Building a Metrics Program

Most organizations know it is important to record business development metrics. Far fewer, however, have a strategy for using metrics to measure and manage their performance.

Introduction

Proposal and BD organizations are typically measured on how well their business entities perform in winning business, especially new business. Most often, these metrics take the form of:

  • Measuring competitive win rate
  • Assessing the status of “top” opportunities
  • Comparing revenue generated to revenue targets
  • Calculating total revenue in the sales funnel versus the revenue plan
  • Comparing investment to either budget or return

Irrespective of the merits of these metrics, a very small percentage of proposal and BD organizations have well-considered, coherent strategies for using metrics to actually manage their performance.

A metrics “program” goes beyond simply having metrics. Rather, it is a structured approach to managing results by measuring performance directed to achieve specific goals. Building such a program requires clear understanding of what the organization wants or needs to achieve, along with a thorough analysis of the factors that need to be controlled to be successful.

A coherent approach to proposal and BD metrics is typically process based and involves multiple measures of performance in areas relevant to desired results. Moreover, it should involve a hierarchy of performance measures that connect management-level reporting metrics to their contributing elements at lower levels in the organization.

Best Practices

1. Define goals to be achieved or supported through a structured metrics program.

Typical BD metrics goals are commonly expressed in phrases such as “achieve business growth defined in the strategic plan” or “meet revenue targets set in the annual plan.” The telling questions, though, are whether an organization has a clear understanding of:

  • What it takes to meet such goals
  • What should be managed to maximize the probability of meeting such goals

Of course, a significant part of the answers to these questions has to do with market factors and customer trends. Proposal and BD organizations are often frustrated that they are held accountable for “win rate” or “capture ratio” when many factors outside of their control can affect outcomes. However, a significant part of the answer can be understood and managed based on industry best practice and research on high-performing BD operations.

One robust approach to building a metrics program for proposal and BD operations focuses on managing the capability required to sustain predictable results over time, as documented in the Business Development Capability Maturity Model®. This model articulates specific goals and practices in five categories—customer behaviors, leadership characteristics, organizational competencies, performance management, and environment—that correlate with predictable results.

In short, the BD-CMM defines both what it takes to meet BD goals and the set of conditions to be managed to maximize probable outcomes. A metrics program based on this approach might look like the example shown in Figure 1.

Figure 1. Sample Metrics Program.

Figure 1. Sample Metrics Program. A metrics program based on BD-CMM would monitor performance in categories relevant to maintaining capability, as described in the model, adjusted for organizational maturity.

2. Consider the relative maturity of the organization and its ability to implement and maintain metrics.

One of the complexities associated with building a BD metrics program is that industry research shows that immature BD operations are unlikely to value and support meaningful metrics (Figure 2). For this reason, you must have a sense of the relative maturity of your organization to plan metrics that are appropriate. You can accomplish this using BD-CMM criteria, which generally suggest the following:

If the organization does not have a formalized BD process, it is unlikely to support more than simple metrics and is not a good candidate for a structured metrics program
If the organization does have a formalized BD process, it can support a structured metrics program in some form
Only highly mature organizations seem to be able to actually implement a metrics program with more robust (i.e., predictive) metrics
The lesson here is simply a caution that a metrics program must be designed for success. That is, one must be able to envision that the metrics included in the program will be able to be supported with data and appropriate resources within the organization.

It takes some level of organizational maturity to go beyond using only trailing, or historical metrics (which rely on historical data and are intended to establish trends and initiate lessons learned), in assessing BD operational performance.

Figure 2. Relative Value of Metrics.

Figure 2. Relative Value of Metrics. Research suggests that an organization must have a reasonable level of maturity before it is likely to be able to support a structured metrics program.

3. Select metrics that can be directly linked to targeted results.

In selecting metrics to be included in the program, clearly envision how the metrics connect to results. One must understand what elements must be controlled to achieve desired goals. For example, in the sample metrics program shown in Figure 1, one of the customer metrics is “proposal quality versus standard.”

A very simple “standard” could be 100-percent compliance to customer requirements. Any given proposal can be seen to have a targeted result of having no stated deficiencies from the customer. This could be applied by even a low-maturity organization. This is a purely quantitative measure that can be easily interpreted and has a clear path for root-cause analysis when deficiencies are identified.

A more sophisticated approach could define the standard with compliance as only one of several measurements. In this case, the organization might define proposal quality to include responsiveness, application of strategy, messaging, document structure, use of visuals, and so forth. In this case, the targeted result is a proposal that has many characteristics considered important for winning.

In the latter case, the metric has both quantitative and qualitative measures that add more depth to the performance analysis. At the same time, it allows for in-process measurement of the product, which can be used to improve the customer deliverable before it is actually delivered.

For most organizations, candidate metrics should also be assessed for their ability to support both “in-process” and “forensic” use:

  • Forensic metrics (or historic metrics) are based on completed activities and apply formal or informal measurements as part of root-cause analysis to try to improve the process. An example of a forensic metric is win rate, because win rate is known only for completed bid.
  • In-process metrics (i.e., measures of work in progress) measure process products and activities that are still in development. They are intended to influence the content of products and how well activities are performed to improve the subsequent quality of the final results. An example of this type of metric would be status of an organization’s top 10 opportunities.

Highly mature organizations often include predictive metrics, as well. Predictive metrics can be correlated with “norms” in estimating probable outcomes. They are intended to influence current decisions in favor of better outcomes. An example of a predictive metric is opportunity alignment with strategic goals.

The problem with in-process metrics is that they do not always tell us how to create new products. They tell us only about current status, not outcomes. Metrics should be selected for their ability to support forensic use in root-cause analysis when failures occur.

For a robust program, goals should be tiered with both management-level and organization-level measures. In the example of proposal quality versus standard, one can imagine that the metric reported to management is “percentage of proposals with no customer-determined deficiencies” or “percentage of proposals passing internal audit of winning characteristics.” What is measured within the proposal organization, on the other hand, might be how each proposal “performs” against each of the various criteria that contribute to “characteristics deemed important for winning.”

4. Establish an appropriate infrastructure for collecting, analyzing, and reporting data in support of the metrics program.

For a metrics program to be successful, it must be reasonably easy to collect, analyze, and report data. It is also imperative that reasonable decisions are made regarding the form in which data are collected and reported.

Formalize a management plan that clearly defines:

  • How often data are to be reported
  • Who is responsible for providing needed input
  • How data are to be analyzed
  • How exceptions are to be handled

The proposal quality versus standard example provides a good illustration. A simple metric like compliance fits collection for each proposal, with records kept by the BD process owner or someone in the Business Opportunity Center, and a rolling average reported monthly or quarterly to senior management.

5. Implement the metrics program, including resource allocation and defined accountability.

One key issue for implementing the metrics program is ensuring that adequate budget and human resources are made available to support the program. Some organizations gain resources by involving their quality function in BD performance management, especially if the approach includes use of performance audits. Nonetheless, resource requirements will vary according to the complexity of the program, although more mature organizations are more likely to have formal budgets and assigned staff associated with their metrics program.

A second important issue is making someone accountable for gathering, analyzing, and reporting data. It’s typically best to have a single individual or small group responsible for managing the metrics program. In mature organizations, the designated process owner is also typically responsible for performance management and metrics. However, it’s also fairly common for this responsibility to reside within a BD unit.

6. Maintain and improve the metrics program through ongoing lessons learned.

A good metrics program should not remain static. At least once a year, review the program for both content and structure. On a more routine basis, it’s important that in-process metrics be shown to contribute to success, and it is good practice to include this consideration in BD project “lessons learned” activities.

Conduct the broader review of metrics program impacts as part of both process analyses and ongoing management process improvements. If a metrics program or individual metrics within the program are not helping the organization achieve its BD goals, investigate causes and identify improvements.

Application in Diverse Environments

Metrics in smaller settings

In building BD metrics programs, one size cannot fit all organizations. Certainly, programs in small settings need to be streamlined and may focus primarily on gathering data that directly feed management-level metrics, rather than having a hierarchy of measurements. The most common goal for metrics in these settings is to support business or revenue growth.

Metrics in larger settings

By comparison, metrics programs in highly mature BD organizations can be quantitatively driven and offer substantially greater predictive capability. For these organizations, the most common goal is to manage performance risk.

Meanwhile, in complex organizations, such as large corporations, the corporate metrics program can be structured to encourage cross-divisional collaboration to leverage broader corporate assets, with overriding goals to both integrate performance across multiple operations and manage corporate risk.

Common Pitfalls and Misconceptions

Failure to develop performance-focused programs

A major problem in developing and deploying a BD metrics program is the fact that BD metrics have remained largely static over time. There has been little emphasis in most organizations on creating robust programs to drive performance, and organizations that have created such programs have seldom shared their data. This has led to a misconception that BD operations cannot benefit from having formal metrics programs and that BD activities are generally not amenable to such management systems. Indeed, this has been fed by many sales and BD professionals, who have historically emphasized the “art” of their activities to the exclusion of the “science” involved.

For those who have been interested in having good metrics, their efforts have often been inhibited by lack of connection between what is most often required of them by senior management and what actually drives performance. This is especially true of the demand to report “win rate.” This metric can be more meaningful in combination with other metrics and when its use focuses on forensic analysis of results.

Summary

  • A metrics program encompasses a well-defined system of measurements that collect into a management reporting scheme and connect to relevant performance drivers.
  • A good metrics program includes both qualitative and quantitative measures, and supports both performance measurement and process improvement.
  • Before implementing a metrics program, professionals should evaluate their organizations’ current BD maturity levels.
  • Metrics incorporated into the program must be selected based on their connection with results desired and measured at the organization level accountable for actions being monitored.
  • When structured around an industry standard like the BD-CMM, a metrics program is most likely to address both near-term results and long-term sustainability.

Terms to Know

See Also

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