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3 strategies and tactics for Problem Identification

What is Problem Identification strategy?

Every great achievement starts with a well-thought-out plan. It can be the launch of a new product, expanding into new markets, or just trying to increase efficiency. You'll need a delicate combination of strategies and tactics to ensure that the journey is smooth and effective.

Crafting the perfect Problem Identification strategy can feel overwhelming, particularly when you're juggling daily responsibilities. That's why we've put together a collection of examples to spark your inspiration.

Transfer these examples to your app of choice, or opt for Tability to help keep you on track.

How to write your own Problem Identification strategy with AI

While we have some examples available, it's likely that you'll have specific scenarios that aren't covered here. You can use our free AI generator below or our more complete goal-setting system to generate your own strategies.

Problem Identification strategy examples

We've added many examples of Problem Identification tactics, including a series of action items. We hope that this will make these examples as practical and useful as possible.

Strategies and tactics for proposing an Innovative Investment Portfolio Strategy

  • ⛳️ Strategy 1: Develop a data-driven portfolio strategy

    • Conduct a comprehensive analysis of market trends and historical data
    • Utilise quantitative metrics such as Sharpe Ratio and standard deviation
    • Apply Modern Portfolio Theory to optimise asset allocation
    • Identify a target audience and tailor the strategy to their needs
    • Develop a risk management framework leveraging diversification and hedging
    • Create sample portfolios to demonstrate potential improvements in returns
    • Design visually appealing charts and graphs to illustrate data findings
    • Prepare a revenue model highlighting potential income streams from the strategy
    • Define unique selling propositions that differentiate the strategy
    • Rehearse the presentation, focusing on engaging storytelling and compelling visuals

  • ⛳️ Strategy 2: Create a tech-enabled investment solution

    • Research existing investment apps to identify gaps and opportunities
    • Design a user-friendly interface for a potential app or digital tool
    • Integrate popular investment models like asset allocation into the app
    • Employ a developer to ensure robust and secure technology foundation
    • Incorporate features that assist users with portfolio rebalancing
    • Estimate the potential financial impact of the solution using feasibility analysis
    • Communicate the core value proposition centred on innovation and ease of use
    • Outline a marketing plan with strategies to reach target demographics
    • Assign team roles, ensuring each member contributes to the project
    • Practice delivering a concise and persuasive pitch to invite interest

  • ⛳️ Strategy 3: Implement a socially responsible investment strategy

    • Identify investment opportunities aligned with ESG (Environmental, Social, Governance) criteria
    • Analyse the impact of socially responsible investments on portfolio performance
    • Develop evaluation metrics to assess the social impact alongside financial returns
    • Craft an educational narrative explaining the relevance of responsible investing
    • Select case studies to demonstrate successful socially responsible investments
    • Use quantitative analysis to underline the viability of the strategy
    • Develop a compelling unique selling proposition focusing on ethical investing
    • Prepare detailed slides highlighting the dual benefit of financial and social returns
    • Train the team in clear communication of the benefits of this strategy
    • Plan a persuasive closing pitch reiterating the importance and feasibility of sustainable investments
investmentdiversificationportfolio-managerapp-developerinvestment-analysis-teammarketing-team
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Strategies and tactics for generating strategy ideas

  • ⛳️ Strategy 1: Identify the problem

    • Conduct a thorough analysis of the current situation
    • Identify the root causes of the problem
    • Gather input from key stakeholders
    • Review any relevant data or documentation
    • Define the scope of the problem
    • Prioritise the most pressing issues
    • Convene a brainstorming session
    • Evaluate the impact of the problem
    • Research industry best practices
    • Draft a problem statement

  • ⛳️ Strategy 2: Define success criteria

    • Establish clear and measurable goals
    • Identify key performance indicators
    • Consult with stakeholders to define expectations
    • Review existing benchmarks or standards
    • Determine a timeline for achieving success
    • Assess the resources available
    • Align success criteria with organisational objectives
    • Draft a success criteria document
    • Obtain commitment from key stakeholders
    • Review and revise the criteria as necessary

  • ⛳️ Strategy 3: Develop multiple scenarios

    • Identify potential future scenarios
    • Evaluate the likelihood of each scenario
    • Assess the potential impact on the organisation
    • Draft action plans for each scenario
    • Consult with experts to validate scenarios
    • Determine the resources needed for each scenario
    • Identify key decision points
    • Develop contingency plans
    • Review scenarios with stakeholders
    • Revise scenarios based on feedback
problem-identificationscenario-planningstrategy-analyststakeholder-consultantbusiness-development-teamscenario-planning-team
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Strategies and tactics for enhancing an analytics strategy

  • ⛳️ Strategy 1: Summarise the analytics strategy

    • Review the proposed use of logistic regression to detect vehicle insurance fraud
    • Document the dataset details, including size and fraud rate
    • Outline the reduction of variables from 33 to 15 key risk factors
    • Note the top indicators identified: Fault, Policy Type, Vehicle Category, and Address Change Claims
    • State the achieved AIC value
    • Describe the 50% probability threshold used for case escalation
    • Highlight the strengths of the strategy including its practical business focus
    • Summarise the interpretability benefits of the model
    • Identify weaknesses such as lack of model validation
    • List concerns about data quality and decision threshold simplicity

  • ⛳️ Strategy 2: Evaluate the analytics strategy

    • Assess the appropriateness of logistic regression for fraud detection
    • Evaluate the systematic approach using backward elimination for feature selection
    • Identify the effective alignment of probability outputs with business decision needs
    • Analyse the model's practical implications as demonstrated in case studies
    • Identify gaps in model validation such as train/test splits
    • Critique the arbitrary 50% decision threshold
    • Examine the quality of data exploration and preprocessing
    • Note the absence of performance metrics like accuracy and recall
    • Evaluate the limits of using only raw variables without feature engineering
    • Determine opportunities for further analytical insights and model improvements

  • ⛳️ Strategy 3: Suggest improvements to the analytics strategy

    • Implement a robust validation framework using train, validation, and test data splits
    • Calculate comprehensive performance metrics including precision, recall, and F1-score
    • Conduct cost-benefit analysis to optimise probability thresholds
    • Evaluate and compare advanced modelling techniques such as Random Forest
    • Enhance feature engineering with derived variables and interaction terms
    • Establish a data quality framework with systematic cleaning and imputation
    • Develop a real-time monitoring system for model performance tracking
    • Incorporate external data sources like weather and traffic patterns
    • Explore unsupervised learning for advanced fraud detection
    • Build capabilities for automated model retraining as new data arrives
vehicle-fraudlogistic-regressiondata-analystmachine-learning-engineerdata-science-teamfraud-detection-team
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How to track your Problem Identification strategies and tactics

Having a plan is one thing, sticking to it is another.

Setting good strategies is only the first challenge. The hard part is to avoid distractions and make sure that you commit to the plan. A simple weekly ritual will greatly increase the chances of success.

A tool like Tability can also help you by combining AI and goal-setting to keep you on track.

More strategies recently published

We have more templates to help you draft your team goals and OKRs.

Planning resources

OKRs are a great way to translate strategies into measurable goals. Here are a list of resources to help you adopt the OKR framework:

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