1. Use your 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 your 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 your 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.

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

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.

All of these examples and many other requirements can be met with the Right90 “Copy/Paste Rows Special” functionality available on the Forecast screen. While a separate, more basic Copy/Paste row option enables you to quickly copy an entire row of data from one line item to another, “Copy/Paste Rows Special” gives you more advanced capabilities to copy and paste data selectively as well as for multiple lines of data. With this feature, you can:

• Copy data from specific lines from one time range to another
• Copy data from specific lines to different lines within a selected time range
• Copy data from specific lines from one plan to another plan that’s at the same level of detail.

Here’s a simple example to help you get acquainted with this time saving feature. Let’s copy several months of forecast for 3 products to the same months for 3 different products.

Step 1: Right click on any row in your forecast and choose “Copy Row(s) Special”.

Step 2: Complete the RightCaster information, pick affected rows, and hit the Copy button to copy.

Step 3: Right click on any row in your forecast and choose “Paste Row(s) Special”.

Step 4: Complete the RightCaster information, pick affected rows, and hit the Apply button.

In this example, the results below show that you’ve copied revenue forecast data for the first 3 rows to the last 3 rows on the screen.

Hit “Save” and you can see with just a few clicks that you’ve successfully replicated data from one place to another. Try using Copy/Paste Rows Special to save time updating your forecast!

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.

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.

• Maximizing sales adoption
• Sales and marketing collaboration
• Driving trust in the sales forecast.

In this blog, I’m delighted to share the key learnings around driving trust in the sales forecast (no, this was not a short discussion!).

Key learning #1: Trust in God, but all others pay cash.

“Visibility increases trust in the forecast.” One thought leader had forecasted that the coming quarter would be the best quarter ever for his company. No one believed him, yet he was right and delivered 21% quarter over quarter growth in a down economy. The best part was that the rest of his competition had negative growth that quarter. As a result of this lack of trust, he looked at the sales forecast process across the company. His key learning? The key to increasing trust across the company was getting visibility into each group’s assumptions (what were they thinking?) and making sure the hand offs between groups were well understood. It might be great that the Sales team is forecasting a big jump in sales for that product, but did they know Marketing just killed that promotion Sales assumed was still running?

Having a complete, visible sales forecast across Sales, Product, Marketing, Finance and Operations builds trust in what the sales forecast contains. Improving the process helps companies to more quickly understand what the forecast is telling them, and then take action. As another thought leader said, “When they’re arguing adamantly or are totally quiet, I don’t trust the forecast. I trust it more when there’s dialogue.”

Key learning #2: It’s gut check time.

While forecasting is a process, it’s in large part about human behavior. What’s the human behavior driving the output? Is it emotion driven or logic driven? Interestingly, our thought leaders gave great weight to the emotional and intuitive element as they’ve seen it be as effective as a logical, mathematically derived forecast. “Some sales people have an inherent knack to know if a customer is going to buy something or not. Some of the best forecasters are in better touch with their customers’ emotions, not the logical attribute checklist.” Another chimed in that academics were picking up on this—rules of thumb most of the time came within a small percentage of, or beat, heavy-duty statistical analysis.

A company’s ability to incorporate gut and science is like a pilot transitioning from visual to instrument flying. They must believe the instruments, but initially they’ll fixate on one instrument rather than instrument scan. After a while, they learn to look at the whole instrument panel, and their eye will go to what’s wrong. Forecasting is the same, judgment includes both logic and emotions (do I feel right about this?) and is honed over time and experience.

Key learning #3: Accuracy or performance management?

Trust can be evaluated in two different areas of the sales forecast – how much do you trust the individual forecaster and how much do you trust the overall sales forecast? A lot of the trust in the individual resides in knowing what their biases are and their performance over time. It’s not so much getting people to be the most accurate, it’s knowing how to adjust for their biases. Don’t get me wrong, accuracy and “truthiness” are important! Whether it’s a sales rep, or a customer giving a forecast, you need to measure accuracy to begin building trust. But, managing to performance incorporates managing over the “long trend line”, knowing what has changed, and why. Then the various stakeholders in the company can react appropriately. It’s not so much knowing that the forecast is wrong, it’s knowing which parts of the forecast are wrong.  Ultimately, the forecast is a human generated number; being able to use analytics to understand how good the humans are, and what parts of the forecast are good builds confidence in the sales forecast.

The bottom line is you want to get to a point where the company trusts its sales forecast and the sales people can trust they will get what they forecast. That means revenue.

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.

This option simultaneously copies current changes to other plans — which can save you valuable time and keystrokes.  A list of plans will appear in the pop-up menu that are at the same level of detail as your initial entry plan and are ones where you have authority to edit.  Simply save the changes to multiple sets of data all at once.

Depending on your company’s forecasting process, there could be many situations when it’s helpful to consolidate your updates across plans.  And don’t forget — you can include comments in Right90 with all your changes to provide additional context for others.

• Maximizing sales adoption
• Sales and marketing collaboration
• Driving trust in the sales forecast.

Sales and marketing collaboration proved to be a lively topic. Not surprisingly, the cats and dogs don’t get along much of the time. However, an actionable sales forecast can foster productive discussions that lead to better collaboration not just between sales and marketing, but between sales and other areas like operations. Our key learnings follow:

Key learning #1: Each area of the company has a different view and something to add to the party.

Our thought leaders had many different processes for getting from sales forecast to fulfillment. One company’s process was that sales generated the forecast, marketing vetted it and gave it to operations who built exactly to their forecast. Another’s was that the factory built to maximize their profit margin and then sent it to sales and marketing to sell, sell, sell, regardless of whether their customers wanted it or not. Either extreme doesn’t do a good job of linking feedback to the forecast. In the former, inventory is not optimized. In the latter example, the company could be doing 10-20% more revenue per year if marketing and sales had the  product they wanted, not just what the factory built. Both of these companies put sales forecasting systems in place to bring the various views together, as the forecast application can capture the different views of sales, marketing and operations into a common system of record, while tracking changes. This lets the organizations focus collaboration on what the data is telling them, not on whether or not the data is good. This is a much more productive discussion for these companies – focus on optimizing the unconstrained customer demand forecast with what the company can deliver.

Key learning #2: Responsibility and compensation drive behavior.

Regardless of the process, compensation drives behavior. When marketing runs the forecast, they can impact sales compensation by constraining product to make their inventory bonus, while sales doesn’t have the right products to sell. When the factory runs the process, their comp is to optimize inventory, not revenue which impacts both marketing and sales compensation. When sales runs the process, they can affect operations compensation (e.g. sales may have visibility to margin, but if they’re not comped on it like operations, they won’t optimize their forecast for margin in addition to revenue). Putting in the right compensation drivers is difficult for most companies. Many focus on one end or the other of the sales forecast to fulfillment process. As one thought leader asked another, “Your marketing forecast affects the sales team’s comp when the units are wrong. How does sales like you then?” The reply? “More when inventory is available.” All of the companies recognized that aligning compensation across all the functions is an area of opportunity to drive better behavior for their company.

Key learning #3: New product introductions are the most fun.

New product introductions (NPI) are the riskiest part of any forecast. Run rate or recurring business is much easier to forecast, and is more predictable.  But creating a forecast for an NPI is a work of art, not science. One thought leader’s company has the product team focus on the idea of what the product will look like in the context of macro level items, like input from big customers, consumer data, and competitive products. Sales, of course, has a different view. Their forecast for NPI starts with sales, then goes to marketing, and then to sourcing. This insures that input from sales on new items is definitely seen by marketing, and seen earlier. It is better to have sales call the baby ugly, and let marketing know. And many times, sales was right.

The best analogy of this session — one of our thought leaders likened a new product introduction to flying an F18 by the wire. For those of us who don’t know, when an F18 takes off from an aircraft carrier the pilot is not actually flying the plane. Pilots make too many changes to the stick and the on-board computers can’t keep up. The computer takes care of the plane’s takeoff, then the pilot takes over once airborne. For a NPI, marketing should launch the product, and then sales can fly it. Of course, operations had to build the plane first!