Retail used to be simple: retailers found a store format that worked and then simply opened more stores. It was operationally complex, but conceptually simple. For most retailers customers were anonymous, but same-store sales were a great proxy for customer relevance.
One retail CEO explained how powerful same store sales were for understanding his business saying, “If all my stores are up 5%, life is good. If a couple of stores are down versus last year, I can visit those stores and diagnose in a few minutes what’s going on.”
The ability to walk a store and observe customers made diagnosing performance more about experience than detailed analysis. Additionally, retailers also had a relatively small set of tactical levers to pull. Decisions about markdowns, promotions and inventory allocation were limited and made in tried and tested ways.
Today, retail is more complicated, with more variables than ever before. As such, retailers must remodel their strategies to withstand the fast-paced industry, much like Wall Street did when it went digital. As retail undergoes a similar transformation, retail executives can look to the trading floor as a blueprint.
Now Retail is Far More Complex
The shift from store retail to online and omnichannel has changed the equation. Growth is now driven by the more complex dynamics of customer acquisition and retention, and digital marketing has introduced a new set of variable costs. Ongoing global crises — from COVID-19 waves to the Russia-Ukraine conflict — combined with the economic complexities of inflation and supply chain volatility have massively amplified global risks and uncertainty.
In contrast to the limited data and levers available in physical retail, an omnichannel retail environment creates a firehose of data — the millions of data points generated by the browsing and buying of products by customer, marketing channel, device, price point, color, size, style, etc. Making sense of this data is critical to knowing where to focus attention and take quick action.
In one example, an apparel brand observed that its customer churn was increasing and attempted to slow the decline with aggressive win-back promotions. When the retailer drilled into the data, the analysis showed that most of the supposedly churned customers were still engaged and actively browsing the retailer’s website. The retailer concluded that the real problem was that it had poor availability of the customers’ sizes. The solution was to change the replenishment strategy, not ratchet up promotions.
Lessons from the Digitization of the Trading Floor
Thirty-five years ago Wall Street was human-powered and share trading was manual. The digitization of the trading floor created an enormous amount of data and trading complexity. The velocity and volume of trades that digitization catalyzed led to the automated algorithms that dominate the financial markets today. These algorithms codify the logic and rules that actually make the trading decisions. In extreme cases, high frequency trading funds are making trades every millisecond. The management of trading floors has evolved to cope with this complexity. A critical enabler is the ability for managers to review the profitability of trades in real time and intervene as the situation requires. Speed, transparency and a focus on profitability are all critical to make this work.
We are now at a similar inflection point for retailers. As an ever-greater proportion of any retailer’s business is conducted digitally, retailers need to recognize that success is increasingly driven by automated algorithms that govern critical decisions including CRM, paid marketing, personalization, allocation, replenishment, pricing and fraud.
Every Retailer Should be Inspired by the Trading Floor
The new challenge for leaders is learning how to manage an algorithm-powered retail business. Retailers need to envision a future where managers will operate more like stock traders on the trading floor, orchestrating marketing, merchandising and digital levers to manage category profitability.
For example, one homeware retailer was outsourcing paid marketing to an agency that received a commission on media spend, and consequently was always incentivized to spend more. Performance was reviewed monthly. The retailer brought its paid marketing in-house as a way to save costs, but that decision had some unintended beneficial consequences. Specifically, it highlighted the marketing-merchandising trade-off and started a new conversation around whether the best tactical action for a specific product was to reduce price versus increase exposure via paid marketing. This led to a rethinking of both the product feed and pricing algorithms.
Working in an algorithm-powered retail business requires developing a new operating model and mindset. Experience and intuition need to be complemented with detailed, data-based analysis to understand both how the algorithms have performed and also, critically, where they can be improved.
Finally, new “control towers” are needed to bring together the vast array of data that enables smart decision making. Too often retailers are drowning in reports but lack the simple insights required to make better decisions. Moving forward, retailers must consider how internal and external data can be used to make better, faster and more surgical business decisions.
The New Speed of Retail
Ever-shifting consumer tastes, the speed of omnichannel, environmental and social concerns and ongoing supply chain and other systemic shocks are spurring retail management to move faster than ever before. Automated algorithms, like those used on Wall Street, alongside an operating mindset characterized by speed, transparency and a focus on profitability are all critical to making informed data-driven decisions.
Since 2007, Michael Ross has been the Co-founder and Chief Scientist of DynamicAction (and former parent company eCommera), which delivers technology and decision analytics solutions for multichannel retailers including Neiman Marcus, ASDA, Brooks Brothers, Jaeger and many others. Ross has spent the last 20 years in the ecommerce world. He joined McKinsey in 1994 and spent five years consulting in the early days of the internet. From 1999 to 2006, he was Co-founder and CEO of figleaves.com, which was sold to N.Brown in 2010. He is also a non-executive director of Abcam plc and Wex photographic. He is currently the SVP of Retail Science at EDITED and is based in London.