Max transforms the traditional, error-prone approach to sales pipeline forecasting and valuation by applying probability theory and Monte Carlo simulation. The result is an easy-to-use model that accurately simulates the way sales leads evolve into booked revenue. This model gives sales and finance professionals unique knowledge and insight into sales pipeline dynamics and potential results by graphically presenting the outcome of opportunity combinations.

The current state of the art in pipeline management represents the application of simplistic discounting practices: a company typically discounts the total potential value of deals within a sales cycle stage using a discount-factor-based general rule of thumb. The cumulative discounted value of these phases is summed to provide a total estimated value of the deals in progress.

This traditional approach to pipeline valuation does not reflect the way a sales opportunity becomes booked revenue because it lacks the ability to account for:

• Fluctuations in opportunity closure rates
• That all opportunities aren’t equal in value and risk
• That fact that individual opportunities either convert into booked revenue—or they don’t.

While the fundamental basis of pipeline valuation has remained rooted in past capabilities, automation has been implemented to improve the collection and aggregation of data. Sales opportunity tracking is a base function of all CRM systems. Max leverages this sales opportunity data to provide sales and financial professionals insight into sales pipeline dynamics, allowing for better decision making and better results.

Max reproduces the dynamic through which an individual opportunity converts to booked revenue. By extending the opportunity management capabilities of CRM with advanced modeling techniques, Max provides insight into the real dynamics of sales pipeline performance. Max arms the sales and finance professional with unprecedented insight into true pipeline results.

Although the name refers to the famous casino in Monaco, Monte Carlo simulation methods were developed in the 1940s for the study of nuclear fission. The methods include taking a large number of random inputs into a model and recording the resulting values of combinations of these inputs. These values are then categorized by range to provide a statistical view of probable outcomes.

Monte Carlo simulation methods are very useful when trying to understand the potential outcomes of situations with multiple variables that aren’t fully known or understood. By analyzing outcomes produced through different combinations of variables, Monte Carlo simulation provides unique insight into the probable results of specific situations such as quantum physics scenarios, cinema special effects—and business opportunities.

In recent years, businesses have adopted analytical models based on Monte Carlo simulation. Using these methods, they can perform financial analysis related to ROI, NPV, risk, and other business problems. The output from a Monte Carlo simulation model provides a unique view of the business opportunity and provides a rational foundation for better and faster decision-making.

You can learn more about Monte Carlo simulation at:
en.wikipedia.org/wiki/monte_carlo_method

Although we don’t recognize it as such, most of us use triangular distribution in our everyday decision-making. We commonly refer to choices as “best, most likely, and worst” cases. We then skew the probable results of the decision to the most likely case, and view the worst case as the riskiest scenario and the best case as “upside.”

In Monte Carlo simulation models, a triangular distribution is often used to define a range of values for unknown variables as input to the model. During a model run, the system will select a potential value for the variable between the worst and best cases that is skewed towards the most likely value. This approach allows the model to reproduce a set of results that represents potential real-world outcomes.

Max leverages the use of triangular distributed variables in simulating the likelihood that an opportunity will convert into booked revenue. This approach lets Max’s simulation model account for the uncertainty of customer purchase action. By defining closure rates through a triangular distributed variable, Max provides a true picture of probable outcomes for combinations of sales opportunities.

Implementing and using Max is simple and doesn’t require expensive resources or the involvement of your IT group. The following are required to run Max:

• PC with Microsoft Internet Explorer or FireFox browser, and with the Adobe Flash Player installed.
• Subscription or license to a CRM system such as SugarCRM, Salesforce, or RightNow.

That’s it! After you subscribe, Max will be activated, and you can access Max through your CRM service.

Aha! Software can provide you a Max private edition solution. Contact Aha! Customer Support for Max.

MAX DISPLAY GRAPHS
Max displays model run results using a standard bar chart format. The horizontal axis represents the value of each simulation that is run. For usability, all model runs are divided into 15 separate result bins, each represented by one display bar. While the vertical bars in the graph actually represent bin value ranges generated by simulation runs, the actual $ label on the bar represents the low value for the represented range. The value range is bounded by the label of the next vertical bar. The vertical axis represents the percentage of results each bin represents. The height of the bar represents the percentage of model runs of the resulting number of the sales opportunities that fall between the lower and upper values for that bin.

A normal simulation run will generate a bell curve display. This graphical display of potential sales pipeline outcomes provides you with the true revenue potential of the pipeline, the risk of the pipeline, and the probability of revenue yield from the pipeline.

Graphs with $0 value: Max calculates potential pipeline results based on probability and statistical odds of closing deals. In situations where there may be few opportunities in a sales pipeline stage, you will often get displays that show the potential for a zero value outcome in the pipeline. These zero values represent potential and severe revenue disruptions to your organization.

To manage these situations, closely monitor the opportunities in the pipeline to maximize their odds of revenue conversion. You may also want to focus on increasing the number of opportunities in the pipeline.

Bimodal Graphs: Bimodal graphs are sometimes referred to as “Camel Hump” displays. In a Max simulation, these often occur when there are several opportunities that have a significantly higher value than other opportunities in the sales pipeline.

To maximize revenue in these situations, identify the high value opportunities and set specific selling action activities and commitments and closely monitor their progress.

Spiked Graphs: A spiked graph has high value bars scattered throughout the display. These results are typically generated when there are a limited number of opportunities in the sales pipeline.

To maximize revenue in these situations, identify the critical opportunities and manage them closely to reduce risk. In addition, work to increase the number of opportunities in the sales stage.

Empty Values: No selected opportunity records were sent to Max. You need to check any filters you have set to see if there are records that meet the filter criteria. Opportunities that have closed (won or lost) are not included in the model runs, as these would distort the results.


MAX KEY DATA FIELDS
A Max model run returns 4 key data fields. These returned data fields provide the user with additional insight into the sales pipeline.

Single Number View Data Fields
Single number views provide a reference point that can be utilized in interpreting the Max Model results. Because they are calculated using traditional pipeline analyzation techniques, it allows users to transition into a better understanding of true pipeline potential.

Gross Stage Value
One key performance indicator that can be used in managing the sales pipeline is understanding the gross value of all opportunities in a sales stage required to produce specific revenue yield. This data field provides the gross cumulative value of all leads within a specific pipeline stage. By monitoring this value, users can gain insight into the relationship of gross stage value against potential revenue yield.

Forecast Value
This data field represents the value generated through a traditional forecasting approach whereby the values of all opportunities are summed and then discounted based on the closing value. To calculate this field, Max utilizes the default closing probability for the stage defined in the CRM system. This data field is designed to give the user a perspective of how a traditional forecast value would be positioned within the actual potential sales pipeline results.

Simulation View Data Fields
Simulation view data fields provide the user with the absolute best and worst case scenarios.

Minimum Potential Pipeline Value
This data point represents the potential sales pipeline scenario with the minimum value based on the opportunity closing probability. It provides an understanding of the lower bound for potential sales results.

Maximum Potential Pipeline Value
This data point represents the potential sales pipeline scenario with the maximum value based on the opportunity closing probability. It provides an understanding of the upper bound for  potential sales results.

Max model output can be copied using a screen or flash image capture utility. While Aha! Software does not endorse any specific screen or image capture tools, we have used the following utilities in a Microsoft Windows environment:

Graphs and annotations can be captured and saved as images or as Flash files using FlashCapture from Dreamingsoft. This is a commercially licensed product, and information can be found at:
http://flashcapture.com/flashcapture/quicktour.htm

A screen capture utility can also be used that can select a rectangular area of a screen and save it to the clipboard or to your disk. This image can then be inserted into a document. There are many commercial and freeware products that can be used for this purpose. One such tool is ScreenGrabber from customsigngenerator.com. It is a free tool that can be downloaded from:
http://www.customsigngenerator.com/tools/ScreenGrab.zip

Complete screens can be captured and saved in the clipboard using the PrintScrn button on your keyboard. You can then paste the image into an image-processing tool such as Microsoft Paint, or directly into your document.

The number of iterations needed to provide a valid model depends on the number of opportunity records being modeled. Simulations with less than 500 records typically don’t need more than 5,000 iterations. Simulations with more than 500 opportunity records may require as many as 10,000 iterations.

Max automatically sets the closure probability for a stage, with the most likely value representing the default closure rate defined in the pipeline stage, -10% for the worst case and +10% for the best case. These values are in fact the default values when you first run Max.

You can improve the accuracy of the model by setting these based on known historical data. An example would be setting the worst case at the lowest conversion rate you have experienced in the past 12 months, the best case at the highest true conversion rate in the past 12 months, and the most likely case at the average conversion rate for the past 12 months.

Opportunity records can be filtered using the standard selection process of CRM service or software (SugarCRM, Salesforce, Seibel On Demand, etc.). Refer to the CRM Help documentation to find out how to set filters for your specific needs.

Adobe’s Flash Player is required to display model results. You can download a free version of Flash from Adobe: www.adobe.com

On rare occasions, Flash may have problems displaying graph data. Uninstalling and reinstalling your Flash Player usually clears up the problem. Information about uninstalling and reinstalling the Flash Player is available here:

Uninstall:
http://www.adobe.com/cfusion/knowledgebase/index.cfm?id=tn_14157
Install:
http://www.adobe.com

Several things can cause pipeline stages not to display. The most common cause is that no opportunity records met the filter requirements. If you have a pipeline stage that doesn’t display, we suggest that you run Max against all opportunity records and view the generated graphs. If you still get blank or missing graphs, check your opportunity records to verify that there are valid records for modeling.

Contact Aha! Customer Support for Max

In order to utilize Max, you must first implement the Max Interface module within your SugarCRM On-Demand instance and then activate your Max Modeler subscription.

The Max Interface Module provides a system integration of Max with your CRM system. This module provides a user interface consistent with your CRM system and allows Max to utilize sales stage definitions and opportunity records stored within your CRM environment. Implementation is a simple process that can be performed in less than 15 minutes without requiring scarce IT resources.