Tag Archives: effectiveness


Strategic plans are designed to help organizations achieve long-term goals and objectives, but for a strategic plan to remain relevant and guide an organization effectively, it needs to be reviewed on a regular basis and updated when necessary. The optimal frequency for reviewing and updating a strategic plan can vary depending on factors like the organization’s industry, size, resources, and rate of change in its external environment. Most experts recommend conducting comprehensive reviews of the strategic plan at least once a year, with some interim reviews throughout the year as well.

Conducting an annual review allows an organization to assess progress made against the strategic plan on a regular cadence. It provides an opportunity to revisit goals, objectives, strategies, and initiatives outlined in the plan and evaluate whether they are still appropriate given changes that may have occurred internally or externally over the past year. An annual review meeting typically involves gathering key stakeholders from across the organization who were involved in developing the original plan. During the meeting, participants discuss what strategic priorities and tactics worked well over the past 12 months and which may need refining. They also look at whether the overall vision and mission still align with the organization’s current direction or if updates are warranted. Data on key performance indicators is analyzed to determine what strategic priorities drove the most success and where improvements are needed. The annual review culminates with an assessment of whether any elements of the plan, such as timelines, budgets, or departmental responsibilities need modification to optimize results over the coming year.

While an annual comprehensive review provides the necessary periodic check-in, some organizations also find value in conducting interim reviews on a quarterly or biannual basis. These shorter check-ins allow for more frequent monitoring of progress against objectives and timelines outlined in the plan. They provide opportunities to course correct sooner if implementation is lagging or external factors arise requiring an adjustment of strategic priorities mid-year. During interim reviews, participants typically focus the discussion on a subset of strategic initiatives, priorities or key performance indicators to keep the meetings efficient. Any recommended changes uncovered during an interim review would then be documented and fully evaluated during the next annual review meeting when a comprehensive refresh is conducted if needed.

For organizations operating in dynamic industries or markets that change rapidly, it may even make sense to review the strategic plan on a semi-annual basis to ensure it remains optimally aligned. Reviews that are conducted too frequently, such as monthly, run the risk of disrupting implementation efforts by constantly refining priorities before they have had enough time to take hold. There also needs to be a balance between reviewing frequently enough to stay nimble without expending too many resources on the review process itself.

The timing of annual reviews is also an important consideration. Most experts recommend scheduling the annual strategic plan review meeting towards the end of the fiscal or calendar year, typically in the last quarter. This allows time following the meeting to refine implementation plans for the coming year based on insights from the review. It also provides a natural checkpoint at the close of the year to evaluate performance and progress made against the existing plan. Some organizations find value in conducting a portion of the annual review mid-year as well to incorporate any learnings or adjustments into the second half implementation.

Regardless of review frequency or timing, it is critical that strategic plan reviews involve gathering input from leaders and contributors across all divisions and levels of the organization. Getting diverse perspectives is important for identifying opportunities or risks that may not be as obvious from an executive level view. The review process also needs to incorporate analysis of both qualitative and quantitative performance data to ensure any recommended updates to strategies or priorities are firmly grounded in facts rather objective opinions. With regular, systematic reviews built into the process, an organization’s strategic plan has the best chance of remaining an effective roadmap to drive long-term success even as internal or external conditions inevitably change over time.

Most experts agree that reviewing a strategic plan at minimum on an annual basis, with some organizations benefitting from additional interim reviews quarterly or biannually, provides the necessary cadence to evaluate progress and ensure the plan remains optimally aligned. The overriding goal of maintaining a regular review schedule is to continuously refine implementation strategies based on learnings so the organization can dynamically respond to opportunities while navigating challenges to stay on track with its long-term vision.


Accuracy: Accuracy is one of the most common and straightforward evaluation metrics used in machine learning. It measures what percentage of predictions the model got completely right. It is calculated as the number of correct predictions made by the model divided by the total number of predictions made. Accuracy provides an overall sense of a model’s performance but has some limitations. A model could be highly accurate overall but poor at certain types of examples.

Precision: Precision measures the ability of a model to not label negative examples as positive. It is calculated as the number of true positives (TP) divided by the number of true positives plus the number of false positives (FP). A high precision means that when the model predicts an example as positive, it is truly positive. Precision is important when misclassifying a negative example as positive has serious consequences. For example, a medical test that incorrectly diagnoses a healthy person as sick.

Recall/Sensitivity: Recall measures the ability of a model to find all positive examples. It is calculated as the number of true positives (TP) divided by the number of true positives plus the number of false negatives (FN). A high recall means the model pulled most of the truly positive examples within the net. Recall is important when you want the model to find as many true positives as possible and not miss any. For example, identifying diseases from medical scans.

F1 Score: The F1 score is the harmonic mean of precision and recall. It combines both precision and recall into a single measure that balances them. F1 score reaches its best value at 1 and worst at 0. The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst at 0. The relative contribution of precision and recall to the F1 score are equal. The F1 score is most commonly used evaluation metric when there is an imbalance between positive and negative classes.

Specificity: Specificity measures the ability of a model to correctly predict the absence of a condition (true negative rate). It is calculated as the number of true negatives (TN) divided by the number of true negatives plus the number of false positives (FP). Specificity is important in those cases where correctly identifying negatives is critical, such as disease screening. A high specificity means the model correctly identified most examples that did not have the condition as negative.

AUC ROC Curve: AUC ROC stands for Area Under Receiver Operating Characteristic curve. ROC is a probability curve and AUC represents degree or measure of separability of the model. It tells how well the model can distinguish between classes. ROC is a plot of the true positive rate against the false positive rate. AUC can range between 0 and 1, with a higher score representing better performance. Unlike accuracy, AUC is a balanced measure and is unaffected by class imbalance. AUC helps visualize and compare overall performance of models across different thresholds.

Cross Validation: To properly evaluate a machine learning model, it is important to validate it using techniques like k-fold cross validation. In k-fold cross validation, the dataset is divided into k smaller sets or folds. The model is trained k times, each time using k-1 folds for training and the remaining 1 fold for validating the model. This process is repeated k times so that each of the k folds is used exactly once for validation. The k results can then be averaged to get an overall validation accuracy. This method reduces variability and helps get an insight on how the model will generalize to an independent dataset.

A/B Testing: A/B testing involves comparing two versions of a model or system and evaluating them on key metrics against real users. For example, a production model could be A/B tested against a new proposed model to see if the new model actually performs better. A/B testing on real data exactly as it will be used is an excellent way to compare models and select the better one for deployment. Metrics like conversion rate, clicks, purchases etc. can help decide which model provides the optimal user experience.

Model Explainability: For high-stake applications, it is critical that the models are explainable and auditable. We should be able to explain why a model made a particular prediction for an example. Some techniques to evaluate explainability include interpreting individual predictions using methods like LIME, SHAP, integrated gradients etc. Global model explanations using techniques like SHAP plots can help understand feature importance and model behavior. Domain experts can manually analyze the explanations to ensure predictions are made for scientifically valid reasons and not some spurious correlations. Lack of robust explanations could mean the model fails to generalize.

Testing on Blind Data: To convincingly evaluate the real effectiveness of a model, it must be rigorously tested on completely new blind data that was not used during any part of model building. This includes data selection, feature engineering, model tuning, parameter optimization etc. Only then can we say with confidence how well the model would generalize to new real world data after deployment. Testing on truly blind data helps avoid issues like overfitting to the dev/test datasets. Key metrics should match or exceed performance on the initial dev/test data to claim generalizability.


Employee engagement surveys are one of the most common and useful tools for HR to measure engagement. Conducting periodic anonymous surveys allows employees to provide confidential feedback on their workplace experiences, how supported and valued they feel, their willingness to advocate for the company, and their overall satisfaction. Care should be taken to ensure the questions are meaningful and provide actionable data. Some examples include using a scale to rate agreement with statements about feeling pride in work, willing to go above and beyond, supported with training and resources to do their job well, treated fairly regardless of personal characteristics, etc. Comparing survey results over time can reveal improving or worsening trends. Benchmarks against other organizations in the same industry can also provide useful context.

Focus groups and exit interviews are another valuable qualitative method. Selecting a representative sample of employees for confidential small group discussions or one-on-one exit meetings allows deeper exploration of drivers of engagement. For example, participants could discuss what specific actions by managers, supervisors or the company most influence how they feel about their jobs. Common themes across responses can highlight organizational strengths to capitalize on and weaknesses to prioritize for improvement. Direct quotes from participants regarding their experiences also personalize the data in a compelling way to motivate action.

Tracking key performance indicators (KPIs) related to engagement such as absenteeism/tardiness rates, turnover rates, number of employee recognition awards, participation in optional development/training programs, can provide objective metrics of how engaged employees are feeling over time. Significant decreases in absence or turnover, or increases in recognition and development participation could suggest engagement initiatives are having a positive impact on employee behaviors and retention. These metrics are also useful for benchmarking against industry/competitor standards, or comparing different departments within the organization.

Monitoring internal communication channels is another effective way for HR to gauge engagement. For example, looking at viewership/readership rates of company newsletters, website, intranet, videos, etc. can provide valuable engagement indicators, particularly if there are year-over-year upward trends. Tracking mentions/shares of company posts on internal social networks demonstrates active participation, two-way communication and advocacy. HR may also consider conducting occasional employee Net Promoter Score (NPS) surveys asking how likely employees are to recommend their employer to others – this can be a useful metric of discretionary effort and engagement levels.

Tracking key performance indicators related to the initiatives themselves is important too. For example, if the company has implemented a formal employee recognition program, HR should monitor metrics like the number of monthly/quarterly recognitions awarded across different teams/levels, compliance rates for managers in taking part, employee feedback about impact of recognition received. Analyzing utilization and dropout rates of any wellness/development programs introduced can also provide insights. Comparing pre/post engagement survey results can help determine impact, with statistically significant improvements directly tied to implemented initiatives.

Finally, HR should also consider some external validation of engagement efforts through third party employer branding surveys. Tools like Indeed’s annual ‘Employer Award’ rankings, Comparably’s workplace culture/compensation ratings, LinkedIn Top Companies lists etc. allow benchmarking engagement against peer organizations as perceived by both employees and job seekers. Significant jumps in external reputation ratings could reflect growing employee pride and advocacy for the employer brand – key outcomes of improved engagement.

Utilizing a blended approach incorporating surveys, focus groups, tracking of objective metrics, monitoring of internal communications, and external validation can provide HR with meaningful multi-dimensional data to benchmark, identify strengths/weaknesses, and truly understand the impact of employee engagement initiatives over time at their organization. With the right measurements in place, HR is better positioned to continuously enhance engagement strategies and optimize the employee experience.


Stepping On is an evidence-based fall prevention program designed for community-dwelling older adults. The program was developed in the late 1990s by a team of researchers and clinicians at the University of Wisconsin Madison. It aims to empower participants to reduce fall risks in their homes and improve their strength and balance through low-impact exercise.

The Stepping On program takes place once a week for 2 hours over 7 weeks. Each session features an educational presentation on a fall risk topic as well as exercise to improve strength and balance. Common topics covered include home hazard assessment, vision and falls, safe footwear, medication management, and safety when out in the community. Exercise is led by a certified fitness instructor and focuses on movements like hip strengthening, steps, and body movements that translate to daily tasks.

Several research studies have found Stepping On to be highly effective at reducing falls among older adult participants. A randomized controlled trial published in 2002 evaluated 224 community-dwelling older adults who were at risk for falling. The study found a 30% reduction in falls for those who took part in Stepping On compared to a control group over a 12-month period. Another clinical trial in Melbourne, Australia involving 360 older adults replicated this finding, with participants experiencing a 31% reduction in falls post-intervention.

Subsequent cost-analysis studies have explored the financial benefits of Stepping On as well. A 2017 study published in the Journal of the American Geriatrics Society compared fall-related healthcare costs over 12 months for Stepping On participants versus a control group. It found the program generated a cost savings of $672 per participant through reductions in fall-related medical expenditures like emergency department visits and hospitalizations. With hospital costs for fall-related injuries totaling over $50 billion annually in the United States, effective community-based programs like Stepping On can help curb rising healthcare spending on fall-related care for older adults.

The Stepping On program has been widely disseminated across the United States and internationally since its inception. As of 2022, over 30 states in the US have trained leaders and regularly offer Stepping On workshops in communities. Fidelity to the original curriculum developed at the University of Wisconsin is emphasized in training new leaders to deliver the program. Standardized training involves a 3-day class for potential leaders, which prepares them to implement all educational and exercise elements of Stepping On.

Fidelity is considered important to Stepping On’s effectiveness given the consistency of positive results demonstrated across multiple research studies. Several implementation studies have confirmed trained leaders adhere closely to the prescribed curriculum and can achieve significant reductions in falls comparable to the initial clinical trials. Participant satisfaction is also quite high. Standard evaluation forms reveal the vast majority believe Stepping On helped improve their balance, strength, and awareness of fall risks.

The low cost and infrastructure needed to implement Stepping On has enabled wide adoption globally as well. Translated curricula and leader trainings exist for populations in countries spanning Australia, Canada, Japan, New Zealand, Brazil, and beyond. The World Health Organization has endorsed Stepping On worldwide due to its success at scale. An analysis published in Age and Ageing estimated that if participation was expanded to just 10% of appropriate older adults, over 18,000 fall-related hospitalizations could be prevented annually in the United States alone.

Over two decades of research supports Stepping On as a highly effective, evidence-based fall prevention program. Its multi-component approach combining education and exercise has demonstrated reliable 30% reductions in falls for older adult participants. The program proves cost-saving for healthcare systems and has experienced broad dissemination nationally and globally. With falls posing a major public health threat, low-cost community interventions like Stepping On can play an important role in improving health and independence for growing aging populations worldwide.


Marketing campaigns generate large amounts of performance data from various online and offline sources. Analyzing this data is crucial to evaluate how well campaigns are achieving their objectives and determining areas for improvement. Here are some effective methods for analyzing campaign performance data:

Set Key Performance Indicators (KPIs) – The first step is to establish the key metrics that will be used to measure success. Common digital marketing KPIs include click-through rate, conversion rate, cost per acquisition, website traffic, leads generated, and sales. For traditional campaigns, KPIs may include brand awareness, purchase intent, and actual purchases. KPIs should be Specific, Measurable, Attainable, Relevant, and Timely to be most useful.

Collect Relevant Data – Data must be gathered from all channels and touchpoints involved in the campaign, including websites, emails, advertisements, call centers, point-of-sale, and more. Data collection tools may include Google Analytics, marketing automation platforms, CRM software, surveys, and third-party tracking. Consolidating data from different sources into a centralized database allows for unified analysis. Personally identifiable information should be anonymized to comply with privacy regulations.

Perform Segmentation Analysis – Segmenting the audience based on demographic and behavioral attributes helps determine which groups responded most favorably. For example, analyzing by gender, age, location, past purchases, website behavior patterns, can provide useful insights. Well-performing segments can be targeted more heavily in future campaigns. Under-performing segments may need altered messaging or need to be abandoned altogether.

Conduct Attribution Modeling – Attribution analysis is important to determine the impact and value of each promotional touchpoint rather than just the last click. Complex attribution models are needed to fairly distribute credit among online channels, emails, banner ads, social media, and external referrers that contributed to a conversion. Path analysis can reveal the most common customer journeys that lead to purchases.

Analyze Time-Based Data – Understanding when targets took desired actions within the campaign period can be illuminating. Day/week/month performance variations may emerge. For example, sales may spike right after an email is sent, then taper off with time. Such time-series analysis informs future scheduling and duration decisions.

Compare Metrics Over Campaigns – Year-over-year or campaign-to-campaign comparison of KPIs shows whether objectives are being met or improved upon. Downward trends require examination while upward trends validate the strategies employed. Benchmarks from industry averages also provide a reference point for assessing relative success.

A/B and Multivariate Testing – Testing variant campaign elements like subject lines, creative assets, offers, placements, and messaging allows identification of highest performing options. Statistical significance testing determines true winners versus random variance. Tests inform continuous campaign optimization.

Correlate with External Factors – Relating performance to concurrent real-world conditions provides additional context. For example, sales may rise with long holiday weekends but dip during busy times of year. Economic indicators and competitor analyses are other external influencers to consider.

Conduct Cost-Benefit Analysis – ROI, payback periods, and other financial metrics reveal whether marketing expenses are worth it. Calculating acquisition costs, lifetime customer values, and profits attributed to each campaign offers invaluable perspective for budgeting and resource allocation decisions. Those delivering strong returns should receive higher investments.

Produce Performance Reports – Actionable reporting distills insights for stakeholders. Visual dashboards, one-pagers, and presentation decks tell the story of what’s working and not working in a compelling manner that galvanizes further decisions and actions. Both quantitative and qualitative findings deserve attention.

Campaign analysis requires collecting vast amounts of structured and unstructured data then applying varied analytical techniques to truly understand customer journeys and optimize marketing performance. With rigorous assessment, strategies can be continuously enhanced to drive ever higher returns on investment.