The Non-linear Way to Enhance Product and Feature Prioritisation Through Quantitative Customer Feedback

Background

In the competitive landscape of product development, the ability to prioritise features, outcomes, and needs based on customer feedback is pivotal. This prioritisation is central to crafting products and services that not only meet but exceed customer expectations. It involves understanding what customers truly value in a product or service, focusing on aligning with their goals, and anticipating future needs to stay ahead in the market.

Foundation of Insightful Decision-Making

The journey to effective prioritisation begins with a comprehensive research process, starting with secondary research to gain an initial understanding of customer preferences and market opportunities. This foundational step is crucial for spotting potential areas where a product or service can stand out. It's complemented by primary research, where direct engagement with customers provides deeper insights into their specific needs, desires, and levels of satisfaction.

Transitioning from Qualitative to Quantitative Analysis

Moving from qualitative insights, such as personal experiences and stories, to quantitative data marks a critical phase in the research process. Quantitative analysis involves measuring the importance and satisfaction associated with various aspects of a product or service. This shift is essential for businesses to discern customer priorities, enabling them to focus on what matters most and make informed improvements to their offerings.

A Comparative Analysis of Scoring Methods

To navigate the complex nature of customer feedback, businesses employ various scoring methods. Traditional approaches like the Net Promoter Score (NPS) and Customer Satisfaction Score (CSAT) provide valuable insights but may lack the granularity needed for detailed prioritisation. The integration of importance metrics, to complement satisfaction (CSAT) or even loyalty intention (NPS), may offer a more nuanced understanding, enhancing inherent scoring methods limitations.

Linear vs. Non-linear Scoring Approaches

Linear scoring methods, such as the Gap Analysis and Opportunity Algorithm, apply a direct, proportional analysis to changes in importance and satisfaction ratings. However, these methods sometimes struggle to differentiate effectively between various levels of importance and satisfaction, especially when they yield identical scores for distinct feedback scenarios.

This becomes more tangible when we consider customer feedback on specific product-agnostic needs (JTBD Desired-Outcomes), such as:

1. Alleviate the monotony of everyday routines
2. Reduce the overload from constant information
3. Cut down on the frequency of creative blocks

The Gap Analysis Formula

Importance - Satisfaction

This method subtracts satisfaction from importance, providing a straightforward metric for identifying areas that need improvement, indicated by positive scores. It also signals when fewer resources are needed, as shown by negative scores. However, its simplicity might overlook the complexity of customer needs. Let's look at this example:

Gap Analysis Formula Example

Need 1: "Alleviate the monotony of everyday routines"

• Importance: 8
• Satisfaction: 6
• Gap Score: 8?6=2

Need 2: "Reduce the overload from constant information"

• Importance: 7
• Satisfaction: 5
• Gap Score: 7?5=2

Analysis

Both value statements result in a Gap Score of 2, suggesting equal priority for improvement despite the different levels of importance and satisfaction.

The Opportunity Algorithm

Importance + max(Importance ? Satisfaction, 0)

Developed by Strategyn, this approach builds on the Gap Formula by adding importance to the positive gap between importance and satisfaction. It weights areas of high importance more heavily, ensuring that features or aspects most crucial to customers receive a higher score and thus are prioritised for improvement. It also includes a max function to ensure that only areas with actual room for improvement (where importance exceeds satisfaction) are considered opportunities. While more nuanced, it still operates linearly, potentially equating different scenarios with similar scores. Let's look at this example:

Opportunity Algorithm Example

Need 1: "Alleviate the monotony of everyday routines"

• Importance: 8
• Satisfaction: 6
• Opportunity Score: 8+max(8?6,0)=8+2=10

Need 3: "Cut down on the frequency of creative blocks"

• Importance: 7
• Satisfaction: 4
• Opportunity Score: 7+max(7?4,0)=7+3=10

Analysis

Utilising the Opportunity Algorithm, both statements yield an Opportunity Score of 10, indicating equal improvement priority. This demonstrates the algorithm's limitation in differentiating between varying levels of customer feedback when the calculated scores are identical.

The Non-linear Formula: Advancing Prioritisation

The Non-linear Formula diverges from linear methods by squaring the importance rating, introducing an exponential differentiation factor. Like the Opportunity Algorithm, it weights areas of high importance more heavily, ensuring that features or aspects most crucial to customers receive a higher score. However unlike the Opportunity Algorithm, this approach significantly refines the prioritisation process, accurately reflecting strategic importance and levels of dissatisfaction. Consider the aim to prioritise those aforementioned customer needs.

The Non-linear Formula

Dissatisfaction x Importance(squared)

The Non-linear Formula sets itself apart by squaring the importance rating, introducing an exponential factor that significantly differentiates priorities based on importance and dissatisfaction levels.

Satisfaction x Importance(squared)

Also, applying satisfaction with the Non-linear Formula shines a spotlight on a business's strengths, multiplying satisfaction by the importance squared to reveal where customers are most content.

The Non-linear Formula Example

Need 1: "Alleviate the monotony of everyday routines"

• Importance: 8
• Satisfaction: 6 / Dissatisfaction: 11?6=5
• Improvement Priority Score: 5×8(squared)=5×64=320

Need 2: "Reduce the overload from constant information"

• Importance: 7
• Satisfaction: 5 / Dissatisfaction: 11?5=6
• Improvement Priority Score: 6×7(squared)=6×49=294

Need 3: "Cut down on the frequency of creative blocks"

• Importance: 7
• Satisfaction: 4 / Dissatisfaction: 11?4=7
• Improvement Priority Score: 7×7(squared)=7×49=343

Analysis

The Non-linear Formula differentiates the value statements by producing distinct scores (320, 294, 343), accurately reflecting the strategic importance and current satisfaction levels. This approach highlights the formula's ability to prioritise improvements based on the compounded impact of importance and dissatisfaction.

• Need 1: receives a high score of 320, indicating a significant opportunity for improvement given its importance and the level of dissatisfaction.
• Need 2: receives a score of 294, suggesting a lower but still significant opportunity for improvement.
• Need 3: scores the highest at 343, highlighting the greatest opportunity for improvement among the scenarios due to the highest level of dissatisfaction.

Conclusion

The Non-linear Formula offers a sophisticated solution for prioritising product features and addressing customer needs based on quantitative feedback. Its ability to discern subtle differences in importance and satisfaction ensures that prioritisation efforts are strategically aligned with customer expectations. Adopting this approach enables businesses to develop products and services that resonate more deeply with customers, enhancing satisfaction and fostering long-term engagement. By embracing this advanced analysis, companies can ensure their product development efforts are not only responsive to current needs but are also strategically poised to anticipate and meet future demands.