New Balance manages pandemic with Tibco Spotfire



Thanks to Tibco Spotfire, New Balance was able to respond quickly when the COVID-19 pandemic hit and revamp its sales and marketing strategy.

New Balance, founded in 1906 and based in Boston, is a manufacturer of athletic footwear and apparel that primarily wholesalers at retail outlets and also operates its own retail stores.

The company started using data to drive its decision-making process in the 1990s, according to Steven Lubitz, applications manager at New Balance, and in 2015 began using Tibco Spotfire as its primary business intelligence platform. .

Spotfire is the primary business intelligence platform for analytics provider Tibco, founded in 1997 and based in Palo Alto, California.

Tibco unveiled Spotfire 11 in September 2020. In September 2021, the vendor introduced new features for Spotfire, including additions to its Mods framework of pre-built apps and visualizations, and enhanced augmented intelligence and human science capabilities. data.

New Balance and Tibco

While offering sports clothing and accessories, New Balance is most closely associated with sneakers and running shoes in particular.

The company is affiliated with the New York City Marathon, offering a collection of TCS NYC Marathon footwear and apparel (Tata Consultancy Services), and it partners with the New York Road Runners to sponsor races.

As a shoe maker, most of New Balance’s sales resulted from in-store purchases. However, when the pandemic hit in March 2020, outlets were closed and that sales pipeline dried up.

Steven Lubitz, Head of Applications at New Balance, speaks at Tibco Now 2021, a virtual conference hosted by analytics provider Tibco.

New Balance needed to find a way to make up for lost store sales revenue and do it quickly.

Tibco Spotfire provided the tools.

“With all of the supply and logistics issues that have arisen as a result of COVID, we’ve made heavy use of Spotfire,” Lubitz said at Tibco Now 2021, the analytics provider’s virtual conference. “We had to make emergency reports to obtain [analysts] the data they need to react quickly to the changing reality of the market. “

Even before the pandemic, New Balance relied heavily on Spotfire.

According to Lubitz, the company had 140 analysts using around 450 reports and analyzes built with Tibco’s platform. These analysts, in turn, used the reports to gain insight into retail and e-commerce operations, consumer behavior, wholesale operations, product development, merchandising, business planning. demand and the company’s value chain.

With all of the supply and logistics issues that have arisen as a result of COVID, we’ve made heavy use of Spotfire.

Steven lubitzApplications Manager, New Balance

Fortunately for New Balance, when in-store sales evaporated, online sales increased.

With gyms being closed due to the pandemic, as sales of home exercise equipment such as dumbbells and on-demand exercise platforms like Peloton have skyrocketed, many have turned. to running for exercise.

“We have primarily been a wholesale business with a retail arm,” Lubitz said. “When COVID shut everything down, not only did it shut down our retail stores, it also shut down the stores of our wholesale customers. The wholesale business shut down. Which kept us afloat for the first few months of the pandemic was online commerce. “

A new strategy

But New Balance couldn’t just rely on an increase in online sales to completely make up for the total loss of its main sales pipeline.

He had to take advantage of the increase in his online sales, take information about these online purchases and use it to understand as much as possible those who buy shoes and clothing online and develop strategies to market to these consumers.

Essentially, even though many stores reopened and some consumers started shopping in person again, New Balance made the switch to an e-commerce retailer and used Tibco Spotfire to do so.

By collecting transactional data and sending it to a third party to obtain demographic information such as age, gender, and income, New Balance was able to better understand its customers. He is now able to compare purchasing patterns and histories with demographics.

It can compare new customers to loyal customers, those who buy running shoes to those who buy baseball cleats, and examine how demographics affect the buying habits of those new customers versus repeat customers, or those who buy running shoes versus those who buy baseball cleats.

New Balance also collects brand affinity data to see whether certain customers primarily buy New Balance products or if they might also purchase Nike, Reebok, Adidas, or other brand shoes and clothing. And again, New Balance adds demographics to this analysis.

It even examines how the color of an item affects shopping habits and whether demographics play a role in buying red or green shoes versus blue shoes.

Increase in use

Just as New Balance was starting to focus more on e-commerce, Tibco introduced Spotfire Mods in Spotfire 11.

New Balance then used the mods to create a new main dashboard to drive the analysis during the pandemic.

“We want to develop the products that are most attractive to our consumer base,” said Lubitz. “When we [altered strategy], we decided to refresh the main dashboard from which we run all of our analytics, and we decided to use the mods to make it a demo dashboard of what Spotfire can do for the organization. “

The result is that the dashboard is more user-friendly, Lubitz continued.

Business analysts using the dashboard can easily dig into the data and see relevant information at a glance in one place rather than having to examine a series of pie charts. Driven by ease useful, the new dashboard developed with Spotfire drives increased user adoption.

And that, according to Lubitz, will lead to increased profitability.

“We anticipate that as we start to use it more and the company makes decisions based on it, there will be financial benefits,” he said. “There’s the promise of being able to do this kind of visualization work and we’re excited that users are getting everything they want from data. “


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