A Decade-Plus of Digital Engagement at Outdoor Retailer Moosejaw
公司规模
Mid-size Company
地区
- America
国家
- United States
产品
- LiveEngage platform
- IBM WebSphere Commerce
技术栈
- Live chat
- Mobile live chat
- Proactive live chat
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Cost Savings
- Revenue Growth
技术
- 平台即服务 (PaaS) - 连接平台
适用行业
- 零售
适用功能
- 销售与市场营销
服务
- 软件设计与工程服务
- 系统集成
关于客户
Moosejaw is a brick-and-mortar and online outdoor retailer headquartered in Madison Heights, Michigan. Founded in 1992, the company is known for delivering a unique customer experience through a marketing methodology known as Moosejaw Madness. Moosejaw was an early adopter of the Internet in the 1990s and launched its corporate website shortly after its founding. The website originally did not have an e-commerce element, but in 1999, the management team added an e-commerce component, allowing customers to find more products online than in retail stores.
挑战
Moosejaw, an outdoor retailer, faced several challenges. They wanted to humanize the digital experience, deliver real-time online customer engagement, drive digital conversion rates, and engage customers at high-impact moments. As an early adopter of the Internet, Moosejaw understood the importance of digital engagement and implemented a live chat solution powered by LivePerson. However, they wanted to evolve beyond chat to digital engagement and become more strategic in how they used the LiveEngage platform. They also wanted to replicate the in-store experience onto the website and find ways to engage with customers through additional channels.
解决方案
Moosejaw implemented a proactive live chat on desktops and mobile engagement channels using the LiveEngage platform. They created rules that launched a chat invitation window if someone stayed on a page longer than a specified timeframe. Over time, they developed additional rules based on specific customer behaviors. Moosejaw also configured its proactive chat rules so that accepted chats go to the agent with the most knowledge about the product at which the customer is looking. In 2013, they implemented LivePerson’s mobile live chat solution to provide customers with a better experience. They also began leveraging conversations analysis to gain insights into information that may not be placed in the right spot on the website and information that currently doesn’t exist on the website.
运营影响
数量效益
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
相关案例.
Case Study
Improving Production Line Efficiency with Ethernet Micro RTU Controller
Moxa was asked to provide a connectivity solution for one of the world's leading cosmetics companies. This multinational corporation, with retail presence in 130 countries, 23 global braches, and over 66,000 employees, sought to improve the efficiency of their production process by migrating from manual monitoring to an automatic productivity monitoring system. The production line was being monitored by ABB Real-TPI, a factory information system that offers data collection and analysis to improve plant efficiency. Due to software limitations, the customer needed an OPC server and a corresponding I/O solution to collect data from additional sensor devices for the Real-TPI system. The goal is to enable the factory information system to more thoroughly collect data from every corner of the production line. This will improve its ability to measure Overall Equipment Effectiveness (OEE) and translate into increased production efficiencies. System Requirements • Instant status updates while still consuming minimal bandwidth to relieve strain on limited factory networks • Interoperable with ABB Real-TPI • Small form factor appropriate for deployment where space is scarce • Remote software management and configuration to simplify operations
Case Study
How Sirqul’s IoT Platform is Crafting Carrefour’s New In-Store Experiences
Carrefour Taiwan’s goal is to be completely digital by end of 2018. Out-dated manual methods for analysis and assumptions limited Carrefour’s ability to change the customer experience and were void of real-time decision-making capabilities. Rather than relying solely on sales data, assumptions, and disparate systems, Carrefour Taiwan’s CEO led an initiative to find a connected IoT solution that could give the team the ability to make real-time changes and more informed decisions. Prior to implementing, Carrefour struggled to address their conversion rates and did not have the proper insights into the customer decision-making process nor how to make an immediate impact without losing customer confidence.
Case Study
Digital Retail Security Solutions
Sennco wanted to help its retail customers increase sales and profits by developing an innovative alarm system as opposed to conventional connected alarms that are permanently tethered to display products. These traditional security systems were cumbersome and intrusive to the customer shopping experience. Additionally, they provided no useful data or analytics.
Case Study
Ensures Cold Milk in Your Supermarket
As of 2014, AK-Centralen has over 1,500 Danish supermarkets equipped, and utilizes 16 operators, and is open 24 hours a day, 365 days a year. AK-Centralen needed the ability to monitor the cooling alarms from around the country, 24 hours a day, 365 days a year. Each and every time the door to a milk cooler or a freezer does not close properly, an alarm goes off on a computer screen in a control building in southwestern Odense. This type of alarm will go off approximately 140,000 times per year, equating to roughly 400 alarms in a 24-hour period. Should an alarm go off, then there is only a limited amount of time to act before dairy products or frozen pizza must be disposed of, and this type of waste can quickly start to cost a supermarket a great deal of money.
Case Study
Supermarket Energy Savings
The client had previously deployed a one-meter-per-store monitoring program. Given the manner in which energy consumption changes with external temperature, hour of the day, day of week and month of year, a single meter solution lacked the ability to detect the difference between a true problem and a changing store environment. Most importantly, a single meter solution could never identify root cause of energy consumption changes. This approach never reduced the number of truck-rolls or man-hours required to find and resolve issues.
