Maven Analytics published a marketing challenge https://www.mavenanalytics.io/blog/maven-marketing-challenge a campaign data with appr. 2200 records of marketing data.

The task is to pitch one recommendation to improve future marketing campaigns.

The data can be found here https://www.mavenanalytics.io/data-playground . Thanks for Maven Analytics for providing the data.

My view is more to find some conclusions rather than providing graphics.

The data looks like this:

Also the data dictionary was attached.

I downloaded the data to Access database. The reason for using Access is to have data in a database.

The data was loaded from Excel. Select external data – new data source – from file – Excel.

The data in Access.

The data is driven to data model. Some data types were changed from decimal to whole number.

Only create connection and add this data to the data model.

Business as usual.

Then we can start creating measures.

All the accepted campaigns were added to one number. We are not analysing single campaigns but all the campaigns are considered as one value.

Check the accepted campaigns per country.

Spain is the main country. Out of eight countries more than half of all accepted campaigns were accepted in Spain. That is the country we are concentrating on.

Create a measure to calculate all the accepted campaigns.

Count percentage.

53 % of all all the accepted campaigns took place in Spain. That is the country to concentrate on.

Birth years are scattered between 1893 and 1996. To analyze the data we need to group the data into buckets. If sentence below groups the birth year per decade.

= Table.AddColumn(#”Changed Type”, “Year_birth_cust”, each if [Year_Birth] < 1950 then 40

else if [Year_Birth] < 1960 then 50

else if [Year_Birth] < 1970 then 60

else if [Year_Birth] < 1980 then 70

else if [Year_Birth] < 1990 then 80

else 90)

A calculated column is added Power Query.

The customers born on seventies have accepted campaigns. This report is without filters.

In Spain those born in seventies have highest acceptance ratio.

Let’s check another parameter, amount spent on sweets.

On Access you can use SQL script eg. to see the lowest and highest value for amount spent for sweets.

Values are scattered between 0 and 263 some groups are needed to analyse the parameter.

if [MntSweetProducts] < 50 then 1

else if [MntSweetProducts] < 100 then 2

else if [MntSweetProducts] < 150 then 3

else if [MntSweetProducts] < 200 then 4

else 5

Again a calculated column is added to Power Query.

Persons not eating too much sweets have accepted a most of campaigns. Those who have spent more than 200 for the sweets have not accepted any single campaigns. This is very clear indicator that campaigns should be targeted for those who do not spent too much money on sweets.

The parameters could be reviewed in similar way further. I just added age and amount spent on sugar as examples.

To pitch one recommendation for future campaigns, that was the task given.

My pitch is concentrate on the persons who

• Those who live in Spain.
• Born in seventies.
• Not eating too much sweets.
• Education graduation, master or PhD.
• Marriage status: married, single, together.
• No kids at home.
• 0 or 1 teen at home.
• Not eating too much meat.
• Not eating too much fish.
• Not spending too much in gold.
• Not making too many deals purchases.
• Not making too many web purchases.

More dimensions can be found of course. Here is one list.