Background
A national outdoor specialty retailer and one of the nation’s largest consumer co-ops, with 17 million active members came to Concentrix Catalyst with a need to improve their customer insights. The retailer has nearly 150 stores in 35 states, with a core purpose to inspire, educate and outfit for a lifetime of outdoor adventures and stewardship.
Challenge
Multi-channel operations offer retailers a wealth of data about customer preferences and behaviors, with in-store analytics alone expected to contribute $61 billion to the retail industry through 2018 due to its ability to improve efficiency, provide real-time information and offer operational and shopper analytics.
It’s not easy, though—retailers are often challenged to harness that data and convert it into actionable customer insights. A recent study by research firm RSR found that half of retailers had implemented capabilities related to capturing and analyzing customer insights through big data, but less than one-fourth reported being satisfied with what they had implemented.
With in-store and online operations in addition to its adventure company, the retailer has access to a lot of raw data. When it began its partnership with Concentrix Catalyst in early 2017, it was inundated with streams of structured and unstructured data that included customer names and addresses, transactions and amounts, emails, and any activities in which the customer had participated.
The team needed access to standardized, clean data updated in a timely manner in order to realize the potential of big data. To stay ahead of the competition, it needed to enable analyses able to drive a better understanding of customer buying patterns and behaviors, increase revenue, lower costs and improve the customer experience.
Solution
Leveraging extensive experience in analytics and the retail industry, Concentrix Catalyst began by standardizing the company’s data and enabling daily updating of reports containing customer information and activities. The team used Tableau to build executive-level dashboards that gave the C-suite a daily snapshot into the health of the business, enabling a quick response to changes. Alteryx was used to allow the company to connect to a variety of data sources — internal, including spreadsheets housed on employees’ laptops, and external, from third-party weather data and demographics to geographic databases.
Geospatial analyses allowed for the determination of how far customers lived from a store, including driving time during peak hours. The team used the programming language R to enable predictive modeling that allowed the organization to anticipate what activities a customer was most likely to undertake next, given his or her current purchases and activities. For example, understanding that hikers often transition into backpacking allows the company to suggest activities and products the customer may find valuable, leading to more loyal, more engaged customers and increased sales.
The team also developed a churn model as part of a larger effort to determine a customer’s lifetime value. The initial model defined the current value for each customer, with a second phase to include a prediction for the future that takes into account an analysis enabling the retailer to define lost and retained customers based on how much they purchase and spend. The churn model not only predicted with 90 percent accuracy which customers were likely to leave but also allowed the outdoor specialty retailer to target its marketing activity to retain the most valuable customers among that group.
Together, these tools delivered customer insights that informed and shaped a variety of business decisions for the retailer, from which products to sell to where to open the next store. Analysis and datasets built on market penetration and growth enabled the company to understand how well it has captured “Outdoor Enthusiasts” as customers in every designated market area of the U.S. The retailer can also identify the strongest growth areas for outdoor activities and understand how differences across age ranges affect customer behavior.
