C5i > 实例探究 > Leveraging a balanced scorecard for performance analysis to identify drivers of financial performance and deliver on brand promise

Leveraging a balanced scorecard for performance analysis to identify drivers of financial performance and deliver on brand promise

公司规模

1,000+

产品

LiNK manager platform
LiNK BI reporting platform

技术栈

Data Warehousing
Multivariate Techniques

实施规模

Enterprise-wide Deployment

影响指标

Customer Satisfaction
Employee Satisfaction
Revenue Growth

技术

分析与建模 - 大数据分析
分析与建模 - 预测分析

适用行业

零售

适用功能

商业运营
销售与市场营销

用例

质量预测分析

服务

数据科学服务

关于客户

The customer is a major retail chain with multiple outlets. They are focused on delivering their brand promise which includes excellent customer service, superior product quality and range, and a highly enjoyable shopping experience. The client believes that the delivery of its brand promise would directly impact customer loyalty, supporting client retention, repeat purchasing and cross sales. They also believe that effective delivery of the promise is driven by employee satisfaction and productivity. To validate these hypotheses and achieve their objective, they needed to merge a multitude of different research studies – past and present - into one single database, and then analyze that data at both the customer and store level.

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挑战

The retail client was in need of a balanced scorecard model that would enable its senior leaders to understand the drivers of store-level financial performance and empower the delivery of the company’s brand promise. The client’s hypothesis was that the delivery of its brand promise would directly impact customer loyalty - supporting client retention, repeat purchasing and cross sales. They also believed that effective delivery of the promise was driven by employee satisfaction and productivity. To prove their hypotheses and achieve their objective, the client needed to merge a multitude of different research studies – past and present - into one single database, and then analyze that data at both the customer and store level.

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解决方案

The solution involved developing a model in three phases. Phase 1 involved in-depth interviews among senior leaders and selected store managers, along with employee focus groups. In addition, all past research was reviewed and a number of data sources were identified to be included in the model. Phase 2 involved warehousing the data using the LiNK manager platform to create a linked data set at individual customer level and then at store level. Phase 3 involved the application of various multivariate techniques with customer-level and store-level data stored in the LiNK database to validate hypothesized relationships between financial data, customer loyalty and employee metrics.

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运营影响

The metrics produced using LiNK provided specific, actionable insights into the relationship between the company’s employees and its customers.
A separate report was generated using the LiNK BI reporting platform for each store that included aggregated customer spending data and employee scores at the store level, enabling store managers to see the direct relationship between employee management and store revenues.
Based on the results, workshops were held with store managers to drive improvement, while ‘impact-planning’ tool kits were developed to facilitate workshops and train-the-trainer programs.

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数量效益

Customers identified as loyal spent 65% more on charge cards relative to those who were identified as non-loyal; they were also 50% more likely to engage in the loyalty program and were less open to sales and discounts
Over a 2-year period, loyalty scores had improved by 16% on the loyalty metric to 26%. There were also clear linkages between store revenue / profitability and employee / customer metrics

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