Tag 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 #3: How Systems Maximize Forecasting Effectiveness

Monday, June 7th, 2010

As we continue our journey through the differences between sales forecasting, CRM and sales analytics applications, we need to consider not only how they foster collaboration between the participants in the forecasting process, but how they can make the forecasting process most effective.

Difference #3: How these systems build a complete sales forecast that maximizes forecasting effectiveness

The core challenge of sales forecasting is to maximize insight into key drivers of the business while at the same time not making the forecasting process too onerous to sales people, product managers and executives. A process that is too “heavy” will kill productivity for the participants while a process that is too “light” will not provide adequate visibility into where the business is going. If a sales forecasting process is too time consuming or complex, adoption is negatively impacted. If forecasting adoption is low, the company will not have the data to find the insights it is looking for. The most effective forecast is one that minimizes effort while maximizing results. How do our three favorite systems minimize the forecasting effort while maximizing effectiveness?

A Sales Forecasting System offers a suite of tools and best practice templates to assist customers in designing a sales forecasting process that balances the need for complete information with the importance of minimizing the time spent forecasting.  A sales forecasting system enables all the constituents that build the forecast (sales, sales ops, sales management, marketing, operations, finance) to use the same tool. The game can be played by all with the same bat and ball. A sales forecasting system supports key capabilities like:

  • multiple plans (revenue, product, regional)
  • customer tiering
  • statistical forecasting baselines for standard or high volume parts
  • plug values for buckets of small customers and partners
  • analytics to uncover insights, and more.

All of these capabilities help companies minimize the time required to create a complete sales forecast and derive actionable insights. This maximizes the effectiveness of the sales forecasting process.

A CRM System is focused on minimizing the time a sales rep spends managing their opportunities. Sales ops often fills the gap between new business and run rate business with spreadsheets. Also, CRM does not provide native support for multiple plans, statistical tool integration, forecasting best practices, or other key sales forecasting requirements.  If companies want these capabilities they need to integrate third party applications, or develop these capabilities themselves. This often adds complexity to the sales forecast process and decreases the quantity and quality of insights a company can obtain.

As mentioned before, a Sales Analytics System does not have independent data capture capabilities, instead sales analytics rely solely upon the data contained in the CRM system to derive insights.  This limited view of opportunities does not help companies much in building the sales forecast and delivers minimal forecasting effectiveness.

Combining CRM and Sales Analytics yields a good way for sales to manage new opportunities, yet leaves the other ball players, and much of the game, off the field.

Having the right application helps companies build a complete forecast of new and recurring revenue with minimal effort from all the players. It also delivers maximum value through actionable insights. Next we’ll discuss how companies get these actionable insights. It starts with assessing the quality and reliability of the forecast.

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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.

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Real-Life Examples of Bookings and Revenue Forecasts

Monday, December 21st, 2009

In two previous posts, I discussed the need for a bookings and a revenue forecast and how to create those two types of forecast. But sometimes the easiest way to learn about something is to hear how others are doing it.

So, here are a few real-life examples of companies driving their business on a bookings forecast and a revenue forecast. (more…)

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