Selling shirts and shoes like airlines sell seats?

    

Consider the number of flights you’ve taken this year. Whether booking a last-minute business trip, or planning a family holiday, we’ve all spent time searching for the best flight price in this highly competitive and dynamic marketplace.

It’s easy to see the price wars at play between carriers by hub and destination. But what’s less apparent is the everyday battles fought seat by seat. Carriers are focused on determining the right price to charge for a specific seat, on a specific flight to maximize profit, while striking the right balance between demand and constrained capacity. This dynamic pricing may look arbitrary, but it’s far from it. Airlines are analysing a broad range of data by applying deep data science to guide optimized, real-time, right time pricing decisions.    

For retailers, the business problems and applications are not all that different from airlines. Akin to seats on a plane, there’s nothing arbitrary about in store products – not the product, placement, price, nor promotion. At the core of this “precision play" is data and analytics, and the constant ratcheting of precision is driving extraordinary productivity for some of today’s more advanced retailers. The constant: a progressive mindset driven by purpose-built analytics that churn through massive amounts of data to ratchet precision at each step of their supply and demand chain. And at the heart of this movement is a salient understanding of the consumer – who they are, where/how they shop, what they buy, how they buy, how much they buy, and what motivates them to buy.

Let’s take, for instance, an end of season clearance for a softline retailer. The 30% off MSRP for bathing suits in September is not a coincidence, and behind the scenes, analytics analyses inventory levels and sell-through. When consumer demand is low, and there’s inventory on hand at the end of the season, price reductions (i.e., markdowns) are applied to entice the consumer to purchase. Anyone can give product away. The trick is finding the right depth and cadence of markdowns to preserve as much revenue and margin as possible. And while the business problem may be similar across retailers, their various approaches to the problem is all over the map. Getting this business problem right cannot be overstated, and for some retailers it literally means the difference between solvency and bankruptcy. 

Airlines have a different price for the same seat from one plane to the next, so why don’t retailers price the same products differently from one store to the next? Some retailers are embracing this tact, but others are still stuck in a linear pricing model, applying markdowns at an aggregate level (e.g. chain or market). This logic is flawed because it assumes all stores have similar levels of inventory at risk, requiring the same clearance incentives to sell through residual product. But what if only a select number of stores have issues? A one price fits all approach is easier to operationalize, but it leaves significant revenue and margin on the table. Why offer an incentive at a location where it’s not needed? If you’re one of those retailers who isn’t pricing at the individual store level, I urge you to revaluate your options.     

For retailers that are more progressive in their thinking, consider the next chapter in the airline playbook – dynamic lifecycle pricing. Store level pricing for markdowns is a basic form of dynamic pricing for end of season merchandise. Why not extend this strategy to a broader range of product across a wider span of the product lifecycle? The degree of “dynamic-ness” may need to be more subtle than how prices are managed for airline seats, but the application is entirely suitable. Imagine analytics behind the scenes, constantly evaluating the price point required to maintain equilibrium between sell rate and end of season target inventory. For longer life cycle products, think about constantly balancing revenue and margin across base price and promotion support, and how the ebb and flow of causal support across brands, flavors and sizes impacts the category and basket? For fast fashion, consider a dynamic pricing strategy for highly unique products where supply is constrained relative to its demand? This approach may be burdensome within the brick and mortar context, but why not think about it for e-commerce? For retailers embracing personalization, rather than pricing at an aggregate demand level that leverages a single elasticity coefficient, why not analyze demand and elasticity at a more granular level, connecting the dots between what is relevant and compelling on a one to one personalized level? 

The world is changing and just like what’s happened in the airline marketplace, the market for retail is increasingly competitive and complex. From product and sourcing differentiation, to customer experience and supply chain efficiency, there are more battles and they’re more complex. Undeniably, doing the same archaic things is no longer sufficient. Need proof? Just think about the household names of the past that no longer exist today.

Though success isn’t determined by a single dimension or factor, leveraging data as an asset, and applying analytics to ratchet organizational productivity, is surely a considerable part of the equation. Suffice it to say, an analytic driven culture is no longer a luxury, but rather a necessity. Analytics is a journey, and while the possibilities are vast, it does require vision and commitment. Begin where you can, but by all means get started.

 

Antuit enables you to predict and measure how pricing levers will impact your demand, revenue and margin. Learn more here.

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