July 18, 2022
In the first post of this series, MakerSights Co-founder and CEO Dan Leahy shared some truly staggering statistics about the retail space: Markdowns now represent 40% of total retail sales, while 60% of products wind up in the landfill within one year of being produced.
This amount of waste is having devastating effects on brands, consumers, and the environment, and much of the blame can be placed on traditional product creation and merchandising practices. These strategies were developed decades ago when brands generated consumer demand by largely establishing and controlling visibility into the latest trends and access to products in stores — long before the rise of eCommerce, fast fashion, and social networks.
Brands spend up to two years and thousands of dollars flying designers around the world for inspiration, creating countless samples, and hashing out subjective stakeholder arguments to develop a single assortment. Along the way, just 60% of product and merchant professionals consider past sales data, while 59% factor in market trend data.
However, the majority of brands still fail to gather information from the source best able to accurately predict preferences and demand in today’s digital, data-rich, and on-demand world: their consumers.
Stepping outside their echo chambers and tuning into the real-time voice of the consumer is the first step for brands to evolve their merchandising strategies to a more agile, iterative, and consumer-obsessed model — one that protects their brand integrity, their market share, and the environment. And that’s what the first stage of MakerSights Modern Merchandising Maturity Model is all about.
Read on to discover whether your brand fits into this stage, the top challenges and biggest opportunities you’ll likely face, and some of the specific actions you can take to reach maturity.
New brands usually invest a lot of time, money, and research into understanding and clearly defining their target consumers. However, it’s inevitable that these consumers and their purchase habits and preferences will evolve over time — especially given today’s rate of change and innovation. Brands may also want or need to expand this initial audience as they explore different styles or develop new product categories.
Knowing which products to create each season, how many of each SKU to order, and even how to market them requires brands to consistently update and refine their target consumer profiles. It’s helpful to segment consumers by demographic, psychographic, and behavioral data to gain a deeper, actionable understanding of both existing and potential customers.
Demographic data refers to population-based and socioeconomic information like gender, zip code, education, age, income, race, and marital status.
Psychographic data includes information about a person’s activities, interests, and preferences, such as their favorite brands, any sports they play, if they like to travel, whether they’re a cat lover or a dog lover, etc.
Behavioral data is information that’s associated with a consumer’s purchase habits, like whether they tend to shop online, via a social app, or in store, or how much they spend on average.
It’s often said that demographic data explains who is buying, psychographic data tells you why they buy, and behavioral data tells you how they buy. While every brand defines its target audience differently, here are a couple examples of what a robust consumer profile segment might look like:
No matter how many years of experience or creative ideas someone has, it’s simply unreasonable to think they can predict what products other people want — particularly with the degree of specificity, accuracy, and consistency required to maximize margins and minimize waste.
Stakeholders must look outside the four walls of their milestone meetings to help objectively guide assortment decisions, whether they’re launching a new category or testing a bold new colorway. Not only does this help decision makers reach consensus faster and with far less pressure and friction, but it also improves the performance of every assortment.
“Looking to the future, retailers should….seek a more diverse set of data sources instead of an overreliance on historical data. Examples include web scraping for social mentions and hashtags to inform geographic-specific assortment, as well as third-party solutions such as MakerSights, which allows retailers to test products and concepts with consumers early in the development process.” — McKinsey
Nearly three-quarters of product and merchant professionals believe their brand must become more data-informed to stay competitive. That’s why we recommend brands begin factoring in data from a number of different sources during the product creation process, including:
While each of these data sources can be helpful in the right context, it’s important to note that the more recent the data, the better. For example, 2019 sales data was not particularly relevant or indicative of future performance during the COVID-19 pandemic in 2020, 2021, and beyond. In contrast, eCommerce data is highly reliable since it can be collected and analyzed in near real-time.
Similarly, information that’s volunteered by your consumers (zero-party), captured directly by your brand (first-party data), or provided by a partner you know and trust (second-party data) is the most reliable kind of data. While information curated and published or sold by vendors (third-party data) can be insightful and useful for supporting or enriching existing data, it tends to be outdated and prone to inaccuracies.
While first-party data like eCommerce performance offers highly dependable insight into recent consumer trends and demand, the most illuminating data a brand can have comes “straight from the horse’s mouth,” as the saying goes. In fact, nearly 95% of CEOs recognize data on customer preferences and needs as critical or important, ranking it the number-one most valuable data type, according to a survey by PwC.
Soliciting product feedback directly from current customers and aspirational consumers empowers brands to get clear and direct answers to specific, highly detailed questions that shape high-performing assortments. Product managers and merchandisers need to collect feedback from their target audiences and test new and experimental styles before they invest in samples or place orders with factories, conserving valuable time, money, and resources. This minimizes risks and stakeholder doubts that get in the way of delivering newness by objectively measuring demand and shoring up the success of each release.
Traditionally, the most common way for brands to capture the voice of the consumer has been through focus groups. However, gathering multiple people in a room and passing around color swatches isn’t exactly fast or easy to scale. As retail veteran, Rothy’s SVP, and MakerSights advisor Chris Hull explained in a recent interview:
“In my days at Nike you’d organize a focus group of maybe 10 to 12 people. It’s time-consuming to do, expensive, and it’s usually dominated by one or two voices, the most opinionated [people in the room]. You come back from those focus groups thinking that you can extrapolate those two voices to be 2,000 voices and that’s going to help make your decision.”
Fortunately, the latest technology allows brands to digitally gather detailed feedback from thousands of consumers across highly specific segments in just a few hours. Assortment management solutions make it possible to evaluate and compare consumer preferences and demand for various products and particular features, including fabrics, colors, patterns, silhouettes, prices, and much more.
For example, pretend your brand sells mens shirts and must decide between two new high-end SKUs. You could run a test for each shirt to your brand’s segment of luxury buyers, asking them to rate their potential purchase intent for shirt A and shirt B on a scale of one to five.
Then, you could ask them to choose which specific attributes they like most about each shirt.
From there, you would be able to see which shirt these target consumers prefer, as well as which features they like best. Imagine you discover that while your audience prefers shirt B to shirt A overall, they rated the pockets as their least favorite feature of shirt B, and their most favorite feature of shirt A. Now your team can test a version of shirt B with pockets like those on shirt A to achieve even greater alignment with your target consumer’s preferences.
Yes, the voice of the consumer is invaluable. But it’s not enough to seek shoppers’ opinions right at the beginning or the end of the product creation and merchandising process. Trends and consumer preferences change rapidly in today’s always-on economy. Sometimes products look different in the sketch or 3D CAD drawing phase than when they’re actually made into samples. Different regions buy different quantities of different styles, colors, or materials.
That’s why the brands that gain the most from consumer feedback are those that seek it at every stage of product development. While exact naming conventions vary by company, the typical product creation process includes five key phases. Each phase has its own decision milestones that benefit greatly from varying consumer inputs.
See the chart below for a breakdown of these five phases, associated decision milestones, and voice of the consumer data brands should consider at each stage:
When you think about data, chances are you imagine numbers, neutrality, and cold hard facts. It’s true that incorporating consumer data throughout the product creation process is intended to help merchants step outside their own opinions and preconceived notions to make smarter, more objective decisions. But even data can fall prey to partiality.
Confirmation bias occurs when people approach or interpret data in a way that affirms their existing beliefs, experiences, and desires, rather than seeking the impartial truth. For example, if someone hates the color pink, they might take the fact that pink doesn’t rank number one among their customers’ top ten favorite colors as confirmation that they don’t need to create pink products — even if pink ranked number three. They may even choose to exclude pink from the color analysis to begin with!
Approaching analyses with confirmation bias defeats the purpose of seeking data in the first place, causing unwitting brands to generate massive waste or miss out on millions of dollars. It often occurs subconsciously, even to those with the best intentions, which means brands that genuinely want to be consumer-obsessed must be aware of and actively fight against confirmation bias.
Too many merchants think they should know what consumers want because they’ve been in the business for a long time. They’re afraid to be wrong because they think others will judge them — or they judge themselves. To successfully overcome confirmation bias, brands must create a culture where learning is more highly esteemed than getting things “right” every time.
One effective tactic is to approach every decision using the scientific method. Refer to personal thoughts and opinions as “hypotheses,” or educated guesses, and seek consumer feedback to “test them” as if you’re conducting a science experiment. Scientists don’t do experiments to be right — they do them because they’re curious and want to get closer to discovering the truth. Product and merchandising professionals should embrace using data to build assortments as opportunities to continuously learn how to better serve their customers, grow their brand, and minimize waste.
Modern consumers are satisfied with products for half as long as they were 15 years ago, and retail overproduction has reached an astonishing 40% per season. Minimizing economic and environmental waste starts with creating products that consumers love. But this requires brands to trade their traditional supply-led merchandising processes for a modern consumer-obsessed model.
Following the steps outlined in this post is the perfect starting point:
MakerSights’ Modern Merchandising Maturity Model distills decades of retail experience, expertise, and success into five distinct stages brands can follow to streamline and accelerate their journeys to consumer-obsessed merchandising. Over the next several weeks, we will release dedicated content across these maturity model stages, each of which presents its own challenges, opportunities, and benefits.
Once you’ve completed the five steps detailed in this post, it’s time to move on to Stage 2: Digitize Milestone Meetings. We will explore the tools and tactics brands can use to digitally transform milestone meetings in a way that streamlines line reviews, accelerates decision making, and helps stakeholders break free from their calendars.
If you're ready to begin your transformation, you can download our comprehensive Modern Merchandising Maturity Model eBook and get started today.
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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