NLP well-formed outcomes- 1st process of any best NLP techniques

NLP’s well-formed outcome is goal setting in well structured NLP way. In this, we will discuss how to create NLP well-formed outcomes.

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NLP well-formed outcomes process

Here is the process for NLP well-formed outcomes,

Step 1 – Create specific and positive outcomes.

The outcomes desired by you must be stated in the positive statements and not in the negative.

Step 2-  Focus on your ability.

When setting up NLP well-formed outcomes, you should focus on your ability, not depending on others.

Step 3- Context or Specific

The well-formed outcomes should be with context, and context brings meaning to the statement.

Step 4- Add NLP Representational systems / sensory modalities

You can add what you will see, hear and feel when you achieve your outcome.

Step 5 – Set Actionable objectives

Once your goal is set, the next task is to break down this goal into small actionable objectives.

Step 6 – Support and resources.

Ask yourself what all resources you will require to achieve this goal. Who can all help you to achieve this goal?

Step 7 – Perform an Ecology check.

Do the ecology check on your NLP well-formed outcomes. Ask yourself the following question.

  • Are there any other goals or objectives hurdle to this outcome?
  • What might be interfering with your outcome?
  • Consider any other obstacles that may cause the problem.

Step 8 – Set up Milestones

Like you have created small actionable objectives in the process, you should also create the milestones.

Step 9 – Write down your goals.

Everyone knows that out of sight is out of mind.

Step 10 Routine Test

Check your progress against the milestones and actionable objectives on fixed frequencies.

Where to use NLP well-formed outcomes?

NLP well-formed outcomes can be used in regular goal setting, making better decisions,s and can be combined with other NLP techniques.

NLP well-formed outcomes PDF

I have found a small NLP well-formed outcomes worksheet in pdf, which you can download from here.


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