Tableau Reveals Key Opportunities in Sales Performance Data
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Tableau Professional
技术栈
- Excel
- Access
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Revenue Growth
技术
- 应用基础设施与中间件 - 数据可视化
适用功能
- 销售与市场营销
用例
- 补货预测
- 需求计划与预测
服务
- 数据科学服务
关于客户
First American Corporation (FAF:NYSE) is the nation’s leading provider of business information. Founded in 1889, First American continues its service commitment by providing the information businesses need to make timely, accurate decisions. Backed by industry leading technology, First American delivers efficiency to support clients’ information needs. First American Real Estate Solutions is the nation’s largest and most comprehensive source for property and ownership information.
挑战
The Operations Department at First American Corporation was responsible for collecting, shaping, and analyzing sales performance data across all segments, products, and geographies. They were using Excel pivot tables, but they were slow, inflexible, and difficult for people to learn. Also, the graphics being generated from Excel didn’t effectively communicate the critical insights to executive management. The primary challenge was to find and implement an analysis application that supported First American’s wide-ranging requirements. The Operations Department was charged with reporting monthly sales performance trends to senior managers categorized by channel, market segment, and product mix, analyzing price points by product then recommending pricing strategies that would positively impact revenue and simplify current customer pricing plans, and investigating dozens of sources of marketing, customer, and financial data to drive strategic initiatives and uncover trends and themes previously not known.
解决方案
First American selected Tableau Professional. Tableau’s “visual analysis” enables Operations to find important trends, relationships, and outliers in large datasets quickly and effectively. Improved insight into product pricing and usage now drives better decision making. The application had to be able to connect to hundreds of thousands of records every month from 4-5 separate production systems via Excel exports and Access, allow data be “sliced” by many attributes (e.g. regions, products, partners, etc.) and be especially flexible when investigations required multiple time dimensions, and deploy easily, enable multiple analysis cycles very quickly, and provide flexible visual displays.
运营影响
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
Pepsico's Transformation to Smarter Sales Forecasting with Designer Cloud
PepsiCo, a global consumer packaged goods company, faced a significant challenge in calibrating sales forecasting to supply the right product quantities to its retailers. The sales forecast incorporated a variety of data, including warehouse data, store stock data, and promotional forecast data, all of which were provided by retailers in different file formats and delivered using various methods. The primary challenge was the speed of preparing a sales forecast. With the existing Microsoft Access and Excel-based processes, the time required to prepare this data was so extensive that analysts could only leverage it once a month or not at all. This inefficiency risked under or oversupplying retailers, potentially impacting PepsiCo's business operations and customer relationships.
Case Study
Gexa Energy and AutoGrid's Innovative Demand Response Programs in ERCOT
Gexa Energy, a leading retail electricity provider in Texas, was seeking to introduce new demand response programs for its commercial and industrial customers in the Electric Reliability Council of Texas (ERCOT) market. The challenge was to provide a platform that would allow these customers to lower their energy bills by adjusting their energy consumption during peak energy demand or high wholesale electricity prices. The solution needed to be intelligent, scalable, and offer both manual and automated options for adjusting energy consumption. The demand response programs needed to include Emergency Response Service (ERS), Real-Time Price Response (RTPR), and 4 Coincident Peak (4CP).
Case Study
ZettaNet's Agile Juniper Network Meets Booming Digital Demand in Australia
ZettaNet, a privately-held company based in Perth, Australia, was facing a significant challenge due to the exponential demand for enterprise network, data center, and cloud services in the region. The company's business growth necessitated an upgrade of their core network to meet the increasing bandwidth requirements of their customers. The customers, which primarily include managed service providers, were demanding 1 Gbps connectivity between locations. These service providers then deliver network, data center, cloud, and voice services to a diverse range of customers including local businesses, schools, hospitals, residential communities, and government offices in Western and Southern Australia. The challenge for ZettaNet was to meet this high-capacity network services demand while maintaining profitability.
Case Study
Procter & Gamble Implements Terra Technology's Demand Sensing for Improved Forecast Accuracy
Procter & Gamble (P&G) faced significant challenges in accurately forecasting short-term demand for their consumer products. Their existing 24-month forecast provided a good overview for monthly or weekly production, but it was insufficient for the immediate needs of supply chain planning and manufacturing teams. These teams required a short-term forecast to plan production effectively and avoid 'fire-fighting' practices. P&G needed a solution that could provide accurate short-term demand forecasts to ensure agility and flexibility in manufacturing, especially for products with very short production and order lead times. The company explored various solutions but found that most big software companies lacked the agility to meet their specific demand sensing needs. Terra Technology's Real-Time Forecasting, later known as Demand Sensing (DS), emerged as a promising solution due to its specialized focus on consumer packaged goods (CPG) demand planning and forecasting.
Case Study
Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting
Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals.
Case Study
MARS Incorporated: Leading a Global Digital Transformation
MARS Incorporated, a multinational manufacturer, faced significant challenges in digitizing and standardizing processes across its mid-markets globally. The company's reliance on legacy solutions necessitated continuous, time-consuming upgrades and made data compilation and comparison across different regions and business units difficult. Excel spreadsheets were extensively used for forecasting in smaller markets, leading to disconnected processes, siloed working environments, and increased risk of data inconsistencies and inaccuracies in demand forecasting. MARS also struggled with a lack of visibility across its midmarket footprint, scattered critical data across various systems and spreadsheets, a low degree of automation, and insufficient statistical analytics for demand planning. The absence of a standard process for demand planning made it challenging to consolidate KPIs and gain a comprehensive view of demand trends and supply chain performance.
