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Moving to a Demand Chain Means Data and Decisions Matter More than Ever

It’s an interesting time to be living in when Twitter is more accurate in predicting the next hot trend than the planning software most apparel businesses currently rely upon.

At the recent NRF Big Show in New York, solutions providers promising to help these companies operate more accurately and efficiently took over the Javits Convention Center. And as the apparel industry continues its evolution from a supply chain to a demand chain, retailers still relying on legacy processes and systems are sure to be outgunned by newer, more agile sellers.

For many of today’s apparel retailers, it’s hampering their ability to maximize revenue opportunities, according to Marc Leveille, vice president of services and business development at merchandise planning provider Foresight Retail.

“When we look at retailers, they’re still planning the old-fashioned way [with] spreadsheets and tools that are not adequate for how fast retailers [should be]. So they lose a lot of visibility [into their supply chain] by not having the proper solutions in place,” said Leveille. “The technology is there. They just haven’t caught up to what the standards are today. We see it every day when we run our analysis. Just on our store forecasts, as an example, they lose millions in lost sales, just because they’re not looking at the details to get it.”

While the majority of retailers may acknowledge the importance of collecting consumer data, many aren’t efficient at harnessing it in a way that makes it actionable. What’s more, consumer demands have ratcheted up so quickly that relying upon historical data is already old news, with signals like social media conversations, weather patterns and location-based buying patterns now more accurate at predicting consumer behavior.

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This type of “unstructured data” can take different forms and be derived from various sources. One example includes using data from Nielsen, which culls Google search results to provide location-based information about keyword search results, said NGC Software president Marc Burstein. “Google can provide you with the keywords that are getting the most hits within 6 miles of each store, so that you can become more granular.”

The ability to access clean data is among the most important aspects, said Ashwin Ramasamy, co-founder and chief marketing officer of Pipecandy, a provider of predictive analytics for e-commerce. Most retailers face a cycle of about a month and a half when it comes to deriving insights from the data they’ve aggregated, he said, which is too long to effectively react to trends. Retailers would be well served to invest in better harmonization of data in order to accelerate this time frame, he said.

“You’re getting data from the stores. You’re getting data from social. You’re getting data from third-party sources, and so on and so forth,” Ramasamy said. “So how fast can your data science team react?”

After all, having mountains of data is pretty useless for those unable to quickly execute on the insight, and those with sluggish reaction times are often stuck marking down piles of unsold stock.

Ramasamy cited the experience of one retailer he worked with during the fidget spinner craze. This seller was on the fence about whether it should stock the spinners, and by the time it decided to pull the trigger, the trend was over, leaving it with over $10 million worth of inventory languishing on store shelves, he said.

“It’s not rocket science,” Leveille said bluntly. “A lack of having good planning means that [retailers] don’t start with the right information, so the execution is poor because the numbers aren’t telling the right story.”

Trusting the data you’re receiving is also important, he noted. “Whether you’re looking at more technology or better insights, there’s a lot of questions around it because [retailers] still go too much with their gut. Buyers and merchandisers don’t think [the data is] what they should go with” because they’re used to operating on instinct, he said. “They have to trust the systems and rely on the information that they get.”

At the end of the day, successful demand planning is what drives long-term customer loyalty, said Burstein, which can be more valuable than simply decreasing markdowns.

“If you think about the brand aspect of it, it’s keeping your customers happy,” he said, “and customer satisfaction is more important to me. I want repeat customers. I want the right quality to go to them. I want them to be really happy with my product so they come back. Having the product when they want to buy it is really important for that.”