diff --git a/R/DESCRIPTION b/R/DESCRIPTION
index 637e88f72..43cd8c2ea 100644
--- a/R/DESCRIPTION
+++ b/R/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: Robyn
 Type: Package
 Title: Semi-Automated Marketing Mix Modeling (MMM) from Meta Marketing Science 
-Version: 3.10.5.9014
+Version: 3.10.5.9015
 Authors@R: c(
     person("Gufeng", "Zhou", , "gufeng@meta.com", c("cre","aut")),
     person("Leonel", "Sentana", , "leonelsentana@meta.com", c("aut")),
diff --git a/R/R/auxiliary.R b/R/R/auxiliary.R
index 847de5174..c4c53ccea 100644
--- a/R/R/auxiliary.R
+++ b/R/R/auxiliary.R
@@ -78,7 +78,7 @@ baseline_vars <- function(InputCollect, baseline_level) {
   stopifnot(baseline_level %in% 0:5)
   x <- ""
   if (baseline_level >= 1)
-    x <- c(x, "(Intercept)")
+    x <- c(x, "(Intercept)", "intercept")
   if (baseline_level >= 2)
     x <- c(x, "trend")
   if (baseline_level >= 3)
diff --git a/R/R/plots.R b/R/R/plots.R
index 138d73164..e6f9e68d2 100644
--- a/R/R/plots.R
+++ b/R/R/plots.R
@@ -1485,8 +1485,13 @@ ts_validation <- function(OutputModels, quiet = FALSE, ...) {
 #' @param solID Character vector. Model IDs to plot.
 #' @param exclude Character vector. Manually exclude variables from plot.
 #' @export
-decomp_plot <- function(InputCollect, OutputCollect, solID = NULL, exclude = NULL) {
+decomp_plot <- function(
+    InputCollect, OutputCollect, solID = NULL,
+    exclude = NULL, baseline_level = 0) {
+  if (is.null(solID) && length(OutputCollect$allSolutions) == 1)
+    solID <- OutputCollect$allSolutions
   check_opts(solID, OutputCollect$allSolutions)
+  bvars <- baseline_vars(InputCollect, baseline_level)
   intType <- str_to_title(case_when(
     InputCollect$intervalType %in% c("month", "week") ~ paste0(InputCollect$intervalType, "ly"),
     InputCollect$intervalType == "day" ~ "daily",
@@ -1501,7 +1506,14 @@ decomp_plot <- function(InputCollect, OutputCollect, solID = NULL, exclude = NUL
     ) %>%
     tidyr::gather("variable", "value", -.data$ds, -.data$solID, -.data$dep_var) %>%
     filter(!.data$variable %in% exclude) %>%
-    mutate(variable = factor(.data$variable, levels = rev(unique(.data$variable))))
+    mutate(variable = ifelse(
+      .data$variable %in% bvars, paste0("Baseline_L", baseline_level), as.character(.data$variable))) %>%
+    group_by(.data$solID, .data$ds, .data$variable) %>%
+    summarise(value = sum(.data$value, na.rm = TRUE),
+              value = sum(.data$value, na.rm = TRUE),
+              .groups = "drop") %>%
+    arrange(abs(.data$value)) %>%
+    mutate(variable = factor(.data$variable, levels = unique(.data$variable)))
   p <- ggplot(df, aes(x = .data$ds, y = .data$value, fill = .data$variable)) +
     facet_grid(.data$solID ~ .) +
     labs(
diff --git a/R/R/response.R b/R/R/response.R
index f9f636d70..7334a7ba0 100644
--- a/R/R/response.R
+++ b/R/R/response.R
@@ -23,56 +23,83 @@
 #' @examples
 #' \dontrun{
 #' # Having InputCollect and OutputCollect objects
+#' ## Recreate original saturation curve
+#' Response <- robyn_response(
+#'   InputCollect = InputCollect,
+#'   OutputCollect = OutputCollect,
+#'   select_model = select_model,
+#'   metric_name = "facebook_S"
+#' )
+#' Response$plot
 #'
-#' # Get marginal response (mResponse) and marginal ROI (mROI) for
-#' # the next 1k on 80k for search_S
-#' spend1 <- 80000
+#' ## Or you can call a JSON file directly (a bit slower)
+#' # Response <- robyn_response(
+#' #   json_file = "your_json_path.json",
+#' #   dt_input = dt_simulated_weekly,
+#' #   dt_holidays = dt_prophet_holidays,
+#' #   metric_name = "facebook_S"
+#' # )
+#'
+#' ## Get the "next 100 dollar" marginal response on Spend1
+#' Spend1 <- 20000
 #' Response1 <- robyn_response(
 #'   InputCollect = InputCollect,
 #'   OutputCollect = OutputCollect,
-#'   metric_name = "search_S",
-#'   metric_value = spend1
-#' )$response
-#' # Get ROI for 80k
-#' Response1 / spend1 # ROI for search 80k
+#'   select_model = select_model,
+#'   metric_name = "facebook_S",
+#'   metric_value = Spend1, # total budget for date_range
+#'   date_range = "last_1" # last two periods
+#' )
+#' Response1$plot
 #'
-#' # Get response for 81k
-#' spend2 <- spend1 + 1000
+#' Spend2 <- Spend1 + 100
 #' Response2 <- robyn_response(
 #'   InputCollect = InputCollect,
 #'   OutputCollect = OutputCollect,
-#'   metric_name = "search_S",
-#'   metric_value = spend2
-#' )$response
+#'   select_model = select_model,
+#'   metric_name = "facebook_S",
+#'   metric_value = Spend2,
+#'   date_range = "last_1"
+#' )
+#' # ROAS for the 100$ from Spend1 level
+#' (Response2$response_total - Response1$response_total) / (Spend2 - Spend1)
 #'
-#' # Get ROI for 81k
-#' Response2 / spend2 # ROI for search 81k
-#' # Get marginal response (mResponse) for the next 1k on 80k
-#' Response2 - Response1
-#' # Get marginal ROI (mROI) for the next 1k on 80k
-#' (Response2 - Response1) / (spend2 - spend1)
+#' ## Get response from for a given budget and date_range
+#' Spend3 <- 100000
+#' Response3 <- robyn_response(
+#'   InputCollect = InputCollect,
+#'   OutputCollect = OutputCollect,
+#'   select_model = select_model,
+#'   metric_name = "facebook_S",
+#'   metric_value = Spend3, # total budget for date_range
+#'   date_range = "last_5" # last 5 periods
+#' )
+#' Response3$plot
 #'
-#' # Example of getting paid media exposure response curves
-#' imps <- 1000000
+#' ## Example of getting paid media exposure response curves
+#' imps <- 10000000
 #' response_imps <- robyn_response(
 #'   InputCollect = InputCollect,
 #'   OutputCollect = OutputCollect,
+#'   select_model = select_model,
 #'   metric_name = "facebook_I",
 #'   metric_value = imps
-#' )$response
-#' response_per_1k_imps <- response_imps / imps * 1000
-#' response_per_1k_imps
+#' )
+#' response_imps$response_total / imps * 1000
+#' response_imps$plot
 #'
-#' # Get response for 80k for search_S from the a certain model SolID
-#' # in the current model output in the global environment
-#' robyn_response(
+#' ## Example of getting organic media exposure response curves
+#' sendings <- 30000
+#' response_sending <- robyn_response(
 #'   InputCollect = InputCollect,
 #'   OutputCollect = OutputCollect,
-#'   metric_name = "search_S",
-#'   metric_value = 80000,
-#'   dt_hyppar = OutputCollect$resultHypParam,
-#'   dt_coef = OutputCollect$xDecompAgg
+#'   select_model = select_model,
+#'   metric_name = "newsletter",
+#'   metric_value = sendings
 #' )
+#' # response per 1000 sendings
+#' response_sending$response_total / sendings * 1000
+#' response_sending$plot
 #' }
 #' @return List. Response value and plot. Class: \code{robyn_response}.
 #' @export
diff --git a/R/man/robyn_outputs.Rd b/R/man/robyn_outputs.Rd
index 8c8312b45..652854da3 100644
--- a/R/man/robyn_outputs.Rd
+++ b/R/man/robyn_outputs.Rd
@@ -60,7 +60,13 @@ robyn_onepagers(
 
 ts_validation(OutputModels, quiet = FALSE, ...)
 
-decomp_plot(InputCollect, OutputCollect, solID = NULL, exclude = NULL)
+decomp_plot(
+  InputCollect,
+  OutputCollect,
+  solID = NULL,
+  exclude = NULL,
+  baseline_level = 0
+)
 }
 \arguments{
 \item{InputCollect, OutputModels}{\code{robyn_inputs()} and \code{robyn_run()}
diff --git a/R/man/robyn_response.Rd b/R/man/robyn_response.Rd
index 71abcb4f7..c1fa0cf45 100644
--- a/R/man/robyn_response.Rd
+++ b/R/man/robyn_response.Rd
@@ -75,55 +75,82 @@ result and selected model build (initial model, refresh model, etc.).
 \examples{
 \dontrun{
 # Having InputCollect and OutputCollect objects
-
-# Get marginal response (mResponse) and marginal ROI (mROI) for
-# the next 1k on 80k for search_S
-spend1 <- 80000
+## Recreate original saturation curve
+Response <- robyn_response(
+  InputCollect = InputCollect,
+  OutputCollect = OutputCollect,
+  select_model = select_model,
+  metric_name = "facebook_S"
+)
+Response$plot
+
+## Or you can call a JSON file directly (a bit slower)
+# Response <- robyn_response(
+#   json_file = "your_json_path.json",
+#   dt_input = dt_simulated_weekly,
+#   dt_holidays = dt_prophet_holidays,
+#   metric_name = "facebook_S"
+# )
+
+## Get the "next 100 dollar" marginal response on Spend1
+Spend1 <- 20000
 Response1 <- robyn_response(
   InputCollect = InputCollect,
   OutputCollect = OutputCollect,
-  metric_name = "search_S",
-  metric_value = spend1
-)$response
-# Get ROI for 80k
-Response1 / spend1 # ROI for search 80k
-
-# Get response for 81k
-spend2 <- spend1 + 1000
+  select_model = select_model,
+  metric_name = "facebook_S",
+  metric_value = Spend1, # total budget for date_range
+  date_range = "last_1" # last two periods
+)
+Response1$plot
+
+Spend2 <- Spend1 + 100
 Response2 <- robyn_response(
   InputCollect = InputCollect,
   OutputCollect = OutputCollect,
-  metric_name = "search_S",
-  metric_value = spend2
-)$response
-
-# Get ROI for 81k
-Response2 / spend2 # ROI for search 81k
-# Get marginal response (mResponse) for the next 1k on 80k
-Response2 - Response1
-# Get marginal ROI (mROI) for the next 1k on 80k
-(Response2 - Response1) / (spend2 - spend1)
-
-# Example of getting paid media exposure response curves
-imps <- 1000000
+  select_model = select_model,
+  metric_name = "facebook_S",
+  metric_value = Spend2,
+  date_range = "last_1"
+)
+# ROAS for the 100$ from Spend1 level
+(Response2$response_total - Response1$response_total) / (Spend2 - Spend1)
+
+## Get response from for a given budget and date_range
+Spend3 <- 100000
+Response3 <- robyn_response(
+  InputCollect = InputCollect,
+  OutputCollect = OutputCollect,
+  select_model = select_model,
+  metric_name = "facebook_S",
+  metric_value = Spend3, # total budget for date_range
+  date_range = "last_5" # last 5 periods
+)
+Response3$plot
+
+## Example of getting paid media exposure response curves
+imps <- 10000000
 response_imps <- robyn_response(
   InputCollect = InputCollect,
   OutputCollect = OutputCollect,
+  select_model = select_model,
   metric_name = "facebook_I",
   metric_value = imps
-)$response
-response_per_1k_imps <- response_imps / imps * 1000
-response_per_1k_imps
+)
+response_imps$response_total / imps * 1000
+response_imps$plot
 
-# Get response for 80k for search_S from the a certain model SolID
-# in the current model output in the global environment
-robyn_response(
+## Example of getting organic media exposure response curves
+sendings <- 30000
+response_sending <- robyn_response(
   InputCollect = InputCollect,
   OutputCollect = OutputCollect,
-  metric_name = "search_S",
-  metric_value = 80000,
-  dt_hyppar = OutputCollect$resultHypParam,
-  dt_coef = OutputCollect$xDecompAgg
+  select_model = select_model,
+  metric_name = "newsletter",
+  metric_value = sendings
 )
+# response per 1000 sendings
+response_sending$response_total / sendings * 1000
+response_sending$plot
 }
 }