A new product decision platform that is tightly aligned to the retail calendar is helping brands including Levi’s, Madewell and Teva to reduce the gap between products made and what consumers buy.
The new iteration of the MakerSights cloud software solution features an artificial intelligence (AI) based decision engine that combines human intuition and machine learning to help product teams accurately determine which styles will be winners and losers.
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Reducing risk at all product development stages helps make it possible to narrow the multi-trillion dollar annual gap between what brands think consumers want and what consumers actually value.
Companies such as Allbirds and Lucky Brand are also using the platform to “de-risk investments in time, capital and creativity throughout their product development and go-to-market cycles by making better decisions from a greater set of data points about how their products are made, merchandised and brought to consumers,” MakerSights says.
“To most effectively support product-related decision-making, we designed the new iteration of our platform to be tied directly to the retail calendar as opposed to sitting outside of it,” explains Matt Field, co-founder and president of MakerSights. “This helps brands seamlessly aggregate consumer feedback, historical sales data and internal hypotheses when assessing the viability of new products. These inputs can then be translated into actionable, easy to understand recommendations.
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“Anyone within a product organisation can access these data-driven suggestions at any point across the product lifecycle to develop better products that will sell to their full potential.”
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By GlobalDataThe new release extends capabilities to provide relevant insights to a wider array of retail teams – from designers and product developers to merchandisers, planners and salespeople, as well as executives looking to build the skill set of data-driven decision-making into their organisations.
Expanding beyond collecting and analysing just consumer data, the underlying AI engine is also said to incorporate a brand team’s internal perspective, as well as historic selling performance to provide more robust and relevant recommendations.
The settings and controls are either pre-configured automatically (for example, how to ask questions to consumers in the right way, how to target appropriate audiences, etc) or can be edited centrally by a MakerSights support team.
Products are then tested with either a brand’s CRM, an externally recruited audience or an internal employee network from the brand. Once feedback has been collected, stage-specific results are generated in real-time to help brands make the best possible product decision at each major product creation or go-to-market phase.
“I believe that big data and AI will play an increasingly important role in how successful brands connect with consumers and formulate their assortments,” says Levi Strauss CEO Chip Bergh. He adds: “MakerSights is helping us evolve into an organisation that embraces data and technology as opportunities to become more responsive to our consumers, and not as threats to the old way of doing things.”
For Wendy Yang, president of Deckers Brands’ performance lifestyle group (Hoka One One, Sanuk and Teva): “In this new era of retail, building and executing a profitable product strategy has grown more and more complex. With MakerSights, our Teva and Hoka teams have an actionable decision framework to leverage at each stage in the product creation process. We’re able to remain bullish on strategic opportunities and identify risks before resources are committed.”
Understanding customer preferences
Separately, a new survey by MakerSights shows that that even though consumers have an increased appetite for providing feedback and being engaged in the product creation process, a top challenge for product teams remains understanding end-consumer preferences.
The inaugural ‘State of Technology in Retail Report’ found that 43% of retail professionals surveyed indicated the toughest challenge they face when bringing new products to market is “understanding customer preferences,” followed by inventory management (41%) and pricing strategy (32%).
In addition, most product professionals believe that integrating technology into their processes positively impacts their business outcomes – yet 58% still rely on legacy tools such as Excel and other manual data entry tools in their go-to-market process. Just 20% report utilising PDM/PLM software and 43% report using demand/inventory forecasting tools.
