Digital native mattress company Casper made headlines this summer by announcing plans to open 200 stores across the U.S. It was the latest in a line of new brands like Warby Parker, Harry’s and Everlane that began with online-only, direct-to-consumer models and then extended into traditional retail stores.
At the same time, traditional brands such as Nike are doubling down on investments in omnichannel. They know they need to effectively serve online buying preferences to stay relevant and competitive.
Alloy has observed companies at both ends of the spectrum trying to emulate aspects of each other. It can be challenging to shift established mindsets or approaches, but it’s worth the effort.
First, let’s look at what traditional brands can learn from their digital counterparts.
Lesson One: Let Data Drive Decisions
Digital natives were born into today’s technology environment, which treats data analytics as foundational to decision-making. Starting out direct-to-consumer, it’s easy for them to access granular sales data, use it to understand what consumers want, and take action to meet that demand.
On the other hand, making data-driven decisions requires more of a mindset shift for traditional brands. They grew up during a time when data was less accessible, and are now encumbered by legacy technologiesthat can’t just be ripped out — there are years of institutional knowledge built into them.
Traditional brands that recognize these challenges are investing in digital transformation. We’ve seen companies achieve the most success in this journey when they:
1. focus on easy-to-use tools that help employees make the transition;
2. adopt an agile framework and iterate quickly instead of trying to solve everything at once, while;
3. making sure to invest in a platform that can scale to today’s growing complexity.
Lesson Two: Focus on the End-Consumer
When you’re used to selling in bulk orders to retailers, it’s easy to become fixated on wholesale metrics, like sell-in and OTIF. However, digital-native brands are quick to remember that, ultimately, it’s the end-consumer who matters most. True demand should always be the core driver for wholesale decisions, and it’s not always what you would expect based on sell-in.
For example, one of our established electronics customers conducted a limited launch of a new product to better gauge consumer response before rollout. They analyzed which ZIP codes drew the strongest demand and were surprised by some of the high performers, including ones in a certain state whose demographic profile differed from their typical consumer. With better insight into the end user, they crafted a retail and marketing strategy around those markets, and had the data to get retail buyers on board.
Lesson Three: Align Individual Metrics and Business Goals
At startups with fewer resources, employees must think about how every action affects overall company health. By contrast, at large companies with specialized roles, individuals can become hyper-focused on delivering against specific metrics whose impact on the company is unclear.
As an example, we work with a large confectionary company whose demand planning and supply chain teams were focused purely on never missing a shipment. But when looking at the sell-out data, we realized that some customers were over-ordering relative to demand, and so missing a shipment wouldn’t hurt sales. It was more concerning that orders weren’t tuned to demand, resulting in excess product that would expire before it was sold.
Team-specific metrics can be helpful, but it’s important to ensure they're also tied back to company goals and will help break down functional silos instead of creating them.
The three examples above of innovative practices are important to stay relevant, but the deep retail experience that established brands have is also incredibly valuable. Companies looking to transition from online-only, direct-to-consumer models into omnichannel sales should look to their traditional peers for a few pointers:
Lesson One: Cross-Channel Data Management Gets Harder
When you’re only selling on your own website, it’s relatively easy to manage data collection and cleansing. But as you expand to sell through partners, complexity quickly escalates. Data comes from retailers in inconsistent formats, gets masked by distributors, and varies in timeliness and granularity. It’s much more challenging to get the data ready for analysis quickly enough to react while the opportunity still exists.
Digital native brands must be prepared for a more complex data pipeline when they begin to sell through retailers, and should invest accordingly in the necessary technology. They can’t expect data collection, cleansing and harmonization to be as seamless as it was when selling direct-to-consumer, and should be ready to adjust as needed.
Lesson Two: Serve the Consumer and the Customer
Traditional brands have invested a lot over time in their relationships with retailers — to the extent that some have employees physically located at retailer offices. They know that buyers deal with hundreds of brands, and that developing a collaborative relationship is key.
To help level the playing field, new entrants should make it as easy as possible to do business with them. When discussing orders and forecasts, they should present everything in the retailer’s “language” and show how their interests are aligned on metrics that matter to the retailer.
For instance, one of Alloy’s customers introduced a new product line and quickly identified that some SKUs didn’t perform as well as others. They recommended to a retail partner that it scale back the order on those products — even though that meant a short-term hit. The action built trust with the retailer and showed that the brand was invested in their mutual long-term success.
Lesson Three: Be Your Own Brand Advocate
Established retailers may seem like they have all the answers, but keep in mind that they actually lack the full product picture for your brand. Retailers don’t have the data that brands do on how products are performing across all channels, nor do they have the analyst resources to use data to optimize inventory for every brand at every store.
Brands should be prepared to make recommendations to buyers based on store-specific performance, as well as on overall product sell-through (without sharing confidential info, of course). Sometimes this may mean advocating for a different order than a buyer originally proposes, but that shouldn’t intimidate brands; after all, optimizing inventory will ultimately benefit both parties.
Today’s retail landscape is more challenging than ever, but the opportunities are also greater than they've ever been. Brands of all sizes can capitalize on trends like omnichannel selling, innovative store experiences and AI-based tools to grow in new ways.
Most importantly, we aren’t seeing a battle between digital natives and traditional brands that will require one side to be crowned the winner. All brands are moving toward an omnichannel, hybrid approach; the ones who combine the best of both worlds will emerge on top.
About the Author
Joel Beal is chief executive officer and co-founder of Alloy, an AI-powered analytics and forecasting platform that helps brands pinpoint demand and accelerate their response to it.