C5i > Case Studies > 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

Customer Company Size

Large Corporate

Product

LiNK manager platform
LiNK BI reporting platform

Tech Stack

Data Warehousing
Multivariate Techniques

Implementation Scale

Enterprise-wide Deployment

Impact Metrics

Customer Satisfaction
Employee Satisfaction
Revenue Growth

Technology Category

Analytics & Modeling - Big Data Analytics

Applicable Industries

Retail

Applicable Functions

Sales & Marketing
Business Operation

Use Cases

Predictive Quality Analytics

Services

Data Science Services

About The Customer

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.

The Challenge

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.

The Solution

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.

Operational Impact

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.

Quantitative Benefit

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