Transforming Customer Data into Insights

Chapter 1. Introduction and Case Studies

Delivering a unified, personalized customer experience across channels is desired, if difficult, for today’s marketers. In this chapter, you’ll learn about how three organizations have become leaders in this pursuit.

Lululemon

Lululemon, a Canadian athletic apparel retailer, operates 460 stores worldwide, along with an ecommerce site. As part of the retailer’s strategy, each store is given large autonomy to serve as a community hub. For example, then-CEO Christine Day told Fortune in 2012 that store managers are free to customize the store’s layout and color scheme; they are also given a budget to give to charities, local auctions, and other events.

Free yoga classes are also offered in stores weekly. This rich community involvement generated a rich amount of customer loyalty—and data. But its use was limited to the regional community; in-store and ecommerce data were disconnected, offering a fragmented experience to customers.

To solve this problem, Lululemon integrated myriad sources of data—ranging from customer profiles to transactions to web data—and processed it in real time to deliver the right experience at the right time to customers. As a result, Lululemon can now, for example, gather information about a customer’s attendance at their local store’s yoga class. Based on this information, they can target that customer with customized campaigns and offers through multiple channels like email, direct mail, and in-person experiences.

By crafting a personal experience from customer data that works across multiple channels, Lululemon experienced a 50% increase in site visits and a 10–15% increase in baseline revenue from digital marketing campaigns.

Moosejaw

Moosejaw is a Michigan-based recreation apparel and gear retailer operating 11 locations, along with an online presence. As a boutique retailer, Moosejaw needed a way to stand out from lower-priced ecommerce competition. Sending emails that offered heavy discounts to largely undifferentiated customer segments was eroding both margins and the brand’s customer experience.

Using machine learning algorithms on unified online and in-store customer data, Moosejaw analyzed patterns in customer purchase behavior and used the results to develop more targeted segments. As a result, company revenue per email increased by 9%, while customer acquisition costs dropped by 10%. Email continues to provide 20–30% year-over-year growth for the retailer.

In Moosejaw’s stores, sales associates carry point of sales (POS) software with them on a mobile device. With this, they can order out-of-stock or online-only items for customers, which are delivered to customers for free. They can also complete sales transactions right on the floor with the devices, sparing customers from waiting in line at a sales terminal.

Moosejaw’s CEO Eoin Comerford tells Retail Info Systems that the mobile POS is a celebrated technology among the floor staff: “They like that it enables them to engage directly with customers without a bulky cash wrap getting in the way.”

Arçelik

Delivering a unified customer experience doesn’t just pay off for outdoor and athletic retailers: take Arçelik, for example. A Turkish multinational household appliances manufacturer, Arçelik operates across independent dealers, authorized service providers, and company-owned stores and service centers. Due to this variety of endpoints, the manufacturer had limited visibility into past customer interactions.

Arçelik worked to create a unified customer profile across data from any endpoint, including call centers, social media, POS systems, and more. As a result, 250 million disintegrated customer records were transformed into 60 million unified, actionable entities. Arçelik could then integrate this data into a call script tool, allowing for better personalization from the call center. As a result, conversion rates from the call center have reached 24%, and SMS conversions have improved by as much as six times.

This chapter profiled how three organizations aligned not just their marketing technology but their larger marketing strategy to deliver a more personalized and unified customer experience—and reaped big rewards. How can your organization do the same? Maybe some of these elements, such as segmented email lists or personalized advertisements, sound familiar. Synchronizing a personalized message across channels and customer phases, however, might be less so.

Chapters 2 and 3 will provide a more generalized view of the marketing landscape over the past several decades and the advance of marketing technologies culminating in the CDP. You will see how the CDP can be used to augment the features of customer relationship management (CRM) and, more recently, data management platform (DMP) programs.