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Spike and Drop Story

Any metric can be tracked based on its increasing or decreasing movement. Thus, Spike and Drop story types can alert you of sudden changes quickly, helping you monitor changes that may become new trends over time. 

Below are some examples of how functional teams use Spike and Drop stories: 

  • Marketing teams track ad performance to see if CPM is increasing significantly.
  • Finance teams track sudden changes to cash flow. 
  • Sales teams use this story type to track performance by account executive and territory. 
  • Customer Success teams can be quickly alerted of a sudden rise of service tickets, following a new shipping offering.

An Operations team that is concerned about supply chain shortages, as shown in this story.

Read more to see how you can track any distribution metic.

A Global Pharmaceutical Company Identifies Inventory Shortage within their Supply Chain
A Global Pharmaceutical Company Identifies Inventory Shortage within their Supply Chain.
Outlier Tracks Retail Product Checkouts
Outlier Tracks Retail Product Checkouts

Why Spike and Drop Stories Matter?

An enterprise dashboard can show you increases and drops too, but it only shows you the metric or KPI that you know to look for and choose to track. Outlier automatically monitors for increases and decreases of a metric and all of its related segments over days, weeks, and months, comparing values to their expected range. Not only does Outlier show you the spikes and drops that you might not be expecting and aren’t tracking in your dashboard, but it also alerts those small changes that could strongly impact your business. 

An eCommerce team that is concerned about product checkouts, as shown in this story.

Read more to see how you can track any conversion metric.

Automated Business Analysis platforms help you find these small (or big) changes quickly.

Outlier delivers you a diversity of insights in a semantic-free approach. Instead of relying on the meaning behind your data, Outlier focuses on the numeric properties of your data to find interesting and important patterns.

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