies, new business is a subset of recurring business.

A Sales Analytics System allows other departments to see how the new business pipeline in CRM is evolving but does not otherwise aid cross department collaboration, or in creating a complete forecast collaboratively.

Sales forecasting is an extremely collaborative process that touches both the front office and the back office of every company. Many companies support that process with a hodgepodge of applications and systems, from CRM to Excel to analytics/BI to ERP demand planning. It’s almost as if each function brings their own bat and ball to the party. This makes for a very interesting and chaotic game for most companies.

Next, we’ll look at how companies have brought this game under control.

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Sales Forecasting Pain and Success: It Starts with the Process

Wednesday, December 2nd, 2009

For most organizations, sales forecasting is a time-consuming painful process that results in very little value. As stated in a blog I recently read, the typical process goes something like:

  1. Collect the forecast from sales reps, and assemble in a spreadsheet
  2. Roll-up forecast to managers who apply their subjective judgment
  3. Roll-up manager forecast to VPs who apply their own personal bias
  4. Roll-up the VP forecast to executives, who change it because it is too low
  5. Hand over the forecast to operations and finance who toss it in the garbage because they don’t trust it and it’s not timely enough to make an impact to production or planning.

(more…)

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A Well-Rounded Sales Forecast

Friday, November 6th, 2009

Hybrid or SUV?

Imagine being a fly on the wall in the Chrysler board room in 2001. Margins were jumping as sales of SUVs boomed; times were good. If management used a statistical forecast based on the previous years’ shipments to predict the future, they would be told one thing:  Build more SUVs!

We all know the story: 5 years later (about the time it takes to get a car from design to market), Toyota Priuses were selling like hot cakes and Chrysler was heading toward bankruptcy. Clearly, running your business on a statistical forecast could cause you to miss future trends.

It would be equivalent of driving by looking in the rear view mirror.

(more…)

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CIOs Are Relevant Again

Friday, October 30th, 2009

I found some great observations in this post about why CIOs are becoming relevant again.

You could argue that a parallel process has been happening in the manufacturing world for some time and with the same effect. As companies that for years were running their own factories and infrastructures, moved to an outsourced model, the operational leaders needed to adapt. No longer was the operational executive as concerned with the internal operation of the infrastructure but rather, with the changing relationships and complexities with the ecosystem of partners and systems that they had established.

In the software world, a move to cloud computing is very similar. While a company may transfer responsibility of a system to a partner or group of partners, the burden of that system and how it impacts the business of the company is not relinquished and must be managed. The simple act of an upgrade or the unanticipated retiring of a feature could have profound implications to the company’s use of the software or how it integrates into a larger group of systems. While on-premise ownership affords a CIO the luxury of denying or delaying an upgrade for simplicity sake, the cloud offers no such shelter. This, by example, offers insight into the problems now faced by an organization in it’s move to the cloud. Like the operational transition in manufacturers, the end result is a new (slimmer) technical organization with similar skills and new responsibilities.

As the leader of the technical organization, the CIO must be charged with making sure this new business paradigm does not impact the company in a negative way. This has been the case in manufacturing and the operational executive has been the one to manage the organization through the transition. The role of the executive evolved – CIOs are more relevant than ever.

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