DataSelf AI's rolling-period variance tool compares sales across adjustable time windows, defaulting to the last 120 days versus the prior 120 days. Results appear in under 10 seconds. In the demo, Agrolink Foods spots a recent sales drop driven by lower hot dog, bun, and chip sales.
Customer sales from the last 120 days to the 120 days before that.
Under 10 seconds.
30 days.
Agrolink Foods.
Rolling Period Variances is one of three variance analytics that come with dataself's reports library. The other two are year over year variances and same period variances, both of which enable you to compare sales from one period to another, such as June of this year to June of last year or this quarter versus last quarter. Although useful in their own right, some organizations can't afford to wait until the end of a quarter or or end of a month to get meaningful analytics. And many organizations shy away from running sophisticated reports like trend analysis more frequently because of the time, effort and overhead involved. Data Self's Rolling Period Variances changes how you perform trend analysis by default. This dashboard compares customer sales from the last 120 days to the 120 days before that. This type of analysis would, using standard reporting tools, typically be done at the end of a quarter. But what if looking at trends from eight months ago is too late? What if looking at trends from even three months ago is too late? This dashboard is the answer as it lets you perform trend analysis all the way down to day to day. And it does so by producing its results in under 10 seconds, with subsequent drill down and slice and dice happening instantaneously. So let's change our rolling days from 120 to 30. Now, Agrolink Foods has had a recent decrease in sales. Let's find out why. With Data Cell's infinite drill down, we can slice into the results by item. And let's change our measure from sales variance to quantity sold. The results A drop in the sales of hot dogs, buns and chips, most likely due to their seasonal nature, gives us the insight we need. So if you want to uncover trends while they're happening and not 30, 60 or 90 days later, rolling period variances is your way to that insight.
