Panic buying was definitely something I did for the pandemic. I bought everything I thought my family and I would need in the coming weeks, maybe months. Soup, mac ‘n cheese, peanut butter, toilet paper – you name it, it was in my cart. The one thing I didn’t think to get was a bike. I didn’t even know I wanted/needed a bike until two months later when I tried to ride my old bike. It was too small and though it managed to uphold a 25 mile bike ride, it was high time for a new bike. No problem, I thought, I’ll just go to the retail store nearby and pick one up.
Well, apparently unbeknownst to me, not only would that store not have any bikes (well not any in my $200-$300 budget range anyway), but neither would the local bike shops or other retail stores across the country. I took for granted the days when bike racks were fully stocked, ready for perusal at any given moment. At the start of the pandemic, The NPD Group reported that “children’s/BMX bike sales were up 56% and adult leisure bikes were up 121%”. Thus ensued a nationwide bike shortage due to the pandemic and people being forced outdoors for entertainment.
The following weeks consisted of me scouring the internet for a bike within my budget only to be met with frustration. I finally decided on a bike I wanted to get and that I would wait for. I then used a retail store’s website and checked every single day, multiple times a day to see if the bike I wanted was in stock anywhere within driving distance. At one point, I was even willing to drive to Massachusetts for the one bike in stock that they had. The catch – there was no guarantee that it would still be there after the 4 hour drive. The way this particular store’s service worked, even if I ordered it online, I could be out bought by someone else who either bought it before me online, or walked into the store and bought it off the shelf. The store claimed that their site did not update fast enough and could sometimes allow purchase of products that were not in fact in the store anymore. Frustrating to say the least.
On top of that, whenever I called these stores, they could never tell me when they were getting another shipment of bikes or what the next shipment would even contain at all. I resigned myself to the fact that I may not get a bike anytime soon, but would keep trying because eventually I would get one, right? Well, one fine summer morning, I checked the store’s site and found my bike in stock at a store 40 miles away; a trip worth taking. I ran to pay for it as quickly as possible so I wouldn’t lose it, got the confirmation email and we were cruising. I was ecstatic!
I got my bike home, assembled it, and have been riding ever since!
I’m still very happy with my purchase and grateful that I was able to secure a bike for the whole summer and well before the original time of the fall, yet wish the process had been easier. The point of this anecdote is to portray the important role of big data. Big data could’ve been a huge help in this whole process. During one of the conversations I had with the store’s customer service, I asked if there was a database of inventory information for bikes across their whole fleet of stores; if there was, would it then be possible to order from another store that had this bike in stock and have it shipped. Neither were able to be done. My bike was classified as an “item not available for shipping”; which, is ironic because a few weeks later, my mom ordered the same bike and was able to get it shipped to her house and in a shorter amount of time than the estimated shipping time of a month. Maybe that customer service lady took my suggestion into account after all because my mom’s bike buying process was a lot more seamless than mine had been. We still had to check the site everyday for a bike, but the shipping feature made for a much more manageable experience.
Data is essential for everything. Being able to have had that inventory data would have helped the store distribute more bikes and allow customers to buy them faster. There were always clusters of bikes available in the midwest, and not having shipping as an option made for an excruciating wait. The store could have also used predictive analytics to predict customer demand by store location, and use business intelligence solutions to understand which stores were selling out faster than others so those ones could’ve been replenished faster. Sure, these were, as the phrase of the year states, unprecedented times and no one could have really predicted this bike shortage or done a lot to prevent it. However, using data to try to restore any sense of normalcy and get the bikes out there would have been/is very helpful. Having clear inventory data is crucial for businesses and consumers alike.
Big data is essential for improving business practices. The use of big data to gauge people’s interests, shifting activities, and purchases with the pandemic would have been able to reduce the shortage of many products. Businesses get exorbitant amounts of data daily. Being able to sift through that data to discover patterns and trends would allow businesses to make more informed decisions. For example, the age-old thought correlation between selling diapers and beer together had stores arranging their item displays to boost sales. The key is to look at consumer behavior and make decisions based on the data from such behavior. In this instance, lots of people were buying bikes. Using big data techniques such as predictive analytics, product tracking, real-time insights, and big data analytics for inventory and production in the supply chain would add great help for retailers.
Predictive analytics uses history of sales of products, taking into account seasons, levels of demand (Inside Big Data.com). With this information, retailers can gain insight into which products sell faster and when to better prepare stock products. As the name suggests, you use this to make predictions and can adjust manufacturing accordingly. Product tracking is exactly what it sounds like – tracking a product’s rate of return, demand, etc. This would also aid in predicting sales and demands (Inside Big Data.com).
Real-time insights can mean analyzing the data as soon as it is received so as to get the most out of the data as quickly as possible (Inside Big Data.com). This will also help to give you a leg up on competition. By having your data analyzed right away, allows you to start making more informed decisions immediately. Bike retailers were already doing well due to the boom of sales from COVID, yet if this had been used, their customers may have been able to buy bikes faster due to the insights showing larger than normal sales for bikes , generating more income.
Finally, analytics for inventory and production in the supply chain would give stores, manufacturers, and other parties involved in the process of selling a product, clear insight into what products were produced and how many more were coming so as to better prepare the stores and provide them with shipment information. Dashboards of such information would be vital – clearing displaying this information. Knowing what the popular bikes are, would allow these companies to stay on top of when they were coming in, be able to tell customers, and potentially produce more to get back on track.
As a customer, these tools would most likely have allowed me to purchase a bike much sooner. Using previous years’ data about popular bike purchasing seasons – as we were heading into said season – and having dashboards of clear data about which bikes were going the fastest and sharing that information with the supply chain would enable consumers to not only potentially have their desired products faster, but know when they would be in stock next. Consumers would not have the frustration of never knowing when there will be a bike or checking websites constantly to see if by chance there would be a bike. This also helps the stores. In deploying these analytics tools, stores will build rapport with customers and create higher satisfaction rates. It shows the customers that the stores know there is a problem and are working to fix it quickly for the customer and that the store cares about getting inventory restocked as fast as possible for their customer; they care for their customer. And, not to mention, the fact that the stores would probably see higher revenue for being able to stock their shelves faster, due to predictions made based on previous years. It really would be a win-win situation for everyone involved.
Analytics can really change the way businesses make decisions. My frustration in trying to find a bike does not have to be everyone’s experience, as I suggest using such tools to help people find bikes. The answers are always in the data and can really turn around how companies do things, as well as, increase ROI – which is always an end goal, right? To all those still in search of a bike, I hope you get one soon and that big data can help!
