Opportunity Recognition and Market Foresight

Introduction

Brand innovation begins before a product is designed or a campaign is proposed. It begins with the disciplined recognition of a meaningful opportunity: a persistent customer problem, an emerging cultural tension, an underserved segment, a changing technology, or a shift in the rules of a market. Opportunity recognition is therefore not simply the generation of creative ideas. It is the process of discovering, interpreting, and validating possibilities for creating value.

For brand managers, this process has a distinctive character. An opportunity must be commercially viable, but it must also be consistent with the brand’s identity, distinctive capabilities, customer promise, and ethical responsibilities. A technically feasible product may be a poor brand opportunity if consumers do not perceive its relevance. Conversely, a small unmet need may become strategically important when it aligns with a powerful cultural or technological change.

Market foresight extends this work into the future. It does not attempt to predict one certain future. Rather, it helps managers identify forces that may shape several plausible futures and prepare responses before competitors do. Effective opportunity recognition combines environmental scanning, trend analysis, jobs-to-be-done thinking, category analysis, cultural insight, and direct customer-problem discovery.

Learning Objectives

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

1. Explain the relationship between opportunity recognition, innovation, entrepreneurship, and brand management.
2. Distinguish trends, fads, drivers, signals, scenarios, and forecasts.
3. Conduct a structured environmental scan using macro- and market-level evidence.
4. Apply the jobs-to-be-done framework to uncover functional, emotional, and social needs.
5. Analyse categories, competitors, substitutes, and points of friction.
6. Use cultural insight and customer discovery methods responsibly.
7. Prioritise opportunities using transparent criteria rather than intuition alone.
8. Evaluate the ethical, practical, and evidential limitations of opportunity analysis.

1. What Is an Opportunity?

An opportunity is a favourable possibility for creating value by addressing a problem, desire, inefficiency, or emerging change. It is not identical to an idea. An idea is a proposed solution; an opportunity is a validated or potentially valuable situation in which a solution could create benefits for customers and the organisation.

A useful distinction is:

• Problem: an undesirable condition experienced by a person, organisation, or community.
• Need: a requirement or desired outcome.
• Insight: an interpreted explanation of why a problem occurs or matters.
• Idea: a possible response to the problem or need.
• Opportunity: a problem or need connected to a feasible, desirable, and economically or socially valuable response.

Entrepreneurial opportunities can be discovered, constructed, or both. The discovery perspective suggests that changes in technology, regulation, demographics, or consumer behaviour reveal possibilities that already exist. The construction perspective emphasises experimentation: entrepreneurs and customers jointly shape an opportunity through prototypes, conversations, and market tests. In brand innovation, both perspectives are useful. A demographic shift may reveal an opening, but the eventual meaning of the brand is often constructed through interaction with users.

A strong opportunity generally has five characteristics:

1. Relevance: it concerns a problem or aspiration that matters to a defined group.
2. Evidence: observations, data, or repeated customer accounts support its existence.
3. Distinctiveness: the proposed response can be meaningfully differentiated.
4. Feasibility: the organisation can deliver the proposed value with available or developable capabilities.
5. Timing: conditions make action appropriate now or within a foreseeable planning horizon.

Opportunity recognition should not be confused with trend imitation. Entering a fashionable market without understanding the underlying customer problem often produces undifferentiated offerings and weak brand legitimacy.

2. The Opportunity-Recognition System

Opportunity recognition is best treated as a system rather than a single technique. The system moves through interconnected activities:

Scanning → interpreting → framing the problem → generating hypotheses → investigating customers → testing desirability and feasibility → prioritising → experimenting

Environmental scanning identifies changes. Interpretation explains why those changes may matter. Problem framing defines the customer or stakeholder tension. Customer investigation tests whether the tension is real and important. Experimentation produces evidence about possible responses.

The process is iterative. A customer interview may challenge an assumed trend; a prototype may reveal a different use case; a regulatory change may make an apparently attractive opportunity infeasible. Consequently, opportunity work should preserve uncertainty rather than conceal it behind overconfident forecasts.

Opportunity statements

A useful opportunity statement identifies a target group, context, unmet need, and potential value without prematurely specifying a product. For example:

Urban renters who want indoor plants struggle to maintain them during travel and irregular work schedules; they need a low-effort way to experience the emotional and environmental benefits of greenery without becoming expert gardeners.

This statement is stronger than “launch a smart plant pot” because it leaves room for several solutions and identifies the underlying job.

3. Environmental Scanning

Environmental scanning is the systematic collection and interpretation of information about forces that may affect an organisation, category, or brand. It includes both broad external forces and immediate market conditions.

Macro-environmental scanning

A PESTLE scan considers:

PESTLE is a scanning framework, not a prediction machine. A political or technological change does not automatically create a profitable opportunity. Its significance depends on how it affects customer behaviour, category economics, and organisational capabilities.

Industry and market scanning

At the market level, managers examine customers, competitors, suppliers, distributors, substitutes, complementary products, and new entrants. Porter’s competitive forces remain useful here, especially for understanding rivalry, buyer power, supplier power, threats of substitution, and barriers to entry. However, category boundaries should be defined from the customer’s perspective. A cinema may compete not only with other cinemas but also with streaming services, restaurants, gaming, and social activities.

Signals and evidence quality

Scanning produces many signals, but not all signals deserve equal confidence. Evidence can be classified as:

• Weak signal: an early, ambiguous indication of possible change.
• Pattern: repeated observations that suggest a relationship or direction.
• Trend: a sustained movement in behaviour, technology, values, or markets.
• Driver: a force with substantial capacity to shape future conditions.
• Discontinuity: a sudden break from existing patterns.

Managers should record the source, date, geography, affected population, and interpretation for every signal. Search popularity, social-media engagement, sales data, expert interviews, ethnographic observations, and regulatory documents measure different things. None should be treated as a complete representation of demand.

4. Trend Analysis and Market Foresight

A trend is a directional and relatively sustained change. A fad is a rapid, highly visible, and generally short-lived adoption pattern. The difference cannot always be known at the beginning, so managers should examine the underlying mechanism rather than relying on popularity alone.

A trend hypothesis should answer four questions:

1. What exactly is changing?
2. For whom and in which contexts is it changing?
3. What forces are causing or reinforcing the change?
4. What would have to remain true for the change to continue?

Trend triangulation

Trend analysis becomes more reliable when several types of evidence converge. For example, a claim that consumers want repairable electronics might be examined through search behaviour, retailer offerings, customer interviews, repair-business growth, legislation, and observed willingness to pay. Triangulation does not eliminate uncertainty, but it reduces dependence on a single source.

Forecasting versus foresight

Forecasting often extends observed patterns to estimate likely future values. Foresight is broader: it explores alternative futures, including discontinuities and low-probability/high-impact events. A forecast might estimate the growth of a market; a foresight exercise might ask how the category changes if regulation bans a material, a new platform controls distribution, or customers reject ownership altogether.

Scenario thinking

Scenario planning develops several internally coherent accounts of how the future might unfold. Scenarios are not predictions and should not be assigned false precision. A basic process is:

1. Define the focal question and time horizon.
2. Identify major drivers and uncertainties.
3. Select two critical uncertainties that create contrasting conditions.
4. Construct a scenario matrix.
5. Describe customer behaviour, competition, technology, regulation, and category economics in each scenario.
6. Identify robust moves, contingent moves, and warning indicators.

A brand may discover that an opportunity is attractive in several scenarios, making it a robust opportunity. Alternatively, it may require a specific assumption, such as cheap data access or permissive regulation. That dependency should be made explicit.

5. Jobs to Be Done

The jobs-to-be-done (JTBD) perspective asks what customers are trying to accomplish in a particular situation. Customers do not simply buy product attributes; they “hire” products, services, or brands to make progress.

A job may be:

• Functional: completing a practical task.
• Emotional: feeling secure, confident, relaxed, or inspired.
• Social: expressing identity or achieving recognition and belonging.

The job is not the product. “Buy a drill” describes a solution category; “create a precise hole in a wall without damaging the room” describes a job. This distinction expands the opportunity space and reveals substitutes.

A job statement should include context, desired progress, and constraints. For example:

When preparing a quick evening meal after work, I want to produce something nutritious with minimal planning and cleanup, so that I can eat well without feeling that cooking has consumed my evening.

Job maps and struggle points

A job can be decomposed into stages such as defining the task, locating resources, preparing, executing, monitoring, modifying, completing, and evaluating. At each stage, customers may experience anxiety, delay, cost, inconvenience, or uncertainty. These struggle points are potential innovation opportunities.

JTBD is particularly valuable when category labels hide competition. A meal-kit brand competes not only with other meal kits but with takeaway food, frozen meals, home cooking, and skipping dinner. The relevant question is not “Who sells meal kits?” but “What alternatives help customers accomplish this job?”

Limitations of JTBD

Jobs are interpretations, not objective facts. Different researchers may describe the same behaviour differently. The framework can also understate structural constraints, social inequality, habit, and collective consumption. It should therefore be combined with observation, cultural analysis, and market evidence.

6. Category Analysis and White-Space Identification

A category is a socially and commercially recognised set of alternatives that customers consider related. Category analysis examines how the category is defined, organised, communicated, and experienced.

Managers should investigate:

• category entry points: situations that trigger consideration;
• customer segments and underserved users;
• functional and symbolic benefits;
• category conventions and codes;
• price and channel structures;
• dominant claims and points of parity;
• sources of frustration and switching;
• substitutes and non-consumption;
• regulatory and technological constraints.

White space

White space is an underdeveloped position or customer need within a market. It is not simply an empty space on a perceptual map. A gap may exist because it is unattractive, technically difficult, legally restricted, or already served by an unrecognised substitute. Therefore, white-space analysis must distinguish between:

• Unserved space: customers have a need with no satisfactory solution.
• Underserved space: existing solutions provide inadequate value.
• Unoccupied positioning space: a communication position is available, but the underlying offer may not be.
• Artificial space: a visual gap created by an incomplete or misleading map.

A perceptual map can support discussion, but its axes must represent meaningful customer dimensions, and the data used to position brands must be credible. Managers should not infer demand merely because a quadrant appears empty.

7. Cultural Insight

Brands operate within systems of meaning. Cultural insight examines how products, practices, identities, rituals, symbols, and institutions shape what people consider desirable or acceptable.

A cultural insight is more than an interesting observation. It connects a social pattern to a human tension and a possible implication for action. For example:

• Observation: people share photographs of carefully arranged home workspaces.
• Interpretation: the workspace has become a visible expression of competence and self-control, not merely a functional location.
• Tension: people want professional identity and comfort simultaneously.
• Opportunity implication: workspace brands may need to design for identity, wellbeing, and displayability together.

Useful cultural research methods include semiotic analysis, social listening, diary studies, participant observation, cultural probes, and analysis of media and public discourse. Researchers must avoid treating online subcultures as representative of an entire population. Cultural interpretations should be tested with people who inhabit the relevant context.

Cultural insight also requires reflexivity. Researchers bring assumptions about normality, age, class, gender, ethnicity, disability, and consumption. Inclusive research asks whose experience is visible, whose voice is missing, and who may bear the costs of a proposed innovation.

8. Customer Problem Discovery

Customer discovery tests whether a suspected problem is real, important, frequent enough, and inadequately served. It should precede excessive investment in a solution.

Discovery interviews

Effective interviews focus on recent behaviour rather than hypothetical preference. Questions such as “Tell me about the last time…” reveal context, sequence, workarounds, expenditure, and emotional consequences. Questions such as “Would you buy this?” often produce unreliable answers because respondents are polite, speculative, or unable to imagine future behaviour accurately.

A practical interview structure is:

1. Establish context and consent.
2. Ask about a recent relevant episode.
3. Reconstruct actions, decisions, alternatives, and obstacles.
4. Explore consequences and coping strategies.
5. Identify existing expenditure or effort.
6. Ask what would make the situation better, without presenting a preferred solution too early.

Observation and diary methods

Observation can reveal workarounds that customers fail to mention because they are habitual. Diary studies capture problems over time and are useful for episodic activities such as commuting, health management, or grocery planning. Digital analytics may reveal behaviour at scale, but it usually cannot explain motivation without qualitative inquiry.

Problem evidence

The strength of a problem hypothesis increases when customers:

• experience it repeatedly or at a consequential moment;
• already spend money, time, or effort to address it;
• use improvised workarounds;
• express strong emotional or practical consequences;
• actively search for alternatives;
• can identify what a better outcome would mean.

Evidence is weaker when respondents merely express general enthusiasm, agree with a leading question, or describe an issue without taking any action.

9. Prioritising Opportunities

Organisations commonly identify more opportunities than they can pursue. Prioritisation should make assumptions visible and allow different kinds of evidence to be compared.

A simple weighted scoring model can be used as a discussion aid:

Here, S is the total opportunity score, wᵢ is the weight assigned to criterion i, and rᵢ is the rating of the opportunity on that criterion. The weights should sum to 1 if ratings use a common scale. Typical criteria include customer importance, evidence strength, strategic fit, distinctiveness, feasibility, timing, and ethical or environmental impact.

The score is not an objective truth. It is a structured judgement. Ratings must use a defined scale, such as 1 to 5, and reviewers should record the evidence behind each rating. Sensitivity analysis is useful: if a small change in an uncertain rating reverses the ranking, the decision requires further research.

Illustrative scoring example

Suppose a team evaluates an opportunity using four criteria. Customer importance receives a weight of 0.30 and a rating of 5; evidence strength receives 0.25 and a rating of 4; strategic fit receives 0.25 and a rating of 3; feasibility receives 0.20 and a rating of 2. The weighted score is:

The score is on a 1–5 scale because the ratings are on that scale and the weights sum to 1. It does not establish that the opportunity will succeed. Its value lies in showing that feasibility is a weakness requiring investigation or capability development.

10. Applied Case: Reimagining Urban Laundry

The following is an illustrative case, created for educational purposes.

Background

A fictional brand, UrbanEase, sells household cleaning products in a large city. Its management team notices growing interest in compact living, shared housing, sustainability, and app-enabled services. The initial idea is to launch a premium detergent subscription.

Problem discovery

Before developing the subscription, the team conducts interviews, two-week diary studies, and observations in shared laundries. Participants report that detergent is not their main difficulty. Their recurring problems include transporting laundry, deciding when machines are available, separating loads, avoiding forgotten clothes, and managing laundry around unpredictable schedules. Some customers use excessive detergent because dosage instructions are confusing; others delay washing until they have enough clothing for a full load.

JTBD analysis

The central job is not “buy detergent.” It is:

When laundry accumulates in a constrained living environment, complete washing and drying with minimal coordination, waiting, and risk of inconvenience.

The job has functional dimensions, but also emotional dimensions: customers want to feel organised rather than overwhelmed. Socially, they want shared spaces to remain respectful and conflict-free.

Category and cultural analysis

The team maps alternatives: home machines, laundromats, pickup-and-delivery services, hand washing, and delaying the task. It also studies the cultural meaning of domestic competence. In the target group, convenience is valued, but so are affordability and reduced waste. A premium subscription limited to detergent would address only one small part of the job.

Approach and solution hypothesis

UrbanEase develops a pilot service combining a compact, concentrated detergent system with machine-availability information, dosage guidance, and optional pickup partnerships. The pilot is deliberately narrow. It tests whether customers value coordination and reduced uncertainty, not merely whether they like the app interface.

The team defines success measures before launch: repeat use, reduction in reported waiting time, correct dosage behaviour, customer retention, and unit economics. It also monitors accessibility, data privacy, and the environmental consequences of packaging and delivery.

Outcome

The pilot shows strong interest in scheduling support but weak demand for a premium detergent-only subscription. A smaller group values pickup partnerships, while price-sensitive users prefer information and dosage guidance without a recurring fee. UrbanEase therefore revises its opportunity: detergent becomes an enabling component of a broader laundry-management proposition rather than the centre of the brand.

Learning points

1. The first idea was not the opportunity; it was a possible solution.
2. JTBD revealed substitutes and coordination problems outside the original category.
3. Customer evidence changed the value proposition.
4. Small experiments reduced the cost of being wrong.
5. Ethical and operational constraints must be examined alongside desirability.

11. Technology, AI, and Evidence Governance

Digital tools can accelerate opportunity recognition. Search data, social listening, customer-service transcripts, transaction data, and natural-language processing can help identify recurring themes. Generative AI can assist with coding interview transcripts, comparing scenarios, or producing alternative hypotheses.

However, automated pattern detection is not the same as insight. AI systems may reproduce sampling bias, confuse correlation with causation, overrepresent highly vocal users, or generate plausible but unsupported interpretations. Sensitive customer data also raises privacy, consent, security, and data-minimisation concerns. Managers should document data sources, preserve human review, test interpretations with customers, and avoid using protected characteristics irresponsibly.

AI-generated recommendations should be treated as hypotheses. Decisions involving vulnerable populations, exclusion, pricing, eligibility, or personalisation require appropriate governance and accountability. A brand’s desire to anticipate needs does not justify covert surveillance or manipulative design.

12. Challenges and Limitations

Opportunity recognition is subject to several recurring errors:

• Confirmation bias: seeking evidence that supports an attractive idea.
• Availability bias: overestimating vivid or recently observed events.
• Trend inflation: treating a small online subculture as a mass-market movement.
• Solution fixation: asking customers to evaluate a predetermined product.
• Sample bias: interviewing convenient participants who do not represent the target context.
• Survivorship bias: studying successful brands while ignoring failed attempts.
• False precision: presenting uncertain scores or forecasts as objective facts.
• Category blindness: overlooking substitutes and non-consumption.
• Ethical blindness: identifying commercial value while ignoring social or environmental harm.

Good practice includes maintaining an evidence log, separating observation from interpretation, actively seeking disconfirming evidence, using mixed methods, testing assumptions in sequence, and involving diverse perspectives. A promising opportunity should be challenged, not merely celebrated.

Professional Implications for Brand Managers

Brand managers who can recognise opportunities contribute beyond communication. They help define markets, identify emerging customer needs, shape propositions, allocate experimentation budgets, and protect brand meaning during growth. Employability in innovation roles depends on the ability to move between qualitative insight and commercial reasoning.

In practice, students should be able to produce an opportunity brief containing:

• the customer and situation;
• the problem or job;
• evidence and evidence limitations;
• relevant trends and environmental drivers;
• category alternatives and competitors;
• the proposed value space;
• assumptions and risks;
• an experiment plan;
• ethical, legal, and sustainability considerations.

The strongest briefs are concise but not simplistic. They show what is known, what is uncertain, and what must be learned next.

Chapter Summary

Opportunity recognition is the disciplined process of identifying and validating possibilities for value creation. It differs from idea generation because it links a meaningful customer problem or emerging change to evidence, feasibility, distinctiveness, timing, and responsible action.

Environmental scanning uses PESTLE and market analysis to identify forces affecting customers and categories. Trend analysis distinguishes sustained change from temporary popularity and uses triangulation to improve confidence. Market foresight explores multiple plausible futures rather than pretending that one forecast is certain.

The jobs-to-be-done framework focuses on the progress customers seek in particular contexts and reveals functional, emotional, and social dimensions. Category analysis identifies conventions, substitutes, underserved users, and genuine rather than merely visual white space. Cultural insight connects social meanings and tensions to potential brand implications. Customer discovery uses interviews, observation, diaries, and behavioural evidence to test whether problems are real and important.

Opportunities should be prioritised transparently. Weighted scoring can structure judgement, but scores are not facts and must be supported by evidence and sensitivity analysis. Prototyping and experimentation help organisations learn before committing substantial resources. Finally, ethical governance, privacy, inclusion, and environmental consequences must be considered from the beginning. Market foresight is most valuable when it produces better questions, more responsible experiments, and stronger strategic choices.

Key Terms

Opportunity: A possibility for creating value by addressing a problem, need, or emerging change.

Opportunity recognition: The process of identifying, interpreting, and validating potential opportunities.

Environmental scanning: Systematic monitoring of external forces that may affect a market or organisation.

PESTLE: A framework for analysing political, economic, social, technological, legal, and environmental forces.

Weak signal: An early and ambiguous indication of possible change.

Trend: A relatively sustained directional movement in behaviour, technology, values, or markets.

Fad: A highly visible but generally short-lived adoption pattern.

Driver: A force with significant capacity to shape future conditions.

Market foresight: Exploration of plausible future conditions and their implications.

Scenario planning: Construction of alternative, internally coherent accounts of possible futures.

Jobs to be done: The progress a customer seeks to make in a particular situation.

Category: A recognised group of related alternatives considered by customers and organisations.

Substitute: An alternative way of addressing the same customer job.

White space: An underdeveloped or underserved position or need in a market.

Cultural insight: An interpretation connecting social meanings, behaviours, and tensions to action.

Customer discovery: Research designed to test the reality and importance of customer problems.

Triangulation: Comparing different evidence sources or methods to improve confidence in an interpretation.

Opportunity hypothesis: A provisional, testable claim about a customer problem and potential value.

Weighted scoring: A structured evaluation method combining criterion weights and ratings.

Evidence log: A documented record of observations, sources, interpretations, and uncertainties.

Review Questions

Short Answer Questions

1. How does an opportunity differ from an idea?
2. What is the purpose of a PESTLE scan?
3. Distinguish a trend from a fad.
4. What are the functional, emotional, and social dimensions of a job to be done?
5. Why are recent-behaviour questions generally more useful than hypothetical purchase questions?

Long Answer Questions

1. Explain how environmental scanning and market foresight can support brand innovation. Include the limitations of forecasting.
2. Describe the jobs-to-be-done framework and explain how it can reveal competition outside a conventional category.
3. Discuss how cultural insight can contribute to opportunity recognition while avoiding stereotyping and overgeneralisation.
4. Explain the stages of customer problem discovery and evaluate the strengths and weaknesses of interviews, observation, and diary studies.
5. Discuss how a brand manager should prioritise opportunities using evidence, strategic fit, feasibility, and ethical considerations.

Analytical / Application Questions

1. Select a familiar category and construct an opportunity statement that identifies a target group, context, unmet need, and potential value without naming a specific product.
2. A team believes that a viral social-media practice represents a major market trend. Design a triangulated research plan to test this claim and identify the evidence that would challenge it.
3. Using the weighted scoring model described in this chapter, evaluate two hypothetical opportunities. Choose your own criteria and weights, calculate the scores, and explain how uncertainty or ethical concerns might change the decision.

Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes an opportunity from an idea?

A. An opportunity is always a technological invention.

B. An opportunity connects a meaningful problem or need with a potentially valuable and feasible response.

C. An opportunity is a creative concept with no need for evidence.

D. An opportunity is identical to a product feature.

Correct Answer: B. An opportunity connects a meaningful problem or need with a potentially valuable and feasible response.

Question 2

Which framework examines political, economic, social, technological, legal, and environmental forces?

A. JTBD

B. PESTLE

C. SWOT pricing grid

D. Service blueprinting

Correct Answer: B. PESTLE

Question 3

What is the main purpose of scenario planning?

A. To predict one certain future with precision

B. To replace customer research with expert opinion

C. To explore several plausible and internally coherent futures

D. To calculate current market share

Correct Answer: C. To explore several plausible and internally coherent futures

Question 4

Which question is most appropriate for customer problem discovery?

A. Would you definitely buy our proposed product?

B. Do you agree that our brand is innovative?

C. Tell me about the last time you experienced this situation.

D. Would you prefer a blue or green version?

Correct Answer: C. Tell me about the last time you experienced this situation.

Question 5

In jobs-to-be-done analysis, “buy a drill” is best understood as:

A. The underlying customer job

B. A possible solution to a broader job

C. A cultural driver

D. A market forecast

Correct Answer: B. A possible solution to a broader job

Question 6

A weighted opportunity score uses ratings from 1 to 5 and weights that sum to 1. What does the resulting score represent?

A. A guaranteed probability of success

B. A structured summary of judgements across selected criteria

C. The exact future market share

D. The legal risk of the opportunity

Correct Answer: B. A structured summary of judgements across selected criteria

Question 7

Which finding provides the strongest evidence that a customer problem is consequential?

A. Respondents say the idea sounds interesting.

B. Customers already spend time or money using workarounds.

C. The problem appears in one online comment.

D. A manager believes the problem is important.

Correct Answer: B. Customers already spend time or money using workarounds.

Question 8

What is genuine white space?

A. Any empty quadrant on a perceptual map

B. An underserved need or underdeveloped value position

C. A market with no competitors under any circumstances

D. A communication slogan without an operational offer

Correct Answer: B. An underserved need or underdeveloped value position

Question 9

Why should AI-generated opportunity insights be treated as hypotheses?

A. AI cannot process any customer data.

B. Automated systems may reflect bias, sampling problems, or unsupported interpretations.

C. AI outputs are always legally prohibited.

D. Human research is never affected by bias.

Correct Answer: B. Automated systems may reflect bias, sampling problems, or unsupported interpretations.

Question 10

A team gives an opportunity ratings of 5, 4, 3, and 2 with weights 0.30, 0.25, 0.25, and 0.20 respectively. What is the weighted score?

A. 2.95

B. 3.25

C. 3.65

D. 4.20

Correct Answer: C. 3.65