The app also allows you to perform fast cohort, lost items, number of items, and like for like analysis.

Select a column for which you want to analyze cohorts (for instance to split customers according to when they were acquired), lost items (purchases by customers lost in each period), number of unique items, like for like analysis.

The app will generate a set of dimensions (such as "Customer Since", "Customer Lost") and of metrics (such as "Number of Customers", "Sales by Customer", "Margin by Customer", "Units by Customer") you can select for your plots.

To know how like for like customers (the customers that were active in all three years) did in terms of sales, under the Tag ­čĆŤ´ŞĆ Columns panel click on Compare Like to Like.

If you are analyzing two periods (say previous year vs actual) select "Two periods" in the "Calculate like for like across" widget. If you want to study like for like clients across all the periods present in the dataset (aka the clients that were active in all periods), click on "All periods".

If you set the dimension to plot to Client and the metric to Number of Client you get the number of clients that were active in all three years

How much did they buy on average after discounts? Set the metric to "Sales net discount by Client" and hit Submit.

What if we were only interested, to match our AC (actual) vs PY (previous year) analysis only in comparing the customers active this year and last year? Select "Two periods" in the Calculate like to like across widget and hit submit.

To compare this to the company average (that also includes the like for like customers), you might want to filter out the customers acquired in the current year (they probably have less than 12 months of sales).

In the Filter ­čÜČ Data panel, exclude Client Since of the current year.

Under Tag ­čĆŤ´ŞĆ Columns unselect "Compare like for like".

Go to Plot ­čôŐ Charts and hit Submit.

You can do similar analysis with other metrics (average price per customer, average discount per customer, average units per customer,...).

Please note that if the metric is not stackable, the "per dimension" plots will only be shown as small multiples.