Retail’s challenge: Getting customers what they want, when they want it
December 17, 2014
Ritu Jain was shopping at a high-end retailer during last year’s holiday season when she found a top she liked, but not in her size. She had the salesperson order it for her – and was pleasantly surprised when it was delivered to her doorstep, free of charge, the very next day.
“It just makes for a fantastic customer experience,” Jain said.
It’s also a trick that Jain, the director of industry marketing for the data blending and advanced analytics software provider Alteryx, knows can be very complicated to pull off.
Anyone who works in retail will tell you how important it is to keep customers happy. Still, experts say many traditional retailers continue to lag in one key area: Getting customers exactly what they want, when they want it – without much hassle or added cost.
The growing availability of big data should be making this problem easier to solve. After all, companies have more information than ever before about their customers’ buying habits, along with detailed supply chain data about where their company’s products are being manufactured and stored.
But experts say the problem is that too often, the various technologies that house all that data don’t work well together.
Jain said it can be a huge challenge for companies to get one view of customer demand across both online and in-store channels, and to have a complete picture of the inventory across various different distribution centers. That makes it even more challenging to tie customer demand data to inventory data and ensure the right products – in the correct sizes, colors, and styles – are available at the right store.
In short, companies are still struggling to understand where people are more likely to buy size 2 sweats, where they’re more apt to want size 12 sweaters, and how to get that inventory to the right places.
“Retailers have done a good job of understanding who their customers are,” said Saken Kulkarni, a solution principal with Slalom Consulting’s New York office who specializes in big data analytics. “That’s all great, but what if the distribution channel that’s feeding all of your products to a (store) in Minneapolis is the same one feeding your customers in Atlanta? You’re not having that kind of (communication) between the distribution center and the customer input.”
A more complicated request, like getting Jain’s top shipped to her home overnight, can be even more cumbersome because it involves pulling in a nearby store to act as a distribution center. That’s a function that traditional supply chains were not designed for.
“The most difficult part is the fact that customer data and supply chain data often reside in different places,” Kulkarni said. “Within an organization, you can have a customer data warehouse (and a) transactional data system, and they don’t talk to each other.”
In addition, Kulkarni said, the mounds of data can often be of poor quality, because of older technology and issues as mundane as data entry errors.
Those challenges are multiplied for the nation’s largest retailers, who may have grown by acquiring other brands with their own unique technologies, and also are likely to have set up separate systems and warehouses for their online and brick-and-mortar stores.
Jain recalls one retailer that had 15 different databases for their customer data alone. In addition, she said, large retailers may have more than a dozen distribution centers, plus ones that are set up solely for online sales. The end result: A shortage of products in one city or channel, and too many in another.
“On one side you are marking down,” she said. “On the other side, you have no inventory.”
We’ve come a long way … sort of
When Michael Levy was getting his Ph.D. in marketing several decades ago, he said few people gave much thought to a supply chain that was efficient and responsive to customer needs. Instead, that side of the business was often run by people who came up the ranks through warehouse work, and had little to do with the buyers and other people who looked at what the customer wanted.
Many retailers have gotten better at making sure their supply chains are more aligned with their customers’ buying habits, said Levy, a professor of marketing and head of the Babson Retail Supply Chain Institute at Babson College, but the improvements have been uneven.
Some so-called “fast fashion” retailers, like Zara and H&M, have figured out how to get new products into stores just weeks after higher-end versions are spotted on the runways, while others are still struggling with the best way to return an item bought online in a physical store.
“A lot of companies have come a long way,” he said. “Some companies haven’t done much of anything, and it shows.”
‘What I want, when I want it’
Pallavi Tyagi, a principal consultant in Slalom’s analytics practice, said she thinks retailers are starting to prioritize making their supply chains more responsive to customers. That’s because they are moving away from predominantly worrying about cost-cutting and price wars, and thinking more about setting themselves apart from competitors.
Retailers are starting to see the competitive advantages to having things like sophisticated delivery methods that can quickly ship products from a nearby store instead of a faraway distribution center.
They also see the financial benefits of complex simulations that better predict things like which customers are more likely to shop online, and which are more likely to visit an actual store. That can help them be better prepared with the right inventory in the first place.
“They’re realizing the importance of looking at the supply chain to differentiate,” Tyagi said.
A host of new technologies is making that process cheaper and easier. Alteryx, the company Jain works for, provides people with technology to access and combine all sorts of data, even if it is in different formats or systems. So, if you have customer data that is stored in one place, and your supply chain data is stored in another, you can still combine that data and run an analysis to see where your company can be more efficient.
The results also can be exported into visualization tools like Tableau, so company executives can literally see what’s working, and what’s not.
Kulkarni said those tools can improve customer service, and also the bottom line. That’s because companies can do a better job forecasting what products will sell where, based on the buying habits of customers in those areas. They’re also able to more easily anticipate when they might need to move inventory around, reducing the risk of markdowns.
The technology is incredibly helpful, but experts say that alone won’t solve all the problems retailers are facing. Companies also need to change their structure and culture.
“Data is part of it. Analytics is part of it. What a lot of organizations fail to do is get aligned from an organizational perspective,” Tyagi said.
For many companies, the holy grail is something called “omnichannel retailing,” in which the customer experience is the same online, in store, and via other channels. That allows for things like shipping products from a store to a person’s home, and from a warehouse to a store for immediate in-store pickup.
Tyagi noted that most customers don’t care what the industry calls it – as long as it works for them.
“The customer is not going to say, ‘I want the omnichannel experience.’ They say, ‘I want what I want, when I want it,’” Tyagi said.