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## Summary

In this guide you will learn how to fetch your marketing data and apply a Marketing Mix Model to elucidate which source presents the best marketing performance. Marketing Mix Modelling is a statistical analyses such as multivariate regressions on sales and marketing time series data to estimate the impact of various marketing tactics on a given variable related to reward (e.g., number of clicks).

Our aim will be to elucidate which marketing platform presents the highest number of clicks per spend for the last year. More concretely, we will consider Facebook and Google Ads data.

First, we will use windsor.ai API to get our marketing data from Facebook Ads and Google Ads. Then, we will clean the data and prepare it for the analyses. Finally we will perform the analysis to answer our question.

## Get started

First of all we need to install the packages we will use.

library(dplyr) # data cleaning
library(ggplot2) # plotting
library(PerformanceAnalytics) # to calculate correlations
library(mctest) # to evaluate models assumptions (lineality)
library(lmtest) # to evaluate models assumptions (homocedasticity)

## Get data using Windsor.ai API

Now we will use two packages that allows us to get marketing data from windsor.ai API for Facebook (facebookadsR) and for Google (googleadsR).All you need before starting to get your data is to create and account in windsor.ai and get an API key. You have a 30 days free trial.

By using the following code, you will download marketing data from 2022-01-01 to now. More concretely, we are interested in the variables clicks, representing the number of clicks which will be our measure related to marketing performance and spend as a measure of the money invested in each platform per date. We will also need a variable identifying the source (here, Facebook or Google) and the date.

Then, we can unify the data into an integrated data base containing both Facebook and Google Ads marketing data.

date_from = "2022-01-01",
date_to = Sys.Date(),
fields = c("source", "date", "clicks", "spend")) %>%
dplyr::mutate(source = as.character(source),
date = as.Date(date),
clicks = as.numeric(clicks),
spend = as.numeric(spend)) %>%
aggregate(. ~ date + source, FUN = sum)

date_from = "2022-01-01",
date_to = Sys.Date(),
fields = c("source", "date", "clicks", "spend")) %>%
dplyr::mutate(source = as.character(source),
date = as.Date(date),
clicks = as.numeric(clicks),
spend = as.numeric(spend)) %>%
aggregate(. ~ date + source, FUN = sum)

Let’s have a quick look at the data we just downloaded and unified.

glimpse(my_integrated_data)

#> Rows: 607
#> Columns: 4
#> \$ date   2022-01-01, 2022-01-02, 2022-01-03, 2022-01-04, 2022-01-05, 20…
#> \$ clicks 10, 9, 2, 2, 8, 6, 5, 3, 5, 4, 4, 3, 3, 4, 4, 1, 3, 7, 4, 3, 3,…
#> \$ spend  4.89, 5.05, 5.01, 4.95, 4.95, 5.02, 5.10, 4.92, 4.86, 4.91, 4.9…

## Clean data

Now, we will proceed to prepare our data for the analyses. We will start by aggregating the data by week. First, we need to create a new varaible identifying each week for the period we have.

my_integrated_data\$week <- lubridate::week(my_integrated_data\$date)

Then, we can aggregate the data by week as it follows:

weekly_spend_df <- aggregate(spend ~ week + source,
data = my_integrated_data,
sum) %>%
rename(weeklyspend = spend)

weekly_clicks_df <- aggregate(clicks ~ week,
data = my_integrated_data,
sum) %>%
rename(totalclicksweek = clicks)

weekly_df <- merge(weekly_spend_df,
weekly_clicks_df,
by="week")

Let’s have a look at the data before we further proceed.

#>   week   source weeklyspend totalclicksweek
#> 1    1 facebook       34.97           17345
#> 2    1   google    24877.29           17345
#> 3    2 facebook       34.88           14109
#> 4    2   google    28053.36           14109
#> 5    3 facebook       35.05           14653
#> 6    3   google    26890.79           14653

Let’s have a look at the structure of the data too:

glimpse(weekly_df)

#> Rows: 88
#> Columns: 4
#> \$ week            1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, …
#> \$ weeklyspend     34.97, 24877.29, 34.88, 28053.36, 35.05, 26890.79, 261…
#> \$ totalclicksweek 17345, 17345, 14109, 14109, 14653, 14653, 17673, 17673…

Finally, in order to apply marketing mix modelling, we will need to transform the data from narrow to wide format.

weekly_reshaped_clicks <- reshape(weekly_df,
idvar = c("totalclicksweek","week"),
timevar = "source",
direction = "wide")

weekly_reshaped_clicks[is.na(weekly_reshaped_clicks)] <- 0

This is how our wide data looks like:

#> 1     1           17345                34.97           24877.29
#> 3     2           14109                34.88           28053.36
#> 5     3           14653                35.05           26890.79
#> 7     4           17673                34.94           26123.59
#> 9     5           23039                34.91           22211.05
#> 11    6           26069                34.73           29579.07

## Marketing Mix Modelling

Now, we are ready to apply our Marketing Mix Model. We will use the lm function, which implements a linear model. We will set the total clicks per week (log-transformed to improve its normality) as the response variable related to reward and weekly spend in Facebook and weekly spend in Google as our predictors of interest. By calling the summary of the model we can further see and interpret our results.

data = weekly_reshaped_clicks)
summary(mmm_1)

#>
#> Call:
#>     data = weekly_reshaped_clicks)
#>
#> Residuals:
#>     Min      1Q  Median      3Q     Max
#> -0.4418 -0.1013  0.0423  0.1379  0.3963
#>
#> Coefficients:
#>                       Estimate Std. Error t value Pr(>|t|)
#> (Intercept)          8.891e+00  2.384e-01  37.288  < 2e-16 ***
#> weeklyspend.facebook 2.130e-03  3.122e-03   0.682    0.499
#> weeklyspend.google   3.490e-05  4.954e-06   7.045 1.11e-08 ***
#> —
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1
#>
#> Residual standard error: 0.2179 on 43 degrees of freedom
#> Multiple R-squared:  0.552,  Adjusted R-squared:  0.5312
#> F-statistic: 26.49 on 2 and 43 DF,  p-value: 3.176e-08

So, we can see how there is a significant positive effect of the spend in Google with the number of clicks, meaning that the number of spend in this platform Ads present a statistical relationship with a higher number of clicks. So we can answer our question based on our data, which points that investment in Google Ads is translated into a higher number of clicks. This model can be applied then to multiple platforms to compare them and see which ones are related to different response variables related to reward, providing extremely useful insights that will allow us to elucidate which are the platforms that are performing better.

### Model assumptions

Finally, we need to test if model assumptions are meet in order to know if we can trust our results. Specifically, we will test for clolinearity and homocedasticity. To do so we will use two premade functions. If the p-value of the tests are > 0.05, we can confidently say that our model meets the assumptions of linear models.

First, let’s check for colinearity:

imcdiag(mmm_1, method = "VIF")

#>
#> Call:
#> imcdiag(mod = mmm_1, method = “VIF”)
#>
#>
#>  VIF Multicollinearity Diagnostics
#>
#>                         VIF detection
#>
#> NOTE:  VIF Method Failed to detect multicollinearity
#>
#>
#> 0 –> COLLINEARITY is not detected by the test
#>
#> ===================================

No colinearity detected.

Now, let’s check for heterocedasticity:

lmtest::bptest(mmm_1)

#>
#>  studentized Breusch-Pagan test
#>
#> data:  mmm_1
#> BP = 1.3884, df = 2, p-value = 0.4995

No heterocedasticity detected, so linear models assumptions are meet and we can confidently trust our results.

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