Tech Viewpoint: Three omnichannel insights from Momentum 2019
Earlier this week, I traveled to Phoenix to attend the annual Manhattan Associates Momentum conference. As a provider of enterprise technology solutions, Manhattan Associates has great insight into how workflows continue to seamlessly blend across channels.
Following are three specific omnichannel learnings I gleaned from the conference.
An order is an order is an order
This insightful bit of wisdom from Clark Linstone, CFO of Lamps Plus, came up during a session led by executives from the specialty lighting retailer. At surface level, “an order is an order is an order” means an order placed from a PC is no different than an order placed from a mobile phone, or from within a brick-and-mortar store. The retailer should not view them, or fulfill them, with any type of channel-related siloing. A customer should be able to pick up an online order at a store, or have an in-store purchase delivered to their home at a later date.
Beneath the surface, “an order is an order is an order” means retailers need a single, real-time view of their entire enterprise. One cloud-based system needs to handle payment, customer experience, and fulfillment across all channels. Channel alignment is no longer enough – truly seamless order management requires channel elimination.
Small stores equal big complexities
In a one-on-one discussion Chris Shaw, director of product marketing for Manhattan Associates, explained just how complex the store environment really is. A single store can offer more variables and complications than a distribution center fulfilling dozens of stores. These range from unpredictable store events that interrupt scheduled associate activities, to SKU-level differences in product movement among individual stores.
Omnichannel retailers also face complexities in profitably turning stores into order fulfillment hubs. To maximize the ROI of ship-from-store and click-and-collect activities, retailers need to utilize machine learning and artificial intelligence (AI) tools. This enables them to analyze factors beyond geographic distance when selecting a store to fulfill an online order. For example, shipping a product from a more distant store where it is on markdown may prove less costly than shipping it from a local store where it is selling at full price.
Anticipate, don’t react
George Lawrie, VP and principal analyst, Forrester Research, discussed the importance of being ahead of demand. Instead of reacting to trends in consumer product preference once they occur, Lawrie urged attendees to leverage machine learning technology to predict trends before they occur and act accordingly.
This includes setting product assortments, price points, and allocations across channels, before demand is actually set. Retailers need to know whether their best option is to fulfill a product from a distribution center or a store, well ahead of any distribution taking place.
In addition to collecting and analyzing internally produced data, accurate predictive demand planning also requires retailers to study externally created data. Social media chatter, weather and climate patterns, and broader consumer demographic shifts are just a few examples of the types of heterogeneous information that are crucial for retailers staying ahead of the demand curve.
Following are three specific omnichannel learnings I gleaned from the conference.
An order is an order is an order
This insightful bit of wisdom from Clark Linstone, CFO of Lamps Plus, came up during a session led by executives from the specialty lighting retailer. At surface level, “an order is an order is an order” means an order placed from a PC is no different than an order placed from a mobile phone, or from within a brick-and-mortar store. The retailer should not view them, or fulfill them, with any type of channel-related siloing. A customer should be able to pick up an online order at a store, or have an in-store purchase delivered to their home at a later date.
Beneath the surface, “an order is an order is an order” means retailers need a single, real-time view of their entire enterprise. One cloud-based system needs to handle payment, customer experience, and fulfillment across all channels. Channel alignment is no longer enough – truly seamless order management requires channel elimination.
Small stores equal big complexities
In a one-on-one discussion Chris Shaw, director of product marketing for Manhattan Associates, explained just how complex the store environment really is. A single store can offer more variables and complications than a distribution center fulfilling dozens of stores. These range from unpredictable store events that interrupt scheduled associate activities, to SKU-level differences in product movement among individual stores.
Omnichannel retailers also face complexities in profitably turning stores into order fulfillment hubs. To maximize the ROI of ship-from-store and click-and-collect activities, retailers need to utilize machine learning and artificial intelligence (AI) tools. This enables them to analyze factors beyond geographic distance when selecting a store to fulfill an online order. For example, shipping a product from a more distant store where it is on markdown may prove less costly than shipping it from a local store where it is selling at full price.
Anticipate, don’t react
George Lawrie, VP and principal analyst, Forrester Research, discussed the importance of being ahead of demand. Instead of reacting to trends in consumer product preference once they occur, Lawrie urged attendees to leverage machine learning technology to predict trends before they occur and act accordingly.
This includes setting product assortments, price points, and allocations across channels, before demand is actually set. Retailers need to know whether their best option is to fulfill a product from a distribution center or a store, well ahead of any distribution taking place.
In addition to collecting and analyzing internally produced data, accurate predictive demand planning also requires retailers to study externally created data. Social media chatter, weather and climate patterns, and broader consumer demographic shifts are just a few examples of the types of heterogeneous information that are crucial for retailers staying ahead of the demand curve.