Online sales are rising as brick-and-mortar stores decline, yet retailers and brands need both, and thus are looking for ways to integrate the customer experience across both physical and online channels. This has created new requirements for collecting and utilizing data in the retail space, and could result in a radical change in data models that benefits customers and retailers alike.
The COVID era has accelerated the decline of high-street shops as people turned to online shopping to buy all kinds of items, from clothing to food and other daily necessities. We’re already seeing a lot of empty space on many traditional shopping streets. Younger generations in particular prefer online shopping and they are becoming an important segment for many brands.
But this doesn’t mean physical stores are obsolete. Experts have predicted for years that e-commerce spelled the demise of brick-and-mortar stores, but even today, people still want to see the products for themselves and get new ideas when walking in malls or looking at shop windows. This is why it’s very difficult to create high-end brands only on the internet.
The dilemma is that many customers want physical stores to see items for sale, but many also prefer to buy them online – and these are not necessarily separate groups of people. This trend is especially remarkable in the case of more expensive items.
The changing role of stores
The problem is that stores can no longer justify their traditional role based on in-store sales volume. Sometimes, it doesn’t even make sense to maximize sales in certain stores.
Many brands are now thinking about the concept of showrooms – retail shops that are not focused on selling, but instead are marketing spaces for brands to develop customer relationships and engagement. One advantage of this model is that it can save shopping space in high street stores, malls, and airports, as you don’t need to keep lots of stock in the store – customers can come in, look around, and purchase items online for delivery.
The other advantage is that it preserves the physical space as a data collection point.
Obviously, e-commerce thrives on analytics, using all kinds of digital user data, including browsing history, purchase history, site clicks, demographics, and data acquired from third parties. But retail chains have also collected customer data for years, such as purchase history and demographics, which is usually applied to things like loyalty programs.
Retail stores also analyze data that isn’t directly linked to individual customers. For example, price sensitivity for different products in different areas (e.g. rich vs. poor areas, or areas with differing age demographics), product range optimization for each store, demand estimates, and how to get maximum value from their shelves spaces. Some retailers also collect customer behavior data from stores – e.g. which routes customers walk in the stores, where they stop, which products they look at and/or pick up, and which ones they end up buying.
Data from online and physical stores
Data from online and offline retail sites is valuable in its own right, but there’s even more value in combining the two data sets to see how they work together and serve each other. Many retail brands are already looking at online-to-offline (O2O) commerce models to combine physical and online data not only to increase sales volumes but to increase engagement and deliver a better customer experience that is consistent across both channels.
Online customer behavior data can help improve the physical design of retail stores. Furthermore, customer behavior in physical stores can inform online offerings, pricing, and targeting. There are plenty of other possibilities.
However, combining both data sets isn’t that straightforward. For a start, different brands also have different priorities. For example, luxury brands see customer relationships and emotional engagement as critical – this also requires a better customer understanding. For sellers of low-cost goods, their KYC requirements may not require as much depth.
Combining data from both channels requires an effective collection model, which will also require tearing down silos where a lot of that data has traditionally resided. Anonymous behavior and profiling data from both channels are easier to utilize. It gets harder when we try to know if an individual customer decided to buy something in a store and then went to buy it somewhere online.
However, oftentimes it’s not even necessary to analyze individual customers. For example, anonymous behavior data can be instrumental in helping to understand things like the preferences of different types of customers and which products get much attention. Some items can draw people into the stores or help them fall in love with a brand, but they may end up buying something else. A known fact is that many fashion brands make a significant part of their revenue from lower price items, e.g., perfumes or sunglasses, while the items they advertise and base their brand image on are too expensive for many people.
There are three interesting scenarios to consider when we look at how physical and online data analytics – and subsequent customer engagement – could evolve in the coming years:
- Some companies will begin to dominate both physical and online channels, whiuch is likely for the biggest online stores and some local leading retail chains.
- We will see new platforms that offer brands both physical and digital sales channels as well as data analytics and customer engagement tools. These ‘platforms’ mean not only something in the cloud, but also solutions to enable physical sales and demos in shop spaces, and to collect on-site data.
- As the user-held data model grows in popularity, consumers will be empowered to use their own data in physical and online stores to find the best deals based on their preferences.
In reality, we will probably see a combination of all three scenarios. Option #1 works for many everyday items and lower price products. In this model, the sales channel actually owns the customer. Option #2 is probably better for high-end brands: they want to better control their customer relationship and own their customers themselves.
At the same time, the user-held data model is likely to become important, because it gives customers more control over how and where their data is used, and provides them with tools to find the best deals and items they prefer.
More to the point, data regulations such as the GDPR or CCPA have imposed specific “data minimization” requirements to curtail the amount of data that brands collect about their consumers. Therefore, retail chains and brands also want to start working with user-held data models to motivate consumers to share some data by, say, offering some benefits and incentives in return.
The whole retail business is now undergoing remarkable disruption that will change the format of brick-and-mortar stores and how the shopping streets look, and lead retailers and brands to integrate their physical and online data models. At the same time, consumers are also being empowered with better tools to get value from their own data. All of this adds up to big changes in retail data collection, analytics and customer engagement tools.