15.2 Building a Change Analytics Strategy

APMG Change Management Foundation Exam

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1 Chapter 1: The v3 Foundation Exam and the Organisational Context

1.1 Exam Structure, the Five Syllabus Areas and the Certification Path 1.2 Scene Setting: External and Internal Factors Shaping Change 1.3 Change Management, Benefits and Dis-benefits

2 Chapter 2: Understanding the Change Context

2.1 Kelman's Three Levels of Adoption 2.2 VUCA and the Cynefin Framework 2.3 Planned Change versus Emergent Change 2.4 Systems Thinking in Change 2.5 Organisational Culture and Its Three Levels

3 Chapter 3: Diagnosing and Structuring the Change Approach

3.1 Lewin's Force Field Analysis 3.2 Co-Design 3.3 The ADKAR Model 3.4 Evolving the n-Step Process and Kotter's Eight Accelerators

4 Chapter 4: Delivery Strategy, Agile Practices and the Change Strategy

4.1 Delivery Strategy: Big Bang, Phased, Voluntary and Iterative 4.2 Agile Practices and Agile Change Practices 4.3 The Continuous Change Management Cycle 4.4 The Change Strategy

5 Chapter 5: People and Change — Individual Difference and Response

5.1 Differences in People and Personality Preferences 5.2 Neuroscience and Change: SCARF, Mindset and Insight 5.3 The Change Curve 5.4 Bridges' Transition Model

6 Chapter 6: Motivation, Resilience and Learning

6.1 Rewards, Punishment and Pink's Three Motivators 6.2 Positive Psychology, Resilience and Appreciative Inquiry 6.3 The Beckhard-Harris Change Formula and Schein's Two Anxieties 6.4 Competence, Learning and the Learning Dip 6.5 Learning Preferences: Kolb, Honey and Mumford

7 Chapter 7: Key Roles in Change Leadership

7.1 Key Roles in the Change Process 7.2 The Role of the Change Sponsor 7.3 The Change Agent's Role and Personal Influence 7.4 The Role of Local Leadership in Change

8 Chapter 8: Psychological Safety, Leadership and Culture

8.1 Psychological Climate and Psychological Safety 8.2 Carolyn Taylor: How Leadership Shapes Culture 8.3 Leadership Styles and Organisational Climate

9 Chapter 9: Teams and Change

9.1 Team Structures and Change Agent Networks 9.2 Tuckman's Stages of Team Development 9.3 Lencioni: Assessing and Growing Team Effectiveness 9.4 Harvesting the Value of Team Diversity

10 Chapter 10: Stakeholder Identification, Analysis and Engagement

10.1 Stakeholder Definition and Identification 10.2 Stakeholder Analysis, Segmentation and Empathy Mapping 10.3 Stakeholder Mapping and Sources of Power 10.4 Barriers to Engagement 10.5 Planning Stakeholder Engagement

11 Chapter 11: Communication Theory, Messaging and Emotional Connection

11.1 Communication Theory and the Communication Process 11.2 Barriers to Communication and Cognitive Biases 11.3 Messaging: Setting Context and the Tell, Sell, Include Approach 11.4 Appealing to Hearts and Minds

12 Chapter 12: Communication Channels and Planning

12.1 Ways of Communicating and VARK Language Style Preferences 12.2 Communication Channels: One-Way, Two-Way, Lean and Rich 12.3 Communications Planning

13 Chapter 13: Assessing Change Impact and Severity

13.1 Change Management as Risk Management and the Two-Step Assessment 13.2 Step 1: Categorising Impacts and Identifying Impact Areas 13.3 Step 1 Continued: Gap Analysis, Impact Profiles and Monitoring 13.4 Step 2: Change Impact Severity

14 Chapter 14: Resistance to Change

14.1 Causes of Resistance and Oreg's Four Neural Factors 14.2 Common Reasons for Resistance and How to Deal With Them 14.3 Building a Strategy to Manage Resistance

15 Chapter 15: Change Analytics and Exam Readiness

15.1 Measurement Types and the Change Delivery Scorecard 15.2 Building a Change Analytics Strategy 15.3 Exam Technique and Final Revision

15.2 Building a Change Analytics Strategy

Key Takeaways Explain This Topic AI

A change analytics strategy is how you will capture, manage and use data to support the success of the change

It should cover change readiness, adoption and benefits realisation, plus the effectiveness of communications, engagement and learning interventions

Qualitative data is subjective and harder to measure, while quantitative data can be observed and measured numerically

Leading measures are result-oriented dynamic factors that signal progress, while lagging measures are output focused and measure the outcome of the change

In the lead-up to change readiness teams rely on qualitative data, while for adoption the emphasis shifts to quantitative metrics

Last updated: September 2026

A change analytics strategy refers to how you will capture, manage, and use data to support the success of the change.

Once you have a framework for your balanced scorecard, consider how you will measure change readiness, adoption, and benefits realisation . This should also cover assessing the effectiveness of your communications, engagements, and learning and development interventions .

That second sentence is significant. Change analytics is not only about whether the change is being adopted; it is also about whether the change management itself is working . Measuring the effectiveness of communication and engagement is what stops those activities being reported as an activity count.

The four evaluation methods

Method Definition
Qualitative Data that is subjective and deemed harder to measure
Quantitative Data you can observe and measure , numerical in nature

Leading Result-orientated. These are dynamic factors that signal progress. Like qualitative measures, these are harder to quantify

Lagging Output focused. They measure the outcome of the change

The two pairs answer different questions. Qualitative versus quantitative is about the nature of the data — subjective judgement versus countable observation. Leading versus lagging is about timing — signals of progress versus measurement of the result.

Examples that fix the distinctions:

Measure Type
Interviews and questionnaires about how people feel about the change Qualitative
Number of staff who have completed training Quantitative
Proportion of teams that have run their own local adoption session Leading (signals progress towards adoption)
Reduction in processing time six months after go-live Lagging (measures the outcome)

Interviews and questionnaires are a qualitative evaluation measure — a point the exam tests directly.

Applying measures appropriately

Quantitative measures are the most attractive method when capturing data due to their tangible nature. However, different stages of the change lifecycle will require different mixes of data types to show progress and critical mass:

In the lead-up to change readiness, the change teams will be reliant on qualitative data. For example, do people know what the change is and what it is trying to achieve?

For adoption, the emphasis will shift to quantitative metrics. For example, what activities have the change network held? How many people attended?

This progression is worth internalising because it contradicts a common instinct. Early in a change, when pressure for hard numbers is highest, the honest answers are qualitative — nobody is using the new system yet because it does not exist, so the only meaningful readings are about understanding, sentiment and readiness. Producing quantitative readiness metrics at that stage usually means counting activity rather than measuring readiness.

As the change moves into adoption, countable behaviour becomes available and the emphasis shifts. A change analytics strategy should plan for that shift rather than applying one measurement approach throughout.

Limitations of data analytics

CM3 names four limitations:

Limitation What it means in practice
Volume of data More data than can be meaningfully analysed or acted on; signal buried in noise

Quality of data Incomplete, inconsistent or inaccurate underlying data producing confident but wrong conclusions

Inappropriate handling of data Poor collection, storage or analysis practice — including drawing conclusions the data cannot support

Data distortion Data affected by how it is collected or by people's awareness of being measured

Data distortion deserves particular attention in change work, because measurement itself changes behaviour. If adoption is measured by logins, people log in. If engagement is measured by survey scores and those scores affect a manager's rating, the scores improve without the engagement doing so. This is why the actionable factor and the involvement of users in metric selection matter: measures chosen with people and used to help them are distorted far less than measures imposed on them and used to judge them.

Quality of data is the most common practical problem. Change teams frequently build analytics on system data collected for another purpose entirely, and the mismatch between what the field records and what the change team believes it records is not visible in the resulting chart.

Assembling the strategy

A change analytics strategy that satisfies CM3 covers:

What will be measured — readiness, adoption, benefits realisation, and the effectiveness of communications, engagement and learning

How — the mix of qualitative, quantitative, leading and lagging methods, and how that mix shifts across the lifecycle

How data will be collected — sources, including system data already available, and the cadence for each
Who acts on it — the accountable owner for each measure and what response each signal triggers

What the limitations are — an honest statement of what the data can and cannot show, so conclusions stay within what the evidence supports