Category Archives: Sales Forecasting

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Spotlight of the Week: Top 3 Ways to Use Right90 Comparative Analytics

Thursday, July 22nd, 2010

Why should you use Right90 Comparative Analytics — also known as the Analyze screen? The Analyze screen provides a rolled up view of your forecast by customer, product, region, or user in a table view.  While you have the option to drill down to see more details, you’re not bogged down with all those details. In addition to just viewing the forecast, here are three other popular ways to use Analyze:

  1. Use top-3-ways-comparative-analyticsyour snapshots to compare your forecast at points in time — for example, how has the 2010 forecast changed from the beginning of the year to today?  In this example, we’re looking at the variance by region.
  2. Compare top-3-ways-comparative-analyticsyour forecast to targets or goals so that you can keep track of how you’re doing against your objectives. In this example, we can see how each sales person is delivering against their targets.
  3. Compare top-3-ways-comparative-analyticsyour forecast to actuals or shipments to monitor attainment during the quarter. In this example, we can see attainment by customer.

Whenever you get a view of the data that you’ll want to see on a regular basis, save time by creating a Favorite. If you don’t have targets or actuals in Right90, your Right90 Administrator can help facilitate getting that data so you can take full advantage of Right90 Comparative Analytics to help identify and manage exceptions in your business.

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S&OP: What’s Next After 30 Years?

Wednesday, July 14th, 2010

S&OP BalanceAs its forefathers including Oliver Wight would remind us, S&OP is a very mature process. The objective of S&OP: to align demand and supply in a financially sound manner. Sounds simple, yes? Yet after more than 30 years of practice, most companies still struggle to achieve success with S&OP. AMR Research’s current survey (as presented by Jane Barrett) found that 67% of companies are stuck at Stage Two of what AMR describes are the four stages of S&OP maturity. Let’s look at the state of S&OP practices today and some key implications of recent S&OP and Integrated Business Planning (IBP) research from AMR and Gartner, Inc.

We all know the importance of effective sales and marketing organizations. Especially in a fast-changing economy, sales and marketing effectiveness drives profitability disproportionately. When sales, marketing and channel partners (the “front office”) are effectively engaged with finance, demand planning and supply chain (the “back office”), the company fires on all cylinders. Case in point, Bob Johnson of Gartner Inc. recently presented research demonstrating it was excellence in the front-office side of S&OP that had the greatest impact on allowing many semiconductor companies to stay profitable during the last downturn. The ideal process starts with the front office accurately sizing up the total future demand, or shall we say, revenue potential.  The inputs to S&OP from the front office are bottom-up forecasts of both units (volume) and ASP (price) from sales reps, marketing/product managers and their executives. This rich forecast becomes the baseline for finance, demand planning and supply chain to engage.  Strategic S&OP issues and scenarios can then be escalated to executives for evaluation, while past performance and forward-looking metrics are assessed. The end result? The products customers want are built, and available for delivery, to their delight!

Guess what’s the #1 gap in S&OP today? AMR’s 2010 survey ranked sales and marketing input as the most important aspect of S&OP, yet one of worst performing areas. S&OP leaders assume the demand planning process can adequately incorporate sales and marketing forecasting, including new product forecasts. However, the tools they use to manage the process—demand planning applications—were not built for the sales and marketing people managing sales pipelines, new product launches and marketing campaigns. Even S&OP solutions like Demantra and SAP APO (demand planning applications with S&OP lipstick) lack the basic capabilities needed by sales and marketing—such as an intuitive user-interface and real-time data integration with CRM systems like Oracle CRM On Demand and salesforce.com’s Sales Cloud. It’s no surprise that 51% of companies still use Excel spreadsheets and the like for S&OP (per AMR’s 2010 S&OP survey). But when go-to-market strategies, or detailed sales forecast inputs are incorporated from Excel spreadsheets, accuracy and credibility are lost with operations, and therefore the possibility of reaching reaching a high level of S&OP maturity. Note that on the “OP” side in contrast, supply chain applications (often well-entrenched from over the past decade) can provide a reasonable means to gauge the capability to meet demand (AKA supply planning) and even quickly gauge the impact of S&OP scenarios.

So what lies ahead? S&OP has a bright future. Notably Tim Payne, Research Director, Gartner, Inc., predicts the market for S&OP solutions will grow at around 15% to 20% per year for the next few years1. The challenge for companies wanting to evolve from a reactive S&OP process to one that is collaborating and orchestrating is to get effective S&OP engagement from sales and marketing, including their executives. In my next post, we’ll take a look at how Right90 customers are reaching greater S&OP maturity with a purpose-built forecasting solution for sales and marketing that effectively engages sales and marketing. Even the executives.

1MarketScope for Sales and Operations Planning, 20 October 2009

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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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Demand Planning’s Achilles’ Heel

Thursday, June 10th, 2010

The IBF Demand Planning & Forecasting conferences are always enlightening, including the latest event in San Francisco. While the demand planning vendors touted their latest statistical forecast algorithms and growing S&OP functionality, and customers shared stories about their new forecasting process, I kept seeing a soft spot in the demand planning process. This weak link was preventing many companies from achieving “best-in-class” demand planning performance.

Despite the ever-sophisticated demand planning algorithms, reporting and best practices, it struck me that customers still had a tremendous pain getting sales to provide a good forecast and demand planning system were not adequate. Here’s how some of the attendees described how they obtained the sales forecast:

“Sales always has the latest forecast insight and so we give sales a voice in our demand planning process by having them email us key account updates every month.”

“We take CRM opportunities from Sales and get a rough short-term forecast by taking the quantity times probability, as a weighted forecast. This gives my demand plan a reality check in case there’s a trend difference.”

“We initially planned to have the Sales team input a forecast in our demand planning system but reverted back to spreadsheets. At any rate, sales folks can be very accurate in their forecasting.”

I have heard comments just like these many times in my career. Because of my passion for forecasting and planning, I have worked at leading-edge companies like i2, Manugistics (both now JDA),  Steelwedge, and now Right90. My views have been informed by working with 50 or so companies across many industries. I have seen the demand planning processes that work with Sales effectively, but more often than not, they work not-so-well.   Demand planning systems from Demantra, JDA and SAP APO DP are complex and powerful but not designed for the busy sales team.

Companies have also used statistical forecasting solutions for the past 35 years (e.g. Autobox), yet those have not solved the problem either. Effective sales forecasting solutions are only now emerging.  This is an encouraging development, as IDF customers know the value of a sales forecast, and the value of including it in the demand planning process. After all, sales is closest to customer, and if their input wasn’t valuable, why would they have gone to all the trouble of manually including sales inputs from email and spreadsheets?

As a friend of mine says, “There’s a pony in there somewhere”. I’d like to explore how to get that pony out and on the racetrack. I’ll start by exploring the optimal process for connecting sales forecasting to demand planning and S&OP. As always, your thoughts are very welcome in this journey of discovery.

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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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