It’s no secret that 2023 will be a year of economic uncertainty with possible headwinds and dynamic market circumstances. Virtually every industry could be affected, but prominent B2C sectors, particularly the retail space, are likely to be hit hard if consumer sentiment wanes. This shift in consumer purchasing patterns will very likely have the largest impact on the fashion and home goods retail segments—which largely rely on discretionary spending. In fact, experts already note that 69 percent of consumers are cutting back on non-essential items in response to the global cost-of-living crisis.
Fashion and home goods leaders alike are preemptively hedging for an economic dip by zeroing in on cost-outs to cushion their bottom lines. But if history—and macroeconomics—has taught us anything, it’s the cyclical nature of the market. Every recession has been followed by a recovery period. And while it’s natural for companies to cut costs before or during a downturn, if they don’t simultaneously promote growth or cut too deeply—they’ll be playing catch-up during the ensuing market cycle and return to growth.
So how can the fashion and home goods segments implement cost-out methods without growth-out repercussions? Let’s look at four tactics that not only cut spending, but also promote business growth and cycle-proof dynamism:
The bedrock of almost every cost-out strategy relies on an organization’s demand forecasting capabilities. Unfortunately, many apparel, footwear, and home goods companies struggle to predict consumer demand and consumption—even in normal market conditions.
Common forecast error rates at large brands may represent no better than a “50/50” gamble. However, when done properly, reducing just 1 percent of forecast error can equate to millions in captured value.
Reducing demand forecast by 1 percent may not sound challenging, but as it stands—most companies across the broader retail and fashion sectors don’t have accurate, predictive solutions in place to minimize forecasting errors. Companies lack varied data sources, proper data management strategies, advanced modeling techniques or suitable data platforms. Without these ingredients for interpreting consumption signals, forecasting solutions may lack sufficient accuracy. And without an accurate prediction of consumer demand, enterprises won’t know what their customers want, or when, or where—let alone, understand the underlying drivers that unlock accurate predictions. In the longer term, the ripple effect caused by inaccurate predictions will also reverberate throughout an organization—from purchasing and production to distribution and marketing—leaving profitable growth and savings opportunities on the table for analytics-savvy competitors to potentially cultivate and capture.
Going back to Macroeconomics 101, we know the flip side of demand is supply—or in this specific case, inventory optimization. Once an enterprise can forecast demand with high accuracy, they’ll know how to properly supply their customers and consumers. It may seem simple, but without a robust data strategy to enable demand forecasting and validate inventory optimization, it’s actually quite complex. Over-supply the customer base, and you’ll increase unnecessary costs. Items will sit on shelves. Products will be pushed through the supply chain when they aren’t even wanted, incurring carrying and logistics costs, margin (or brand reputation) dilution from clearance promo costs, or even indirect costs reverberating potentially all the way to manufacturing and sourcing.
Under-supply consumers, and you may cut costs, but you’ll also miss out on opportunities to increase revenue and monetize demand. Plus, you may suffer indirect consequences, such as penalties from franchise partners, wasted trade spend (such as those arising from out-of-stocks), or consumers switching to another brand.
Inventory optimization can be a cost-out and growth engine: Get it right, and you can lower holding costs in your supply chain AND improve line availability. The art of fulfilling just what consumers want relies on understanding consumption signals—and never goes out of style (be it economic crest or trough).
Marketing mix models
Marketing is often one of the first business functions to receive significant cuts during economic downturns. Many fashion and home goods leaders may think if the market isn’t looking to spend, then why spend (as much) on marketing? This approach leads companies to cut their marketing campaigns to the bone and without understanding the relative impact on sales, leaving them poorly positioned to make a splash in the market when the economy inevitably swings back.
What’s more, organizations have struggled to understand the ROI behind their marketing campaigns. Social media influence has compounded attribution, requiring advanced, statistical techniques. And many can’t optimally allocate their budgets and align media spend to ROI because they do not have digital solutions in place, such as predictive analytics simulations, to provide a forward-looking view of the market. Leaders can appropriately distribute marketing funds and tailor their marketing mix models during recessionary periods by identifying the areas with the lowest ROI and reducing those budgets. And when the market improves, share gain marketing tactics are just a simulation away. MMM or MMO works wonders with the appropriate data strategy—rain or shine.
Digital and physical shelf optimization
Some may argue this isn’t quite a cost-out tactic, but improving “shelf health” using data-powered sensing and prescriptive analytics solutions in stores and online is most definitely a growth tactic and a way to mitigate potential revenue losses from inefficient market execution—like stock-outs that hurt a big promotional push—during economic downturns. Taking opportunity cost out of retail execution is good business. Digital and physical shelf optimization allows apparel, footwear, and home goods enterprises to improve the consumer experience, product promotion and store management by fundamentally aligning supply to demand. These seemingly subtle changes ultimately get the right products in front of the right consumers at the right time in the right location—and that maximizes sales.
Cutting costs will always be the driving force when leaders face economic headwinds—and it should be. But to stay competitive in today’s consumer goods and retail market, it’s not enough to react in the short term. Companies must prepare for the long-term and position themselves to succeed in both recessionary and recovery periods. It’s no easy feat cutting costs and promoting growth. In fact, it almost sounds like an oxymoron. But it is possible if leaders can predict consumer demand and optimize inventory, marketing budgets, and digital and in-store operations. The right mix of data-driven, dynamic solutions never go out of style. So start investing in long-term equity and transformation, today, that’ll endure, tomorrow, too.
Dinand Tinholt is a vice president with Capgemini’s Insights & Data Global Business Line where he is responsible for the North American Consumer Products, Retail and Services Market and globally responsible for Capgemini’s Data & AI Strategy offer. He focuses on all aspects of engagements—from go-to-market strategy and sales to client engagement and delivery—covering a broad spectrum of data & analytics work, including data strategy and governance, data-driven transformation, data estate modernization, data science, data engineering, data management, data ecosystems, data privacy, data ethics, data monetization, data literacy, and data-driven decision-making.
Jason Fisher serves as a sector leader on the consumer products, retail and services team in Capgemini’s Insights & Data global practice. He helps leading brands enable valuable, analytics use cases by connecting business (cases, users, and context) to technological capabilities for profitable growth. He’s an expert in supply chain, sales, and marketing domains with a proven track record in commercial transformation and innovation.