| Title: | Van Westendorp Price Sensitivity Meter Analysis |
|---|---|
| Description: | An implementation of the van Westendorp Price Sensitivity Meter in R, which is a survey-based approach to analyze consumer price preferences and sensitivity (van Westendorp 1976, isbn:9789283100386). |
| Authors: | Max Alletsee [aut, cre] |
| Maintainer: | Max Alletsee <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.3.0 |
| Built: | 2024-11-04 04:19:11 UTC |
| Source: | https://github.com/max-alletsee/pricesensitivitymeter |
pricensitivitymeter is an implementation of the van Westendorp Price Sensitivity Meter method to analyze consumer price preferences and price sensitivity in R. Besides the estimation of optimal price points and price ranges, it can also model the optimal price in terms of reach or revenue (based on the so-called Newton Miller Smith extension).
To read the documentation for the function's syntax,
see psm_analysis and
psm_analysis_weighted (for weighted data).
Max Alletsee [email protected]
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the 29th ESOMAR Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
Newton, D, Miller, J, Smith, P, (1993) "A market acceptance extension to traditional price sensitivity measurement" Proceedings of the American Marketing Association Advanced Research Techniques Forum.
Useful links:
Report bugs at https://github.com/max-alletsee/pricesensitivitymeter/issues
psm_analysis() performs an analysis of consumer price
preferences and price sensitivity known as van
Westendorp Price Sensitivity Meter (PSM). It takes respondent's
price preferences (from survey data) as an input and estimates
acceptable price ranges and price points. For a description of
the method see the Details section.
psm_analysis( toocheap, cheap, expensive, tooexpensive, data = NA, validate = TRUE, interpolate = FALSE, interpolation_steps = 0.01, intersection_method = "min", acceptable_range = "original", pi_cheap = NA, pi_expensive = NA, pi_scale = 5:1, pi_calibrated = c(0.7, 0.5, 0.3, 0.1, 0), pi_calibrated_toocheap = 0, pi_calibrated_tooexpensive = 0 )psm_analysis( toocheap, cheap, expensive, tooexpensive, data = NA, validate = TRUE, interpolate = FALSE, interpolation_steps = 0.01, intersection_method = "min", acceptable_range = "original", pi_cheap = NA, pi_expensive = NA, pi_scale = 5:1, pi_calibrated = c(0.7, 0.5, 0.3, 0.1, 0), pi_calibrated_toocheap = 0, pi_calibrated_tooexpensive = 0 )
toocheap, cheap, expensive, tooexpensive
|
If a
data.frame/matrix/tibble is provided in the If no data.frame/matrix/tibble is provided in the If the |
data |
data.frame, matrix or tibble that contains the
function's input data. |
validate |
logical. should only respondents with consistent price preferences (too cheap < cheap < expensive < too expensive) be considered in the analysis? |
interpolate |
logical. should interpolation of the price curves be applied between the actual prices given by the respondents? If interpolation is enabled, the output appears less bumpy in regions with sparse price information. If the sample size is sufficiently large, interpolation should not be necessary. |
interpolation_steps |
numeric. if |
intersection_method |
"min" (default), "max", "mean" or "median". defines the method how to determine the price points (range, indifference price, optimal price) if there are multiple possible intersections of the price curves. "min" uses the lowest possible prices, "max" uses the highest possible prices, "mean" calculates the mean among all intersections and "median" uses the median of all possible intersections |
acceptable_range |
"original" (default) or "narrower". Defines which intersection is used to calculate the point of marginal cheapness and point of marginal expensiveness, which together form the range of acceptable prices. "original" uses the definition provided in van Westendorp's paper: The lower end of the price range (point of marginal cheapness) is defined as the intersection of "too cheap" and the inverse of the "cheap" curve. The upper end of the price range (point of marginal expensiveness) is defined as the intersection of "too expensive" and the inverse of the "expensive" curve. Alternatively, it is possible to use a "narrower" definition which is applied by some market research companies. Here, the lower end of the price range is defined as the intersection of the "expensive" and the "too cheap" curves and the upper end of the price range is defined as the intersection of the "too expensive" and the "cheap" curves. This leads to a narrower range of acceptable prices. Note that it is possible that the optimal price according to the Newton/Miller/Smith extension is higher than the upper end of the acceptable price range in the "narrower" definition. |
pi_cheap, pi_expensive
|
Only required for the Newton
Miller Smith extension. If |
pi_scale |
Only required for the Newton Miller Smith extension. Scale of the purchase intent variables pi_cheap and pi_expensive. By default assuming a five-point scale with 5 indicating the highest purchase intent. |
pi_calibrated |
Only required for the Newton Miller Smith extension. Calibrated purchase probabilities that are assumed for each value of the purchase intent scale. Must be the same order as the pi_scale variable so that the first value of pi_calibrated corresponds to the first value in the pi_scale variable. Default values are taken from the Sawtooth Software PSM implementation in Excel: 70% for the best value of the purchase intent scale, 50% for the second best value, 30% for the third best value (middle of the scale), 10% for the fourth best value and 0% for the worst value. |
pi_calibrated_toocheap, pi_calibrated_tooexpensive
|
Only required for the Newton Miller Smith extension. Calibrated purchase probabilities for the "too cheap" and the "too expensive" price, respectively. Must be a value between 0 and 1; by default set to zero following the logic in van Westendorp's paper. |
The Price Sensitivity Meter method for the analysis of consumer price preferences was proposed by the Dutch economist Peter van Westendorp in 1976 at the ESOMAR conference. It is a survey-based approach that has become one of the standard price acceptance measurement techniques in the market research industry and is still widely used for during early-stage product development.
Price acceptance and price sensitivity are measured in van Westendorp's approach by four open-ended survey questions:
At which price on this scale are you beginning to experience ... (test-product) as cheap?
At which price on this scale are you beginning to experience ... (test-product) as expensive?
At which price on this scale you are beginning to experience ... (test-product) as too expensive – so that you would never consider buying it yourself?
At which price on this scale you are beginning to experience ... (test-product) as too cheap – so that you say "at this price the quality cannot be good"?
Respondents with inconsistent price preferences (e.g. "cheap" price larger than "expensive" price) are usually removed from the data set. This function has built-in checks to detect invalid preference structures and removes those respondents from the analysis by default.
To analyze price preferences and price sensitivity, the method
uses cumulative distribution functions for each of the
aforementioned price steps (e.g. "how many respondents think
that a price of x or more is expensive?"). By
convention, the distributions for the "too cheap" and the
"cheap" price are inverted. This leads to the interpretation
"how many respondents think that a price of up to
x is (too) cheap?".
The interpretation is built on the analysis of the intersections of the four cumulative distribution functions for the different prices (usually via graphical inspection). The original paper describes the four intersections as follows:
Point of Marginal Cheapness (PMC): Below this price point, there are more respondents that consider the price as "too cheap" than respondents who consider it as "not cheap" (intersection of "too cheap" and "not cheap"). This is interpreted as the lower limit of the range of acceptable prices.
Point of Marginal Expensiveness (PME). Above this price point, there are more respondent that consider the price as "too expensive" than there are respondents who consider it as "not expensive" (intersection of "not expensive" and "too expensive"). This is interpreted as the upper limit of the range of acceptable prices.
Indifference Price Point (IDP): The same number of respondents perceives the price as "cheap" and "expensive" (intersection of "cheap" and "expensive"). In van Westendorp's interpretation, this is either the median price paid in the market or the price of an important market-leader.
Optimal Price Point (OPP): The same number of respondents perceives the product as "too cheap" and "too expensive" (intersection of "too cheap" and "too expensive"). van Westendorp argues that this is the value for which the respondents' resistance against the price is particularly low.
Besides those four intersections, van Westendorp's article advises to analyze the cumulative distribution functions for steep areas which indicate price steps.
To analyze reach (trial rates) and estimate revenue forecasts,
Newton/Miller/Smith have extended van Westendorp's original
model by adding two purchase intent questions that are asked for
the respondent's "cheap" and "expensive" price. The purchase
probability at the respondent's "too cheap" and "too expensive"
price are defined as 0. The main logic is that the "too
expensive" price point is prohibitively expensive for the
respondent and a price at the "too cheap" price level raises
doubts about the product quality.
By combining the standard van Westendorp questions with those two additional purchase intent questions, it becomes possible to summarize the purchase probabilities across respondents (using linear interpolation for the purchase probabilities between each respondent's cornerstone prices). The maximum of this curve is then defined as the price point with the highest expected reach. Moreover, by multiplying the reach with the price, it also becomes possible to estimate a price with the highest expected revenue.
It has to be noted that the van Westendorp Price Sensitivity Meter is useful in some cases, but does not answer every pricing-related question. It may be a good tool to assess very broadly if the consumers' price perceptions exceed the actual production costs. For more complex analyses (e.g. defining specific prices for different products to avoid cannibalization and drive at the same time incremental growth), other methodological approaches are needed.
The function output consists of the following elements:
data_input: |
|
validated: |
|
invalid_cases: |
|
total_sample: |
|
data_vanwestendorp: |
|
pricerange_lower: |
|
pricerange_upper: |
|
idp: |
|
opp: |
|
NMS: |
|
weighted: |
|
data_nms: |
|
pi_scale: |
|
price_optimal_reach: |
|
price_optimal_revenue: |
|
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the ESOMAR 29th Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
Newton, D, Miller, J, Smith, P, (1993) "A market acceptance extension to traditional price sensitivity measurement" Proceedings of the American Marketing Association Advanced Research Techniques Forum.
Sawtooth Software (2016) "Templates for van Westendorp PSM for Lighthouse Studio and Excel". Online available at https://sawtoothsoftware.com/resources/software-downloads/tools/van-westendorp-price-sensitivity-meter
Examples for companies that use a narrower definition than van Westendorp's original paper include Conjoint.ly (https://conjointly.com/products/van-westendorp/), Quantilope (https://www.quantilope.com/resources/glossary-how-to-use-van-westendorp-pricing-model-to-inform-pricing-strategy), and Milieu (https://www.mili.eu/learn/what-is-the-van-westendorp-pricing-study-and-when-to-use-it)
The function psm_analysis_weighted() performs the same
analyses for weighted data.
set.seed(42) # standard van Westendorp Price Sensitivity Meter Analysis # input directly via vectors tch <- round(rnorm(n = 250, mean = 5, sd = 0.5), digits = 2) ch <- round(rnorm(n = 250, mean = 8.5, sd = 0.5), digits = 2) ex <- round(rnorm(n = 250, mean = 13, sd = 0.75), digits = 2) tex <- round(rnorm(n = 250, mean = 17, sd = 1), digits = 2) output_psm_demo1 <- psm_analysis(toocheap = tch, cheap = ch, expensive = ex, tooexpensive = tex) # additional analysis with Newton Miller Smith Extension # input via data.frame pint_ch <- sample(x = c(1:5), size = length(tex), replace = TRUE, prob = c(0.1, 0.1, 0.2, 0.3, 0.3)) pint_ex <- sample(x = c(1:5), size = length(tex), replace = TRUE, prob = c(0.3, 0.3, 0.2, 0.1, 0.1)) data_psm_demo <- data.frame(tch, ch, ex, tex, pint_ch, pint_ex) output_psm_demo2 <- psm_analysis(toocheap = "tch", cheap = "ch", expensive = "ex", tooexpensive = "tex", pi_cheap = "pint_ch", pi_expensive = "pint_ex", data = data_psm_demo) summary(output_psm_demo2)set.seed(42) # standard van Westendorp Price Sensitivity Meter Analysis # input directly via vectors tch <- round(rnorm(n = 250, mean = 5, sd = 0.5), digits = 2) ch <- round(rnorm(n = 250, mean = 8.5, sd = 0.5), digits = 2) ex <- round(rnorm(n = 250, mean = 13, sd = 0.75), digits = 2) tex <- round(rnorm(n = 250, mean = 17, sd = 1), digits = 2) output_psm_demo1 <- psm_analysis(toocheap = tch, cheap = ch, expensive = ex, tooexpensive = tex) # additional analysis with Newton Miller Smith Extension # input via data.frame pint_ch <- sample(x = c(1:5), size = length(tex), replace = TRUE, prob = c(0.1, 0.1, 0.2, 0.3, 0.3)) pint_ex <- sample(x = c(1:5), size = length(tex), replace = TRUE, prob = c(0.3, 0.3, 0.2, 0.1, 0.1)) data_psm_demo <- data.frame(tch, ch, ex, tex, pint_ch, pint_ex) output_psm_demo2 <- psm_analysis(toocheap = "tch", cheap = "ch", expensive = "ex", tooexpensive = "tex", pi_cheap = "pint_ch", pi_expensive = "pint_ex", data = data_psm_demo) summary(output_psm_demo2)
psm_analysis_weighted() performs a weighted analysis
of consumer price preferences and price sensitivity known as
van Westendorp Price Sensitivity Meter (PSM). The function
requires a sample design from the survey package as the
main input. Custom weights or sample designs from other packages
are not supported.
To run a PSM analysis without weighting, use the function
psm_analysis.
psm_analysis_weighted( toocheap, cheap, expensive, tooexpensive, design, validate = TRUE, interpolate = FALSE, interpolation_steps = 0.01, intersection_method = "min", acceptable_range = "original", pi_cheap = NA, pi_expensive = NA, pi_scale = 5:1, pi_calibrated = c(0.7, 0.5, 0.3, 0.1, 0), pi_calibrated_toocheap = 0, pi_calibrated_tooexpensive = 0 )psm_analysis_weighted( toocheap, cheap, expensive, tooexpensive, design, validate = TRUE, interpolate = FALSE, interpolation_steps = 0.01, intersection_method = "min", acceptable_range = "original", pi_cheap = NA, pi_expensive = NA, pi_scale = 5:1, pi_calibrated = c(0.7, 0.5, 0.3, 0.1, 0), pi_calibrated_toocheap = 0, pi_calibrated_tooexpensive = 0 )
toocheap, cheap, expensive, tooexpensive
|
Names of the variables in the data.frame/matrix that contain the survey data on the respondents' "too cheap", "cheap", "expensive" and "too expensive" price preferences. If the |
design |
A survey design which has been created by the
function |
validate |
logical. should only respondents with consistent price preferences (too cheap < cheap < expensive < too expensive) be considered in the analysis? |
interpolate |
logical. should interpolation of the price curves be applied between the actual prices given by the respondents? If interpolation is enabled, the output appears less bumpy in regions with sparse price information. If the sample size is sufficiently large, interpolation should not be necessary. |
interpolation_steps |
numeric. if |
intersection_method |
"min" (default), "max", "mean" or "median". defines the method how to determine the price points (range, indifference price, optimal price) if there are multiple possible intersections of the price curves. "min" uses the lowest possible prices, "max" uses the highest possible prices, "mean" calculates the mean among all intersections and "median" uses the median of all possible intersections |
acceptable_range |
"original" (default) or "narrower". Defines which intersection is used to calculate the point of marginal cheapness and point of marginal expensiveness, which together form the range of acceptable prices. "original" uses the definition provided in van Westendorp's paper: The lower end of the price range (point of marginal cheapness) is defined as the intersection of "too cheap" and the inverse of the "cheap" curve. The upper end of the price range (point of marginal expensiveness) is defined as the intersection of "too expensive" and the inverse of the "expensive" curve. Alternatively, it is possible to use a "narrower" definition which is applied by some market research companies. Here, the lower end of the price range is defined as the intersection of the "expensive" and the "too cheap" curves and the upper end of the price range is defined as the intersection of the "too expensive" and the "cheap" curves. This leads to a narrower range of acceptable prices. Note that it is possible that the optimal price according to the Newton/Miller/Smith extension is higher than the upper end of the acceptable price range in the "narrower" definition. |
pi_cheap, pi_expensive
|
Only required for the Newton Miller Smith extension. Names of the variables in the data that contain the survey data on the respondents' purchase intent at their individual cheap/expensive price. |
pi_scale |
Only required for the Newton Miller Smith extension. Scale of the purchase intent variables pi_cheap and pi_expensive. By default assuming a five-point scale with 5 indicating the highest purchase intent. |
pi_calibrated |
Only required for the Newton Miller Smith extension. Calibrated purchase probabilities that are assumed for each value of the purchase intent scale. Must be the same order as the pi_scale variable so that the first value of pi_calibrated corresponds to the first value in the pi_scale variable. Default values are taken from the Sawtooth Software PSM implementation in Excel: 70% for the best value of the purchase intent scale, 50% for the second best value, 30% for the third best value (middle of the scale), 10% for the fourth best value and 0% for the worst value. |
pi_calibrated_toocheap, pi_calibrated_tooexpensive
|
Only required for the Newton Miller Smith extension. Calibrated purchase probabilities for the "too cheap" and the "too expensive" price, respectively. Must be a value between 0 and 1; by default set to zero following the logic in van Westendorp's paper. |
The main logic of the Price Sensitivity Meter Analysis is
explained in the documentation of the psm_analysis
function. The psm_analysis_weighted performs the same
analysis, but weights the survey data according to a known
population.
The function output consists of the following elements:
data_input: |
|
validated: |
|
invalid_cases: |
|
total_sample: |
|
data_vanwestendorp: |
|
pricerange_lower: |
|
pricerange_upper: |
|
idp: |
|
opp: |
|
weighted: |
|
survey_design: |
|
NMS: |
|
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the ESOMAR 29th Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
Newton, D, Miller, J, Smith, P, (1993) "A market acceptance extension to traditional price sensitivity measurement" Proceedings of the American Marketing Association Advanced Research Techniques Forum.
Sawtooth Software (2016) "Templates for van Westendorp PSM for Lighthouse Studio and Excel". Online available at https://sawtoothsoftware.com/resources/software-downloads/tools/van-westendorp-price-sensitivity-meter
# assuming a skewed sample with only 1/3 women and 2/3 men input_data <- data.frame(tch = round(rnorm(n = 250, mean = 8, sd = 1.5), digits = 2), ch = round(rnorm(n = 250, mean = 12, sd = 2), digits = 2), ex = round(rnorm(n = 250, mean = 13, sd = 1), digits = 2), tex = round(rnorm(n = 250, mean = 15, sd = 1), digits = 2), gender = sample(x = c("male", "female"), size = 250, replace = TRUE, prob = c(2/3, 1/3))) # ... and in which women have on average 1.5x the price acceptance of men input_data$tch[input_data$gender == "female"] <- input_data$tch[input_data$gender == "female"] * 1.5 input_data$ch[input_data$gender == "female"] <- input_data$ch[input_data$gender == "female"] * 1.5 input_data$ex[input_data$gender == "female"] <- input_data$ex[input_data$gender == "female"] * 1.5 input_data$tex[input_data$gender == "female"] <- input_data$tex[input_data$gender == "female"] * 1.5 # creating a sample design object using the survey package # ... assuming that gender is balanced equally in the population of 10000 input_data$gender_pop <- 5000 input_design <- survey::svydesign(ids = ~ 1, # no clusters probs = NULL, # hence no cluster samling probabilities, strata = input_data$gender, # stratified by gender fpc = input_data$gender_pop, # strata size in the population data = input_data) # data object used as input: no need to specify single variables output_weighted_psm <- psm_analysis_weighted(toocheap = "tch", cheap = "ch", expensive = "ex", tooexpensive = "tex", design = input_design) summary(output_weighted_psm)# assuming a skewed sample with only 1/3 women and 2/3 men input_data <- data.frame(tch = round(rnorm(n = 250, mean = 8, sd = 1.5), digits = 2), ch = round(rnorm(n = 250, mean = 12, sd = 2), digits = 2), ex = round(rnorm(n = 250, mean = 13, sd = 1), digits = 2), tex = round(rnorm(n = 250, mean = 15, sd = 1), digits = 2), gender = sample(x = c("male", "female"), size = 250, replace = TRUE, prob = c(2/3, 1/3))) # ... and in which women have on average 1.5x the price acceptance of men input_data$tch[input_data$gender == "female"] <- input_data$tch[input_data$gender == "female"] * 1.5 input_data$ch[input_data$gender == "female"] <- input_data$ch[input_data$gender == "female"] * 1.5 input_data$ex[input_data$gender == "female"] <- input_data$ex[input_data$gender == "female"] * 1.5 input_data$tex[input_data$gender == "female"] <- input_data$tex[input_data$gender == "female"] * 1.5 # creating a sample design object using the survey package # ... assuming that gender is balanced equally in the population of 10000 input_data$gender_pop <- 5000 input_design <- survey::svydesign(ids = ~ 1, # no clusters probs = NULL, # hence no cluster samling probabilities, strata = input_data$gender, # stratified by gender fpc = input_data$gender_pop, # strata size in the population data = input_data) # data object used as input: no need to specify single variables output_weighted_psm <- psm_analysis_weighted(toocheap = "tch", cheap = "ch", expensive = "ex", tooexpensive = "tex", design = input_design) summary(output_weighted_psm)
psm_plot() uses ggplot() to show the standard van
Westendorp Price Sensitivity Meter plot that allows to see the
acceptance for each price on each of the four variables.
It takes the object created by psm_analysis() or
psm_analysis_weighted() as an input.
psm_plot(psm_result, shade_pricerange = TRUE, line_toocheap = TRUE, line_tooexpensive = TRUE, line_notcheap = TRUE, line_notexpensive = TRUE, point_idp = TRUE, point_color_idp = "#009E73", label_idp = TRUE, point_opp = TRUE, point_color_opp = "#009E73", label_opp= TRUE, pricerange_color = "grey50", pricerange_alpha = 0.3, line_color = c("too cheap" = "#009E73", "not cheap" = "#009E73", "not expensive" = "#D55E00", "too expensive" = "#D55E00"), line_type = c("too cheap" = "dotted", "not cheap" = "solid", "not expensive" = "solid", "too expensive" = "dotted"))psm_plot(psm_result, shade_pricerange = TRUE, line_toocheap = TRUE, line_tooexpensive = TRUE, line_notcheap = TRUE, line_notexpensive = TRUE, point_idp = TRUE, point_color_idp = "#009E73", label_idp = TRUE, point_opp = TRUE, point_color_opp = "#009E73", label_opp= TRUE, pricerange_color = "grey50", pricerange_alpha = 0.3, line_color = c("too cheap" = "#009E73", "not cheap" = "#009E73", "not expensive" = "#D55E00", "too expensive" = "#D55E00"), line_type = c("too cheap" = "dotted", "not cheap" = "solid", "not expensive" = "solid", "too expensive" = "dotted"))
psm_result |
Result of a Price Sensitivity Meter analysis,
created by running |
shade_pricerange |
logical value. Determines if the acceptable price range is shown as a shaded area or not. |
line_toocheap |
logical value. Determines if the line for the "too cheap" price curve is shown or not. |
line_tooexpensive |
logical value. Determines if the line for the "too expensive" price curve is shown or not. |
line_notcheap |
logical value. Determines if the line for the "not cheap" price curve is shown or not. |
line_notexpensive |
logical value. Determines if the line for the "not expensive" price curve is shown or not. |
point_idp |
logical value. Determines if the Indifference Price Point is shown or not. |
point_color_idp |
character vector, specifying the color of the Optimal Price Point. Can be a hex color (e.g. "#7f7f7f") or one of R's built-in colors (e.g. "grey50"). |
label_idp |
logical value. Determines if the label for the Indifference Price Point is shown or not. |
point_opp |
logical value. Determines if the Optimal Price Point is shown or not. |
point_color_opp |
character vector, specifying the color of the Optimal Price Point. Can be a hex color (e.g. "#7f7f7f") or one of R's built-in colors (e.g. "grey50"). |
label_opp |
logical value. Determines if the label for the Optimal Price Point is shown or not. |
pricerange_color |
character, specifying the
background color for the accepted price range. Can be a
hex color (e.g. "#7f7f7f") or one of R's built-in colors
(e.g. "grey50"). You can see all of R's built-in colors
with the function |
pricerange_alpha |
numeric between 0 and 1,
specifying the alpha transparency for the shaded area of
the the accepted price range. Is only applied if
|
line_color |
character vector, specifying the line
color for each of the price curves shown. Color
definitions must match the lines you have defined via
|
line_type |
vector, specifying the line type for each
of the price curves shown. Definitions must match the lines
you have defined via |
The function output is a ggplot2 object.
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the ESOMAR 29th Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
The vignette "Visualizing PSM Results" shows a similar way and more custom way to plot the data.
# set up example data and run psm_analysis() tch <- round(rnorm(n = 250, mean = 5, sd = 0.5), digits = 2) ch <- round(rnorm(n = 250, mean = 8.5, sd = 0.5), digits = 2) ex <- round(rnorm(n = 250, mean = 13, sd = 0.75), digits = 2) tex <- round(rnorm(n = 250, mean = 17, sd = 1), digits = 2) output_psm_demo <- psm_analysis(toocheap = tch, cheap = ch, expensive = ex, tooexpensive = tex) # create the plot (note that ggplot's convention # is to *not* show it by default) ## Not run: psm_result_plot <- psm_plot(output_psm_demo) # to show the plot, call the object (and maybe # additional ggplot functions if you like) psm_result_plot + ggplot2::theme_minimal() ## End(Not run)# set up example data and run psm_analysis() tch <- round(rnorm(n = 250, mean = 5, sd = 0.5), digits = 2) ch <- round(rnorm(n = 250, mean = 8.5, sd = 0.5), digits = 2) ex <- round(rnorm(n = 250, mean = 13, sd = 0.75), digits = 2) tex <- round(rnorm(n = 250, mean = 17, sd = 1), digits = 2) output_psm_demo <- psm_analysis(toocheap = tch, cheap = ch, expensive = ex, tooexpensive = tex) # create the plot (note that ggplot's convention # is to *not* show it by default) ## Not run: psm_result_plot <- psm_plot(output_psm_demo) # to show the plot, call the object (and maybe # additional ggplot functions if you like) psm_result_plot + ggplot2::theme_minimal() ## End(Not run)
"psm"
Class "psm" is a class for outputs of Price Sensitivity Meter analyses as performed by the psm_analysis function.
The main purpose is to create a custom summary function for objects of class "psm".
Objects are usually created as a result of a call of the psm_analysis function.
data_input:Object of class "data.frame". Contains the data that was used as an input for the analysis.
validated:Object of class "logical". Indicates whether the "validate" option of the psm_analysis function has been used (to exclude cases with intransitive price preferences).
invalid_cases:Object of class "numeric". Number of cases with intransitive price preferences.
total_sample:Object of class "numeric". Total sample size of the input sample before assessing the transitivity of individual price preferences.
data_vanwestendorp:Object of class "data.frame". Output data of the Price Sensitivity Meter analysis. Contains the cumulative distribution functions for the four price assessments (too cheap, cheap, expensive, too expensive) for all prices as well as the inversed distributions "not cheap" and "not expensive" (that are required for the acceptable price range).
pricerange_lower:Object of class "numeric". Lower limit of the acceptable price range as defined by the Price Sensitivity Meter, also known as point of marginal cheapness: Intersection of the "too cheap" and the "not cheap" curves.
pricerange_upper:Object of class "numeric". Upper limit of the acceptable price range as defined by the Price Sensitivity Meter, also known as point of marginal expensiveness: Intersection of the "too expensive" and the "not expensive" curves.
idp:Object of class "numeric". Indifference Price Point as defined by the Price Sensitivity Meter: Intersection of the "cheap" and the "expensive" curves.
opp:Object of class "numeric". Optimal Price Point as defined by the Price Sensitivity Meter: Intersection of the "too cheap" and the "too expensive" curves.
weighted:Object of class "logical". TRUE if the function has used weighted data to calulate the price points; FALSE if unweighted data has been used.
survey_design:Object of class "survey.design2". If weighted data has been used, the survey design object from the survey package is returned here. Please refer to the documentation in the survey package for more details.
NMS:Object of class "logical". Indicates whether the additional analyses of the Newton Miller Smith Extension were performed.
data_nms:Object of class "data.frame". Output of the Newton Miller Smith extension: calibrated mean purchase probabilities for each price point.
pi_scale:Object of class "data.frame". Shows the values of the purchase intent variable and the corresponding calibrated purchase probabilities as defined in the function input for the Newton Miller Smith extension.
price_optimal_reach:Object of class "numeric". Output of the Newton Miller Smith extension: Estimate for the price with the highest reach (trial rate).
price_optimal_revenue:Object of class "numeric". Output of the Newton Miller Smith extension: Estimate for the price with the highest revenue (based on the reach).
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the 29th ESOMAR Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
Newton, D, Miller, J, Smith, P, (1993) "A market acceptance extension to traditional price sensitivity measurement" Proceedings of the American Marketing Association Advanced Research Techniques Forum.
To understand the main function that creates an object of class "psm", see psm_analysis or psm_analysis_weighted.
To understand how the summaries of objects of class "psm" look like, see summary.psm.
For a documentation of objects of class "survey.design2", see the documentation of the survey package.
showClass("psm")showClass("psm")
summary method for class "psm".
## S3 method for class 'psm' summary(object, ...)## S3 method for class 'psm' summary(object, ...)
object |
an object of class |
... |
further arguments from other methods - will be ignored in the summary |
Van Westendorp, P (1976) "NSS-Price Sensitivity Meter (PSM) – A new approach to study consumer perception of price" Proceedings of the 29th ESOMAR Congress, 139–167. Online available at https://archive.researchworld.com/a-new-approach-to-study-consumer-perception-of-price/.
Newton, D, Miller, J, Smith, P, (1993) "A market acceptance extension to traditional price sensitivity measurement" Proceedings of the American Marketing Association Advanced Research Techniques Forum.
A comprehensive description of all elements of an object of
class "psm" can be found in psm_analysis
and psm_analysis_weighted.
The generic summary function.