Analytics 50: How big data innovators reap results

Five winners of the 2016 CIO.com and Drexel University Analytics 50 awards share details of their projects, lessons learned and advice.

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ian dewar online The North Face

Ian Dewar, senior manager of The North
Face’s Consumer Lifecycle unit.

The North Face: Customers for all seasons

California-based apparel company The North Face has built a highly recognizable global brand focused primarily on cold-weather gear — winter coats, ski jackets and warm fleeces. But that strong association has had a downside: Customers primarily purchase once a year and don’t buy much in spring or summer.

Moreover, though loyal, its customers don’t necessarily come back every year to buy new products.

“Customers were not returning; not due to dissatisfaction, but because the quality of the brand’s products was too high,” says Ian Dewar, senior manager of The North Face’s Consumer Lifecycle unit. “The level of ongoing engagement with customers was not strong beyond the first major purchase.”

Focus on activities

The company realized that to build repeat business, it needed to push beyond the winter jacket and fleece market. To do that, it had to identify other activities its customers enjoyed and other brands of products they used.

Whereas traditional segmentation focuses on finding the products people buy the most and then marketing additional options, The North Face needed to find the category of products its customers use the most, not just those they purchase the most.

“We began working on big data in 2013 with a pilot project proposed as an innovation experiment,” Dewar says. “We had great results, so we launched a second-phase pilot in 2014. Those two sets of results formed the basis for our recommendation to incorporate advanced analytics into our 2016 plan.”

Both pilots focused on using transactional data, social data and data on spending behavior to predict future purchases. “We have incorporated that learning into our current program in partnership with Tibco and SAS,” Dewar says.

From there, the company had a consulting firm pull together a collection of teams at The North Face to identify opportunities that could arise as the company gleaned insights from its analytics initiative.

Test and learn

“We identified over 25 unique opportunities across ecommerce, direct-to-consumer retail, brand marketing, sources, procurement and product development,” Dewar says. “For 2016, we established a short list of six key use cases we wanted to test and incorporate into our plans. As we test and learn from each use case, we know we have more to go back to.”

In this case, the company focused on enhancing direct customer engagement via a loyalty program, hoping to translate that into a higher level of engagement and increased sales across all retail channels over time.

Its loyalty program, VIPeak Rewards, allows members to earn redeemable “PeakPoints” for every dollar spent and for participating in local activities — endurance challenges, mountain athletics training sessions, skiing and snowboarding competitions and even lectures by athletes. Data from sales, web searches, event registrations, competitions, surveys and other sources is analyzed using platforms such as Tibco’s Spotfire and SAS and IBM analytics tools. The company examines that data to understand the sporting categories customers show the most interest in.

A wealth of data

Standard RFM (recency, frequency and monetary) analysis of past transactions is applied to identify top potential customers, while predictive analytics take into account the company’s model for selling high-quality, long-lasting outdoor products.

“There is so much data available,” Dewar says. “We initially thought we would be spending a lot of time looking for additional data sources — a.k.a. the big data question — but we have been pleasantly surprised at how much transaction and behavioral data we already have. A key lesson for us has been to maximize use of what we already have, data- and customer-wise, before chasing too much external data or expanding to a broad customer prospecting initiative.”

The North Face’s efforts resulted in a dramatic increase in cross-category sales, with the same customers making purchases more than once, Dewar says. The VIPeak program gives the company the ability to build a 360-degree view of its customers while also strengthening customer and brand engagement and increasing online shopping activity.

“By identifying the key product categories customers are most likely to buy next, The North Face has been able to increase both the annual frequency of purchases and the year-over-year return purchase behavior of the VIPeak customers,” Dewar says. “In addition, the lessons learned with the top loyalty members are now being applied to nonmembers to identify top prospects across the whole direct-to-consumer base.”

Asked about advice he might have for other IT leaders planning analytics initiatives, Dewar offers these tips, drawn from the three keys to the success of the North Face project:

  1. Get executive and cross-functional buy-in prior to committing to the project.
  2. Use your own data first; maximize the opportunity to get more from your existing customers.
  3. Make sure data analytics projects have the same KPIs as the overall business, so key wins can be celebrated across departments and key results from a test and learn protocol can be integrated immediately.

Copyright © 2016 IDG Communications, Inc.

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