October 17, 2022
You don’t have to work in the retail industry to know that it has been through a lot over the last two and a half years. Store closures, supply chain disruptions, stockouts, inventory glut, increasing shipping, import, and storage fees — it has been one issue after the next. And just when it appeared that retail was finally on a path toward something that resembled normalcy, the industry (and the rest of the world) was hit with ballooning inflation. Now we are all living with the threat of a recession hanging over our heads.
That said, the retail industry was in a bit of a precarious position even before the litany of challenges spurred by the COVID-19 pandemic. Business models, systems, and processes were – and still are – in practice that should likely be revisited, challenged, and evolved to align with the highly-digital world in which we now live. Nothing should be accepted simply because it has always been done that way.
Brands have a real opportunity to take advantage of emerging technologies to modernize operations, increase efficiency, and improve the bottom line. For example, garment production began to double year-over-year starting in the early 2000s, but brands have failed to see an equivalent return in revenue. As a result, profit margins have constricted (in general) while markdowns and overstock volumes increased. This supply-driven model is among the first that should be questioned.
Similarly, it would behoove brands to take this time of fluctuating costs and economic uncertainty to evaluate their pricing strategies. Not only has inventory glut compounded the risk of bringing new products to market, but it has also led to severe discounts that harm long-term brand integrity. Meanwhile, inflation and supply chain challenges tempt brands to pass as many rising costs as possible onto consumers, who are taking a hard look at what they can afford and are willing to pay.
Striking the right balance is delicate, to say the least — which is why traditional pricing strategies simply won’t cut it. First, let’s take a look at where most retail pricing tools and tactics fall short. Then, we’ll dig into the four key areas where the latest data science and technology empowers brands to focus and maximize profit margins regardless of market conditions.
Although pricing is an undeniably important component of merchandising, it hasn’t always been approached with the most comprehensive data sets or pricing methodologies:
A common tactic in pricing research is to ask consumers directly about their willingness to pay for a specific item. However, direct questions about prices often lead to responses that are, in fact, lower than what the consumer would pay in reality. Instead, modern voice of consumer platforms use conjoint analysis, which is widely considered to be the most accurate and sophisticated method for determining price sensitivity. Not only does conjoint analysis take a variety of market-, competitor-, and consumer-related factors into consideration when calculating optimal pricing, but it also mimics a genuine shopping experience to indirectly gauge price acceptance.
Many tools use the same, standardized template to analyze pricing across industries. They rely on category pricing bands and competitive benchmarking, and fail to take key nuances into consideration, such as brand equity or differences in price sensitivity across consumer segments. Accurate retail pricing requires analyses to be tailored specifically to apparel, footwear, or accessories brands and their current and aspirational audience targets. For example, MakerSights combines pricing optimization with its extensive retail data library and customers’ own consumer profiles and sentiment data to generate contextualized and holistic pricing recommendations.
Brands often tap well-known consulting or consumer research firms to host focus groups to determine the optimal price points. But these reports don’t come cheap, they can take months to develop, and the analyses aren’t always packaged in a way that is easy for merchants to interpret and put into action. Fortunately, purpose-built voice of consumer platforms are able to spin up price testing and deliver results in a matter of days. Not only are insights displayed via clear visualizations, but drilling into the underlying data to investigate further is also a breeze thanks to drag-and-drop functionality, robust filters, and more.
While past experience, competitive benchmarking, and past sales performance are all valuable price testing inputs, they lack the foresight and first-party nature of direct consumer validation. Fortunately, advancements in technology and data science are enabling smart-to-market brands to zero in on four key pricing pillars that can help navigate today’s realities:
Discounts within a pricing architecture are frequently deployed when a brand wants to move inventory quickly. But as seasoned retail investor Brian O’Malley cautions, “Brands need to be careful not to chase their plans with excessive promotions. First off, this has a negative brand impact. Second, it can train customers to buy on discount, which can permanently damage margins. And third, it can attract a clientele that won’t be as sticky as existing customers.”
When done correctly and with statistically significant datasets, brands can redesign their pricing strategies and architectures with smart Good, Better, Best tiering. Setting prices based on the unique consumers in each market and that shop specific channels also drives long-term customer value and allows for discounting that moves inventory quickly, but doesn’t leave money on the table.
It is no longer enough to create pricing bands by category and expect merchants to know where a given item fits along that scale. The difference of just a few dollars can cause demand to rise and fall significantly, particularly during an economic downturn. Merchants need data-informed pricing that maximizes margins and profits for each individual SKU. This requires insight into a consumer’s true willingness to pay and a clear understanding of pricing elasticity, which illustrates how demand changes when price changes.
MakerSights’ Price Optimization plots optimal prices per style on a demand curve (pictured right) that then allows brands to easily experiment and see how different price points impact demand, profitability, and other key factors.
Often referred to as value-based pricing, value-based attributes help product and merchant teams determine which attributes consumers value most, such as colorways, logo placements, fabrics, graphics, and other accents. These often have a direct and significant impact on the cost of an item, its appeal to consumers, and how much they’re willing to pay for it. This helps guide not only pricing but also product creation and assortment development.
Product creators don’t want to add cost to a particular item for attributes and details that their target consumers do not value and will not pay a premium for, which, in turn, negatively impacts sales. For instance, product creators at an accessory brand may think that consumers want real gold jewelry, which comes at a premium. But testing may show that target consumers would be just as happy with gold-plated pieces at a lower price point.
The need to understand consumers’ pricing awareness and purchasing considerations, especially compared to competitors, is highly relevant today as nearly all eyes around the world are fixated on increasing prices. While protecting margins is mission critical right now, brands also know that this is not the time to risk alienating their target consumers, especially with so many alternatives from around the globe readily available at consumers’ fingertips. Brands must develop a pricing blueprint that can insulate margins from macroeconomic forces, as well as identify pricing whitespace and market share growth opportunities.
Optimized pricing – that balances consumer demand and a brand’s need to protect margins – simply cannot be achieved without a more sophisticated understanding of consumer price sensitivity across products and time. It requires carefully crafted consumer surveys, a statistically significant volume of data, and advanced analytics to achieve.
MakerSights delivers contextualized, actionable pricing optimization tailored to apparel, footwear, and accessories brands. Want to learn more about how MakerSights can help your brand optimize pricing, improve margin, and create products that consumers love? Let’s talk!
We focus on surrounding AI design tools with consumer data to improve accuracy and prevent decision fatigue. Our intuitive product insight platform and team of retail-expert researchers work with brands to uncover their target consumers’ preferences on everything from color, silhouettes, patterns and feature attributes.
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