Multichannel Retailers Differentiate Themselves with Same Day Delivery & Big Data
While the competitive potential of offering same-day delivery is not in dispute, attachment to outdated perceptions and flawed methods slow retailers down in this all-important race. By Supply Chain 247
The intersection of same day delivery and big data is closer than we think. It is happening whenever we order items online for certain. But that is a top-level perspective indeed.
Getting items ordered online to your home on a same-day basis is as important or relevant as it needs to be, and it depends on things like the type of products being ordered and its relative urgency as well.
This was put into better perspective for me during a recent conversation I had with Dr. Victor Allis, CEO of Quintiq, a supply chain vendor specializing in a single optimization and planning platform.
In a recent white paper from Quintiq, entitled “Same-Day Delivery-The Next Retail Game-Changer,” the initial thesis is this: while the competitive potential of offering same-day delivery is not in dispute, attachment to outdated perceptions and flawed methods slow retailers down in this all-important race.
Qunitiq explained that multichannel retailing in recent years has taken down the wall between online and brick and mortar stores, with the latter’s physical dimensions no longer limiting the amount or variety of products available to shoppers, as they can place an order online or in a store kiosk for home delivery or an in-store pick up.
What’s more, Quintiq explained that the Internet as of last year is now at the top of the list for favorite shopping venues. And behind a successful multichannel delivery is fast and reliable delivery, Quintiq notes.
“Looking at the movements in the marketplace, same-day delivery…is one of the bigger topics people are looking at,” Allis explained. “If you look at it from a macroeconomic perspective, say, as an example 30 years from now we are all locked into our houses and not allowed to use our cars for whatever reason and suppose that same-day delivery is working very well.
In that situation, my question is would all the miles driven in the U.S. would have gone up or down? My answer would be that miles have dramatically gone down as part of the total miles driven. If I drive to Wal-mart to pick something up and come back, it is a point to point in that it is a single delivery.
I go out to pick up and then come back home. It is the same thing if I were to go out for pizza, but in many cases people go out and back to get one thing. All of that can be replaced by sitting on the couch or my deck and using an app on my phone to get an item from Amazon or a chain store or a collection from local shops used to fend off the bigger ones.”
This could also lead to consumers buying things based on interests spurred on by online searches that result in ads popping up, as well as previous purchases, that can influence consumers, too, he noted.
This is where big data comes in, too, because items ordered online are not always going to just one person in your neighborhood; they are also going to other people that live near you, too. That, in turn, reduces miles traveled for delivery, while products are delivered in real-time, with the consumer not needing to get into a car and go to an actual store.
Influencing Customer Demand
Updating all routing intelligence in real time provides a powerful advantage; it allows for the dynamic quoting of delivery slots. Every time a customer places an order (and in many cases, even before), the system evaluates each possible time window for its efficiency.
For instance, in the chart below, there is almost no cost in delivering a new order between 9am and 10am because an actual delivery at a nearby address has already been scheduled at 9:38. Delivering at 11:20 would require a small detour and incur a small cost, while delivering just before 12pm would mean a significant detour and greatly add to cost.
Account information – including delivery address – becomes available the moment a customer logs in or puts something in his or her shopping cart. Within seconds, the system generates a number of choices for different time windows with related costs/discounts.
Access to information about the incremental cost for each time window enables retailers to offer customers different service levels. These can be based on any number of criteria, including the requested slot, a customer’s loyalty status, the length of their time window (from an 8-hour to a 30-minute slot), or the size of their purchase.
On the other side, though, Allis noted there are some industries than can be viewed as “immature,” when it comes to optimization, like stores that are open 24×7 that do not truly influence consumers in terms of when to go there to make a purchase, instead a consumer just goes to that store when he or she feels like it.
“You don’t get charged more for going to a store at 4 A.M. than 4 P.M.,” he said. “Instead, the store is observing demand and if things are slow only one register or kiosk might be open, whereas if it is 5 P.M., they will all be open. Organizations respond to whatever type of demand there is because they can. But in other sectors, like airlines, there is finite capacity available in terms of seats on planes. So if all tickets were prices the same, flights would be full at certain times like Friday nights and Monday morning, while others would be empty because all tickets are priced the same.”
So how do 24×7 retail stores and airplanes relate to same-day delivery then?
Allis laid out an example in which the solution to same-day delivery is a combination of a few different elements. He said if he had a 90-year old woman as a neighbor that does not have to leave her house and is home all day, anything she orders online can be delivered at most anytime during the day, because she is always home. But if he is ordering groceries online, he needs them at a certain time, in this case between 5:30-6 to start preparing for dinner.
These differences in delivery time specifications, he said, allow big data real time optimization to occur, because delivering his groceries and his neighbors items at the same time meets the needs of both him and his neighbor.
And here is where that all truly comes together: “If you take the combination of these concepts of what needs to be delivered based on a big data forecast, it allows you to forecast over what routes are going to be and allows you to see how efficient or inefficient it will be to offer certain time slots and therefore pricing that is similar to reality and offer a certain time slot that fits with the delivery route and offer a better deal for the consumer.”
Again, that is at a pretty high level, with the blocking and tackling required to meet customers needs getting more detailed and complicated in order to meet the same-day delivery needs of myriad customers.
But with online shopping only gaining traction, these are the types of things being worked on daily by express delivery carriers and retailers alike, and, um, Amazon, too.
Quintiq concludes its white paper by explaining that same-day delivery represents an opportunity for retailers to differentiate themselves, but cautions that using the wrong approach can translate into “exploding operational costs and too many factors that can cause delays and broken commitments to customers,” with failure to deliver on time potentially resulting on customers trying another retailer. And it added that consumers want deliveries on at a time that is convenient onto them and not be subjected to the dreaded “between 9 and 5” delivery window either.
There are many more innings to go before the same-day delivery game is over. And while there are challenges ahead that come with the proliferation of e-commerce, it stands to reason retailers and delivery providers will step up and do their best to deliver a same-day home run.