CRM Systems and Customer Data Management

Introduction

Customer relationship management (CRM) is the organised use of people, processes, data and technology to create, maintain and improve profitable customer relationships. In digital marketing and sales, CRM is more than a software package. It is a management capability that connects customer information with decisions and actions across the customer life cycle.

A CRM system may record an enquiry, assign a lead to a salesperson, track an opportunity, send a personalised email, register a service complaint and report customer value. Its effectiveness depends not only on technical functionality but also on data quality, process design, employee adoption, privacy governance and the organisation’s ability to learn from customer interactions.

This chapter examines four broad CRM perspectives—operational, analytical, collaborative and social CRM—and explains how organisations manage customer records, sales pipelines, lead scoring, system integrations, data quality and the single customer view.

Learning Objectives

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

• distinguish operational, analytical, collaborative and social CRM;
• explain the structure and purpose of customer records and sales pipelines;
• design a basic lead-scoring approach;
• describe CRM integrations and the role of APIs and data flows;
• assess data quality and propose improvement controls;
• explain the purpose and limitations of a single customer view;
• apply CRM principles to a practical marketing and sales situation; and
• recognise ethical, legal and governance responsibilities in customer-data management.

1. CRM as a Business Capability

CRM can be understood at three levels. First, it is a business philosophy that places customer value and relationship development at the centre of decision-making. Second, it is a set of processes, such as lead management, opportunity management, customer service and retention. Third, it is a technology environment containing databases, workflows, dashboards, communication tools and integrations.

These levels must be aligned. Installing software without redesigning processes commonly produces an expensive database with incomplete records. Conversely, a customer-oriented process without reliable information makes consistent execution difficult. A useful CRM system therefore answers four questions:

1. Who is the customer or prospect?
2. What has happened in the relationship?
3. What is likely to happen next?
4. What action should the organisation take, and who is responsible?

CRM supports both customer value and organisational value. Customers may receive faster service, relevant offers and more consistent treatment. The organisation may obtain improved conversion, retention, productivity and forecasting. These benefits are not automatic: excessive contact, inaccurate personalisation or intrusive data use can reduce trust.

Important CRM terminology

• Lead: a person or organisation that may have commercial potential but has not yet been fully qualified.
• Prospect: a lead assessed as having a meaningful fit or need.
• Account: an organisation or household represented in the CRM.
• Contact: an identifiable individual associated with an account or acting independently.
• Opportunity: a qualified potential sale with an estimated value and expected closing date.
• Activity: an interaction or task, such as a call, email, meeting or demonstration.
• Touchpoint: any customer interaction with the organisation or its brand.
• Lifecycle stage: the customer’s position in a relationship sequence, such as subscriber, lead, customer or advocate.
• Workflow: a defined sequence of tasks, rules, approvals or communications.
• Record owner: the employee or team accountable for maintaining a record and progressing its next action.

2. Four Perspectives on CRM

2.1 Operational CRM

Operational CRM supports routine, customer-facing and process activities. Its main functions include:

• contact and account management;
• lead capture and assignment;
• sales-force automation;
• opportunity and quotation management;
• campaign execution;
• customer-service case management;
• task reminders and workflow automation; and
• activity and communication histories.

For example, a website form may create a lead, allocate it to a salesperson according to territory, generate a follow-up task and record the resulting conversation. Operational CRM improves coordination and reduces dependence on personal spreadsheets or individual memory.

Its central question is: What action should be performed now, by whom, and according to which process?

Operational CRM has limitations. Automation can reproduce a poor process at greater speed. A workflow that sends repeated promotional messages may damage the customer experience. Automation should therefore include exception handling, approval rules and human review for sensitive situations.

2.2 Analytical CRM

Analytical CRM uses customer and transaction data to support interpretation and decision-making. It may examine:

• acquisition source and conversion;
• customer profitability and value;
• purchase frequency and recency;
• campaign response;
• churn or inactivity;
• product affinity;
• service quality; and
• sales forecasting.

Analytical CRM combines descriptive, diagnostic, predictive and, in some cases, prescriptive analysis. Descriptive analysis reports what happened; diagnostic analysis explores why it happened; predictive analysis estimates what may happen; and prescriptive analysis recommends possible actions.

A simple retention analysis might divide customers into recency, frequency and monetary-value segments. This is useful for campaign planning, but segments are not explanations of customer behaviour. Managers should test whether a segment responds differently before investing in a targeted programme.

Analytical CRM depends on valid data and suitable definitions. If “active customer” is defined differently by marketing, sales and finance, a dashboard may create apparent disagreement that is actually a measurement problem.

2.3 Collaborative CRM

Collaborative CRM enables departments, channels and sometimes external partners to share relevant customer information. Marketing may pass campaign-response data to sales; sales may provide promised delivery dates to service; service may report recurring product issues to product management.

Collaboration can occur through:

• shared customer records;
• common case and complaint histories;
• partner or distributor portals;
• shared calendars and tasks;
• documented handover rules; and
• integrated communication channels.

Collaborative CRM addresses organisational fragmentation. However, sharing does not mean unrestricted access. Employees should receive the information necessary for their role, subject to security and privacy controls.

2.4 Social CRM

Social CRM extends CRM into social media, online communities, ratings, reviews and other customer-generated channels. It includes monitoring conversations, responding to public enquiries, identifying advocates, handling complaints and learning from community behaviour.

Social CRM differs from simply posting on social media. It links social interactions to broader relationship processes where appropriate. A public complaint might become a service case; a product suggestion might be routed to product development; a positive review might indicate advocacy potential.

Social data requires caution. A public comment is not automatically permission to combine every available detail about its author with a private customer profile. Organisations must consider platform rules, reasonable expectations, consent, data minimisation and the context in which information was obtained.

Comparison of CRM perspectives

These perspectives are complementary. A service case may be handled operationally, shared collaboratively, analysed for recurring causes and monitored socially if it becomes a public issue.

3. Customer Records and the Data Model

A CRM record is a structured representation of a person, organisation, interaction or business event. A basic data model often contains:

• Account table: organisation name, sector, size, address and account status;
• Contact table: name, role, communication details, preferences and account relationship;
• Lead table: source, stated need, qualification status and owner;
• Opportunity table: value, stage, probability, expected close date and competitors;
• Activity table: calls, emails, meetings, tasks and outcomes;
• Case table: service issue, priority, status, resolution and satisfaction; and
• Consent and preference table: lawful basis, permissions, channel preferences and withdrawal records.

The same individual may appear in several tables, but should not be treated as several unrelated people. Unique identifiers and relationship keys connect records. In business-to-business marketing, the distinction between an account and a contact is especially important: several contacts may influence one organisational purchase.

A well-designed record contains more than raw facts. It also contains ownership, source, timestamp, confidence or verification status, and the purpose for which the data may be used. Free-text notes can provide valuable context but create inconsistency and may include inappropriate personal information. Structured fields should be used for information needed in reporting and automation; free text should be governed and reviewed.

Record lifecycle

A record normally passes through creation, enrichment, qualification, use, maintenance, archiving and deletion. The organisation should define:

• who may create or edit it;
• which fields are mandatory;
• how duplicates are resolved;
• when it is reviewed;
• how long it is retained; and
• how a correction or deletion request is handled.

A record should have a clear next action. A database full of contacts with no owner, lifecycle stage or planned follow-up is not an effective pipeline.

4. Managing the Sales Pipeline

A sales pipeline represents opportunities progressing through defined stages. A useful stage design reflects observable customer or seller actions, not merely optimistic opinions. Example stages are:

1. inquiry received;
2. initial qualification;
3. discovery completed;
4. solution or proposal presented;
5. commercial negotiation;
6. decision pending; and
7. won or lost.

Each stage should have an entry criterion, an exit criterion, a responsible owner and a required next action. For example, “proposal presented” should mean that a proposal was actually delivered to an identified decision group, not merely that a salesperson intends to send one.

Pipeline management supports prioritisation and forecasting. A simple weighted pipeline estimate is:

where E is the expected pipeline value, Vᵢ is the value of opportunity i, and pᵢ is its estimated probability of closing, expressed as a decimal between 0 and 1. The calculation assumes opportunities are sufficiently independent for a simple aggregate estimate and that probabilities are reasonably calibrated. It is an estimate, not a guaranteed revenue forecast.

Worked example. Three opportunities have values of $10,000, $25,000 and $40,000, with estimated probabilities of 0.20, 0.50 and 0.75. The expected value is:

E = (10,000 × 0.20) + (25,000 × 0.50) + (40,000 × 0.75)

E = 2,000 + 12,500 + 30,000 = $44,500

The result should not be interpreted as a promise that exactly $44,500 will be received. Probabilities may be biased, opportunities may not be independent, and timing, margin and capacity are not represented.

Pipeline managers should examine stage ageing, win rates, average sales cycle, loss reasons and missing next actions. A large pipeline can conceal weak qualification or stalled opportunities. A smaller pipeline with clear evidence may be more valuable.

5. Lead Management and Lead Scoring

Lead management connects demand generation with sales capacity. It includes capture, identification, enrichment, qualification, routing, follow-up and recycling of leads that are not yet ready to buy.

A lead-scoring model assigns points to characteristics or behaviours associated with commercial readiness. A basic illustrative score is:

where S is the lead score, xⱼ is the value of criterion j, and wⱼ is the weight assigned to that criterion. The variables may be binary, categorical or numerical, but the scoring scale must be defined consistently. The formula is a prioritisation rule, not proof that a lead will purchase.

Example criteria might include:

• target-sector fit: 0–20 points;
• relevant job role: 0–15 points;
• requested product information: 0–15 points;
• attendance at a high-value demonstration: 0–25 points;
• repeated website engagement: 0–10 points; and
• explicit purchase timeframe: 0–15 points.

A score of 60 or more might trigger sales review, while a lower score might trigger educational nurturing. Thresholds should be tested against actual outcomes. A person who downloads many resources may be curious rather than purchase-ready, while a low-engagement procurement manager may have substantial authority.

Good lead scoring follows five principles:

1. Fit: Does the lead resemble the intended customer?
2. Need or intent: Is there evidence of a relevant problem or buying project?
3. Recency: Is the behaviour recent enough to be meaningful?
4. Evidence: Are scores based on validated relationships with outcomes?
5. Reviewability: Can staff understand and challenge the score?

Scores should decay or be reviewed when behaviour becomes old. They should also be monitored for unfair effects. If historical sales data reflects unequal access or biased qualification practices, an automated model may reproduce those patterns.

6. CRM Integrations and Information Flows

A CRM rarely operates alone. Common integrations connect it with:

• websites and landing pages;
• email and marketing-automation platforms;
• advertising systems;
• e-commerce and payment platforms;
• enterprise resource planning and finance systems;
• customer-service and contact-centre tools;
• calendars and collaboration software;
• mobile applications;
• data warehouses and business-intelligence tools; and
• identity, consent and access-management systems.

An integration may be one-way or two-way, real-time or batch-based, and event-driven or scheduled. An API is a defined interface through which one system requests or sends data to another. Middleware or an integration platform may transform fields, manage authentication, queue messages and log failures.

Before integrating, managers should specify the business purpose and the system of record for each data element. For example, finance may be authoritative for invoices, while CRM is authoritative for sales activities. Without this decision, two-way synchronisation can overwrite accurate information with stale information.

Integration design should address:

• field mapping and data formats;
• unique identifiers;
• duplicate prevention;
• timing and latency;
• error handling and retry rules;
• consent and purpose limitations;
• access permissions;
• audit logs; and
• data deletion or correction propagation.

A successful integration is not defined merely by technical connection. It must deliver reliable business outcomes without creating hidden privacy, security or operational risks.

7. Data Quality Management

Data quality is fitness for a defined purpose. Common dimensions include:

Poor quality arises from hurried data entry, inconsistent definitions, duplicate imports, outdated contact details, disconnected systems and weak accountability. It creates practical costs: wasted sales time, failed deliveries, inaccurate segmentation, poor forecasts and customer frustration.

A data-quality programme should begin with critical data elements. The organisation can then establish standards, assign data owners, measure defects, correct existing records and prevent recurrence. Common controls include email and telephone validation, address standardisation, duplicate detection, drop-down values, format rules, mandatory ownership and periodic data-health reviews.

Data cleansing must be controlled. Automatically merging records with similar names can combine two different people; failing to merge records can produce repeated communications. Matching rules should use several identifiers and route uncertain cases to human review.

A useful management measure is a defect rate:

where D is the percentage defect rate, n is the number of records failing a stated quality test, and N is the number of records tested. The measure is meaningful only when the test definition and sample are clear. For example, a “missing valid email” rate is different from an overall data-quality rate.

8. The Single Customer View

A single customer view (SCV) is an integrated, usable representation of a customer’s relevant relationship with an organisation. It may bring together identity, transactions, interactions, service cases, preferences, consent and predicted needs.

The SCV is not necessarily one physical database or one screen containing every possible detail. It is better understood as a consistent view assembled from governed sources. Its purpose is to reduce fragmentation and enable relevant, coordinated action.

An SCV typically requires:

1. Identity resolution: linking records that refer to the same person or organisation.
2. Common identifiers: stable customer IDs and account relationships.
3. Shared definitions: agreement about terms such as active customer and churn.
4. Source governance: clear authority for each data element.
5. Permission controls: access based on role and purpose.
6. Time context: dates showing when information was captured or updated.
7. Confidence and provenance: knowledge of where information came from.

An SCV has limits. Some information should not be combined merely because it is technically possible. Household identity may be uncertain; a shared email address may represent several people; offline purchases may not link reliably to online activity. A perfect 360-degree view is therefore unrealistic. The goal is a sufficiently accurate and lawful view for a defined business purpose.

9. Governance, Ethics and Regulation

Customer data management creates responsibilities concerning privacy, fairness, security and accountability. The exact legal requirements depend on jurisdiction, but sound practice generally includes:

• transparency about what data is collected and why;
• a lawful and documented basis for processing;
• data minimisation;
• purpose limitation;
• accurate and correctable records;
• retention limits;
• appropriate security;
• controls for access, export and deletion; and
• procedures for handling incidents and individual requests.

Consent, where used, should be meaningful, specific and withdrawable. A customer’s agreement to receive a newsletter does not automatically authorise unrelated profiling. Organisations should distinguish operational communications, marketing communications and sensitive processing.

Security controls include strong authentication, role-based access, encryption where appropriate, secure backups, logging, staff training and vendor due diligence. Employees should not copy customer lists into personal spreadsheets or unapproved generative-AI tools.

Automated scoring and personalisation raise fairness concerns. Organisations should document the purpose of a model, test outcomes across relevant groups, monitor performance drift and provide human review where decisions have significant effects. Explainability is particularly important when a score determines access to service, credit, pricing or sales attention.

10. Technology, Automation and AI

Modern CRM platforms may use automation and artificial intelligence to summarise calls, predict propensity, recommend next actions, draft messages and identify service themes. These capabilities can improve productivity, but they do not remove managerial responsibility.

AI outputs may be inaccurate, overly confident, based on incomplete data or affected by historical bias. Before deployment, an organisation should define acceptable use, protect confidential information, verify outputs, retain audit evidence and specify when human approval is mandatory. AI-generated text should not invent customer commitments, and automated recommendations should not be treated as objective simply because they are numerical.

The most effective use of AI is usually augmentation: helping staff locate relevant information, identify priorities or prepare a draft while retaining human judgement. Data quality, governance and process clarity remain prerequisites.

11. Illustrative Case: Northstar Office Supplies

Background

Northstar Office Supplies sells equipment and consumables to small and medium-sized businesses. Marketing captures leads through search advertising, webinars and downloadable guides. Salespeople keep additional notes in spreadsheets, while service uses a separate ticketing system.

Problem

Customers receive duplicate emails because marketing and sales use different lists. Sales managers cannot distinguish new opportunities from inactive enquiries. Service agents cannot easily see recent quotations. Reports disagree about the number of customers because finance defines a customer as an account with an invoice, while marketing includes newsletter subscribers.

Analysis

The problem is not simply a lack of software. Northstar has operational fragmentation, weak data definitions and no ownership for duplicate resolution. Its proposed SCV must therefore begin with governance rather than immediate automation.

Approach and solution

Northstar takes five steps:

1. It defines common lifecycle stages and agrees that finance remains authoritative for invoices, while CRM is authoritative for sales activities.
2. It introduces a stable account ID and separates account, contact, lead, opportunity and service-case records.
3. It applies duplicate matching using business email, organisation name and address, with uncertain matches reviewed manually.
4. It creates a transparent lead score based on target-sector fit, product interest, webinar attendance and stated timeframe. A score triggers sales review, not automatic acceptance.
5. It integrates the service platform with CRM so authorised staff can see open cases and recent interactions, while sensitive service notes remain restricted.

A data steward monitors missing owners, invalid email addresses, stale opportunities and duplicate rates each month. Managers also establish a suppression rule so that a customer with an unresolved service complaint is not sent an inappropriate promotional message.

Outcome

After implementation, Northstar has a more consistent customer history, fewer duplicate communications and clearer sales accountability. Forecast discussions focus on evidence such as stage criteria and next actions rather than spreadsheet totals. The organisation still recognises that some records cannot be confidently matched and that data must be maintained continuously.

Learning points

• CRM improvement is a process and governance project as well as a technology project.
• A single customer view should be purpose-based and permission-aware.
• Definitions and ownership are necessary before reliable reporting is possible.
• Lead scoring should prioritise human attention rather than replace judgement.
• Integration quality must be measured by business outcomes and customer experience.

12. Implementation Best Practices

A practical CRM implementation can follow this sequence:

1. Clarify objectives: connect CRM activity to outcomes such as response time, retention or qualified pipeline.
2. Map customer processes: document current journeys, handovers, exceptions and pain points.
3. Define the minimum viable data model: collect information that has a clear use.
4. Assign ownership: identify process owners, data stewards and system administrators.
5. Set standards: establish field definitions, lifecycle stages, naming conventions and retention rules.
6. Clean and migrate carefully: profile data, test mappings, preserve audit information and validate samples.
7. Integrate selectively: prioritise high-value flows and define systems of record.
8. Pilot with users: obtain feedback from marketing, sales, service and customers where appropriate.
9. Train and reinforce: explain not only how to use the system but why accurate entries matter.
10. Measure and improve: review adoption, data quality, customer outcomes and unintended effects.

CRM metrics should include both activity and outcomes. Activity measures might include response time, completed follow-ups or record completeness. Outcome measures might include qualified conversion, retention, complaint resolution and customer satisfaction. A high number of logged activities is not evidence of high performance if customers experience irrelevant contact.

Challenges and Limitations

CRM systems face recurring challenges:

• employee resistance and low adoption;
• over-customised software that is difficult to maintain;
• duplicate or outdated records;
• conflicting departmental objectives;
• integration failures and data latency;
• excessive automation;
• privacy and cybersecurity incidents;
• biased scoring or segmentation;
• dependence on vendor platforms; and
• confusion between customer information and customer understanding.

CRM cannot compensate for an unattractive offer, poor service or inadequate fulfilment. It is an enabling system, not a substitute for sound marketing and management. Organisations should also avoid measuring relationship quality solely through short-term sales. Trust, satisfaction, consent and long-term value matter.

Chapter Summary

CRM is a business capability combining customer-oriented strategy, cross-functional processes, information and technology. Operational CRM manages everyday activities such as lead routing, sales tasks and service cases. Analytical CRM converts customer data into insights, forecasts and segments. Collaborative CRM coordinates information across departments and partners. Social CRM connects relationship management with social conversations and communities.

Effective customer records require clear entities, identifiers, ownership, lifecycle rules and permission controls. Sales pipelines should use observable stage criteria and realistic next actions. Weighted pipeline value and lead scoring can support prioritisation, but both are estimates whose usefulness depends on validated probabilities, reliable data and managerial judgement.

Integrations should define systems of record, identifiers, field mappings, error handling and governance. Data quality includes accuracy, completeness, consistency, timeliness, uniqueness, validity and integrity. A single customer view depends on identity resolution, shared definitions, provenance and lawful access; it is a useful purpose-based representation rather than an assumption that all data should be combined.

Finally, CRM requires privacy, security, fairness and accountability. Automation and AI can augment employees, but they must be governed and reviewed. Successful CRM implementation is continuous: organisations must measure customer and business outcomes, maintain data quality and improve processes as customer expectations and technologies change.

Key Terms

CRM: The coordinated management of customer relationships using strategy, processes, people, data and technology.

Operational CRM: CRM supporting routine customer-facing processes and workflow execution.

Analytical CRM: CRM using customer data for reporting, insight, prediction and decision support.

Collaborative CRM: CRM enabling information sharing and coordinated work across teams or partners.

Social CRM: CRM involving social media, online communities and customer-generated interactions.

Lead: A person or organisation with possible commercial potential.

Prospect: A lead assessed as having relevant fit, need or buying potential.

Account: An organisation or household represented in a CRM.

Contact: An individual associated with an account or relationship.

Opportunity: A qualified potential sale being actively pursued.

Pipeline: The sequence and collection of opportunities progressing toward a decision.

Lead scoring: Assigning points to lead characteristics or behaviours to support prioritisation.

API: A defined interface allowing systems to exchange requests or data.

Data quality: The fitness of data for a specified purpose.

Identity resolution: The process of determining which records refer to the same entity.

Single customer view: An integrated and governed representation of relevant customer information.

Data steward: A person responsible for maintaining data standards and quality.

Purpose limitation: Using personal data only for defined and compatible purposes.

Workflow: A structured sequence of tasks, rules or approvals.

Data provenance: Information about the source, history and transformation of data.

Review Questions

Short Answer Questions

1. What is the difference between operational CRM and analytical CRM?
2. Why should a CRM record have an owner and a next action?
3. Define a single customer view.
4. Identify four dimensions of data quality.
5. What is the purpose of a system of record in an integration?

Long Answer Questions

1. Explain how operational, analytical, collaborative and social CRM can work together in a customer-service process.
2. Describe the main stages of a well-designed sales pipeline and explain why observable stage criteria matter.
3. Explain how an organisation can design, test and govern a lead-scoring model.
4. Discuss the organisational and technical requirements for developing a single customer view.
5. Evaluate the ethical and privacy risks associated with CRM automation and AI-based personalisation.

Analytical / Application Questions

1. A company has duplicate records, inconsistent customer definitions and disconnected service data. Diagnose the likely causes and propose a phased CRM improvement plan.
2. Design a lead-scoring framework for a business-to-business software provider. Specify at least five criteria, their rationale, and how the threshold would be validated.
3. A manager claims that a large weighted pipeline guarantees future revenue. Use the assumptions and limitations of weighted pipeline analysis to assess this claim and recommend better forecasting practices.

Multiple Choice Questions with Answers

Question 1

Which CRM perspective primarily supports routine lead routing, sales tasks and service cases?

A. Operational CRM

B. Analytical CRM

C. Collaborative CRM

D. Social CRM

Correct Answer: A. Operational CRM

Question 2

What is the main purpose of a system of record in CRM integration?

A. To ensure every system stores identical copies of all data

B. To identify which system is authoritative for a particular data element

C. To eliminate the need for data-quality controls

D. To prevent any data from being shared

Correct Answer: B. To identify which system is authoritative for a particular data element

Question 3

An opportunity worth $20,000 has an estimated closing probability of 0.50. What is its contribution to a simple weighted pipeline estimate?

A. $5,000

B. $10,000

C. $20,500

D. $40,000

Correct Answer: B. $10,000

Question 4

Which data-quality dimension asks whether a real entity has been represented more than once?

A. Timeliness

B. Completeness

C. Uniqueness

D. Validity

Correct Answer: C. Uniqueness

Question 5

What is the most appropriate interpretation of a lead score?

A. A guaranteed prediction that the lead will purchase

B. A prioritisation aid based on defined characteristics or behaviours

C. A legal classification of the customer

D. A replacement for all salesperson judgement

Correct Answer: B. A prioritisation aid based on defined characteristics or behaviours

Question 6

Which feature is most important when defining a sales-pipeline stage?

A. A subjective optimism rating only

B. An observable entry or exit criterion

C. The salesperson’s preferred colour code

D. The number of fields displayed on screen

Correct Answer: B. An observable entry or exit criterion

Question 7

What does identity resolution attempt to determine?

A. Whether a campaign has enough creative content

B. Which records refer to the same person or organisation

C. Whether an employee has completed training

D. Which product has the highest price

Correct Answer: B. Which records refer to the same person or organisation

Question 8

Which practice best supports privacy-aware CRM management?

A. Collecting every available detail regardless of purpose

B. Sharing all customer information with every employee

C. Limiting data use to defined purposes and authorised access

D. Keeping inaccurate data to preserve historical records

Correct Answer: C. Limiting data use to defined purposes and authorised access

Question 9

Which CRM perspective is most directly concerned with learning from online reviews and public customer conversations?

A. Social CRM

B. Operational CRM

C. Transactional accounting

D. Inventory CRM

Correct Answer: A. Social CRM

Question 10

If 80 of 1,000 tested records fail a stated quality test, what is the defect rate?

A. 0.8%

B. 8%

C. 12.5%

D. 80%

Correct Answer: B. 8%