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Predictive Data-Driven Models Drive Ecommerce Growth for Retailers

John Reagh

Drop-ship and marketplace models help retailers expand inventory and rapidly adapt to shifting demand patterns without incurring more cost.
The past few years have disrupted inventory paradigms throughout every sector of the retail industry. As retailers seek to streamline logistics while significantly expanding their product selection, many are scaling back the inventory they purchase and warehouse, and are leaning instead toward models like marketplace and drop ship, in which third-party brands take ownership of manufacturing, storing and shipping the majority of a retailer’s inventory.

Innovation and data-driven insights are making smart retailers more agile and flexible.
More excitingly, an extensive commerce network that powers drop-ship and marketplace models generates massive amounts of data that can serve as a powerful resource for intelligent, predictive consumer demand modeling, which enables retailers to more quickly adapt their offerings to changing patterns of demand — and even to forecast upcoming demand spikes based on data rather than intuition.

What’s more, since retailers don’t have to purchase or warehouse their inventory when adopting an unowned inventory approach, they’re able to expand their product assortments quickly in response to highly specific market signals — featuring products from manufacturers with suddenly trendy products, or those with low carbon footprints, for example, or from brands that publicly support social-justice causes, or from minority-owned business in their local neighborhood, and make these specific choices in an agile, flexible and more efficient way. This specificity enables retailers to sell more of the exact products each customer wants, without incurring the costs and headaches of managing unneeded inventory.

We’ve seen numerous retailers experience measurable growth by leveraging predictive demand modeling in hybrid retail models.
Hybrid retail models help retailers complement their owned inventory with unowned inventory on an agile, as-needed basis, while predictive demand modeling enables real-time decision making based on rapidly shifting market signals — empowering retailers to proactively solve supply chain problems in innovative ways. For example, when new customer demand appears, a retailer’s partner network can instantly start shipping just-in-time inventory while the retailer works on the wholesale buy.

Retailers who’ve adopted predictive data-driven hybrid retail models can realize significant growth as a result. Across CommerceHub’s network of brands and retailers, we’ve seen stock-keeping units (SKUs) increase by a full 70 percent year over year from retailers who’ve hybridized their retail models — for an impressive total of 126 million SKUs listed across 158 different marketplaces globally. And they are doing this in a way that can drive profitable growth.

We also recently saw one retailer on our network increase their run rate gross merchandise value (GMV) by more than 200 percent in just 90 days. Another grew their business 100 percent in 12 months — by finding ways to add new products and brand partnerships to their storefront with minimal friction.

By hybridizing their go-to-market models, un-siloing their data flows, and saying “yes” to more brand partnerships, retailers are tapping into the transformative power of an extensive commerce network, driving real-time data-driven decisions that fulfill customer demand through adaptive combinations of owned and unowned inventory. As a result, they’re able to grow their product assortment for happier customers, do it profitably, and continue to shine as tastemakers in the market.

John Reagh

John Reagh

John is SVP of Sales at CommerceHub. He has more than 20 years of success in strategic revenue leadership roles at early-stage tech companies and leading enterprise software companies, including Expertvoice, Infopia, Siebel (Oracle), and NetSuite (Oracle).