Learning Needs Analysis (Part 1): Identifying Performance Gaps

Learning Needs Analysis (LNA) is the process that disciplines one of the most common organizational reflexes: “Let’s run a training and things will improve.” Rather than jumping straight to a course or workshop when a problem appears, LNA slows that reaction down and introduces a more structured chain of reasoning:

Performance Signal → Performance Gap → Importance Screening → Root Cause Analysis → Intervention Design → Evaluation

As Kenney and Reid (1) note, “the quality of training can be no better than the quality that the analysis permits.” In other words, the effectiveness of any learning intervention is largely determined before the training itself begins.

Training initiatives can fail for several reasons. They may target the wrong problem, focus on the wrong capability, or reach the wrong population. In some cases, the underlying issue is not solvable through training at all. When this happens, organizations invest time and resources in interventions that are unlikely to improve performance in any meaningful way.

The consequences can go beyond wasted resources. Ineffective training may create the illusion that a problem has been addressed when only a short-term symptom has been touched. This delays the real solution and allows the underlying issue to persist, or even grow. Furthermore, over time, repeated exposure to poorly targeted or irrelevant training can also weaken trust in organizational learning initiatives. When employees are repeatedly asked to attend trainings that do not help them handle the challenges of their daily work, the credibility of the organization’s learning culture begins to erode.

In this Learning Needs Analysis series, we will walk through the logic of LNA step by step and illustrate how the process works in practice through concrete examples. We will examine how to identify performance gaps, which data sources can reveal capability problems, how learning needs can be distinguished from broader organizational issues, and how identified needs can be translated into interventions that can later be evaluated.

This first article will focus on the starting point of LNA: how a performance problem is defined, how a gap is identified, and how organizations evaluate the impact and risk of that gap.

Note. The terms Learning Needs Analysis (LNA) and Training Needs Analysis (TNA) are often used interchangeably. But, some authors distinguish between learning needs (the performance changes required) and training needs (cases where training is an appropriate intervention) (2). In this article, LNA will be used as an umbrella term encompassing both concepts.

Step 1: Define the Performance Problem

The first step in LNA is to define the problem, issue, or signal in performance terms, rather than learning terms.

In practice, problems usually emerge as operational signals: error rates increase, processes slow down, customer complaints rise, or managers observe inconsistent task execution. These signals indicate that something may be wrong, but they do not yet confirm the existence of a real performance gap. This is where a more structured diagnosis becomes necessary.

The central question is whether a measurable discrepancy exists between current and expected performance. Gap analysis, therefore, functions as a decision gate for the entire process, determining whether the analysis should proceed or stop (4).

Note. Two conditions are particularly important for an effective LNA, whose relevance will become clearer later in the process. First, the analyst must have access to detailed information about the organization’s performance, problems, and future plans (2). Second, the analysis should involve multiple stakeholders. Both data collection and intervention decisions typically require input from several organizational actors (e.g., line managers, HR professionals, and business leaders) rather than relying on a single perspective (3).

In this part, the analysis focuses on two key questions:

1. What is the current state?
The first task is to understand the triggering signal that initiated the analysis. Evidence about current performance should ideally come from objective sources such as operational metrics, audit results, quality indicators, workflow data, or artifact reviews. Relying solely on perceptions or anecdotal observations can be risky, as self-reported needs are prone to bias and may overestimate training requirements.

2. What is the desired state?
Next, the analysis clarifies what effective performance should look like. Desired performance is usually defined through operational targets, business objectives, service-level agreements, regulatory requirements, or internal benchmarks (e.g., “95% of transactions processed error-free”, “Response time under 24 hours”, “Audit compliance rate above 90%”).

Both the current and desired states should ideally be defined in observable and measurable terms.

Step 2: Identify the Gap

Once the current and desired states are defined, the performance gap can be identified as the difference between them. At this stage, the objective is simply to confirm that a meaningful discrepancy exists. The analysis does not yet attempt to explain the causes of the gap.

Note. It is also important to verify that the discrepancy reflects a stable pattern rather than a temporary fluctuation. Isolated incidents, short-term spikes in performance indicators, or a small number of anecdotal observations may create misleading signals (4). For this reason, analysts typically examine data across multiple observations, time periods, or sources.

Illustrative Scenario (Part 1): Detecting a Performance Gap in a Reinsurance Underwriting Team

The signal. During the last quarter, managers begin to notice that complex underwriting cases are taking longer to process. Brokers also report delays in receiving responses for certain reinsurance contracts.

At this point, the issue represents a performance signal, not yet evidence of a training need.

Establishing the current state. The LNA analyst reviews operational data from the underwriting workflow system and recent internal audit reports. The analysis focuses on three indicators: average review time for complex cases, cases requiring senior correction, and escalated cases.

Defining the desired state. Next, the analyst reviews internal underwriting guidelines and service-level agreements with brokers to determine the expected standards for these metrics.

Identifying the gap. Once both states are defined, the discrepancy becomes visible.

Operational indicators – Current vs. Target

Metric	Current	Target	Gap
Review time	72h	48h	+24h
Correction rate	18%	10%	+8pp
Escalation rate	22%	12%	+10pp

Gap magnitude across indicators

Review time — 72h → 48h (+24h)

Correction rate — 18% → 10% (+8pp)

Escalation rate — 22% → 12% (+10pp)

Note. The bar lengths represent the relative deviation from the target value, calculated as (Current − Target) / Target. Expressing the discrepancy relative to the expected standard allows indicators measured in different units (hours and percentages) to be compared on a common scale.

At this stage, the analysis does not attempt to explain why the gap exists. The objective is simply to confirm that a measurable discrepancy between current and expected performance is present.

Step 3: Determine the Importance of the Gap

Any intervention requires organizational resources such as time, money, and personnel. For this reason, the next step is to consider the value and potential impact of the identified gap. Not every discrepancy necessarily requires an intervention.

For instance, in some cases, a performance gap may be temporary or even expected. For example, short-term performance fluctuations can occur during organizational transitions, onboarding periods, or when employees are adapting to new processes or technologies. In such situations, the discrepancy may resolve naturally as individuals gain experience or systems stabilize.

Evaluating the importance of the gap typically involves several questions (4) (5):

• How strongly does the gap affect organizational outcomes?
• What risks or costs are associated with the discrepancy?
• How many roles or teams are affected?
• What constraints or feasibility factors exist (for example available resources, operational capacity, or the trainability of the skill involved)?

This assessment helps determine whether the discrepancy is significant enough to justify further analysis.

Illustrative Scenario (Part 2): Importance Screening

At this stage, the analyst and relevant stakeholders conduct a quick importance screening to determine whether the identified discrepancy is significant enough to justify deeper analysis. A simple 1–5 scale is used to structure the discussion (1 = low importance, 5 = high importance).

Importance screening (1 = low, 5 = high)

Criterion	Observation in this scenario	Score
Business impact	Slower underwriting decisions delay broker negotiations	4
Operational risk	Senior underwriters spend additional time correcting cases	3
Population affected	Pattern appears across most junior underwriters (8 of 10)	4
Consistency of pattern	Similar trend observed over the last three months	4
Feasibility of intervention	Skills involved (case evaluation and documentation) are trainable	3

The table provides a structured summary of the potential importance of the gap. The final interpretation is typically discussed with relevant stakeholders who determine whether the discrepancy is significant enough to justify deeper analysis.

In this scenario, stakeholders conclude that the gap appears operationally relevant and persistent. The analysis, therefore, proceeds to the next stage: investigating the causes of the discrepancy.

So far, the analysis has answered two foundational questions: whether a measurable performance gap exists, and whether that gap appears significant enough to justify further investigation.

What remains unanswered is why the gap exists. A discrepancy in performance can emerge for many different reasons. In some cases, employees may lack the necessary knowledge or skills. In others, the issue may lie in unclear processes, missing resources, poorly designed systems, or conflicting organizational incentives.

Understanding this requires a deeper analysis of the factors initially identified in the preliminary gap analysis, often using additional sources such as interviews, task analysis, observations, and performance reviews.

The next article in this series will focus on that diagnostic stage: how organizations investigate the root causes of performance gaps and determine whether the problem is truly related to learning or driven by broader organizational conditions.

REFERENCES

1. Kenney, J., & Reid, M. (1986). Training interventions. Institute of Personnel Management.
2. Palmer, R. (1999). The identification of organizational and individual training needs. In Human resource development: Learning & training for individuals & organizations (p. 117).
3. Cotes, J., & Ugarte, S. M. (2019). A systemic and strategic approach for training needs analysis for the International Bank. Journal of Business Research, 127, 464–473. https://doi.org/10.1016/j.jbusres.2019.05.002
4. Surface, E. A. (2012). Training needs assessment: Aligning learning and capability with performance requirements and organizational objectives. In M. A. Wilson, W. Bennett, Jr., S. G. Gibson, & G. M. Alliger (Eds.), The handbook of work analysis.
5. Ferreira, R. R., & Abbad, G. (2013). Training needs assessment: Where we are and where we should go. BAR - Brazilian Administration Review, 10(1), 77–99. https://doi.org/10.1590/s1807-76922013000100006

Image 1 by Hanna Pad