Sales Planning, Forecasting, Budgets and Quotas

Introduction and Context

Strategic sales management converts organisational strategy into coordinated market-facing action. Sales planning determines where the organisation intends to compete, which customers and offerings it will prioritise, how resources will be deployed, and what results will constitute success. Forecasting estimates what is likely to happen; budgeting allocates financial resources; and quota-setting translates organisational expectations into measurable responsibilities for territories, teams and individuals.

These activities are interdependent but not identical. A forecast is an evidence-based estimate of expected performance, whereas a target or quota is a managerial commitment or expectation. A budget expresses planned resource use in monetary terms. A sales plan integrates all three with market analysis, segmentation, channel decisions, account strategy and implementation controls.

Poor planning produces predictable consequences: unrealistic targets, excessive discounting, neglected accounts, inaccurate inventory decisions, low morale and unreliable financial projections. Effective planning is therefore both an analytical and a behavioural discipline. It combines data with managerial judgement, and it must be revised as market conditions change.

Learning Objectives

By the end of this chapter, learners should be able to:

• explain the relationship between sales plans, forecasts, budgets and quotas;
• prepare a coherent sales plan aligned with corporate and marketing strategy;
• apply qualitative and quantitative forecasting methods;
• calculate and interpret pipeline coverage, forecast error and forecast bias;
• use scenario planning to examine uncertainty;
• construct activity, revenue, profit and strategic quotas;
• evaluate the ethical, behavioural and technological implications of target-setting.

1. The Strategic Sales Planning System

A sales plan is a formal statement of intended sales actions and results over a specified period. It normally includes market priorities, objectives, strategies, programmes, resources, responsibilities, schedules, assumptions and control measures.

A useful planning hierarchy is:

1. Corporate strategy defines the organisation’s overall direction, such as growth, stability, market development or diversification.
2. Marketing strategy specifies target markets, positioning, value propositions, pricing and channel design.
3. Sales strategy determines how customers will be acquired, developed and retained.
4. Sales programmes translate strategy into campaigns, territory actions, account plans, partner initiatives and selling processes.
5. Budgets and quotas assign resources and measurable expectations.
6. Controls and reviews compare actual performance with plans and initiate corrective action.

Planning should be both top-down and bottom-up. Senior management provides strategic boundaries, financial requirements and resource constraints. Sales managers and representatives contribute customer intelligence, opportunity evidence and practical estimates. A purely top-down process may impose unattainable goals; a purely bottom-up process may embed excessive caution or local optimisation.

Components of a professional sales plan

Objectives should be specific, measurable, achievable but stretching, relevant and time-bound. However, the familiar SMART principle should not be interpreted as permission to make targets merely easy to measure. Strategic objectives may include qualitative outcomes such as improving executive relationships, entering a priority sector or developing partner capability. These should be supported by observable indicators.

2. Sales Forecasting

A sales forecast is an estimate of future sales for a defined product, market, territory, channel and period. It should specify the unit of measurement: units, orders, revenue, gross margin or contribution. Forecasts are conditional statements based on assumptions; they are not promises and should not be confused with quotas.

A sound forecasting process identifies:

• the forecast horizon and time intervals;
• the products, territories, channels and customer segments included;
• the dependent measure, such as units or net revenue;
• data sources and quality standards;
• assumptions concerning price, supply, competition and customer behaviour;
• the owner of the forecast and the review timetable;
• uncertainty ranges or alternative scenarios.

2.1 Qualitative forecasting methods

Qualitative methods are particularly valuable when historical data are limited, the market is changing rapidly, or a new product is being introduced.

Executive opinion collects estimates from senior managers. It is fast and useful for strategic direction, but hierarchy and optimism may suppress disagreement.

Sales-force composite aggregates estimates from salespeople. Representatives possess current customer knowledge, although they may understate potential to make quotas easier or overstate deals to appear successful.

Customer intention surveys ask existing or prospective customers about planned purchases. Stated intentions may not become actual orders, especially when budgets or priorities change.

The Delphi method uses several anonymous rounds of expert questioning. A facilitator summarises responses between rounds until opinions converge or the reasons for disagreement become clear. Anonymity reduces the influence of status, but the result remains judgement-based.

Analogy and market research infer demand from comparable products, related markets, pilot launches, distributor intelligence or industry studies. The analogy must be tested because differences in price, regulation, adoption and competitive intensity can be substantial.

Qualitative evidence should be documented. A forecast review should record who contributed, what assumptions were made, how confidence was assessed and what evidence would cause revision.

2.2 Quantitative forecasting methods

Quantitative methods use historical observations or measurable drivers. They are not automatically superior to judgement: poor data, structural market changes and inappropriate models can produce precisely calculated errors.

#### Naïve forecast

For a stable series, the next-period forecast may equal the most recent actual value:

Here, F_t+1 is the forecast for period t+1, and A_t is actual sales in period t. This method is simple and provides a benchmark. It assumes that the latest observation is the best available estimate and is unsuitable when strong trend or seasonality is present.

#### Moving average

A simple moving average of the most recent n observations is:

The variables have the same units as sales. The method smooths random variation but can lag behind turning points. A longer window provides greater smoothing but responds more slowly to change.

Worked example. Quarterly unit sales were 900, 1,000 and 1,100. A three-period moving-average forecast for the next quarter is:

The calculation assumes each of the three periods deserves equal weight and that the recent history is relevant to the next period.

#### Weighted moving average

When recent observations are more informative, weights can be assigned:

Weights are non-negative in the usual application. If the latest three observations are weighted 0.5, 0.3 and 0.2 respectively, using 1,100, 1,000 and 900 units gives:

The weights reflect managerial judgement or may be selected through validation against historical data.

#### Exponential smoothing

Simple exponential smoothing updates the previous forecast using the latest forecast error:

α is the smoothing constant, A_t is actual sales and F_t is the prior forecast. A high α responds quickly to new information; a low value produces greater stability. The method is most appropriate for level data without pronounced trend or seasonality. Trend and seasonal data require extended methods, such as Holt or Holt–Winters procedures, and should not be treated as simple level series.

#### Trend projection and regression

A basic linear trend can be written as:

Here, y_t is sales in period t, a is the intercept, b is estimated change per period, and ε_t is the unexplained error. The model assumes an approximately linear relationship over the forecast range and reasonably stable conditions. Extrapolation far beyond the observed data is risky.

Regression can also relate sales to explanatory variables such as active customers, advertising expenditure, price, sales capacity or economic indicators. Correlation does not establish causation. A variable may be associated with sales because both respond to a third factor, and a model estimated from historical data may fail after strategy or market structure changes.

2.3 Opportunity and probability forecasting

For an opportunity-based forecast, expected value is often calculated as:

EV is expected value, p_j is the estimated probability of winning opportunity j, and V_j is its relevant value, usually expected net revenue. Probabilities should be calibrated using historical conversion rates and stage definitions, not invented to make a forecast appear credible.

For example, three opportunities have values of $20,000, $40,000 and $60,000 with probabilities of 0.8, 0.5 and 0.25. Their expected revenue is:

This is a risk-adjusted estimate, not a guaranteed order. Opportunities may not be independent, probabilities may be conditional on the same market event, and revenue timing may differ from booking timing.

3. Forecast Accuracy, Bias and Pipeline Coverage

Forecasting must be evaluated systematically. A forecast that appears close in total may conceal severe errors by product, territory or month.

For period t, forecast error may be defined as:

where A_t is actual sales and F_t is forecast sales. Under this convention, a positive error means actual sales exceeded the forecast.

Common measures include:

Mean absolute error, MAE, measures average error in the original sales units.

Root mean square error, RMSE, gives greater influence to large errors and is expressed in the original units.

Mean absolute percentage error, MAPE, expresses average absolute error as a percentage. It is not suitable when actual values are zero or close to zero, because the denominator makes the measure undefined or unstable. In such cases, MAE or a scaled alternative is preferable.

Forecast bias can be monitored through mean error:

A positive ME under the stated convention indicates systematic under-forecasting; a negative value indicates systematic over-forecasting. Bias should be examined by role and period. Persistent over-forecasting can cause inventory and staffing problems, while persistent under-forecasting may lead to missed growth investment.

Pipeline coverage

Pipeline coverage compares qualified opportunity value with the remaining target. A basic ratio is:

If qualified pipeline is $1.2 million and remaining quota is $400,000, coverage is 3.0×. This does not mean the organisation will achieve quota. Coverage must be interpreted alongside win rate, average deal size, sales-cycle length, opportunity age, stage quality, concentration risk and capacity.

A more decision-useful estimate is risk-adjusted pipeline:

Suppose remaining quota is $400,000 and three qualified opportunities have risk-adjusted values of $160,000, $120,000 and $80,000. Risk-adjusted pipeline equals $360,000, or 0.9× remaining quota. The unweighted coverage ratio could still look satisfactory if the gross opportunity value were high, but the probability-adjusted view signals a shortfall.

Pipeline metrics are vulnerable to manipulation. Managers should define entry and exit criteria for each stage, audit stale opportunities, separate new pipeline from recycled pipeline and avoid assigning high probabilities solely because an opportunity has remained open for a long time.

4. Sales Budgets

A sales budget is a quantified plan for expected sales, selling costs and related resource requirements. It supports resource allocation, coordination and control. A budget may include revenue, cost of goods sold, gross margin, compensation, travel, promotion, technology, training, recruitment and customer-service costs.

A simple gross-margin identity is:

If net revenue is $1,000,000 and cost of goods sold is $620,000, gross margin is $380,000 and the gross-margin percentage is:

Revenue alone can mislead. A sales plan that achieves revenue through excessive discounting may reduce profit. Net revenue should account for discounts, returns, rebates and allowances.

Budgeting approaches

Incremental budgeting adjusts the prior budget for expected changes. It is efficient but may preserve ineffective spending.

Zero-based budgeting requires activities to be justified from the beginning of each cycle. It can improve discipline but is time-intensive and may undervalue long-term capability building.

Activity-based budgeting links costs to operational drivers such as sales calls, demonstrations, proposals, travel days or onboarding events. It is useful when managers understand the resource requirements of each activity.

Flexible budgeting adjusts expected costs or resources to the actual level of activity. It supports fair variance analysis when sales volume differs substantially from plan.

A budget should distinguish controllable from uncontrollable variances. For example, a representative may control travel scheduling but not an unexpected regulatory closure. Variance analysis should seek causes rather than assign blame automatically.

5. Quota Design

A quota is a quantified performance expectation assigned to a salesperson, territory, account team, channel partner or business unit for a defined period. Quotas direct effort, support compensation and provide a basis for performance evaluation. They must be challenging enough to stimulate performance but credible enough to maintain legitimacy.

Types of quotas

• Revenue quotas: expected bookings, billings or recognised sales. The organisation must define which revenue concept applies and when it is recorded.
• Unit quotas: expected number of products, subscriptions or contracts.
• Profit or margin quotas: expected gross margin, contribution or profit. These discourage unprofitable volume but require reliable cost allocation.
• Activity quotas: expected calls, meetings, demonstrations, proposals or account reviews. Activities should be linked to outcomes rather than treated as ends in themselves.
• Strategic quotas: objectives such as new-logo acquisition, target-sector penetration, product mix, retention, cross-selling or partner activation.
• Combination quotas: weighted measures balancing immediate results and strategic development.

Principles of quota allocation

1. Alignment: quotas must support corporate, marketing and sales strategy.
2. Controllability: the measure should reflect factors the role can materially influence.
3. Consistency: definitions, periods and crediting rules should be comparable.
4. Capacity: territory potential, account workload, selling time and service constraints must be considered.
5. Transparency: representatives should understand assumptions and dispute procedures.
6. Balance: revenue, profitability, customer quality and strategic progress should be considered together.
7. Reviewability: quotas should be revisited when there is a material change in territory, product availability or market conditions.

A quota may be allocated using historical share, market potential, workload, capacity or a hybrid approach. Historical allocation is simple but can reward previously advantaged territories. Potential-based allocation is more strategic but depends on reliable market and account data.

Activity-to-revenue logic

Activity quotas should not be selected merely because they are easy to count. A manager may estimate required activity from a sales funnel. If the annual target is 24 new customers, the historical close rate from qualified proposal to customer is 25%, and the qualification rate from first meetings to qualified proposals is 40%, then the required numbers are:

This calculation assumes stable and meaningful conversion rates, comparable opportunity quality and sufficient time to perform the activities. It should be used as a planning estimate, not as proof that activity volume alone will create sales.

6. Scenario Planning and Sensitivity Analysis

A single-point forecast conceals uncertainty. Scenario planning develops internally consistent descriptions of alternative futures, usually including a base case, an upside case and a downside case. Scenarios should differ in meaningful drivers such as demand, price, win rate, sales-cycle duration, competitor action, capacity or supply availability.

A scenario is not the same as a probability forecast. A scenario describes a coherent possibility; a probability forecast assigns likelihood to an outcome. Managers may use both, but they should not present an unsupported scenario probability as fact.

Illustrative scenario table

The implied bookings are calculated as number of opportunities × average value × win rate. These figures are not forecasts unless the assumptions have been assessed and the timing of bookings is feasible.

Sensitivity analysis changes one driver while holding others constant. If a 5-percentage-point decline in win rate causes a major profit shortfall, management may prioritise coaching, qualification or deal support. If results are most sensitive to supply capacity, increasing lead generation may be counterproductive until fulfilment constraints are resolved.

Scenario planning should lead to trigger-based actions. For example, if qualified pipeline falls below 2.0× remaining quota for two consecutive reviews, the organisation may launch targeted prospecting; if win rates fall below the agreed threshold, it may initiate deal-quality reviews; if supply constraints arise, it may revise customer promises and quota crediting.

7. An Integrated Planning Process

A practical annual or quarterly process can follow these stages:

1. Diagnose the situation. Analyse market trends, customers, competitors, prior performance, capacity and strategic priorities.
2. Define planning assumptions. Record price, product availability, headcount, conversion rates, economic conditions and known risks.
3. Set strategic objectives. Specify financial and non-financial outcomes, including profitability and customer objectives.
4. Estimate demand. Combine historical analysis, opportunity data, market research and informed judgement.
5. Design coverage. Decide territories, segments, channels, account ownership, capacity and role specialisation.
6. Build the sales budget. Estimate revenue, cost of sales, selling expenses, technology, training and capacity investment.
7. Allocate quotas. Apply transparent rules, test feasibility and check for unintended territory inequities.
8. Develop scenarios. Identify downside and upside conditions, assumptions and management responses.
9. Communicate and enable. Explain definitions, provide tools and align compensation and coaching.
10. Review and learn. Compare actuals with forecast and quota, analyse error and bias, and revise assumptions where justified.

Forecast reviews should separate three questions: What has happened? What is likely to happen? What should management do? Mixing these questions encourages defensive reporting and obscures the difference between facts, estimates and decisions.

8. Technology, Data and Artificial Intelligence

Customer relationship management systems, sales analytics and business-intelligence dashboards can improve visibility of pipeline, conversion, activity and forecast changes. Automated probability scoring may identify patterns that humans overlook. Machine-learning systems can also estimate propensity to buy, expected deal duration or churn risk.

Technology does not eliminate managerial responsibility. Models inherit the quality and biases of training data. If historical quotas systematically disadvantaged a territory, an algorithm trained on those outcomes may reproduce the disadvantage. Data leakage, inconsistent opportunity stages, duplicate accounts and missing outcomes can create false precision.

Good governance includes:

• documented definitions for opportunity stages and revenue;
• role-based access and protection of customer information;
• audit trails for forecast overrides and quota changes;
• validation of model performance across segments and territories;
• human review of unusual recommendations;
• monitoring for discrimination, privacy breaches and gaming;
• clear communication that a model estimate is not a guaranteed result.

AI-generated forecasts should be treated as decision support. Managers remain accountable for customer promises, employee fairness, financial reporting and compliance with applicable privacy, competition and employment requirements.

9. Ethics, Behaviour and Control

Targets influence behaviour. Excessive pressure may produce sandbagging, premature opportunity advancement, mis-selling, discount abuse, channel stuffing, neglect of low-value customers or manipulation of CRM data. A quota system can therefore create ethical risk even when the numerical design appears rational.

Controls should include realistic crediting rules, approval for exceptional discounts, customer-outcome measures, quality and retention indicators, compliance gates and confidential channels for reporting pressure. Managers should distinguish poor performance from poor system design. A representative cannot reasonably be held fully accountable for a product outage, a territory without sufficient potential or a quota changed after the selling period begins.

Compensation and quotas should reinforce sustainable value. For example, a portion of incentive may depend on profitable revenue, customer retention or verified strategic outcomes. However, adding too many measures can dilute focus and create administrative complexity. The best scorecard uses a limited number of understandable indicators with clear trade-offs.

Applied Case: Meridian Workplace Solutions

This is an illustrative case.

Background

Meridian Workplace Solutions sells subscription-based workplace software through a direct sales team. The company has grown rapidly, but its quarterly forecasts have been consistently optimistic. Salespeople record many opportunities at late pipeline stages, while finance reports that discounts and implementation costs are reducing margins. The chief executive wants 20% growth next year, while the sales director proposes identical revenue quotas for all territories.

Problem

The territories differ substantially. Territory A has many mature accounts and produced $900,000 last year; Territory B is a new region with high market potential but produced only $400,000. The proposed equal increase would assign quotas of $1,080,000 and $480,000. However, pipeline data show that A has $1.2 million of qualified opportunities with a historical win rate of 30%, whereas B has $1.0 million with a win rate of 12%. Delivery capacity is limited, and the average implementation cost is higher in B.

Analysis

Gross pipeline coverage is 1.11× in A and 2.5× in B when compared with the proposed quotas. That comparison is misleading because the pipeline quality differs. Risk-adjusted pipeline is:

The territories also have different strategic value. B may justify investment because of market potential, but its first-year objective should not be represented as equivalent near-term revenue capacity without additional enablement, references and implementation support.

Approach and solution

Meridian adopts a hybrid plan. It sets a company revenue objective, but allocates territory quotas using historical productivity, market potential, capacity and pipeline quality. It introduces a margin floor and separates bookings from recognised revenue. B receives a lower first-year revenue quota but strategic quotas for qualified new accounts, partner development and reference customers. A receives a higher retention and cross-sell expectation.

The company also changes forecasting rules: opportunities cannot enter the commit category without verified customer need, decision process, timing and commercial approval. Forecasts are submitted with a base estimate and a confidence range. Weekly pipeline reviews are replaced by fortnightly reviews focused on evidence and action. Implementation capacity is added before launching a large prospecting campaign in B.

Outcome

The revised plan produces a less dramatic headline target but a more credible operating budget. Managers identify that B’s principal constraint is not lead volume but implementation capability and low customer proof. Forecast bias declines in the following quarter, and discount approval reduces unprofitable bookings. The company does not achieve the original uniform quota proposal, but it improves forecast reliability and creates a stronger foundation for sustainable growth.

Learning points

• Gross pipeline coverage can conceal weak conversion quality.
• Equal quotas are not necessarily equitable quotas.
• Market potential should be matched with capacity and enablement.
• Revenue, margin, retention and strategic development may require different measures.
• Forecast governance must define evidence standards, not merely reporting dates.

Challenges and Limitations

Sales planning is affected by structural breaks, incomplete customer data, long sales cycles, new products, channel conflict and changing competitive behaviour. Historical models may fail after a pricing change or regulatory event. Pipeline probabilities may be subjective, and activity measures may encourage quantity over quality. Scenario analysis can also create an illusion of completeness if all scenarios share the same flawed assumptions.

Managers should therefore triangulate methods, use ranges rather than unjustified precision, examine errors by segment, test assumptions, and update plans when evidence changes. The objective is not perfect prediction. It is disciplined preparation, transparent decision-making and rapid learning.

Best-Practice Checklist

• Define the forecast measure, period and ownership.
• Separate forecast, budget, objective and quota.
• Combine historical data with customer and market intelligence.
• Document assumptions and confidence levels.
• Measure error and bias, not only forecast attainment.
• Use risk-adjusted pipeline and audit opportunity stages.
• Allocate quotas using potential, capacity and controllability.
• Balance revenue with margin, customer and strategic outcomes.
• Use downside and upside scenarios with trigger actions.
• Govern AI and CRM systems through data quality, fairness and human review.
• Review plans regularly without changing targets opportunistically.

Chapter Summary

Sales planning integrates market analysis, strategy, programmes, resources, forecasts, budgets, quotas and control. Forecasting estimates likely future performance, while budgets allocate resources and quotas establish accountable expectations. Qualitative methods are valuable where data are scarce or conditions are changing; quantitative methods provide structured benchmarks but depend on suitable data and assumptions.

Moving averages, weighted averages, exponential smoothing, trend models and opportunity-based expected values each have specific conditions and limitations. Forecast quality should be assessed through error, absolute error, percentage error and bias. Pipeline coverage is useful only when stage quality, win rates, timing and capacity are considered. Budgets should include revenue, costs and margins, while quotas should balance financial, activity and strategic measures.

Scenario planning exposes uncertainty and links alternative assumptions to management actions. Ethical quota design requires fairness, transparency, controllability and safeguards against manipulation. Technology and AI can improve visibility and analytical support, but governance, data quality and human accountability remain essential. The central managerial task is not to predict perfectly, but to make assumptions explicit, allocate resources responsibly and learn continuously from results.

Key Terms

Sales plan: A coordinated statement of sales objectives, strategies, actions, resources and controls.

Sales forecast: An estimate of future sales for a specified scope and period.

Sales budget: A quantified plan for sales revenue, costs and related resources.

Quota: A formally assigned performance expectation.

Pipeline: The set of active sales opportunities at different stages of the selling process.

Pipeline coverage: Qualified pipeline value divided by remaining quota.

Risk-adjusted pipeline: Pipeline value weighted by estimated probabilities of success.

Forecast error: The difference between actual and forecast performance.

Forecast bias: Persistent directional error in forecasts.

Moving average: An average of a defined number of recent observations.

Exponential smoothing: A forecasting method that weights recent observations more heavily through a smoothing constant.

Regression: A statistical method for modelling relationships between a dependent variable and explanatory variables.

Expected value: The sum of possible values weighted by their probabilities.

Scenario planning: Analysis using coherent alternative future conditions.

Sensitivity analysis: Examination of how results change when one or more assumptions change.

Revenue quota: A target based on sales value.

Profit quota: A target based on margin, contribution or profit.

Activity quota: A target based on specified selling activities.

Strategic quota: A target supporting longer-term priorities such as retention or market entry.

Quota crediting: The rules determining when and to whom sales performance is attributed.

Review Questions

Short Answer Questions

1. Distinguish a sales forecast from a sales quota.
2. State two limitations of using a simple moving average.
3. What does a pipeline coverage ratio measure?
4. Why can revenue growth fail to improve profitability?
5. Identify two governance risks associated with AI-based sales forecasting.

Long Answer Questions

1. Explain the relationship between corporate strategy, marketing strategy, sales planning, budgeting and quota-setting.
2. Compare qualitative and quantitative forecasting methods and discuss when each is appropriate.
3. Explain how MAE, RMSE, MAPE and mean error assist forecast evaluation, including a limitation of MAPE.
4. Discuss the principles that should guide fair and strategically useful quota allocation.
5. Explain how scenario planning can improve sales decisions under uncertainty.

Analytical / Application Questions

1. A team has a remaining quota of $500,000 and qualified opportunities worth $1,250,000. Historical win probability is 32%. Calculate gross pipeline coverage and risk-adjusted pipeline, then explain why the two measures lead to different managerial conclusions.
2. A salesperson must win 30 new customers. The proposal-to-close rate is 20%, and the first-meeting-to-proposal rate is 50%. Estimate the required proposals and first meetings. State the assumptions behind your calculation.
3. A company has high revenue attainment but declining gross margin, frequent discounts and poor retention. Design a revised quota scorecard and explain how it could reduce dysfunctional selling behaviour.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a sales forecast from a sales quota?

A. A forecast is an estimate, while a quota is an assigned expectation.

B. A forecast is always lower than a quota.

C. A quota is calculated only from historical averages.

D. A forecast is a measure of selling activity only.

Correct Answer: A. A forecast is an estimate, while a quota is an assigned expectation.

Question 2

Quarterly sales for the last three periods were 900, 1,000 and 1,100 units. What is the three-period simple moving-average forecast for the next quarter?

A. 900 units

B. 950 units

C. 1,000 units

D. 1,100 units

Correct Answer: C. 1,000 units

Question 3

A pipeline contains $800,000 of qualified opportunities and the remaining quota is $400,000. What is gross pipeline coverage?

A. 0.5×

B. 1.0×

C. 2.0×

D. 3.0×

Correct Answer: C. 2.0×

Question 4

Which forecast measure is expressed in the original sales units and gives greater weight to large errors?

A. MAPE

B. RMSE

C. Mean error percentage

D. Win rate

Correct Answer: B. RMSE

Question 5

Three opportunities are valued at $10,000, $20,000 and $40,000, with probabilities of 0.8, 0.5 and 0.25. What is their expected value?

A. $18,000

B. $20,000

C. $28,000

D. $70,000

Correct Answer: B. $20,000

Question 6

Why is MAPE problematic when actual sales are zero or close to zero?

A. It cannot use percentages.

B. The forecast becomes automatically unbiased.

C. The actual-value denominator makes the measure undefined or unstable.

D. It measures only profit.

Correct Answer: C. The actual-value denominator makes the measure undefined or unstable.

Question 7

Which quota type is most directly intended to discourage unprofitable sales volume?

A. Unit quota

B. Activity quota

C. Profit or margin quota

D. Call quota

Correct Answer: C. Profit or margin quota

Question 8

A company wants to estimate required proposals when it needs 20 wins and the proposal-to-close rate is 25%. How many proposals are required, assuming a stable conversion rate?

A. 5

B. 25

C. 80

D. 100

Correct Answer: C. 80

Question 9

What is the principal purpose of scenario planning?

A. To guarantee the most optimistic forecast

B. To describe alternative conditions and prepare appropriate responses

C. To replace all quantitative analysis

D. To eliminate managerial judgement

Correct Answer: B. To describe alternative conditions and prepare appropriate responses

Question 10

Which practice best supports ethical and reliable pipeline governance?

A. Allowing representatives to assign any probability they choose

B. Advancing opportunities automatically after a fixed number of days

C. Defining evidence-based stage criteria and auditing stale opportunities

D. Counting every contact as a qualified opportunity

Correct Answer: C. Defining evidence-based stage criteria and auditing stale opportunities