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.

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.

• 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!

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:

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

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.

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

I’ll be covering each topic in separate blogs – there were so many great insights that were shared amongst the attendees.

First, maximizing sales adoption. This discussion focused around one of those deceptively simple and critically underestimated phases in the rollout of any new process and application. Rolling out any new application for sales people is especially challenging, but sales adoption can be greatly improved with these key learnings.

Key learning #1: Sales forecasting is fundamental to improving revenue and maximizing sales.

Make the sales forecast not only easy to create but also easy to consume. This will drive a more effective dialog between sales, products/marketing, and operations. This dialogue becomes a discussion about how the data can be leveraged by these groups to drive the various aspects of the business rather than a dialogue focused on who has the right data. Adoption of the forecast across the company is just as important as within sales. Nothing fosters adoption faster than seeing the results of your efforts delivering value back to you.

Key learning #2:  User adoption revolves around people’s motivations.

Many companies view the forecast as an incredible time sink that takes the sales team away from selling and is widely ignored by the rest of the organization. A highly motivating situation. How can you combat that?

First, motivate your sales people by showing them it’s easier than what they were doing before (remove the pain). You may not want to go cold turkey on them and pull their familiar forecast spreadsheet, but one of the thought leaders did. They found that when their sales team actually tried the new way, they realized the new application was much better. Why? Because it allowed them to forecast faster, and then provided them with improved visibility “around the curves”. The forecast became the tool that prevented them from slamming into walls at the end of the quarter. Less hassle, better insights will always motivate people.

Key learning #3: Show them you care.

We heard stories of sales people randomly changing their Excel-based forecast just to see if anyone was watching. Sadly, at many companies, no one is. But, if you give feedback on the forecast consistently and directly to the sales team, they will adopt the behavior you want. One of our VPs of Sales occasionally sends out emails to the sales reps making changes in their forecast asking questions about their updates. Combine this level of executive attention with an easy to use application for forecasting, and the sales team pays attention.

Key learning #4: A little competition goes a long way.

Carrot or stick? With sales people, competition and a carrot can really move the needle. For example, set up a competition between the forecast sales produces vs. marketing’s. Or, between sales teams in different regions or countries. For extra credit, set up a competition between operations and sales. Winning any of these competitions really means the company wins by getting an actionable forecast.

Next, key learnings around sales and marketing collaboration.

I aim to prove, with real life examples, that you can.

Increased forecast accuracy by 15%

A telecommunications equipment manufacturer was in a situation where operations teams would state that the sales and marketing forecast was sometimes 100% wrong. In fact, there was so little trust between the two groups that operations would build to its own forecast without sales and marketing even knowing. However, as you can imagine, inventory overages and stock-outs caused lost orders and encouraged the company to improve its forecasting. By following a proven four-step process to forecasting and putting structure around that process, the company was able to measure forecast accuracy and pinpoint the areas of forecast on which to focus.

Once operations, marketing and sales could see the areas of the forecast that required attention, they were able to increase the forecast accuracy by 25%. As I argued in a previous post, the intelligence to address the forecasting challenge exists in the minds of the people doing the forecast, they just need to focus their energy in the proper places. Now this telecommunications equipment manufacturer wins more business with shorter lead times and has reduced inventory levels by up to 25% by simply having a forecast on which operations knows exactly which elements can be trusted.

Decreased inventories by 20%

A semiconductor company that competes in a lead time-based market consistently had high levels of inventory to ensure that it could always have parts available to meet the customer demand. Unfortunately, this would result in excess and obsolete inventory that weighed negatively on its earnings and growth. It needed to stay competitive without carrying excess inventory. It implemented a similar four-step sales forecasting process and began measuring and enforcing forecast accuracy rules. The company quickly determined which products were the high accuracy products and changed its process to build to forecast on those products.

The company also found the lowest accuracy products and focused the sales team’s energy on improving the accuracy of those products. Just by building the high-accuracy parts to forecast, instead of to stock, the company decreased its inventory by 20%.

With improvements it is seeing in forecast accuracy of the other parts, the company expects to decrease its inventory levels another 20%. What can it do with all that extra money? Hire more sales folks? Build better products? Invest in growth?

The point is, decreasing inventory is not only good for operations—it is good for entire company.

I hope, with two real world examples, I have reduced your skepticism that there is gold in your forecast. It may be hard to find, but it is there. Through process and technology, like Right90 Trust Analytics™, you can find it. If you are still skeptical or you simply want to learn more, I encourage you to read other success stories or come to talk to us.

In my 20 years in the high tech world, I’ve never heard an enterprise application described as “super sexy.”

For this, I have to thank James Ward, a Technical Evangelist at Adobe who saw Right90 Trust Analytics™ app at Dreamforce and thought it was one of the best, see his Adobe Flex blog for the recording of the app here. James, thanks for bragging rights I never thought I’d be able to claim!

Once I got beyond the fun of it all, I thought about how the adoption of enterprise applications is greatly influenced by the user experience. Over and over, the biggest inhibitor to the success of a enterprise application deployment has been user adoption. Think of the phrase “shelf-ware” that described business’ purchase of enterprise apps that just sat on the shelf and were never deployed.

If users don’t adopt the application, no data gets entered, no analysis can take place, and no insights that can benefit the business are derived. It’s a simple hierarchy that drives a business’ success. CEOs can mandate the use of an application, but that use will still be spotty unless the users like the application and get more benefit from using it than not.

When I reflect on who our audience is for our applications, I’m very proud to have our app called “super sexy”. The users of our applications—sales reps and sales management primarily—are some of the toughest folks to please. (We all know they love their CRM systems and log in every morning like they do with their email.) If we can increase user adoption by providing them with an application that is a pleasure to use and delivers great value through the insights that are presented, I know that our customers will be successful. Sales forecasting has been a tough problem to solve for large companies, and I hope that applications like ours will inspire them to move out of the “do nothing abyss” and move forward to finally fix the chronic sales forecasting problem.

My thanks to James Ward for giving us some extra exposure; my thanks to our CTO, Dean Skelton, who is the brains behind this application; and finally to Elaine Cleary, our Customer Success Manager who so ably and beautifully demo’d our new application.  In addition to our applications, Elaine is one of the reasons that our customers have achieved success, she helps to guide their use and adoption of our applications. She also provides a weekly spotlight blog that can be accessed here.

Wouldn’t it be nice if you had a simple pie chart telling you where to focus?

Imagine that you had a simple pie chart that told you which parts of your forecast were trustworthy, which ones were medium risk, and which ones were completely untrustworthy? You would count on the green ones, focus your energy on the yellow ones and put the red ones in upside.

Today, we’re announcing Right90 Trust Analytics™, which provides exactly this capability. You can watch our Right90 Trust Analytics demo here as well.

Use historical performance to evaluate trustworthiness

By measuring the historical performance of the forecast in your organization, you can determine which segments of the forecast you can trust. For example, if a particular forecaster has historically been 95% accurate when forecasting in a specific region, then you are more likely to trust that person’s forecast for that particular region. In fact, there are four key elements of historical performance on which you can gauge trust in the forecast.

To that end, Right90 Trust Analytics features the ABCs of Trust:

• Accuracy: A percentage measurement of actual shipments versus the forecast (as of a specific time period prior to actual shipment).
• Bias: A percentage measurement, of the positive or negative variance between actual shipment and forecast. For example, a bias of -20% would mean the forecast was 20% below actual shipments, whereas a bias of 35% would mean the forecast was 35% above actual shipments.
• Completeness: A percentage measurement of how many forecast items have been updated in a specified time period. For example, if 80% of all accounts’ forecast have been updated in the past 2 weeks, that represents 80% completeness of the account based forecast.
• Consistency: A positive or negative measurement of forecast variance over time. For example, a sales rep with bias of 20% in Q1 and bias of -25% in Q2 is less consistent than a sales rep with bias of 30% in both Q1 and Q2.

By combining all of these historical measures into a single measure called the Right90 Trust Factor, you can segment your forecast into elements that can be highly trusted (for example, a trust factor between 8 – 10), or have moderate trust risk (4– 7) and those that are highly risky (1 – 3).

Now that you have a single number on which you can gauge trust in forecast, you can focus your energy on the areas of your forecast on which you can have an impact. If there is a customer or set of products that is subject to a particularly risky forecast, spend your time to ensure that order will come in or so that operations builds the units required to meet the customer demand and win that business.

Real value ($)

Enterprises weigh each of the ABCs to determine the relative importance in quantifying trust. The system automatically computes and presents the trust factor based on the weighting. Users can drill down into the data and make sales performance management, decisions, plan inventory and reduce risk.

By aggregating and segmenting the Trust Factor across the entire forecast, the patented technology of Right90 highlights the specific areas of risk. An executive can drill into the risk areas, judge the forecast, take necessary action, and create a trusted actionable sales forecast. Global 1,000 companies are utilizing a trusted, actionable forecast to make the key business decisions in time to immediately impact the business.

Many companies don’t realize it, but there is gold in the forecast. The hard part isn’t mining the gold – most sales executives can do the right thing if they know where to focus their energy. The hard part is knowing where to look for that gold. With the Right90 Trust Factor, you have that capability. In the final post of this 3 part series, I will describe companies that have you used a trusted forecast to increase revenues and forecast accuracy while decreasing inventory levels.

We built the application using Adobe Flex. In fact, James Ward, a Technical Evangelist at Adobe thought it was one of the best implementations of Adobe Flex in the enterprise he as ever seen. He recorded a demo video at this year’s Dreamforce and called it “super sexy.”

“Right90 Trust Analytics software really stands out due to its innovative and intuitive user interface which was built with Adobe Flex,” Ward said.

If you would like to know more about Right90 Trust Analytics, we’re going to be conducting an introductory webinar at 11 am on Jan. 22. You can register here. This webinar will show you how to:

• Maximize revenue and quota attainment by identifying and focusing on key areas of the forecast that require attention
• Improve sales forecasting performance by measuring and coaching forecast performance
• Get the necessary support from Operations while reducing stock-outs and minimizing lead times.

And, if you’re unable to make the webinar, please register now anyway and you will be able to access the recording as soon as it’s available.

For more information about Right90 Trust Analytics, you can read the press release or go to the product page.