The pandemic complicated retail operations in unprecedented ways. Operators managing retail and manufacturing facilities must now anticipate the impact of supply chain delays, inflation, COVID restrictions, and worker shortages—to name a few. Peloton is a recent example of a company that failed to optimize their operations.
Peloton experienced supreme demand in 2020 during lockdown conditions, but the company struggled to fulfill orders. They expanded corporate and manufacturing operations as a result with the expectation of continued sales growth. Unfortunately, the sales didn't materialize, and Peloton was forced to lay off employees and shutter manufacturing plants.
This suggests a failure of Peloton's internal data systems. Did the company rely only on sales data to make operational decisions?
Take a look at what foot traffic indicated about Peloton's operations:
We made the assumption Peloton's Bristol, Pennsylvania warehouse served orders made at their Pennsylvania stores. Within the past two years, consumer interest at the brand's retail locations peaked in February 2020, with a secondary increase in June of the same year. Peloton's warehouse didn't respond to consumer demand until August. The warehouse maintained high traffic levels until January 2021. Traffic waned somewhat in the remainder of 2021, but interestingly, neighboring retail locations hit a stable low starting in June 2021. There was no comparable dip in warehouse traffic during this time.
There's a clear disparity between demand at Peloton retail sites and warehouse activity.
Traffic levels in the warehouse were at their lowest in June 2020, three months after peak visitation at Pennsylvania's retail locations. This suggests a huge lag in warehouse responsiveness to consumer activity. It's possible Peloton was impacted by worker shortages and supply issues, but they were playing against a stacked deck if they only relied on historical sales to make operational decisions.
We argue that in the current retail climate, businesses can't model current demand based on past behavior. They need exact and timely metrics to gauge fluctuations in consumer interest and adapt manufacturing operations accordingly.
Peloton could have benefited from understanding how retail-level activity would drive demand (and need for workers) at their warehouse. This speaks to warehouse efficiency, site selection for optimal warehouse placement, and overall operational planning for workers and supply. Location data is the accurate and timely data that can bridge the gap between retail performance and manufacturing operations.
What's the takeaway?
+Businesses like Peloton should use foot traffic at retail locations as a metric of downstream manufacturing needs
+Sales data can't tell the whole story in our volatile retail climate
+Facility operators need accessible data that speaks to cross-departmental performance in order to make optimal decisions
Here we've examined site to site visitation, but bear in mind location data can also be used for model predictions of consumer behavior with only a four-day lag. Location data overall can be essential to brand's looking to optimize their operations, and it's also beneficial for investors and competitors scoping brand performance and seeking insight into black-boxed data.
Interested in hearing more about site performance? Our recent Amazon piece discussed what foot traffic revealed about their closure of off-shoots Amazon 4-Star, Pop-Up, and Books. If you have questions or want a data sample: schedule a meeting, check out our blog, or simply talktous@unacast.com.
