To see the Right90 forecast in Salesforce CRM, just go to your regular “Reports” tab and choose to view the “Right90 Demand Forecast Reports” Folder. There will be some default views in that folder (including a standard “Weighted Revenue Forecast”) that you can use as a starting point for creating customized reports and dashboards that meet your needs.

In addition to your sales forecast data, keep in mind that we can also write back other “plans” or sets of data that you maintain in Right90 as long as it’s at the same level of detail as your original sales forecast. So for example, if you have a marketing forecast that’s at the same detail as your sales forecast, you can use Salesforce CRM to report on it as well. In fact, we’ll even help you get started—just send a request to the Right90 Support team. We want to make sure you’re taking advantage of all the features your integrated solution has to offer!

The role of the front office, specifically sales and marketing executives, is to own the revenue and mix forecast of the company. This is “Sales and Marketing Management’s commitment” to the process as Oliver Wight puts it. To be clear, this means operation’s demand planners, typically using supply chain tools, are not the owners (see Demand Planning’s Achilles’ Heal). This complete Sales and Marketing forecast drives the supply planning and at minimum influences, or in fact is the seed for, the finance plan.

As highlighted in “S&OP: What’s next after 30 years“, there is a need for easier, more effective S&OP tools for Sales and Marketing to define their commitment to the CEO and on down the food chain. This is key to getting to the next level per AMR’s S&OP maturity model. Among the tools available today for the Front Office, the worst may be ERP. Surprising or not, I have seen sales operations enter long term “forecast” sales orders (on behalf of Sales and Marketing) into ERP systems like SAP and Oracle, that are then exported and rolled-up into Excel to represent an S&OP forecast (at Fortune 100 companies, mind you). With my supply chain background (at i2 and Manugistics, now JDA) I have also seen advanced supply chain demand planning tools rolled out to Sales and Marketing including SAP APO and Oracle Demantra—all in vain—too many times, much like complex financial planning systems like Cognos or Hyperion.  The latest Gartner S&OP Marketscope echos this tool gap.  Excel, as hellish as it can be, surprisingly may be, at least, a better option than the latter ones.

Another tool often experimented with is CRM with its user-friendly and mature  “web 2.0″ technology (i.e. sales force automation like Oracle CRMOD, Microsoft Dynamics or salesforce.com). The world of leads, campaigns and opportunities is indeed the “language” of sales and marketing, but as many learn the hard way, CRM is not the right tool for forecasting mix and for departmental collaboration, despite its demand-related data richness and ease-of-use for sales (see CRM vs forecasting post).

What are we left with for Sales and Marketing? Looking at the quadrant above, is there an S&OP option for Sales and Marketing with Demand Planning and Financial Planning’s forecasting robustness and CRM’s ease of use and forward-looking sales data? In my next blog I will detail how companies like Sharp and Lineage Power have gotten to the next level in S&OP and reaped tremendous benefits. These companies equipped Sales and Marketing to quickly ramp up and own the S&OP forecast for the company as best practices would have it.

Specifically I will dive into how the right front-office solution can transform each step of the Executive S&OP process (per Oliver Wight):

1. Marketing/Events Activity Review
2. Demand Review
3. Supply Review
4. Demand and Supply Balancing Review and
5. The Executive Review.

Until then, let me know how you collect Sales and Marketing input for your S&OP process, and how much ownership of the forecast your Sales and Marketing teams claim!

For example, the chart to the right shows that the 2010 forecast for Apple has increased by $26M in the last 6 months — with most of that contribution coming in Services forecast as detailed in the Change Summary to the right of the graph.

The other two lines on the chart represent the forecast for two of your other key accounts — Sony and Verizon Wireless. Changing the “Focused On” from Apple to either of these other two allows you to quickly toggle between each customer to understand what’s driving the forecast changes.

Since we’re capturing forecast changes by customer, product, and region, you have a lot of flexibility in determining the type of comparisons you want to see on the graph and also which views of the Change Summary detail on the right will be most meaningful. Get started today by going to Right90 Change Analytics, select “Create New View” in the top right corner of the screen, and start learning more about how your forecast is changing.

What better way to do that than through a great sales forecast? Our final difference is how companies can use their forecast to drive their key business processes with the three systems.

Difference #5: How systems help companies apply the forecast to drive key business processes

Once a sales forecast is captured, vetted, rolled-up and scored, businesses need a way to use the forecast to drive key business processes.  For example, manufacturers use their sales forecast to match demand and supply (demand-supply matching), manage their product mix, forecast their revenue and margin, manage promotions, and prioritize customer service levels. Their ability to do this depends greatly on the capabilities of the forecasting system that they are using.

A Sales Forecast System has features and capabilities that are designed to help construct the complete sales and marketing forecast and use it to drive these different business processes. A sales forecasting system supports multiple types of inter-related plans (e.g., sales commit, marketing judgment, channel, upside, etc.), multidimensional roll-up and analysis, change and accuracy analytics, and other capabilities that are necessary to integrate the sales forecast into the operational fabric of the company. A sales forecast system is the means for Sales and Marketing to take ownership of the end-to-end bottoms-up revenue forecast and to make well informed predictions about what the shape of customer and product demand is likely to be.

A CRM System is focused on opportunity forecasting and pipeline management. CRM systems are generally focused on predicting the dollar value and likelihood to close of new customer and new product related opportunities. CRM based forecast processes do not usually address run-rate business, channel forecasts and new product forecasts. They do not usually provide a complete view of anticipated customer and product demand or provide pre-built capabilities to facilitate the driving of other functional business processes based specifically on the forecast. While great tools for managing opportunities, they do not help the rest of the company see and understand the complete sales and marketing forecast. For this reason, many operational and finance executives are obligated to assemble other, sometimes conflicting, parts of the forecast that live in spreadsheets outside of the CRM system to get complete visibility into anticipated sales and marketing outcomes.

A Sales Analytics System can be configured to provide visibility into data that exists in the CRM system but needs to be customized to provide insight into data that spans other systems (SCM, CPM, ERP). Often, this customization occurs at the data warehouse level and is more aligned to CPM (corporate performance management) initiatives. To achieve sales forecasting, a lot of business process and application logic needs to be built around the basic business intelligence framework that most CPM systems provide. This means a larger IT effort with more cost and time to deploy than a pre-built sales forecasting system.

So this brings me back to the big question from the audience at the Sales 2.0 Conference, “What’s the difference between CRM, sales forecasting software and sales analytics software?” The 5 key differences between CRM, sales forecasting and sales analytics systems lie in:

• handling run-rate and new business
• collaborating across departments
• building a complete sales forecast
• scoring the reliability and quality of the sales forecast
• using the forecast to drive key business processes.

Let me know if you agree.

^{Photo from Flickr user ralphbijker}

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.