Change management professionals are increasingly requested to provide measurement, data, and insights to various stakeholder groups. Not only does this include tracking various change outcomes such as business readiness or adoption, but stakeholder concerns also include such as change saturation and visibility of incoming initiative impacts.
To become better at working with data there is much that change managers can learn from data scientists (without becoming one of course). Let’s explore how change management can benefit from the practices and methodologies employed by data scientists, focusing on time allocation, digital tools, system building, hypothesis-led approaches, and the growing need for data and analytical capabilities.
1. Time Allocation: Prioritizing Data Collection and Cleansing
Data scientists spend a substantial portion of their time on data collection and cleansing. According to industry estimates, about 60-80% of a data scientist’s time is dedicated to these tasks. This meticulous process ensures that the data used for analysis is accurate, complete, and reliable.
In the below diagram from researchgate.net you can see that for data scientists the vast majority of the time is spent on collecting, cleansing and organising data.
You might say that change managers are not data scientists because the work nature is different, and therefore should not need to carve out time for these activities? Well, it turns out that the type of activities and proportions of time spent is similar across a range of data professionals, including business analysts.
Below is the survey results published by Business Broadway, showing that even business analysts and data analysts spend significant time in data collection, cleansing, and preparation.
Lessons for Change Management
a. Emphasize Data Collection and Cleansing: For change managers, this translates to prioritizing the collection of reliable data related to change initiatives. This might include stakeholder feedback, performance metrics, impact data and other relevant data points. Clean data is essential for accurate analysis and insightful decision-making. Data projects undertaken by change managers are not going to be as large or as complex as data scientists, however the key takeaway is that this part of the work is critical and sufficient time should be allocated and not skipped.
b. Allocate Time Wisely: Just as data scientists allocate significant time to data preparation, change managers should also dedicate sufficient time to gathering and cleaning data before diving into analysis. This ensures that the insights derived are based on accurate and reliable information.
It also depends on the data topic and your audience. If you are presenting comparative data, for example, change volume across different business units. You may be able to do spot checks on the data and not verify every data line. However, if you are presenting to operations business units like call centres where they are very sensitive to time and capacity challenges, you may need to go quite granular in terms of exactly what the time impost is across initiatives.
c. Training and Awareness: Ensuring that the change management team understands the importance of data quality and is trained in basic data cleansing techniques can go a long way in improving the overall effectiveness of change initiatives. Think of scheduling regular data sessions/workshops to review and present data observations and findings to enhance the team’s ability to capture accurate data as well as the ability to interpret and apply insights. The more capable the team is in understanding data, the more value they can add to their stakeholders leveraging data insights.
2. Leveraging Digital Tools: Enhancing Efficiency and Accuracy
Data scientists rely on a variety of digital tools to streamline their work. These tools assist in data collection, auditing, visualization, and insight generation. AI and machine learning technologies are increasingly being used to automate and enhance these processes.
Data scientists rely on various programming, machine learning and data visualisation such as SQL, Python, Jupyter, R as well as various charting tools.
Lessons for Change Management
a. Adopt Digital Tools: Change managers should leverage digital tools to support each phase of their data work. There are plenty of digital tools out there for various tasks such as surveys, data analysis and reporting tools.
For example, Change Compass has built-in data analysis, data interpretation, data audit, AI and other tools to help streamline and reduce manual efforts across various data work steps. However, once again even with automation and AI the work of data checking and cleansing does not go away. It becomes even more important.
b. Utilize AI and Machine Learning: AI can play a crucial role in automating repetitive tasks, identifying patterns, data outliers, and generating insights. For example, AI-driven analytics tools can help predict potential change saturation, level of employee adoption or identify areas needing additional support during various phases of change initiatives.
With Change Compass for example, AI may be leverage to summarise data, call out key risks, generate data, and forecast future trends.
c. Continuous Learning: Continuous learning is essential for ensuring that change management teams stay adept at handling data and generating valuable insights. With greater stakeholder expectations and demands, regular training sessions on the latest data management practices and techniques can be helpful. These sessions can cover a wide range of topics, including data collection methodologies, data cleansing techniques, data visualisation techniques and the use of AI and machine learning for predictive analytics. By fostering a culture of continuous learning, organizations can ensure that their change management teams remain proficient in leveraging data for driving effective change.
In addition to formal training, creating opportunities for hands-on experience with real-world data can significantly enhance the learning process. For instance, change teams can work on pilot projects where they apply new data analysis techniques to solve specific challenges within the organization. Regular knowledge-sharing sessions, where team members present case studies and share insights from their experiences, can also promote collective learning and continuous improvement.
Furthermore, fostering collaboration between change managers and data scientists or data analysts can provide invaluable mentorship and cross-functional learning opportunities. By investing in continuous learning and development, organizations can build a change management function that is not only skilled in data management but also adept at generating actionable insights that drive successful change initiatives.
3. Building the Right System: Ensuring Sustainable Insight Generation
It is not just about individuals or teams working on data. A robust system is vital for ongoing insight generation. This involves creating processes for data collection, auditing, cleansing, and establishing governance bodies to manage and report on data.
Governance structures play a vital role in managing and reporting data. Establishing governance bodies ensures that there is accountability and oversight in data management practices. These bodies can develop and enforce data policies, and oversee data quality initiatives. They can also be responsible for supporting the management of a central data repository where all relevant data is stored and managed.
Lessons for Change Management
a. Establish Clear Processes: Develop and document processes for collecting and managing data related to change initiatives. This ensures consistency and reliability in data handling.
b. Implement Governance Structures: Set up governance bodies to oversee data management practices. This includes ensuring compliance with data privacy regulations and maintaining data integrity. The governance can sponsor the investment and usage of the change data platform. This repository should be accessible to stakeholders involved in the change management process, promoting transparency and collaboration. Note that a governance group can simply be a leadership team regular team meeting and does not need to be necessarily creating a special committee.
c. Invest in system Infrastructure: Build the necessary system infrastructure to support data management and analysis that is easy to use and provides the features to support insight generation and application for the change team.
Data scientists often use a hypothesis-led approach, where they test, reject, or confirm hypotheses using data. This method goes beyond simply reporting what the data shows to understanding the underlying causes and implications.
Lessons for Change Management
a. Define Hypotheses: Before analyzing data, clearly define the hypotheses you want to test. For instance, if there is a hypothesis that there is a risk of too much change in Department A, specify the data needed to test this hypothesis.
b. Use Data to Confirm or Reject Hypotheses: Collect and analyze data to confirm or reject your hypotheses. This approach helps in making informed decisions rather than relying on assumptions or certain stakeholder opinions.
c. Focus on Actionable Insights: Hypothesis-led analysis often leads to more actionable insights. It is also easier to use this approach to dispel any myths of false perceptions.
For example: Resolving Lack of Adoption
Hypothesis: The lack of adoption of a new software tool in the organization is due to insufficient coaching and support for employees.
Data Collection:
Gather data on the presence of managerial coaching and perceived quality. Also gather data on post go live user support.
Collect feedback from employees through surveys regarding the adequacy and clarity of coaching and support.
Analyse usage data of the new software to identify adoption rates across different departments.
Analysis:
Compare adoption rates between employees who received sufficient coaching and support versus those who did not.
Correlate feedback scores on training effectiveness with usage data to see if those who found the training useful are more likely to adopt the tool.
Segment data by department to identify if certain teams have lower adoption rates and investigate their specific training experiences.
Actionable Insights:
If data shows a positive correlation between coaching and support, and software adoption, this supports the hypothesis that enhancing coaching and support programs can improve adoption rates.
If certain departments show lower adoption despite completing coaching sessions, investigate further into department-specific issues such as workload or differing processes that may affect adoption.
Implement targeted interventions such as additional training sessions, one-on-one support, or improved training materials for departments with low adoption rates.
5. Building Data and Analytical Capabilities: A Core Need for Change Management
As data and analytical capabilities become increasingly crucial, change management functions must build the necessary people and process capabilities to leverage data-based insights effectively.
Lessons for Change Management
a. Invest in Training: Equip change management teams with the skills needed to manage data and generate insights. This includes training in data analysis, visualization, and interpretation.
b. Foster a Data-Driven Culture: A lot of organisations are already on the bandwagon of Encourage a culture where data is valued and used for decision-making. The change management needs to promote this equally within the change management function. This involves promoting the use of data in everyday tasks and ensuring that all team members understand its importance. Think of incorporating data-led discussions into routine meeting meetings.
c. Develop Analytical Frameworks: Create frameworks and methodologies for analyzing change management data. This includes defining common key metrics, setting benchmarks, and establishing protocols for data collection and analysis for change data. Data and visual templates may be easier to follow for those with lower capabilities in data analytics.
Practical Steps to Implement Data-Driven Change Management
To integrate these lessons effectively, senior change practitioners can follow these practical steps:
Develop a Data Strategy: Create a comprehensive data strategy that outlines the processes, tools, and governance structures needed to manage change management data effectively.
Conduct a Data Audit: Begin by auditing the existing data related to change management. Identify gaps and areas for improvement.
Adopt a Hypothesis-Led Approach: Encourage the use of hypothesis-led approaches to move beyond descriptive analytics and derive more meaningful insights.
Invest in Technology: Invest in the necessary digital tools and technologies to support data collection, cleansing, visualization, and analysis.
Train the Team: Provide training and development opportunities for the change management team to build their data and analytical capabilities.
Collaborate Across Functions: Foster collaboration between change management and data science teams to leverage their expertise and insights.
Implement Governance Structures: Establish governance bodies to oversee data management practices and ensure compliance with regulations and standards.
By learning from the practices and methodologies of data scientists, change management functions can significantly enhance their effectiveness. Prioritizing data collection and cleansing, leveraging digital tools, building robust systems, adopting hypothesis-led approaches, and developing data and analytical capabilities are key strategies that change management teams can implement. By doing so, they can ensure that their change initiatives are data-driven, insightful, and impactful, ultimately leading to better business outcomes.
Successful change management relies on having the right metrics to measure progress, gauge impact, and communicate with stakeholders. Moreover, the right metrics can drive continuous improvement and help directly achieve change outcomes. However, not all metrics are beneficial, and some can mislead or fail to meet stakeholder needs. Let’s check out the top change management metrics to avoid and go through examples to take note.
Understanding the Disconnect: Change Managers vs. Business Stakeholders
A significant reason certain change management metrics fall short is the differing perspectives between change managers and business stakeholders. Change managers are trained to view metrics through the lens of change management frameworks and methodologies, focusing on detailed assessments and structured approaches. These include applying ratings and judgments on aspects such as impact levels.
In contrast, business stakeholders prioritize business operations, strategic outcomes, and practical implications. The busy business stakeholder is often looking for practical implications from metrics that can be used to directly drive decision making, meaning “what do I do with this data to improve the ultimate business outcome”.
Of course, different stakeholder have different data needs, and you need to show the right metric to the right type of stakeholder. For example, operations focused stakeholders expect fairly detailed metrics and data and what that means in terms of organisation, coordination, capacity and performance perspectives. Senior managers may prefer higher level data with focus on strategic impacts, overall progress and adoption indicators.
This disconnect can lead to the use of metrics that do not resonate with or are misunderstood by stakeholders.
Metrics from a Change Manager’s Perspective
Change managers may leverage metrics that are derived from the various change management documents such impact assessments, training plan or communications plan. Metrics also often chosen for ease of use and ideally are not overly complicated to execute.
For example, impact assessments typically involve rating stakeholder groups and initiatives on a traffic light system (red, amber, green) based on their impact. While this approach is systematic, it can be problematic for several reasons:
Lack of Sufficient Stakeholder Context: Business stakeholders might not understand the practical implications of these ratings. For instance, an “impact rating per initiative” may not clearly convey what the rating means for day-to-day operations or strategic goals. For example, if an initiative has a red impact rating, stakeholders might not grasp the specific operational changes or strategic adjustments needed, in essence, “what do I do with this?”.
Misinterpretation of Traffic Light Ratings: The red, amber, green system can be misleading. Stakeholders might interpret red as an indicator of alarm or imminent risk, while green may be seen as a sign that no action is needed. This is because stakeholders are trained to interpret traffic light ratings this way (from the various project/business updates they’ve attended). In reality, red might simply mean high impact, requiring focused attention, and green might indicate a low impact but still require monitoring. For instance, a red rating might indicate significant process changes that need careful management, not necessarily a negative outcome.
Hard to defend ratings if prompted: Business stakeholders may also want to drill into how the ratings are determined, and based on what basis. They may expect a logical data-backed reasoning of how each colour scheme is determined. If a rating is based on an overall ‘personal judgment’ this may be hard to defend infront of a group of stakeholders.
Examples of Potentially Misleading Metrics
Certain metrics, although straightforward, can be easily misinterpreted and fail to provide a realistic picture of change impacts. Often these are selected because they are easy to report on. However, easy, make not give you the outcome you are looking for.
Number of Go-Lives: Tracking the number of Go-Lives over time might seem like an effective way to represent change volume. However, the most significant impacts on people often occur before or after the Go-Live date. For example, the preparation and training phase before Go-Live and the adoption phase afterward are critical periods that this metric overlooks. A Go-Live date might indicate a milestone but not the challenges, progress or impacts faced during the implementation phase.
Number of Activities Implemented: Similar to Go-Lives, this metric focuses on quantity rather than quality. Simply counting the number of activities does not account for their effectiveness or the actual change they drive within the organisation. For example, reporting that 50 training sessions were conducted does not reveal whether employees found them helpful or if they led to improved performance.
Number of impacts or stakeholders impacted: Again, using a numerical way to indicate progress can be very misleading, or unmeaningful. This is because it may be ‘interesting’ but with no real action for your stakeholder to take in order to somehow lead to a better overall change outcome. If metrics do not result in some kind of action, then over time it will not shape your change(s) toward the targeted outcomes. Or worse, your stakeholders may lose interest and lose confidence in the strategic impact of these metrics.
Another common way to report change metrics is to use the number of impacts or number of stakeholders impacted. This can be in terms of the following:
Number of divisions impacted
Number of stakeholder groups impacted
Number of employees impacted
Number of initiatives per division/stakeholder
Metrics That May Be Too Operational
Metrics that are overly operational can fail to capture meaningful progress or adoption. Perhaps if the metric are for reporting within the Change Management team that may be OK. However, when you are showing metrics to stakeholders, a different set of expectations should be cast.
If you are presenting metrics to senior managers, you need to ensure that they hit the mark for that audience group. If the group is more interested in strategic impact, and higher level progress outcomes, you need to tailor accordingly.
Examples of metrics that may be too operational include:
Number of Communications Sent: This metric measures activity but not effectiveness. Sending numerous emails or messages does not guarantee that the message is received, understood, or acted upon by stakeholders. For instance, stakeholders might receive 100 emails, but if the content is unclear, the communication effort is wasted. Or worse, the emails may not even have been read.
Number of Training Sessions Attended: This one is a classic. While training is crucial, the number of sessions attended does not necessarily reflect the attendees’ understanding, engagement, or the practical application of the training. For example, employees might attend training but not apply the new skills if the training is not relevant to their roles for various reasons.
Number of workshops/meetings: Another way of articulating the change management progress in terms of activities is the number of workshops or meetings conducted with stakeholders. Again, this may be good to track within the change management team. However, presenting this metric to stakeholders may not be appropriate as it may not mee their needs.
The way metrics are presented is just as important as the metrics themselves. Poor visualization can lead to misinterpretation, confusion, and misguided decisions. Here are some common pitfalls to avoid:
Ineffective Use of Pie Charts
Pie charts can be misleading when used to show data points that are not significantly different. For example, using a pie chart to represent the percentage of divisions impacted by a change might not effectively communicate the nuances of the impact if the differences between the divisions are minimal. A pie chart showing 45%, 30%, and 25% might not convey the critical differences in impact levels among divisions.
Misleading Traffic Light Ratings
Using red, amber, and green to indicate high, medium, and low impacts can send the wrong message. Stakeholders might associate these colours with good and bad outcomes rather than understanding the actual levels of impact. Stakeholder may be used to interpreting these in the context of their usual project or business updates where red indicated alarm and ‘bad’. This can lead to unnecessary alarm or complacency. For instance, a green rating might suggest no need for action, while in reality, it might require ongoing monitoring.
Overuse of Colours
Using too many colours in charts and graphs can overwhelm stakeholders, making it difficult to discern the key message. Using colours in data visualisation can be two-edged sword. Colour can effectively point your stakeholders are the area where you want them to focus on. But, too many colours can lose your audience. A cluttered visual can obscure the critical data points and lead to misinterpretation. For example, a graph with ten different colours can confuse stakeholders about which data points are most important.
Practical Takeaways for Senior Change Managers
To ensure that change management metrics are effective, consider the following practical takeaways:
Align Metrics with Stakeholder Perspectives
Understand Stakeholder Priorities: Engage with stakeholders to understand their priorities and concerns. Tailor your metrics to address these aspects directly. For example, if stakeholders are concerned about operational efficiency, focus on metrics that reflect improvements in this area.
Use Business Language: Frame your metrics in a way that resonates with business stakeholders. Avoid change management jargon and reference, and ensure that the implications of the metrics are clear and actionable. For example, instead of using technical terms, explain how the metrics impact business outcomes. Think in terms of business activities, milestones, busy periods, and capacity challenges.
Focus on Meaningful Metrics
Measure Outcomes, Not Just Activities: Prioritize metrics that reflect the outcomes and impacts of change, rather than just the activities performed. For example, instead of counting the number of training sessions, measure the improvement in employee performance or knowledge retention post-training.
Example: Instead of reporting that 100 employees attended training sessions, report that 85% of attendees showed improved performance in their roles after training, or that certain level of competencies were gained.
Track Engagement and Adoption: Monitor metrics that indicate the level of engagement and adoption among stakeholders. This could include surveys, feedback forms, or direct measures of behaviour change.
Example: Use post-training surveys to measure employee confidence in applying new skills or managerial rating of application of learnt skills. Track the percentage of employees who actively use new tools or processes introduced during the change.
Improve Metric Visualization
Simplify Visuals: Use clear, simple visuals that highlight the key messages. Avoid clutter and ensure that the most important data points stand out.
Example: Use bar charts or line graphs to show trends over time rather than pie charts that can be harder to interpret.
Contextualize Data: Provide context for the data to help stakeholders understand the significance. For example, instead of just showing the number of Go-Lives, explain what each Go-Live entails and its expected impact on operations. Or better, focus on showing the varying levels of impact on different stakeholders across time within the initiative.
Example: Accompany a Go-Live count with a visual showing the varying impact level of various implementation activities of the changes.
Narrative Approach: Combine metrics with a narrative that explains the story behind the numbers. This can help stakeholders understand the broader context and implications.
Example: Instead of presenting raw data, provide a summary that explains key trends, successes, and areas needing attention.
Educate your stakeholders: Depending on stakeholder needs you may need to take them on a phased approach to gradually educate them on change management metrics and how you ultimately want them to drive the outcomes.
Example: You may start the education process to focus on more simplistic and easy-to-understand measures, and as your stakeholders are more change-mature, move to drill into more detailed metrics that explain the ‘why’ and ‘how’ to drive outcome success.
Continuously improvement: Provide regular updates on key metrics and adjust them based on feedback from stakeholders. Continuous communication ensures that everyone remains aligned and informed.
Example: Hold monthly review meetings with stakeholders to discuss the latest metrics, address concerns, and adjust strategies as needed.
Examples of Effective Metrics
Employee Adoption and Engagement
Percentage of Employees Adopting New Process/System: This metric measures the rate at which employees are using new processes or systems introduced during the change. High adoption rates indicate successful integration.
Implementation: Use software usage analytics or surveys to track tool adoption rates.
Visualization: A graph showing adoption rates over time.
Employee Feedback Scores: Collect feedback on change initiatives through surveys or stakeholder ratings to measure sentiment/feedback and identify areas for improvement.
Implementation: Conduct regular surveys asking employees about their experience with the change process. Do note that depending on the change you may expect negative feedback due to the nature of the change itself (vs the way it was implemented).
Visualization: Bar/Line charts comparing feedback scores across different departments or time periods. Bar/Line charts are the standard go-to for data visualisation. They are easy to understand and interpret.
Impact on Business Outcomes
Improvement in Key Performance Indicators (KPIs): Track changes in KPIs that are directly impacted by the change initiatives, such as productivity, customer satisfaction, or financial performance.
Implementation: Identify relevant KPIs and measure their performance before and after change initiatives.
Visualization: Use line/bar graphs to show trends in KPI performance over time.
Operational Efficiency Metrics: Measure improvements in operational processes, such as reduced cycle times, error rates, or cost savings.
Implementation: Track specific operational metrics relevant to the change initiatives.
Visualization: Bar charts or heatmaps showing improvements in efficiency metrics across different operational areas.
Effective change management requires metrics that not only measure progress but also resonate with business stakeholders and accurately reflect the impact of change initiatives. Avoiding common pitfalls such as relying on easily misinterpreted or overly operational metrics is crucial. By aligning metrics with stakeholder perspectives, focusing on meaningful outcomes, improving visualization, and communicating effectively, senior change and transformation professionals can ensure that their metrics truly support the success of their change initiatives.
The top change management metrics to avoid are those that fail to provide clear, actionable insights to business stakeholders. By understanding and addressing the disconnect between change managers and business stakeholders, and by prioritizing metrics that truly reflect the impact and progress of change, you can drive more effective and successful change management efforts by influencing your stakeholders in your organisation.
Chat with us if you would like to discuss more about leveraging AI and technology to generate high-impact change management metrics and data for your stakeholders, both at project and portfolio levels.
Scaled Agile Framework (SAFe) has emerged as a leading methodology to address organisational change demands of fostering flexibility, collaboration, and continuous improvement. A cornerstone of SAFe is the principle of “Measure and Grow,” which emphasizes using data and fact-based decisions to enhance change outcomes over time. Despite its centrality, SAFe does not explicitly detail the change management components essential for its success. Here we outline how change management practitioners can effectively apply the “Measure and Grow” principle to lead change and improve outcomes to support the Scaled Agile environment.
The “Measure and Grow” Principle in Scaled Agile
“Measure and Grow” is integral to SAFe, focusing on systematic measurement and continuous improvement. By leveraging data and analytics, organizations can make informed decisions, identify areas needing attention, and iteratively enhance performance. For change management professionals, this principle translates into a structured approach to evaluate the effectiveness of change initiatives, pinpoint areas for improvement, and implement necessary adjustments.
In a Scaled Agile environment, “Measure and Grow” is a core tenant or principle that applies in all types of agile environments. By continuously assessing and refining change efforts, organizations can align their initiatives with strategic objectives, mitigate risks, and ensure sustained success.
In practice, a lot of organisations have not pinpointed exactly how change management measures can make or break the outcome of the change, and in a SAFe environment, across the program, portfolio as well as enterprise.
The ‘Measure and Grow’ principle as a core part of SAFe (From Scaled Agile Framework)
Key Elements of Measuring and Growing Change Outcomes
To operationalize the “Measure and Grow” principle in change management, it is crucial to establish a set of metrics and assessment frameworks. Here are some broad categories of different types of change measurements that are relevant. Note that since we are talking about SAFe, it is not just at the initiative level that we are talking about metrics. More importantly, it is about establishing a system to promote change improvement across the organisation.
Change Management KPIs and OKRs
Key Performance Indicators (KPIs) and Objectives and Key Results (OKRs) are essential tools for tracking the success of change management initiatives. KPIs provide quantitative measures of performance, while OKRs align change efforts with broader organizational goals. A change management stream or function should focus on establishing KPIs or OKRs to achieve laser focus on achieving change outcomes.
Examples of Initiative-Level Change Management KPIs that may roll out to form portfolio views
Employee Engagement Levels: This KPI assesses how change impacts employee morale and engagement, providing insight into the overall acceptance and support of the change initiative.
Learning Achievement Rates: This can include tracking the percentage of employees who have completed necessary training programs, as well as achieving the target level of competence to ensure that the workforce is adequately prepared for the change.
Feedback Scores: Collecting feedback from stakeholders through surveys or feedback forms helps gauge perception and identify areas needing improvement. It is important to note that depending on the change context, stakeholders may not be happy with the content of the change. However, understanding and tracking this perception is still important.
Change Adoption Rate: This KPI measures the percentage of stakeholders who have adopted the change. High adoption rates are the ultimate goal for initiatives.
Issue Resolution Time: Measuring the time taken to resolve user-related issues related to the change highlights the efficiency of support mechanisms and the responsiveness of the change management team. This is especially important during an agile environment where there may be constant changes.
Change Readiness and Stakeholder Engagement Metrics
Evaluating change readiness and stakeholder engagement is crucial to the success of any change initiative. These metrics help assess the organization’s preparedness for change and the level of involvement and support from key stakeholders. Readiness and engagement rates can also roll up at a portfolio level to provide oversight.
Change Readiness Metrics
Readiness Assessments: Conduct surveys or interviews to gauge the organization’s preparedness for the impending change. This can include evaluating awareness, understanding, and acceptance of the change.
Resource Availability: Measure the availability of necessary resources, such as budget, personnel, and tools, to support the change initiative.
Communication Effectiveness: Assess the clarity, frequency, and effectiveness of communication regarding the change to ensure stakeholders are well-informed and engaged.
Stakeholder Engagement Metrics
Engagement Scores: Use surveys or feedback forms to measure the engagement levels of stakeholders, indicating their commitment and support for the change.
Participation Rates: Track stakeholder participation in change-related activities, such as workshops, meetings, and training sessions, to gauge their involvement.
Influence and Support: Assess the influence and support of key stakeholders in driving the change, ensuring that influential figures are actively endorsing the initiative.
By monitoring these metrics, change management professionals can identify potential barriers to change and take proactive steps to enhance readiness and engagement.
Stakeholder Competency Assessment
Successful change initiatives rely on the competence and readiness of key stakeholders. Assessing stakeholder competency involves evaluating the capability of sponsors and change champions to support and drive the change.
Sponsor Readiness/Capability Assessment
Sponsor Engagement: Measure the level of engagement and commitment from sponsors, ensuring they are actively involved and supportive of the change.
Decision-Making Effectiveness: Assess the ability of sponsors to make timely and effective decisions that facilitate the change process.
Resource Allocation: Evaluate the sponsor’s ability to allocate necessary resources, such as budget and personnel, to support the change initiative.
Change Champion Capability Assessment
Training and Knowledge: Measure the knowledge and training levels of change champions to ensure they are well-equipped to support the change.
Communication Skills: Assess the ability of change champions to effectively communicate the change message and address stakeholder concerns.
Influence and Leadership: Evaluate the influence and leadership capabilities of change champions, ensuring they can effectively drive and sustain the change.
By conducting these assessments, change management professionals can ensure that key stakeholders are prepared and capable of supporting the change initiative.
Change Adoption Metrics
Change adoption metrics provide insight into how well the change has been accepted and integrated into the organization. These metrics help assess the effectiveness of the change initiative and identify areas for improvement. At a portfolio level, there may be different levels of change adoption set for different initiatives depending on priority and complexity.
Key Change Adoption Metrics
Adoption Rate: Measure the percentage of stakeholders who have adopted the change, indicating the overall acceptance and integration of the new processes or systems.
Usage Metrics: Track the usage of new tools, processes, or systems introduced by the change to ensure they are being utilized as intended.
Performance Metrics: Assess the impact of the change on key performance indicators, such as productivity, efficiency, and quality, to determine the overall success of the change initiative.
By monitoring these metrics, change management professionals can gauge the success of the change initiative and identify opportunities for further improvement. To read more about change adoption metrics check out The Comprehensive Guide to Change Management Metrics for Adoption.
Change Impact and Capacity Metrics
Understanding the impact of change and the organization’s capacity to manage it is crucial for successful change management. Change impact metrics assess the effects of the change on the organization, while capacity metrics evaluate the organization’s ability to manage and sustain the change.
Change Impact Metrics
Aggregate impacts: Aggregate impacts across initiatives to form a view of how various teams and roles are impacted by various changes.
Risk Assessments: Identify potential risks associated with the change and evaluate their impact, ensuring that mitigation strategies are in place. A particular focus should be placed on business performance during change, across initiatives.
Capacity Metrics
Resource Capacity: Assess the availability of resources, such as personnel, budget, and tools, to support the change initiative.
Change Fatigue: Measure the risk for potential fatigue within the organization and its impact on stakeholders, ensuring that change initiatives are paced and driven appropriately.
Support Structures: Evaluate the effectiveness of support structures, such as training programs, information hubs, and help desks, in facilitating the change. Support structures may also include change champion networks.
By assessing change impact and capacity, change management practitioners can ensure that the organization is well-equipped to manage and sustain the change initiative.
Change Maturity Assessment
Change maturity assessments provide a comprehensive evaluation of the organization’s capability to manage change effectively. These assessments help identify strengths and weaknesses in the organization’s change management practices and provide a roadmap for improvement.
The Change Management Institute (CMI) Change Maturity Model is a comprehensive framework that takes a holistic approach to enhancing an organization’s change management maturity. It’s divided into three core functional domains, each playing a vital role in the overall journey toward maturity:
Project Change Management
Business Change Readiness
Strategic Change Leadership.
These domains serve as the foundation for achieving higher levels of maturity within the organization.
Within each of these domains, the CMI model outlines a structured path, consisting of five distinct maturity levels. These levels represent a continuum, starting at Level 1, which serves as the foundational stage, and progressing all the way to Level 5, the zenith of maturity and effectiveness. This multi-tiered approach offers organizations a clear roadmap for growth and development, ensuring that they have the tools and insights necessary to navigate the complexities of change management.
By conducting regular change maturity assessments, change management professionals can identify areas for improvement and develop targeted strategies to enhance the organization’s change management capability.
The “Measure and Grow” principle is a powerful tool for improving change outcomes in a Scaled Agile environment. By leveraging data and fact-based decision-making, change management professionals can ensure that change initiatives are effective, aligned with strategic objectives, and continuously improving. Establishing robust metrics and assessment frameworks, such as KPIs, OKRs, change readiness and stakeholder engagement metrics, stakeholder competency assessments, change adoption metrics, change impact and capacity metrics, and change maturity assessments, is essential to applying the “Measure and Grow” principle effectively.
Incorporating these metrics and assessments into change management practices enables organizations to identify areas for improvement, make informed decisions, and drive continuous improvement. By doing so, change management professionals can enhance the effectiveness of change initiatives, ensure successful adoption, and ultimately achieve better business outcomes.
Change management is an intricate dance between vision, strategy, execution, and perhaps most importantly, adoption. The ultimate goal of any change initiative is not merely to implement new systems, processes, or regulations, but rather to embed these changes into the very fabric of the organization, ensuring widespread adoption and long-term sustainability.
However, achieving full adoption is no small feat. Many change initiatives falter along the way, failing to garner the buy-in and commitment necessary for success. Even when adoption is initially achieved, sustaining it over time presents its own set of challenges.
Understanding the Dynamics of Change Adoption:
Change adoption is not a one-size-fits-all endeavor. It’s influenced by a myriad of factors, including organizational culture, leadership support, employee engagement, and the nature of the change itself. Therefore, it’s essential to approach the measurement of adoption metrics with a nuanced understanding of these dynamics.
Before diving into specific metrics, let’s explore some fundamental principles of change adoption:
Context Matters: Every change initiative is unique, shaped by its context, stakeholders, and objectives. What works for one organization may not necessarily work for another. Therefore, it’s crucial to tailor adoption metrics to align with the specific goals and dynamics of each initiative.
Focus on Outcomes: Adoption metrics should go beyond mere activities or outputs and focus on outcomes. Instead of measuring how many employees attended training sessions, for example, focus on whether the training resulted in improved performance or behaviour change.
Continuous Monitoring: Change adoption is not a one-time event but an ongoing process. Continuous monitoring of adoption metrics allows organizations to identify trends, address challenges, and make course corrections as needed.
Now, let’s explore adoption metrics across different types of change initiatives:
Metrics for System Implementations:
System implementations, whether it’s a new CRM platform, ERP system, or productivity tool, often represent significant investments for organizations. To ensure a return on investment, it’s crucial to measure adoption effectively. Here are some key metrics to consider:
System Feature Usage Frequency: Measure how frequently employees utilize various features of the new system. This metric provides insights into whether employees are leveraging the system to its full potential and identifies areas for additional training or support.
Process Efficiency: Assess the efficiency gains achieved through the implementation of the new system. This metric quantifies improvements in workflow efficiency, resource utilization, and cycle times.
Customer Conversation Audit: If the change is aimed to improve the quality of customer interactions post-implementation, then the customer conversation should be audited. This metric focuses on whether the system enhances customer information accessibility, improves service representation, and ultimately leads to higher customer satisfaction.
Sales Volume: If the system aims to boost sales, track changes in sales volume post-implementation. This metric provides a tangible indicator of the system’s impact on revenue generation and business performance.
Information Completeness: Measure the completeness of customer information captured by the new system. This metric highlights the system’s effectiveness in capturing and storing relevant data, which is critical for decision-making and customer service.
Customer Satisfaction: Gauge customer satisfaction levels following the system implementation. This metric reflects the system’s ability to meet customer needs, deliver value, and enhance overall satisfaction.
Metrics for Compliance Initiatives:
Compliance initiatives, whether it’s adherence to regulatory standards, industry certifications, or internal policies, require meticulous attention to detail. Here are some key metrics to consider for measuring compliance adoption:
Process Compliance: Monitor adherence to regulatory processes and requirements. This metric ensures that the organization remains compliant with relevant regulations and mitigates the risk of non-compliance penalties.
Rated Compliance of Targeted Behaviours: Evaluate the compliance level of specific behaviours targeted by the regulatory change. This metric provides insights into whether employees are adopting the prescribed behaviours and following compliance protocols.
Frequency of Team Leader Coaching: Track the frequency of coaching sessions conducted by team leaders to reinforce compliance behaviours. This metric emphasizes the role of leadership in driving and sustaining compliance across the organization.
Customer Feedback: Solicit feedback from customers regarding their experience with the organization post-compliance implementation. This metric captures customer perceptions of the organization’s adherence to regulatory standards and its commitment to compliance.
Number of Incidents: Depending on the nature of compliance requirements, track the number of incidents related to non-compliance. This metric serves as an early warning system for identifying areas of weakness in compliance efforts and implementing corrective actions.
Metrics for Restructuring Initiatives:
Restructuring initiatives, whether driven by mergers, acquisitions, organizational realignment, or cost-cutting measures, often have far-reaching implications for employees, departments, and the overall organizational structure. Measuring adoption in restructuring initiatives requires a nuanced understanding of the changes’ impact on employee morale, productivity, and alignment with organizational goals. Here are some key metrics to consider:
Employee Engagement and Morale: Measure changes in employee engagement and morale before, during, and after the restructuring initiative. Surveys, focus groups, and one-on-one interviews can provide valuable insights into employees’ perceptions, concerns, and levels of commitment to the new organizational structure.
Organizational Alignment: Assess the degree to which the restructuring initiative aligns with the organization’s strategic objectives and long-term vision. Key performance indicators (KPIs), such as revenue growth, market share, and customer satisfaction, can help gauge the effectiveness of the restructuring in driving organizational alignment and performance.
Communication Effectiveness: Evaluate the effectiveness of communication channels and messaging during the restructuring process. Metrics such as employee feedback on communication clarity, frequency of updates, and perceived transparency can shed light on the effectiveness of communication strategies in managing change and alleviating uncertainty.
Employee Productivity and Performance: Monitor changes in employee productivity and performance following the restructuring initiative. Key metrics may include employee turnover rates, absenteeism, and performance evaluations. By tracking these metrics over time, organizations can assess the impact of restructuring on employee motivation, workload, and job satisfaction.
Leadership Effectiveness: Assess the effectiveness of leadership in navigating the restructuring process and driving adoption of the new organizational structure. Metrics such as employee ratings of leadership communication, support, and decision-making can provide valuable feedback on leadership effectiveness and its impact on employee morale and commitment.
Team Dynamics and Collaboration: Measure changes in team dynamics, collaboration, and cross-functional cooperation post-restructuring. Surveys, team assessments, and project outcomes can help identify strengths and weaknesses in team dynamics and collaboration, enabling organizations to address barriers to adoption and foster a culture of teamwork and collaboration.
Implementing and Measuring Adoption Metrics:
Once you’ve identified the relevant adoption metrics for your change initiative, the next step is to implement and measure them effectively. Here are some practical strategies to consider:
Surveys: Utilize surveys to gather feedback from employees, customers, and other stakeholders. Design surveys to capture both quantitative data, such as ratings and frequencies, and qualitative insights into the perceived effectiveness of the change initiative.
Observations: Encourage stakeholders, subject matter experts (SMEs), change champions, and leaders to observe and provide feedback on the implementation process. Their firsthand observations can uncover valuable insights into adoption barriers and successes.
System Tracking Data: Leverage data captured by the system itself to track usage patterns, process compliance, and other relevant metrics. Analyze this data to identify trends and areas for improvement in adoption efforts.
Employee or Stakeholder Feedback Sessions: Conduct regular meetings, interviews, or workshops to solicit feedback from employees and stakeholders. Create a safe and open environment for sharing concerns, challenges, and suggestions related to the change initiative.
Continuous Improvement: Use adoption metrics as a basis for continuous improvement. Regularly review and analyze adoption data to identify areas of success and opportunities for enhancement. Make adjustments to strategies, communication plans, and support mechanisms as needed to drive greater adoption.
Measuring Behaviours in System Implementations:
A significant portion of change involved system or digital change. In system implementations, the successful adoption of new technologies and processes often hinges on changes in employee behaviours. While it’s essential to track macro-level outcomes such as system usage frequency and process efficiency, measuring micro-behaviours provides a stronger link to the direct, underlying drivers of adoption. Here’s how to measure targeted and specific micro-behaviours in the context of a system implementation:
User Interface Navigation: Assess employees’ proficiency in navigating the new system’s user interface. Track metrics such as the time taken to complete common tasks, the number of clicks required to access key features, and the frequency of help requests. If these are not available, observational studies and user feedback can also provide valuable insights into usability issues and training needs.
Data Entry Accuracy: Measure the accuracy of data entry performed by employees using the new system. Compare the quality of data input before and after the implementation, looking for improvements in data accuracy, completeness, and consistency. Conduct periodic audits and spot checks to identify errors and areas for improvement.
Workflow Integration: Evaluate the extent to which employees integrate the new system into their existing workflows. Track metrics such as the proportion of tasks completed using the new system versus legacy systems, the frequency of workarounds or manual interventions, and the level of integration with other tools or processes. Interviews and focus groups can uncover barriers to workflow integration and inform targeted interventions.
Collaboration and Knowledge Sharing: Measure employees’ engagement in collaborative activities and knowledge sharing facilitated by the new system. Look for indicators such as the frequency of document sharing, participation in online discussions or forums, and contributions to shared repositories or knowledge bases. Social network analysis and peer assessments can highlight patterns of collaboration and identify key influencers or knowledge brokers within the organization.
Adoption of Best Practices: Assess employees’ adoption of best practices and standardized workflows supported by the new system. Monitor adherence to established guidelines, protocols, and procedures, looking for deviations or non-compliance. Use performance metrics such as error rates, rework cycles, and customer satisfaction scores to evaluate the effectiveness of best practices in driving desired outcomes.
Change Agent Engagement: Measure the engagement and effectiveness of change agents, champions, or ambassadors tasked with promoting adoption of the new system. Track metrics such as the frequency of communication and training sessions led by change agents, the level of participation in peer support networks or mentoring programs, and the impact of their advocacy efforts on adoption rates. Surveys and feedback mechanisms can assess the perceived credibility, accessibility, and responsiveness of change agents.
Implementing and Measuring Micro-Behaviours:
Define Clear and Measurable Objectives: Identify specific behaviours that are critical to the success of the system implementation and define clear, measurable objectives for each behaviour. Ensure alignment with broader adoption goals and desired outcomes.
Select Relevant Metrics: Choose metrics that are closely aligned with the targeted micro-behaviours and are actionable, observable, and trackable over time. Consider a combination of quantitative data (e.g., completion rates, error rates) and qualitative insights (e.g., user feedback, observational data) to provide a comprehensive understanding of behaviour change.
Utilize Multiple Data Sources: Gather data from multiple sources, including system logs, user activity tracking, surveys, interviews, and observational studies. Triangulating data from different sources enhances the reliability and validity of measurement and provides a more holistic view of behaviour change.
Monitor Progress Continuously: Establish a system for continuous monitoring of micro-behaviours throughout the implementation process. Regularly review and analyze data to identify trends, patterns, and areas for improvement. Use real-time feedback mechanisms to address issues and reinforce positive behaviours promptly.
Provide Timely Feedback and Support: Provide employees with timely feedback on their performance and progress toward behaviour change goals. Offer targeted support, training, and resources to address skill gaps, overcome barriers, and reinforce desired behaviours. Celebrate successes and recognize individuals or teams that demonstrate exemplary behaviour change.
Iterate and Adapt: Continuously iterate and adapt your measurement approach based on ongoing feedback and insights. Adjust metrics, data collection methods, and interventions as needed to respond to changing circumstances, emerging challenges, and evolving user needs. Be flexible and open to experimentation to optimize the effectiveness of your behaviour change efforts.
How Many Metrics Should I Use?
When it comes to measuring behaviour change in change initiatives, the age-old adage “less is more” holds true. While it may be tempting to track a multitude of metrics in the hopes of capturing every aspect of adoption, focusing on the critical few behaviours that will have the most direct impact on the outcome of the change is essential. You are also not going to have the bandwidth and resources to measure ‘everything’. Here’s how to determine the right number of metrics to use:
Focus on Key Objectives: Start by identifying the key objectives of the change initiative. What are the primary outcomes you hope to achieve? Whether it’s increased system usage, improved process efficiency, enhanced customer satisfaction, or compliance with regulatory standards, prioritize the behaviours that directly contribute to these objectives.
Prioritize High-Impact Behaviors: Narrow down your list of behaviours to those that have the most significant impact on achieving your key objectives. What are the critical few behaviours that, if changed, would lead to the greatest improvement in outcomes? Focus on behaviours that are both important and feasible to change within the scope of the initiative.
Consider Complexity and Manageability: Be mindful of the complexity and manageability of the behaviours you choose to measure. While it’s important to capture a comprehensive view of behaviour change, tracking too many metrics can become overwhelming and dilute focus. Aim for a manageable number of metrics that are meaningful, actionable, and directly linked to the desired outcomes.
Quantitative vs Qualitative Metrics: Whilst quantitative metrics are usually preferred by executives and easier to report on, sometimes you may need to incorporate qualitative metrics to gain a holistic understanding of behaviour change. Quantitative metrics such as completion rates, error rates, and productivity measures provide objective data on behaviour performance, while qualitative insights from surveys, interviews, and observations offer deeper context and understanding.
Consider Interdependencies and Trade-Offs: Recognize that behaviours are often interconnected, and changes in one behaviour may impact others. Consider the interdependencies and potential trade-offs between different behaviours when selecting your metrics. Focus on behaviours that have a ripple effect and can drive change across multiple dimensions of the initiative.
By focusing on the critical few behaviours that have the most direct impact on the outcome of the change, you can streamline measurement efforts, maintain clarity of purpose, and maximize the effectiveness of your change initiative. Remember, the goal is not to measure everything, but to measure what matters most and use that information to drive meaningful behaviour change and achieve successful adoption of the change.
Change adoption dashboard
Now that you have determined exactly what you want to measure to drive adoption, you may want to create a dashboard. Check out our article on ‘Designing a Change Adoption Dashboard’.
Change adoption is the ultimate goal of any change initiative, and effective measurement of adoption metrics is key to achieving success. By understanding the dynamics of change adoption, selecting the right metrics, and implementing them effectively, change practitioners and leaders can navigate the complexities of change and drive meaningful outcomes for their organizations. Remember, adoption is not a destination but a journey, and with the right metrics and strategies in place, sustainable change is within reach.
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