Tag Archives: CRM

One of These Things is Not Like the Other

Monday, May 10th, 2010

One of our customers recently spoke at the Sales 2.0 Conference in San Francisco. Our customer, Kirk Nichols of LaCrosse Boots, was on a panel with another company who used sales analytics software and CRM software.

The big question from the audience was “What’s the difference between CRM, sales forecasting software and sales analytics software?” An attendee in the audience answered the question beautifully, but it made me realize the depth of confusion in the market around the general topic of sales forecasting. Simple questions often have complex answers, so I’d like to answer that simple question with the 5 key differences between CRM, sales forecasting and sales analytics:

This will be a series of 5 blogs, each blog will explain one of the key differences between these software systems that all touch on the sales forecasting process. At the end, let me know if I’ve succeeded in clarifying that simple question.

Let’s start with the first difference.

Difference #1: How these systems handle the Run Rate Sales Forecast versus the New Business Sales Forecast

In most businesses, selling efforts are divided between acquiring new business (winning new customers or getting design wins for new products) and managing recurring (or run-rate) revenue from existing customers. For many manufacturers, recurring revenue is much higher than the revenue generated by new products and customers. This is generally true even though new products usually have higher margins and are thus key to driving higher profitability. For manufacturers, the business process for forecasting and managing run rate business can be quite different from the forecast process that is used for new business. And the way the forecast is tracked varies as well — a run rate forecast is usually done at the account level while the new business forecast is done at the opportunity level.

A Sales Forecasting System captures a complete view of both the run rate and new business forecast in terms of units, selling price and total revenue over time.  A sales forecasting system can track both the run rate forecast  at the account level and the new business forecast at the opportunity level. A sales forecasting system understands the differences between these two different kinds of revenue and lets you capture and consolidate the two different components into a single comprehensive sales forecast. A sales forecasting system captures forecast data that is not contained in a CRM system and provides analytic capabilities on that data.

A CRM System focuses primarily on tracking and managing new business (opportunities). The opportunity paradigm is useful for tracking the progression  of design wins through the sales cycle and providing visibility into the likelihood of closing a new customer. But CRM does not lend itself naturally to tracking the detailed and ongoing nature of a repeat business forecast. CRM systems are a good complement to sales forecasting systems — CRM is for tracking the progression of new business-oriented design wins, and sales forecasting captures the ongoing recurring business forecast that results from the new business or design wins.

A Sales Analytics System provides multi-dimensional visibility into the opportunity and new business data that is in the CRM system. It can show how the opportunity pipeline has changed over time and where the bottlenecks are that are preventing opportunities from moving forward. It does not capture any data from users itself and is limited to providing insight into what is already captured in the CRM system. Sales Analytics is good at helping to understand how well the new business sales process is working.

So, key difference #1 is in what data is captured and analyzed — new or recurring business? Depending on how your company’s revenue segments into new and recurring business, it may be more important to your company to understand and manage your recurring revenue than your new business. Of course, it’s best to optimize both types of revenue so you can make the current quarter, in addition to the next one!

Speaking of next ones, our next key difference is in how these systems handle collaboration in the forecasting process.

Creating a Bookings Forecast and Revenue Forecast

Monday, December 14th, 2009

I recently blogged on the need for both a bookings forecast and a revenue forecast. Now that we understand the difference and need for both types of sales forecasts, let’s talk about how to create them. While every organization is different in how they utilize bookings and revenue terms, here are a few steps you can follow to ensure you don’t miss anything critical.
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A weighted forecast: all you need from a sales forecast?

Friday, August 14th, 2009

How often have you heard a sales executive commit to a top-line revenue number by handicapping the forecast? It happens in executive meetings around the world. As a quick refresher, handicapping the forecast is usually done by weighting the pipeline revenue number.  Weighting the forecast is the simple process of multiplying the opportunity “probability to close” by the opportunity revenue and aggregating that “weighted revenue” across all opportunities—and “Abracadabra!” you have your final revenue number.

In a large company, it is quite impressive when you can rely on such a simple process to forecast a revenue number 3 months in advance. In my software career, from Siebel to Right90, this simple method has been incredibly accurate at calling the top line revenue number. This isn’t anything new, and many folks have espoused this method.

So, sales forecasting is easy. Call it done, right?

Should I build 55% of the forecasted units?

WRONG! Is the sales forecast used only for calling the top line revenue number? No. It is used across the organization, including by operations teams to determine how many units of which product they need to build.

Example: If Customer A is forecasted to buy 1000 widgets and the probability of that opportunity is 55%, does that mean the factory should build 550 units?

The customer will either buy 1,000 units or 0 units. Building 550 is the worst of both worlds:  if you win the business,  you don’t have enough to fulfill the order (and therefore lower customer satisfaction or lose the business all together).  However, if you don’t get the order you get stuck with 550 units of left over inventory for that product. In this case, a weighted forecast doesn’t work. Instead, a raw, bottoms-up forecast is a better method.

Denis Pombriant, of Beagle Research, explains this issue in great detail in a recent white paper. Let’s take another example of where weighted forecasting is not enough.

Existing business vs new business

For many companies, 80% of revenue comes from ‘run-rate’ or ‘existing’ account business. It goes to reason for these companies, forecasting existing business is more important than forecasting ‘new business.’  In the case of existing business, there is no probability.

Example: We all know HP buys chips from Intel.  Do you think there is an “opportunity” for HP computers in Intel’s CRM system with a probability associated with it?  NO – Intel won that business a long time ago, but Intel still has to forecast the units of each specific chip that HP demands each month. In this case, Intel needs a raw forecast of exactly how many units of which specific chip that HP demands in which month (or week).

This requires a bottoms-up forecasting approach by units, by account and product managers familiar with the HP and Intel relationship. After capturing the raw forecast, Intel likely leverages a statistical forecast and management judgment in arriving at the final unit judged forecast. This is another example of where the weighted forecast method is not sufficient.

There are many other cases where a weighted forecast does not provide all that you need. However, as I stated at the beginning, weighted forecasting is a valuable PART of a sales forecast. This is why we recently added weighted forecasting functionality to the Right90 sales forecasting application, enabling companies to create  a complete sales forecast.  A complete sales forecast in best-in-class companies involves some combination of

  • a bottoms-up raw forecast from direct sales folks
  • a bottoms-up raw forecast from distributors or partners (the channel)
  • A product manager’s forecast
  • A statistical forecast
  • A calculated forecast (based on probability weighting or some sort of historical measure)
  • Management vetting (scrub or review) of the raw forecast
  • Executive judgment

Sales forecasting is part art and part science; best-in-class companies use all information available to them to arrive at a trusted and actionable sales forecast.

Oracle CRM Partner Video

Friday, June 26th, 2009

Our partners over at Oracle CRM on Demand put this great video together talking about the need for enhanced sales forecasting in CRM systems.

In the video Right90 CEO Kim Orumchian talks about how Right90 is a great addition for companies that are running the Oracle CRM on Demand solution. One of our other partners, Big Machines, makes an appearance as well discussing its quoting and proposal management solution:

What we hear from Right90 users is that being able to combine sales forecasting with their CRM makes the entire system more valuable. Right90 is tightly integrated with Oracle CRM on Demand, which addresses the specific sales forecasting needs of high tech companies (including account based forecasting, unit, price revenue over time). High tech companies need a simple and easy to use way to collect their forecast. And, Right90 can help close deals.

Right90 helps customers that want to maximize their Oracle deployments with an on-demand solution that helps deliver a trusted, actionable sales forecast.