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Key Steps in the Van Westendorp Model

1. Survey Questions:
   Respondents are asked four key questions to gauge their price sensitivity:
   • At what price would you consider the product too expensive to buy?
   • At what price would you consider the product too cheap, making you question its quality?
   • At what price would you consider the product a good value for money?
   • At what price would you consider the product a bit expensive, but still worth buying?
2. Plot Responses:
   Responses to these questions are plotted as cumulative frequency distributions on a graph. The four curves represent:
   • Too Cheap: Point where price is perceived as too low to ensure quality.
   • Too Expensive: Point where price is perceived as unaffordable.
   • Not Expensive: Point where price is considered good value for money.
   • Not Cheap: Point where price is still acceptable but considered high.
3. Identify Key Price Points:
   From these curves, the following price points are determined:
   • Optimal Price Point (OPP): Intersection of “Too Cheap” and “Too Expensive” curves, reflecting the price perceived as most acceptable by consumers.
   • Range of Acceptable Prices: Defined by the intersections of “Too Cheap” with “Not Expensive,” and “Too Expensive” with “Not Cheap.”
   • Indifference Price Point (IPP): Intersection of “Not Cheap” and “Not Expensive” curves, where respondents are indifferent about price.
   • Point of Marginal Cheapness (PMC) and Marginal Expensiveness (PME): Represent boundaries of acceptable pricing.

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Advantages of the Van Westendorp Model

• Simple to Conduct: Requires a straightforward survey.
• Identifies Consumer Perceptions: Offers insights into how price influences perceptions of quality and value.
• Defines Price Range: Helps identify the optimal price point and acceptable pricing limits.
• Customizable: Can be adapted to different product types and consumer segments.

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Limitations of the Van Westendorp Model

• Lack of Contextual Factors: Does not consider competition or external market influences.
• Subjective Responses: Relies on self-reported perceptions, which may not always reflect real-world behavior.
• Limited to Single-Use Products: May not work well for subscription models or complex pricing structures.

To create a dataset of 100 survey responses using the Van Westendorp Pricing Model (PSM), you’ll need to focus on four price-related questions for both product variants (with and without Arduino Nicla sensors). Each question should elicit a range of responses reflecting price perceptions.

Survey Questions for Each Variant

1. At what price would you consider the product too expensive to buy?
2. At what price would you consider the product too cheap, making you question its quality?
3. At what price would you consider the product good value for money?
4. At what price would you consider the product a bit expensive, but still worth buying?

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Expected Price Ranges

Given your pricing expectations:

• Without Sensors: Estimated acceptable range is $60–$100, centering around $80.
• With Sensors: Estimated acceptable range is $150–$210, centering around $180.

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Example Dataset Creation

For 100 responses, you could use these strategies:

• Randomly generate responses within the expected ranges, ensuring they align with logical ordering:
  • “Too Cheap” < “Good Value” < “Bit Expensive” < “Too Expensive.”
• Apply some variability to simulate diverse perceptions.

Sample Response Format (Without Sensors):

Respondent ID	Too Cheap ($)	Good Value ($)	Bit Expensive ($)	Too Expensive ($)
1	50	70	90	110
2	45	80	100	120
3	55	75	85	105

Sample Response Format (With Sensors):

Respondent ID	Too Cheap ($)	Good Value ($)	Bit Expensive ($)	Too Expensive ($)
1	120	160	200	250
2	130	180	210	280
3	115	155	190	230

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Simulating the Dataset

Using this structure, we can generate the full dataset programmatically to account for:

• Random variations.
• Skewed distributions (if, for example, users are more sensitive to price increases).

The Newton Miller Smith (NMS) extension is an enhancement to the Van Westendorp Price Sensitivity Meter (PSM), designed to provide more detailed insights into pricing by incorporating additional considerations of demand and revenue optimization. This extension is especially useful for refining pricing strategies and addressing some of the limitations of the traditional PSM.

Key Elements of the Newton Miller Smith Extension

1. Demand Curve Integration:
   The NMS extension uses the PSM data (e.g., “Too Cheap,” “Good Value,” “Bit Expensive,” “Too Expensive”) to estimate a demand curve for the product. It identifies how price changes affect customer willingness to purchase.
2. Revenue Maximization Analysis:
   By integrating demand data, the NMS extension calculates potential revenues at different price points. This helps determine the price point that maximizes revenue rather than just finding an acceptable price range.
3. Additional Metrics:
   The NMS model introduces new price points, including:
   • Revenue Optimal Price (ROP): The price point that maximizes total revenue by balancing unit sales with price.
   • Demand Optimal Price (DOP): The price point at which demand (volume of sales) is maximized, often lower than the ROP.
4. Behavioral Insights:
   The NMS extension accounts for consumer behavior, recognizing that not all respondents are equally price-sensitive. It integrates probabilistic or weighted models to reflect this variability.

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Steps in Applying the NMS Extension

1. Collect PSM Data:
   Use the standard PSM questions to gather price sensitivity data.
2. Estimate Demand at Price Points:
   Use the PSM curves (cumulative distribution functions) to estimate the proportion of customers likely to buy at each price point.
3. Calculate Revenue:
   Multiply the estimated demand at each price point by the corresponding price to compute revenue.
4. Identify Optimal Prices:
   Use the derived demand and revenue data to identify the Revenue Optimal Price (ROP) and Demand Optimal Price (DOP).

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Benefits of the NMS Extension

• Enhanced Revenue Insights: Focuses on profitability, not just acceptability.
• Demand Dynamics: Captures how different price points influence customer behavior.
• Decision-Making Support: Helps identify the most lucrative price points for both short-term revenue and long-term customer growth.

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Example

For the helmet product without sensors:

• Demand decreases as prices increase:
  • At $65, demand is 80%.
  • At $80, demand is 60%.
  • At $100, demand is 40%.
• Revenue calculation (Price × Demand):
  • $65 × 80% = $52
  • $80 × 60% = $48 (Revenue Optimal Price)
  • $100 × 40% = $40

Here, $80 would be the Revenue Optimal Price, even though $65 generates more demand.